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The purpose of this work is to investigate the static performance characteristics of thermohydrodynamic journal bearing operating under nanolubricants (lubricants containing per cent weight concentration of nanoparticles).

Addition of nanoparticles in the lubricant increases lubricant viscosity. To study the effect of this variation on journal bearing, analytical models are developed for the relationship between viscosity, 0-0.5 per cent weight concentration of nanoparticles and temperature range of 300-900°C. To obtain pressure and temperature distribution, modified Reynolds and energy equations are solved by using the finite element method. The viscosity field (varies with temperature and per cent weight concentration of nanoparticles) is updated in these two equations by using the developed analytical model. The steady-state performance characteristics are computed for various values of eccentricity ratios for non-thermoviscous (viscosity of lubricant varies with per cent weight concentration of nanoparticles) and thermoviscous (viscosity of lubricant varies with per cent weight concentration of nanoparticles and temperature) cases. The lubricant and the nanoparticles used for the present work are SAE15W40, copper oxide (CuO), cerium oxide (CeO₂) and aluminum oxide (Al₂O₃).

The pressure and temperature distribution across the lubricant film in the clearance space of journal bearing and static performance characteristics are calculated.

The computed results show that addition of nanoparticles in the lubricant influences the performance characteristics considerable in thermoviscous case than non-thermoviscous case.

The purpose of this paper is to classify different conditions of the rolling element bearing (REB) using vibration signals acquired from a customized bearing test rig.

An effort has been made to develop health index (HI) based on singular values of the statistical features to classify different conditions of the REB. The vibration signals from the normal bearing (N), bearing with defect on ball (B), bearing with defect on inner race (IR) and bearing with defect on outer race (OR) have been acquired from a customized bearing test rig under variable load and speed conditions. These signals were subjected to “modified kurtosis hybrid thresholding rule” (MKHTR)-based denoising. The denoised signals were decomposed using discrete wavelet transform. A total of 17 statistical features have been extracted from the wavelet coefficients of the decomposed signal.

Singular values of the statistical features can be effectively used for REB classification.

REB are critical components of rotary machinery right across the industrial sectors. It is a well-known fact that critical bearing failures causes major breakdowns resulting in untold and most expensive downtimes that should be avoided at all costs. Hence, intelligently based bearing failure diagnosis and prognosis should be an integral part of the asset maintenance and management activity in any industry using rotary machines.

It is found that singular values of the statistical features exhibit a constant value and accordingly can be assigned to each type of bearing fault and can be used for fault characterization in practical applications. The effectiveness of this index has been established by applying this to data from Case Western Reserve University data base which is a standard bench mark data for this application. HIs minimizes the computation time when compared to fault diagnosis using soft computing techniques.

Aluminium metal matrix composites are used in automotive and aerospace industries because of their high performance and weight reduction benefits. The current investigation aims to focus on the development of the stir cast aluminium-boron carbide composites with enhanced mechanical and tribological properties.

The aluminium-boron carbide composites are produced by stir casting process. Aluminium alloy A356 is chosen as the matrix material and three sets of composites are produced with different weight fractions of boron carbide particles. Higher particle size (63 µm) of boron carbide is chosen as the reinforcement material. Aluminium-boron carbide composites are tested for mechanical and tribological properties. The effect of process parameters like load, speed and percentage of reinforcement on the wear rate are studied using the pin-on-disc method. The interaction of the process parameters with the wear rate is analysed by DesignExpert software using RSM methodology and desirability analysis. The coded levels for parameters for independent variables used in the experimental design are arranged according to the central composite design. The worn surface of the pin is examined using a scanning electron microscope. The phases and reaction products of the composites are identified by X-ray diffraction (XRD) analysis.

Aluminium-boron carbide composites reveal better mechanical properties compared to monolithic aluminium alloys. Mechanical properties improved with the addition of strontium-based master alloy Al10Sr. The ultimate tensile strength, hardness and compressive strength increase with an increase in the reinforcement content. The wettability of the boron carbide particles in the matrix improved with the addition of potassium flurotitanate to the melt. Uniform dispersion of particles into the alloy during melting is facilitated by the addition of magnesium. Wear resistance is optimal at 8 per cent of boron carbide with a load 20 N and sliding speed of 348 RPM. The wear rate is optimized by the numerical optimization method using desirability analysis. The amount of wear is less in Al-B₄C composites when compared to unreinforced aluminium alloy. The wear rate increases with an increase in load and decreases with the sliding speed. The wear resistance increases with an increase in the weight fraction of the boron carbide particles.

The produced Al-B₄C composites can retain properties at high temperature. It is used in nuclear and automotive products owing its high specific strength and stiffness. The main applications are neutron absorbers, armour plates, high-performance bicycles, brake pads, sand blasting nozzles and pump seals.

Al/B₄C composites have good potential in the development of wear-resistant products.

The purpose of this paper is to quantify the benefits of contract farming (CF) on farmers’ income and adoption of food safety measures (FSMs) at the farm level. The paper also investigates the determinants of participation in CF.

The study is based on a survey of 600 tomato farmers from Nepal. Descriptive statistics, regression analysis (using instrumental variable) and propensity score matching have been used to accomplish the objectives of the study.

The study found that the CF ensures higher returns to farmers as well as higher adoption of FSMs at the farm level. The contract farmers earned about 38 per cent higher net returns and had 38 per cent higher adoption of FSM as compared to independent farmers. Caste, occupation, farm size and cropping intensity significantly affected farmers’ participation in CF.

The analysis based on cross-section data has limitations to consider unobserved farmer-level individual heterogeneity.

This study will provide an empirical base to promote CF in Nepal. The study will also contribute to bridge the gap in literature on the drivers of CF and its impact on smallholders’ income and compliance with FSM in Nepal.

The present study provides evidence on export advantages of horticultural commodities based on competitiveness, trade balance and seasonality dimensions.

The study delineated horticultural commodities in terms of comparative advantage, examined temporal shifts in export advantages (mapping) and estimated seasonality. Product mapping was carried out using the Revealed Symmetric Comparative Advantage (RSCA) and Trade Balance Index (TBI). Seasonal advantages were examined through a graphical approach along with the objective tests, namely, modified QS-test (QS), Friedman-test (FT) and using a seasonal dummy.

Cucumbers/gherkins, onions, preserved vegetables, fresh grapes, shelled cashew nuts, guavas, mangoes, and spices emerged as the most favorable horticultural products. India has a strong seasonal advantage in dried onions, cucumber/gherkins, shelled cashew nut, dried capsicum, coriander, cumin, and turmeric. The untapped potential in horticulture can be addressed by handling the trade barriers effectively, particularly the sanitary and phytosanitary issues, affecting the exports. Proper policies must be enacted to facilitate the investment in advanced agricultural technologies and logistics to ensure the desired quality and cost effectiveness.

Commodity-specific studies on value chain analysis would provide valuable insights into the issues hindering exports and realizing the untapped export potential.

There is no holistic and recent study illustrating the horticulture export advantages covering a large number of commodities in the Indian context. The study would be helpful to the stakeholders for drawing useful policy implications.

Artificial intelligence (AI) is increasingly applied by businesses to optimize their processes and decision-making, develop effective and efficient strategies, and positively influence customer behaviors. Businesses use AI to generate behaviors such as customer retention (CR). The existing literature on “AI and CR” is vastly scattered. The paper aims to review the present research on AI in CR systematically and suggest future research directions to further develop the field.

The Scopus database is used to collect the data for systematic review and bibliometric analysis using the VOSviewer tool. The paper performs the following analysis: (1) year-wise publications and citations, (2) co-authorship analysis of authors, countries, and affiliations, (3) citation analysis of articles and journals, (4) co-occurrence visualization of binding terms, and (5) bibliographic coupling of articles.

Five research themes are identified, namely, (1) AI and customer churn prediction in CR, (2) AI and customer service experience in CR, (3) AI and customer sentiment analysis in CR, (4) AI and customer (big data) analytics in CR, and (5) AI privacy and ethical concerns in CR. Based on the research themes, fifteen future research objectives and a future research framework are suggested.

The paper has important implications for researchers and managers as it reveals vital insights into the latest trends and paths in AI-CR research and practices. It focuses on privacy and ethical issues of AI; hence, it will help the government develop policies for sustainable AI adoption for CR.

To the author's best knowledge, this paper is the first attempt to comprehensively review the existing research on “AI and CR” using bibliometric analysis.

The purpose of the study is to fabricate a joint between two aluminium metal matrix composites using microwave hybrid heating (MHH).

Taguchi design of experiments was applied to conduct the experimental study. The mechanical properties such as ultimate tensile strength, micro-hardness and porosity were studied. Grey Relational Analysis was applied to understand the significance of fabrication parameters of best performing sample. The dominant factor of fabrication was analysed using ANOVA. The best performance sample was further characterised using X-ray diffraction and field emission scanning electron microscopy. Energy dispersive X-ray was used to analyse the elemental composition of the sample.

The Aluminium Metal Matrix Composite (AMMC) joint was successfully fabricated using MHH. The mechanical properties were mainly influenced by the fabrication factor of exposure time.

The formation of AMMC joint using MHH might explore the way for the industries in the field of joining.

The purpose of the study is to identify the factors affecting the entrepreneur's choice of the dairy value chain and evaluate the impact of the value chain on farm performance (profit).

Primary data were collected from dairy entrepreneurs in India, covering nine states. A multinomial treatment effect model (controlling for selection bias and endogeneity) was used to evaluate the impact of the choice of the value chain on entrepreneurs' profit.

Dairy entrepreneurs operating in any recognized value chain other than the value chain driven by the consumer household realize a comparatively lesser profit. Dairy farmers have established direct linkages with customers in urban areas – who could pay premium prices for safe and quality milk. Food safety compliance is positively associated with profit and entrepreneurs (who have undergone formal training in dairying) preferred partnerships with a formal value chain. The prospects of starting a dairy enterprise are slightly higher in villages compared to urban areas.

Dairy entrepreneurs can make a shift in accordance with the study's findings and boost their profitability. It aids in comprehending how trainees (who obtained advice and training for raising dairy animals from R&D organizations) and non-trainee dairy farmers make value chain selections, which ultimately affect profitability. However, purposive sampling and a small sample size limit the universal implications of the study.

Developing entrepreneurial behavior and startup culture is at the center of policymaking in India. The findings imply that the emerging value chain not only enhances the profit of dairy farmers by resolving consumer concerns about food safety and the quality of milk and milk products but also builds consumer trust.

This paper offers insight into how the benefits of dairy entrepreneurs vary with their participation in the different value chains. The impact of skill development/training programs on value chain selection and farm profitability has not yet been fully understood. Here is an attempt to fill this gap. This paper through light on how trained and educated dairy entrepreneurs are able to establish a territorial market by approaching premium customers – this is an addition to the existing literature.