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The computational model proposed in this work uses the data's of COVID-19 cases in India. From the analysis, it can be observed that the proposed immunity model decides the recovery rate of COVID −19 patients; moreover, the recovery rate does not depend on the age of the patients. These analytic models can be used by public health professionals, hospital administrators and epidemiologists for strategic decision-making to enhance health requirements based on various demographic and social factors of those affected by the pandemic. Mobile-based computational model can be used to compute the travel history of the affected people by accessing the near geographical maps of the path traveled.

In this paper, the authors developed a pediatric and geriatric person’s immunity network-based mobile computational model for COVID-19 patients. As the computational model is hard to analyze mathematically, the authors simplified the computational model as general COVID-19 infected people, the computational immunity model. The model proposed in this work used the data's of COVID-19 cases in India.

This study proposes a pediatric and geriatric people immunity network model for COVID- 19 patients. For the analysis part, the data's on COVID-19 cases in India was used. In this model, the authors have taken two sets of people (pediatric and geriatric), both are facing common symptoms such as fever, cough and myalgia. From the analysis, it was observed and also proved that the immunity level of patients decides the recovery rate of COVID-19 patients and the age of COVID-19 patients has no significant influence on the recovery rate of the patient.

COVID-19 has created a global health crisis that has had a deep impact on the way we perceive our world and our everyday lives. Not only the rate of contagion and patterns of transmission threatens our sense of agency, but the safety measures put in place to contain the spread of the virus also require social distancing. The novel model in this work focus on the Indian scenario and thereby may help Indian health organizations for future planning and organization. The factors model in this work such as age, immunity level, recovery rate can be used by machine leaning models for predicting other useful outcomes.

Artificial Intelligence (AI) has surpassed expectations in opening up different possibilities for machines from different walks of life. Cloud service providers are pushing. Edge computing reduces latency, improving availability and saving bandwidth.

The exponential growth in tensor processing unit (TPU) and graphics processing unit (GPU) combined with different types of sensors has enabled the pairing of medical technology with deep learning in providing the best patient care. A significant role of pushing and pulling data from the cloud, big data comes into play as velocity, veracity and volume of data with IoT assisting doctors in predicting the abnormalities and providing customized treatment based on the patient electronic health record (EHR).

The primary focus of edge computing is decentralizing and bringing intelligent IoT devices to provide real-time computing at the point of presence (PoP). The impact of the PoP in healthcare gains importance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients. The impact edge computing of the PoP in healthcare gains significance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients.

The utility value of sensors data improves through the Laplacian mechanism of preserved PII response to each query from the ODL. The scalability is at 50% with respect to the sensitivity and preservation of the PII values in the local ODL.

This paper aims to address the role of Internet of Things (IoT) in preventing COVID-19. The IoT devices can be used in various ways to track the patients and suspected person. Remote data collection can be done with the help of IoT and sensors. Later, the data can be analyzed with the help of data science engineers and researchers to predict and prevent the COVID-19.

IoT is a creative mean of amalgamating clinical gadgets and their applications to associate with the human services and data innovation frameworks. An investigation on the conceivable outcomes of defying progressive COVID-19 pandemic by implementing the IoT approach while offering treatment to all classes of patient without any partiality in poor and rich. The information sharing, report checking, patient tracking, data social affair, investigation, cleanliness clinical consideration and so forth are the different cloud-based administrations of IoT. It can totally change the working format of the medical services while rewarding the huge volume of patients with a predominant degree of care and more fulfilment, particularly during this pandemic of COVID-19 lockdown. Health workers can quickly focus on patient zero and identify everyone who has come into contact with the infected person and move these people to quarantine/isolation. As COVID-19 has emerged from the Wuhan province of China, IoT tools such as geographic information system could be used as an effective tool to curb the spread of pandemics by acting as an early warning system. Scanners at airports across the world could be used to monitor temperature and other symptoms. This paper addresses the role of IoT in preventing COVID-19.

In the period of continuous pandemic of COVID-19, IoT offers many propelled cloud-based administrations and offices to serve a greater number of patients effectively. The remote medicinal services framework provides a lot of significance in such a crucial time of lockdown. The powerful interconnected arrangement of gadgets, applications, Web, database and so on encourages the consumers to benefit the administrations in smart way. IoT additionally advances its administrations by building up the quality culture of perceptive medicinal services or portable centre. It is a “distinct advantage innovation,” which may totally change the practices universally. Indeed, even its quality administrations in this extreme time make this methodology progressively productive and beneficial. IoT helps in observing and tracking more recognized people and patients in remote areas for their human service prerequisites. The customary medicinal services are probably going to observe a huge change in perspective sooner rather than later, as the computerized revolution would place cutting-edge innovation and its associated items in the possession of the patients and give both patients and doctors in remote areas better access to quality clinical services.

The contemporary exploration study focuses on the proposed IoT system for the treatment of patients in this progressing COVID-19. The working principle of IoT approach incorporates the mix of human services apparatuses, clinical treatment framework, Web organize, programming and administrations. IoT framework empowers the information assortment, report observing, understanding database, testing pictures and investigation and so forth. Data has been collected through online mode; in this study, the authors adopted empirical research design. Total 150 (118/150 = 78.66% respondent response ratio) online questionnaires were sent in the Chennai city of Tamilnadu, India. The participated nature of work is clinical examination in critical care division.

The purpose of this study is to navigate the process of transforming Wikipedia articles into audio files for library readers. The system provides a feasible manner of listening to Wikipedia content, accommodating diverse learning preferences and enlarging knowledge in education society.

This framework has been constructed using the Python programming languages in the Linux operating platform. Application programming interface and Google text-to-speech (TTS) are required as additional software packages to design this prototype system. Transform any Wikipedia pages into audio files through Wikitrola for libraries and information centers. Wikipedia articles are directly transformed into audio, as these integrate the content seamlessly for the user experience. The whole system has been designed and configured on the basis of machine learning to provide dynamic services among the readers.

The viewer could use the machine learning system to turn Wikipedia articles into audio files, allowing them to listen to Wikipedia content in audio format. This would make information more accessible and adaptable to diverse learning modes, allowing written content to be engaged in novel and visionary ways.

The insightful observation in connection with the paper is that it shows how to convert text-based material into audio through the Google TTS and machine learning Python programming and finally incorporate them in Wikipedia articles. A harmonious system of information dissemination and technical education is established. This approach shows the effectiveness of imagination and the use of programming tools to enhance learning and knowledge-seeking processes.

The purpose of this paper is to review phytochemical potential of acacia and its associated health advantages. Acacia a moderate-sized, deciduous tree and recognised as health-promoting species because of availability of essential bioactive components. The bioactive compounds such as tannins, flavonoids, alkaloids, fatty acids and polysaccharides (gums) present in the plant parts of acacia, namely, bark, leaves, flowers, fruits, twigs and seeds, have medicinal value and thus are used to overlay the formulations of plant-based drugs and value-added foods.

Major well-known bibliometric information sources such as Web of Science, Scopus, Mendeley and Google Scholar were searched with keywords such as “nutrition value of acacia”, “bioactive compounds”, “health benefits”, “processing and safety” were chosen to obtain a database of 1,428 papers. The search considered papers in the English language from the past 18 years of publication in journals (2004–2022). The article selection process consisted of the screening of titles and abstracts, based on inclusion and exclusion criteria. Articles that did not have acacia components as a study objective were taken into consideration for exclusion. A final database of 87 scientific sources was made after sorting and classifying them according to different criteria based on topic relevance, country of origin and year of publication. Articles with other random descriptors were also searched to complement the discussion of the results obtained.

The literature reflected that acacia contains all necessary phytochemicals like polyphenols, flavonoids, terpenoids, glucosinolates, alkaloids and carotenoids along with essential macro, micro-nutrients. Furthermore, processing methods such as soaking, cooking, roasting and dehusking significantly reduced the anti-nutritional factors present in acacia seeds of different species. This review also focused on the processing methods that are used to eliminate or lower down the anti-nutritional factors from the seeds. Previous findings related to acacia plant parts with respect to food development are explored and mentioned.

This review emphasised mainly on recent studies that had been reported on ethnomedical acacia plants therapeutically, commercially and exponentially for further studies to increase the utilisation in food processing.

This research aims to analyze the literature on Quality 4.0 and pinpoint the essential factors contributing to its success. Additionally, the research aims to develop a framework that can be used to create a capable workforce necessary for the successful implementation of Quality 4.0.

By following a systematic approach, the authors could ensure that their literature review was comprehensive and unbiased. Using a set of pre-determined inclusion and exclusion criteria, the authors screened 90 research articles to obtain the most relevant and reliable information for their study.

The authors' review identified essential findings, including the evolution of literature in the field of Quality 4.0 and the systematization of previous literature reviews focusing on training and development. The authors also identified several training barriers to implementing Quality 4.0 and proposed a model for building a competent workforce using Kolb's experiential learning model.

The authors' research offers insights into the training barriers that must be considered when building a competent workforce. Using the framework proposed in the authors' research, consultants and managers can better integrate Quality 4.0 into their organizations.

The adoption of Quality 4.0 has significant social implications and is essential for advancing sustainability. It can improve efficiency, reduce waste, minimize environmental impacts and better meet the needs and expectations of stakeholders.

The authors' study stands out as one of the earliest reviews of the literature on Quality 4.0 to incorporate the theory-context-method (TCM) framework, allowing to provide unique insights into future research directions that had not been previously explored.

The work presents a novel implementation of the generalized minimum residual (GMRES) solver in conjunction with the interpolating meshless local Petrov–Galerkin (MLPG) method to solve steady-state heat conduction in 2-D as well as in 3-D domains.

The restarted version of the GMRES solver (with and without preconditioner) is applied to solve an asymmetric system of equations, arising due to the interpolating MLPG formulation. Its performance is compared with the biconjugate gradient stabilized (BiCGSTAB) solver on the basis of computation time and convergence behaviour. Jacobi and successive over-relaxation (SOR) methods are used as the preconditioners in both the solvers.

The results show that the GMRES solver outperforms the BiCGSTAB solver in terms of smoothness of convergence behaviour, while performs slightly better than the BiCGSTAB method in terms of Central processing Unit (CPU) time.

MLPG formulation leads to a non-symmetric system of algebraic equations. Iterative methods such as GMRES and BiCGSTAB methods are required for its solution for large-scale problems. This work presents the use of GMRES solver with the MLPG method for the very first time.

Many investigations are going on in monitoring, contact tracing, predicting and diagnosing the COVID-19 disease and many virologists are urgently seeking to create a vaccine as early as possible. Even though there is no specific treatment for the pandemic disease, the world is now struggling to control the spread by implementing the lockdown worldwide and giving awareness to the people to wear masks and use sanitizers. The new technologies, including the Internet of things (IoT), are gaining global attention towards the increasing technical support in health-care systems, particularly in predicting, detecting, preventing and monitoring of most of the infectious diseases. Similarly, it also helps in fighting against COVID-19 by monitoring, contract tracing and detecting the COVID-19 pandemic by connection with the IoT-based smart solutions. IoT is the interconnected Web of smart devices, sensors, actuators and data, which are collected in the raw form and transmitted through the internet. The purpose of this paper is to propose the concept to detect and monitor the asymptotic patients using IoT-based sensors.

In recent days, the surge of the COVID-19 contagion has infected all over the world and it has ruined our day-to-day life. The extraordinary eruption of this pandemic virus placed the World Health Organization (WHO) in a hazardous position. The impact of this contagious virus and scarcity among the people has forced the world to get into complete lockdown, as the number of laboratory-confirmed cases is increasing in millions all over the world as per the records of the government.

COVID-19 patients are either symptomatic or asymptotic. Symptomatic patients have symptoms such as fever, cough and difficulty in breathing. But patients are also asymptotic, which is very difficult to detect and monitor by isolating them.

Asymptotic patients are very hazardous because without knowing that they are infected, they might spread the infection to others, also asymptotic patients might be having very serious lung damage. So, earlier prediction and monitoring of asymptotic patients are mandatory to save their life and prevent them from spreading.

This study examines whether certification improves the dimensions of total quality management (TQM) and whether the impact of certification is similar across companies of different sizes and industries. The benefits of certification for companies have been widely discussed in recent years. The general debate has been partly marked by the dispute about whether companies will benefit more from certification or the implementation of TQM. This debate has led to numerous studies on the benefits of certification; however, few studies simultaneously have examined traditional TQM issues and the requirements of the new quality standard, ISO 9001: 2015, as well as the updated European Foundation for Quality Management (EFQM) criteria.

This study was conducted via a survey of Finnish SMEs and covered both industrial and service companies. The study comprehensively compared industrial companies with service companies and small companies with medium-sized companies.

In industrial and small enterprises, certification clearly has a positive effect on the dimensions of TQM, but a similar effect was not observed in medium-sized enterprises or in the service sector.

This is one of the first studies to examine the effect of certification on TQM in different types of SMEs while simultaneously considering EFQM and ISO 9001:2015 in Finland. The significant originality of this research lies in the formation of a comprehensive research framework for the dimensions of TQM.

This study investigates the effect of total quality management (TQM) on small and medium enterprises (SMEs) ' profitability by examining whether the new TQM dimensions of risk management, digitalization, stakeholder management and system deployment, facilitate the impact of traditional TQM dimensions on profitability.

A structured survey of 271 Finnish SMEs divided into industry companies and service companies was used.

Risk management facilitates the relation between continuous improvement and company profitability. Digitalization does not facilitate the relation between TQM dimensions and company profitability. Stakeholder management facilitates the relation between management/leadership and company profitability, customer focus and company profitability and continuous improvement and company profitability. System deployment does not facilitate the relation between TQM dimensions and company profitability.

Most prior studies are based on the traditional TQM classification. The TQM dimensions of this study are more comprehensive than previous studies and take into account the latest trends in business development. The findings of this study differ from most previous studies and provide a source of reflection for SME management on how TQM should be implemented so that it affects the company's profitability.