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It has been six months from the time the first case was registered, and nations are still working on counter steering regulations. The proposed model in the paper encompasses a novel methodology to equip systems with artificial intelligence and computational audition techniques over voice recognition for detecting the symptoms. Regular and irregular speech/voice patterns are recognized using in-built tools and devices on a hand-held device. Phenomenal patterns can be contextually varied among normal and presence of asymptotic symptoms.

The lives of patients and healthy beings are seriously affected with various precautionary measures and social distancing. The spread of virus infection is mitigated with necessary actions by governments and nations. Resulting in increased death ratio, the novel coronavirus is certainly a serious pandemic which spreads with unhygienic practices and contact with air-borne droplets of infected patients. With minimal measures to detect the symptoms from the early onset and the rise of asymptotic outcomes, coronavirus becomes even difficult for detection and diagnosis.

A number of significant parameters are considered for the analysis, and they are dry cough, wet cough, sneezing, speech under a blocked nose or cold, sleeplessness, pain in chests, eating behaviours and other potential cases of the disease. Risk- and symptom-based measurements are imposed to deliver a symptom subsiding diagnosis plan. Monitoring and tracking down the symptoms inflicted areas, social distancing and its outcomes, treatments, planning and delivery of healthy food intake, immunity improvement measures are other areas of potential guidelines to mitigate the disease.

This paper also lists the challenges in actual scenarios for a solution to work satisfactorily. Emphasizing on the early detection of symptoms, this work highlights the importance of such a mechanism in the absence of medication or vaccine and demand for large-scale screening. A mobile and ubiquitous application is definitely a useful measure of alerting the officials to take necessary actions by eliminating the expensive modes of tests and medical investigations.

Co-creation of knowledge offers significant opportunities for innovation. This chapter seeks to gain understanding of the process of co-creation of knowledge for innovation and public relations in multi-stakeholder projects by exploring current insights in academic literature. The research questions look at how co-creation of knowledge for innovation has been investigated in the scholarly literature; the roles of end-users; and the modes and challenges of end-user participation and in collaboration relating to communication.

The method of this chapter is a structured literature review, following a series of rigorous steps: a search of databases, analysis of 33 articles found, summarising relevant content using a data extraction table and a data extraction continuum as analysis tools to show the range of projects discussed in the literature to create a comprehensive overview.

The findings indicate that multi-stakeholder networks can be structured for different aims. In the articles found different types of projects were investigated. Four categories of projects were found: (1) co-creation projects benefiting one company; (2) co-creation projects benefiting business-to-business value chain networks; (3) co-creation projects benefiting public entities; and (4) co-creation projects benefiting innovation network stakeholders.

Complexity is highest for multiple stakeholder co-creation projects benefiting innovation network stakeholders, where the roles between stakeholders are fluid and changing constantly. Solving common issues motivates the stakeholders to collaborate and build trust. Open innovation environments may facilitate communication and interaction.

Co-creation of knowledge requires intensive collaboration. Knowing the main challenges to address will help the functioning of co-creation collaboration networks and their public relations.

Purpose: This chapter aims to analyse and highlight the current landscape of performance management (PM) systems, and the benefits of integrating modern technology such as smart analytics (SA) and artificial intelligence (AI) into PM systems. The chapter discusses the application of AI in PM tasks which successively simplify many offline PM tasks.

Methodology: To carry out this analysis, a systematic literature review was performed. The review covers literature detailing PM components as well as research concerned with the integration of SA and AI into PM systems.

Findings: This study uncovers the merits of using SA and AI in PM. SA technology provides organisations with a clear direction for improvement, rather than simply state failure in performance. AI can be used to automate redundant tasks while retaining the human element of decision-making. AI also helps reduce the time required to take action on feedback.

Significance: The findings of this research provide insights into the use of SA and AI to make PM tasks fast, scalable, and error-free.

A local equivalent linearization methodology is proposed to simulate non‐linear shock absorbers and dual‐phase dampers in the convenient frequency domain. The methodology based on principle of energy similarity, characterizes the non‐linear dual‐phase dampers via an array of local damping constants as function of local excitation frequency and amplitude, response, and type of non‐linearity. The non‐linear behaviour of the dual‐phase dampers can thus be predicted quite accurately in the entire frequency range. The frequency response characteristics of a vehicle model employing non‐linear dual‐phase dampers, evaluated using local linearization algorithm, are compared to those of the non‐linear system, established via numerical integration, to demonstrate the effectiveness of the algorithm. An error analysis is performed to quantify the maximum error between the damping forces generated by non‐linear and locally linear simulations. The influence of damper parameters on the ride improvement potentials of dual‐phase dampers is further evaluated using the proposed methodology and discussed.

The purpose of the study is to analyze the risk of violent conflict with the global conflict risk factors in the Middle East economies by using an integrated fuzzy decision approach. For this purpose, five different dimensions and 24 different criteria are defined by analyzing similar studies in the literature. The dataset is borrowed from the European Commission, and experts appointed for the linguistic evaluation of each dimension and criterion. Additionally, fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) methodology is used to weigh dimensions and criteria and Multi-objective Optimization on the basis of Ratio Analysis (MOORA) approach is considered to rank the countries with respect to the conflict risk. Social dimension was concluded to have the highest importance of the Global Conflict Risk Index. Moreover, Syria, Libya, and Saudi Arabia were identified as the countries that have high conflict risk. Because these countries have high risk of facing conflict in the future, it is strongly recommended that they should primarily focus on social factors in order to minimize this risk.

The purpose of this paper is to identify and analyze the interactions among different readiness factors for implementing agility in healthcare organization. Total interpretive structural modeling (TISM) based readiness framework for agility has been developed to understand the mutual interactions among the factors and to identify the driving and dependence power of these factors.

The identification of factors is done by TISM approach used for analyzing the mutual interactions between factors. Cross-impact matrix multiplication applied to classification analysis is utilized to find the driving and dependent factors of agile readiness in healthcare.

This paper identifies 12 factors of readiness for change in literature review, which is followed by an expert interview to understand the interconnection of factors and to study interrelationships of factors. The study suggests that factors like environmental scanning, resource availability, innovativeness, cost effectiveness, organizational leadership, training and development are important for implementing/improving the readiness of agility in healthcare organizations.

This research focuses mainly on readiness factors for agility in healthcare sector.

Top management must stress on readiness factors that have a strong driving power for efficient implementation of agility in healthcare. This study helps the managers to take quick decisions, and continuous monitoring of readiness factors would be more beneficial to improve the quality of service, which makes the organization more agile.

In this research, TISM-based readiness for agile framework structural model has been proposed for healthcare organizations, which is a new effort for implementation of agility in healthcare.

This paper aims to provide a bibliometric analysis of advances in building information modeling (BIM) research globally. It provides a recent state-of-the-art assessment on trends as it relates to the architecture, engineering and construction (AEC) industry. Being a vastly emerging research area, there is a need for the appraisal of research trends.

A comprehensive bibliometric analysis was conducted using a dual step filtering system on an initial volume of 2347 documents in the first stage between 2010 and 2020, and of 311 publications in the final stage of the analyses which emphasized more on 2015–2020 from the WoS database. Frequency analyses on the sources, affiliations, authors and country/ region of publication were assessed in the first stage of the analyses. Co-authorship and evidence of author collaboration were also examined. The second stage included a co-occurrence keyword network analysis. Further, text mining/mapping of the abstract of the documents was performed.

Emerging trends in the field of BIM research include but are not limited to historical building information modeling (h-BIM) applications, the use of blockchain technology, digital twin, Construction Operations Building information exchange (COBiE), Industry Foundation Classes (IFC), dynamo-bim, energy plus software and BIM laser scanning innovations. The possibility of these innovations solving some current BIM challenges were also discussed.

The study provides an insight into the BIM research trends globally while identifying existing challenges. The study uses text mining of unstructured abstracts, which has not been reported in BIM research.

The purpose of this paper is to apply firework optimization algorithm to optimize multi-matching selective assembly problem with non-normal dimensional distribution.

In this paper, a multi-matching selective assembly approach based on discrete fireworks optimization (DFWO) algorithm is proposed to find the optimal combination of mating parts. The approach introduces new operator with the way of 3-opt and also uses a stochastic selection strategy, combines the discrete selective assembly problem with firework optimization algorithm properly and finds the best combination scheme of mating parts with non-normal dimensional distributions through powerful global search capability of the firework optimization algorithm.

The effects of different control parameters, including the number of initial fireworks and the coefficient controlling the total number of sparks generated by the fireworks on the evolution performance, are discussed, and a promising higher performance of the proposed selective assembly approach is verified through comparison with other selective assembly methods.

The best combination of mating parts is realized through the proposed selective assembly approach, and workers can select suitable mating parts under the guidance of the combination to increase the assembly efficiency and reduce the amount of surplus parts.

A DFWO algorithm is first designed to combine with multi-matching selective assembly method. For the case of an assembly product, the specific mapping rule and key technologies of DFWO algorithm are proposed.

The non‐linear damping mechanisms are expressed in two general forms: velocity dependent and displacement dependent. The non‐linear damping phenomena are expressed by an array of ‘local constants’, whose value depends upon excitation frequency, excitation amplitude, and type of non‐linearity. Thus, the non‐linear system is replaced by several localized linear systems corresponding to every discrete frequency and amplitude of excitation. Each of the localized linear systems, thus formulated, characterizes the response behaviour of the original non‐linear system, quite accurately in the vicinity of the specific frequency and amplitude of excitation. An algorithm is developed, which expresses the non‐linear damping by an array of ‘local constants’. The algorithm then employs the usual linear design tools to generate the response characteristics almost identical to the response behaviour of the non‐linear system.

Many organizations have reported significant benefits after the implementation of Lean Six Sigma (LSS). Embracing LSS requires asking some important questions: How Lean Six Sigma Readiness (LESIRE) can be measured? How can an organization identify the barriers for LESIRE? Answers to these questions are critical to both academicians and practitioners. The paper aims to discuss this issue.

This study illustrates the development process of a Lean Six Sigma Readiness (LESIRE) evaluation model to assess an organization’s readiness for LSS deployment using the fuzzy approach. The model was developed from 4 enablers, 16 criteria and 46 attributes of LSS, identified through a literature review.

To demonstrate the efficiency of the model, this study testing the LESIRE evaluation model in three Indian SMEs. Using experts’ ratings and weight, the researchers calculated the Fuzzy Lean Six Sigma index (FLSS) which indicates the LESIRE level of an organization and the Fuzzy Performance Importance Index (FPII) that helps to identify the barriers for LESIRE.

The main limitations of this study are that it did not consider the failure factors of LSS for model development and the LESIRE was only tested in manufacturing industries. Thus, future researchers could focus on developing a model with failure factors. The results obtained from the SMEs show that LESIRE is capable of assessing LESIRE in an industrial scenario and helps practitioners to measure LESIRE for the future decision making process.

The LESIRE model is easy to understand and use without much computation complexity. This simplicity makes the LESIRE evaluation model unique from other LSS models. Further, LESIRE was tested in three different SMEs, and it aided them to identify and improve their weak areas, thereby readying them for LSS deployment.

The main contribution of this study it proposes a LESIRE model that evaluates the organization for FLSS and FPII for LESIRE, which is essential for the organization embarking on an LSS journey. Further, it improves the readiness of the organization that is already practicing LSS.