Kunj Bihari Rana

In recent years, nanotechnology has emerged as a field of study concerned with producing materials at the nanoscale and their usage for research and commercial purposes like nanoelectronics, biomedicals, energy storage, environmental remediation, heat transfer applications, etc. This review provides a comprehensive analysis of nanofluid synthesis methods, specifically the one-step and two-step approaches, with a focus on their scalability and industrial feasibility. The main aim of this review article is to provide a quick reference for researchers working on the synthesis of nanofluids for heat transfer applications in various thermal systems. The development of nanofluids as enhanced thermal fluids is a result of the obvious demand for fluids that are more efficient. The review systematically examines recent advancements in nanofluids, emphasizing key factors influencing their thermal conductivity, such as nanoparticle properties, base fluids, concentration, stability, and dispersion mechanisms. Additionally, the study explores the integration of nanofluid in heat transfer applications such as heat exchangers, internal combustion (IC) engines, convective boiling, and renewable energy systems. Furthermore, to address sustainability and potential ecological effects of nanofluids applications, a discussion on environmental impact of nanofluids is presented, highlighting the need for green synthesis methods and life cycle assessments. This review intends to provide useful information about the development and optimization of nanofluids for efficient thermal management in future energy systems.

Objectives. The current study correlated grip strength (GS) measurements with upper-limb muscle activity (UMA) for three different experience levels of manual workers (beginner, intermediate and advanced). Methods. A total of 182 male and female manual workers were chosen, and GS was assessed in some contexts using a digital handgrip dynamometer. Results. The GS values change significantly depending on the amount of experience of the workers. The difference in GS for different UMA demonstrates that continuous exposure to hand tools in different conditions of the hands (twisting, bending, etc.) significantly impacts manual arm strength. Conclusions. These findings suggest that repetitive use of hand tools in various manual operations (cutting, digging, pruning, spading, etc.) causes GS to vary significantly. In addition, beginner experience-level individuals exhibited much lower GS values in the dominant hand, whereas advanced experience-level workers had the greatest strength. Thus, for generating higher forces, professionals (advanced and intermediate levels) choose to execute safer activity (i.e., more neutral positions) than beginners.

Objectives. The coronavirus outbreak delivered the condition of dying from infection and forced people (especially university student computer users) to perform all working and non-working activities during homestay. In this situation, device usage for a longer duration is mainly responsible for work-related health issues. This study aims to discover the effect of a physical activity intervention (PAI) on computer users’ musculoskeletal health during homestay. Methods. The investigation was performed on 40 university student computer users. To measure body discomfort before and after using the PAI, the body part discomfort scale of Corlett and Bishop was applied. Results. After implementing the PAI, the musculoskeletal disorder (MSD) decrement in major body regions was reported as wrist/forearm (from 8.17 ± 1.45 to 4.57 ± 1.10), lower back (from 8.01 ± 1.42 to 4.40 ± 1.14), elbow (from 7.57 ± 1.71 to 3.49 ± 1.13) and neck (from 7.40 ± 1.71 to 4.02 ± 0.81). Conclusions. PAI significantly decreased the discomfort among users in various body regions. This research suggested that PAIs may reduce the risk of MSDs in the long term for different body regions.

Objectives. Coronavirus (COVID-19) is affecting people throughout the world. People are forced to conduct various activities at home using mobile devices (MDs) as a result of the outbreak. In this case, prolonged use of MDs is the major cause for work-related health problems. Methods. We used systematic cluster random sampling to sample a diverse group of Indians from India’s various states. Subjects filled out a questionnaire with questions about their demographics, MD usage and musculoskeletal symptoms (MSSs) faced. The relationship between MSSs and various factors was investigated using χ² and binomial logistic regression analysis. Results. An online survey yielded 720 responses. More than half of employees registered MSSs in their upper body regions. Age, gender and MD usage were correlated with MSSs in various body regions. According to the binomial logistic regression findings, gender was significantly linked to MSSs in each body region. Conclusions. Results show that MDs can be used effectively in intermediate leisure activities if they are used in accordance with their basic needs.

Among all renewable energy sources, solar power is one of the major sources which contributes for pollution control and protection of environment. For a number of decades, technologies for utilizing the solar power have been the area of research and development. In the current research, thermal performance parameters of multi-gap V-roughness with staggered elements of a solar air heater (SAH) are experimentally investigated. The artificial neural network (ANN) is also utilized for predicting the thermal performance parameters of SAH. Experiments were executed in a rectangular channel with one roughened side at the top exposed to a uniform heat flux. A significant rise in thermal efficiency performance was reported under a predefined range of Reynolds number (Re) from 3000 to 14000 with an optimized value of relative roughness pitch ratio (P/e) and relative staggered rib length (w/g) as 12 and 1, respectively. The maximum thermal efficiency was attained in the range from 42.15 to 87.02% under considered Reynolds numbers for optimum value of P/e as 12 and w/g as 1. A multilayered perceptron (MLP) feed-forward ANN trained by the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm was utilized to predict the thermal efficiency (η_th), friction (f), and Nusselt number (Nu). The thermal performance parameters such as P/e, w/g, Re, and temperature at the inlet, outlet, and plate were the critical input parameters/signals used in the ANN method. The optimum ANN arrangement/structure to predict the Nu, f, and η_th demonstrate higher accurateness in assessing the performance characteristics of SAH by attaining the root mean squared error (RMSE) in prediction and the Pearson coefficient of association (R²) of 1.591 and 0.994; 0.0012 and 0.851; and 0.025 and 0.981, respectively. The prediction profile plots of the ANN demonstrate the influence of various input parameters on the thermal performance parameters.