The aim of this paper is to explore and evaluate previous work focusing on the relationship and links between Lean manufacturing and industrial work. This study reviews the literature on Lean Manufacturing Assessment (LMA) during the last decade; 2008-2017 and analyses the literature from different perspectives. This paper highlights various key words, scopes, objectives, case study, definitions, methodologies, tools and main results. A total of 126 research papers have been reviewed in this study to help the researchers in the evaluation of lean manufacturing practices to identify and benefit from these studies. This paper provides a quantitative descriptive analysis and qualitative thematic analysis to provide an analysis of impact of lean on performance. Consequently, this paper can be considered as a guide for researchers in LMA. It is expected to be a brief reference for future researchers that reduces effort and time consuming during their studies.

The implementation of lean manufacturing practices (LMP) means a systematic approach of several management procedures and practices, which may impact content, nature and quality of people’s work. This implementation process is often performed after an accurate assessment to ensure the effectiveness and efficient execution of these practices. However, the necessity and logic of different assessments according to the situation required by assessment objective, the logic requires general rules for designing assessment with effective and reliable results. The aim of this research paper is to develop a logical perception to explain the impact of LMP on the actual and overall performance of the company and explains the computational basics and mathematical equations contributing to the assessment design of these practices to ensure efficiency and accuracy in the results. Moreover, this study also concludes a novel mathematical equations that can be used in the effective assessment to increase the effectiveness of evaluation for improvement processes in different sectors.

Coved-19 pandemic is spreading fear among the world in several aspects such as health, economic, international relations, political stability, and social stability. It emerged suddenly and attacked the world in a short period without warning. Details about the virus such as the source, symptoms, transmission, diagnosis and treatment are still incomplete.  Subsequently, more than one million people have died and huge economic losses. In order to avoid this issue in future, this paper aims to focus on artificial intelligence in predicting and tracking viral pandemic Disease and to control similar future risks using artificial intelligence, algorithms and cognitive fission theory.

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In this article, the influence of deionized water and Al2O3/deionized water nanofluid to cooling battery ambient temperature is shown in figure 1. The battery temperature is observed to be decrease as the Al2O3/deionized water nanofluids volume concentration and high flow rate is incremented. The Al2O3/deionized water nanofluid exhibits enhancement as compared to deionized water under laminar flow conditions. The 0.60 vol. % concentration of Al2O3 with 10g surfactant and 1 L/min flow rate gives the highest heat transfer rate value among all with 65 % higher as compared to deionized water at laminar flow was observed. It has been observed that 24 hr of ultra-sonication was the best duration in the presence of a surfactant, where it gives the best stability and improved thermal conductivity, this improvement is due to decrease of aggregates within nanoparticle.