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The purpose fo this paper is to Monitor and sense the sysmptoms of COVID-19 as a preliminary measure using electronic wearable devices. This variability is sensed by electrocardiograms observed from a multi-parameter monitor and electronic wearable. This field of interest has evolved into a wide area of investigation with today’s advancement in technology of internet of things for immediate sensing and processing information about profound pain. A window span is estimated and reports of profound pain data are used for monitoring heart rate variability (HRV). A median heart rate is considered for comparisons with a diverse range of variable information obtained from sensors and monitors. Observations from healthy patients are introduced to identify how root mean square of difference between inter beat intervals, standard deviation of inter-beat intervals and mean heart rate value are normalized in HRV analysis.

The function of a human heart relates back to the autonomic nervous system, which organizes and maintains a healthy maneuver of inter connected organs. HRV has to be determined for analyzing and reporting the status of health, fitness, readiness and possibilities for recovery, and thus, a metric for deeming the presence of COVID-19. Identifying the variations in heart rate, monitoring and assessing profound pain levels are potential lives saving measures in medical industries.

Experiments are proposed to be done in electrical and thermal point of view and this composition will deliver profound pain levels ranging from 0 to 10. Real time detection of pain levels will assist the care takers to facilitate people in an aging population for a painless lifestyle.

The presented research has documented the stages of COVID-19, symptoms and a mechanism to monitor the progress of the disease through better parameters. Risk factors of the disease are carefully analyzed, compared with test results, and thus, concluded that considering the HRV can study better in the presence of ignorance and negligence. The same mechanism can be implemented along with a global positioning system (GPS) system to track the movement of patients during isolation periods. Despite the stringent control measurements for locking down all industries, the rate of affected people is still on the rise. To counter this, people have to be educated about the deadly effects of COVID-19 and foolproof systems should be in place to control the transmission from affected people to new people. Medications to suppress temperatures, will not be sufficient to alter the heart rate variations, and thus, the proposed mechanism implemented the same. The proposed study can be extended to be associated with Government mobile apps for regular and a consortium of single tracking. Measures can be taken to distribute the low-cost proposal to people for real time tracking and regular updates about high and medium risk patients.

Online social networks are increasingly important venues for businesses to promote their products and image. However, information propagation in online social networks is significantly more complicated compared to traditional transmission media such as newspaper, radio, and television. In this chapter, we will discuss research on modeling and forecasting diffusion of virally marketed content in social networks. Important aspects include the content and its presentation, the network topology, and transmission dynamics. Theoretical models, algorithms, and case studies of viral marketing will be explored.

Introduction This paper is a summary of an extensive research project which reviewed the store location literature and surveyed the practices of UK multiple retailers' location assessors. Upon this state of the art review, the authors examined the model building theory, before developing an operational model capable of predicting turnover potential for new store locations. In this instance, the paper concentrates on the survey, while endeavouring to introduce the model building concept.

This paper aims to suggest the preferred mode of financing for major sub-sectors of infrastructure: roads, seaports, telecommunication and energy by examining which mode of infrastructure financing – public, private or public–private partnership (PPP) – has the maximum positive impact on the overall GDP of India. The same exercise was carried out for the overall infrastructure sector by integrating data from all the four sub-sectors.

The structural vector autoregressive approach was used with the period of analysis taken from 1995 to 2014. The stationary properties of the variables were checked by the Phillips–Perron unit root.

The PPP mode of financing was found to make the maximum positive impact on the GDP of India. Considering the four sub-sectors individually, it was concluded that the private mode of financing in roads, energy and telecom sectors has the maximum positive impact on the GDP, while the PPP gives optimal benefit to the seaports sector.

Results will aid the Indian Government and policymakers to efficiently design and develop their economic policies accordingly.

The study is novel in a sense that it helps to address the lack of research into the area of infrastructure financing in India.

The purpose of this paper is to ascertain the current status of knowledge management (KM) adoption in the Indian manufacturing organizations and to develop a comprehensive research model to investigate the impact of enabling conditions for KM and the impact of KM on organizational performance through structural equation modelling.

A descriptive research design is adopted and primary data are collected through structured questionnaire. In total, 251 responses were obtained from the top- and middle-level managers and the structural relationships in the research model were tested using the partial least squares method.

The results revealed a moderate adoption of KM by the manufacturing companies. Also, a significant impact of the enablers on KM processes is observed. It is found that Indian manufacturing is operating in labour-intensive traditional methods and KM is still in its infancy. The impact of KM on the performance is moderate and contradicting the extant literature, the impact of KM on innovation is found to be weak.

The study is carried out in companies located in India and hence generalizing the findings should be done with caution. The sample is dominated by small- and medium-sized enterprises (SMEs) which may have implications for the findings.

As manufacturing companies in the developing countries such as India experience a greater competition in the globalized economy, adoption of KM will perk up the performance of the organizations. Practicing managers need to create a culture that facilitates KM adoption. Policy makers shall support SMEs in technology adoption, R&D, skill development and so on.

Previous KM studies in India are fragmented and analysed KM processes and KM enablers in isolation. Also, the holistic studies on KM literature focussed only on one or two facets of KM. A study which investigates the interactions between KM enablers, KM processes and organizational performance and innovation is scarce. The scarcity of empirical studies on KM and a dearth of understanding of the KM concept led to this maiden attempt to provide a comprehensive understanding of KM framework in the Indian manufacturing sector. A validated structured questionnaire for exploring KM practices in the Indian context is developed. Though the importance of the influence of external factors is theoretically emphasized, an empirical investigation is first of its kind.

Data quality is a major challenge in data management. For organizations, the cleanliness of data is a significant problem that affects many business activities. Errors in data occur for different reasons, such as violation of business rules. However, because of the huge amount of data, manual cleaning alone is infeasible. Methods are required to repair and clean the dirty data through automatic detection, which are data quality issues to address. The purpose of this work is to extend the density-based data cleaning approach using conditional functional dependencies to achieve better data repair.

A set of conditional functional dependencies is introduced as an input to the density-based data cleaning algorithm. The algorithm repairs inconsistent data using this set.

This new approach was evaluated through experiments on real-world as well as synthetic datasets. The repair quality was determined using the F-measure. The results showed that the quality and scalability of the density-based data cleaning approach improved when conditional functional dependencies were introduced.

Conditional functional dependencies capture semantic errors among data values. This work demonstrates that the density-based data cleaning approach can be improved in terms of repairing inconsistent data by using conditional functional dependencies.

This paper introduces an approach to minimize the total designer effort for building XML data integration systems. Since fully automatic schema mapping generation is infeasible, in our view such an approach can be used as a semi‐automatic tool for XML schemas mediation. A method is proposed to query XML documents through a mediation layer. Such a layer is introduced to describe the mappings between global XML schema and local heterogeneous XML schemas. It produces a uniform interface over the local XML data sources, and provides the required functionality to query these sources in a uniform way. It involves two important units: the XML Metadata Document (XMD) and the Query Translator. The XMD is an XML document containing metadata, in which the mappings between global and local schemas are defined. The XML Query Translator which is an integral part of the system is introduced to translate a global user query into local queries by using the mappings that are defined in the XMD.

Demand for express parcel delivery has been greatly increasing in South Korea due to the growth of B2C e-commerce activities. It is imperative that parcel carriers have good insights into the performance of their distribution networks with different levels of demand and plan ahead for the adaptations in order to be able to meet future changes in demand. This paper proposes a framework to evaluate the likelihood of parcels arriving on-time to their destinations (local service reliability) with a strong focus being placed on their spatial distribution. The resulting maps of local service reliability allow us to identify specific locales that would suffer the most from a capacity overflow in the networking system, thus pointing to areas that need immediate attention. Furthermore, this paper attempts to identify potential factors which could affect the spatial variation of local service reliability.

XML is becoming one of the most important structures for data exchange on the web. Despite having many advantages, XML structure imposes several major obstacles to large document processing. Inconsistency between the linear nature of the current algorithms (e.g. for caching and prefetch) used in operating systems and databases, and the non‐linear structure of XML data makes XML processing more costly. In addition to verbosity (e.g. tag redundancy), interpreting (i.e. parsing) depthfirst (DF) structure of XML documents is a significant overhead to processing applications (e.g. query engines). Recent research on XML query processing has learned that sibling clustering can improve performance significantly. However, the existing clustering methods are not able to avoid parsing overhead as they are limited by larger document sizes. In this research, We have developed a better data organization for native XML databases, named sibling‐first (SF) format that improves query performance significantly. SF uses an embedded index for fast accessing to child nodes. It also compresses documents by eliminating extra information from the original DF format. The converted SF documents can be processed for XPath query purposes without being parsed. We have implemented the SF storage in virtual memory as well as a format on disk. Experimental results with real data have showed that significantly higher performance can be achieved when XPath queries are conducted on very large SF documents.