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The purpose of this paper is to identify coronavirus contact using internet of things. The disease is said to be highly contagious with the contact of infected persons. Feared to be air-borne, droplets of body fluids can transmit the disease in a matter of hours. The predominant symptoms of the COVID-19 are high fever, cough, breathing problem, etc. Recent studies have demonstrated the evolution of the disease to hide its symptoms. As it is highly transmissible, this disease might spread at an exponential rate costing the lives of thousands of people. The chain of transmission has to be detected with utmost priority through early detection and isolation of infected people. Automated internet of things (IoT) devices can be used in design and implementation of a prediction scheme for reporting the health-care risks of the patients with various parameters such as temperature, humidity and blood pressure.

IoT is a configuration of multiple autonomous and embedded wireless devices for serving a purpose. Every object possesses an individual identity and will serve to register critical events as entries for future learning and decisions. IoT plays an inevitable role in medical industries, detection of vital signs of diseases and monitoring. Among other life-threatening diseases, a new pandemic is on rise among world nations. COVID-19, a novel severe acute respiratory syndrome virus originated from animals in December 2019 and is becoming a serious menace to Governments, despite serious measures of lockdowns.

In this paper, the authors defined an architecture of an IoT system to predict the Covid-19 disease by getting the data from the human through sensors and send the data to the doctor using mobile, computer, etc. The main goal is early health surveillance by predicting COVID-19. Accordingly, the authors are able to identify both symptomatic and asymptomatic patients, which will help in the early prediction of disease.

Using the proposed method, the authors can save the time of both patient and doctor by ensuring timely medical treatment and contribute toward breaking the transmission chain. In so doing, the method also contributes toward avoiding unnecessary expenses and saving human lives.

This study aims to examine the research productivity of Pakistan Agricultural Research Council’s (PARC’s) researchers published during 2001–2020 by using scientometric indicators. The study explored the growth and collaborative trends along with authorship and institutional collaborative patterns at the national and international levels.

The study was conducted in four phases. Firstly, a search strategy was designed to retrieve reliable data sets. During the second phase, data from PARC research was retrieved from Scopus and Web of Science (WoS). In the third phase, the data were combined, and duplications were removed. Finally, the data were analysed using RStudio and VOSviewer.

The study identified 2,868 research publications from 16 communication channels spanning over the period of 2001–2020. The growth rate varied during the study period and the year 2020 was the most productive year of the organization. Most of the research was produced in multi-authorship and five authors were dominant. Pakistan Journal of Botany was the most preferred and cited source. Moreover, PARC research collaboration with Pakistani researchers was more than their international counterparts.

Like other research, this research has some limitations. For example, this research is based on secondary data extracted from WoS and Scopus databases, world-renowned online academic. However, researchers should keep in mind while interpreting the results of this study. Secondly, the research publications published by PARC researchers during 2001–2020 were considered. Finally, this research considered English language literature only.

The study’s key theoretical contribution is its strategy for merging WoS and Scopus in RStudio, while its findings could assist agriculture research stakeholders in identifying new areas of research, awards, promotions and identification of research gaps.

To the best of the author’s knowledge, this study is the first to use scientometric indicators to evaluate PARC’s research productivity. This detailed analysis provides a deeper understanding of PARC’s contribution to agriculture research and its potential implications.

This paper explores the role of Internet of things (IoT) enabling factors in adopting digital supply chain.

Analytical hierarchy process (AHP) was used to rank performance measures and prioritise the enabling factors. Semi-structured interviews were conducted to validate and support key research findings from the AHP analysis.

The results show that level of customer demand is the most important indicator in adopting IoT while the level of product/process flexibility is the least important. System integration and IoT infrastructure are the top two enabling factors in increasing the level of process stability, supply chain connectivity, and product/process flexibility, respectively. Furthermore, the study suggests that the enabling factors for IoT adoption are directly connected with organisational resources/technological capabilities that support the resource-based view theory. This research identified interdependencies between IoT enabling factors and key performance measures for IoT adoption success in managing the digital supply chain.

Supply chain managers can use the empirical findings of this study to prioritise IoT adoption, based on the relative importance of enabling factors and performance measures. The research findings are focused on broader supply chain practices of large companies rather than a specific industry and SMEs. Hence, any industry-specific adoption factors and SMEs were not evident from this study.

This research study empirically established priorities of enabling factors for IoT adoption, along with inter-dependencies among enabling factors as a basis for developing guidelines for IoT adoption.

This research is aimed at predicting tower crane accident phases with incomplete data.

The tower crane accidents are collected for prediction model training. Random forest (RF) is used to conduct prediction. When there are missing values in the new inputs, they should be filled in advance. Nevertheless, it is difficult to collect complete data on construction site. Thus, the authors use multiple imputation (MI) method to improve RF. Finally the prediction model is applied to a case study.

The results show that multiple imputation RF (MIRF) can effectively predict tower crane accident when the data are incomplete. This research provides the importance rank of tower crane safety factors. The critical factors should be focused on site, because the missing data affect the prediction results seriously. Also the value of critical factors influences the safety of tower crane.

This research promotes the application of machine learning methods for accident prediction in actual projects. According to the onsite data, the authors can predict the accident phase of tower crane. The results can be used for tower crane accident prevention.

Previous studies have seldom predicted tower crane accidents, especially the phase of accident. This research uses tower crane data collected on site to predict the phase of the tower crane accident. The incomplete data collection is considered in this research according to the actual situation.

The purpose of this paper is to present a new bond slip model for reinforced concrete structures. It consists in an interface element (3D) which represents the interface between concrete (modeled in 3D) and steel, modeled using 1D truss elements.

The formulation of the interface element is presented and verified through a comparison with an analytical solution on an academic case. Finally, the model is compared with experimental results on a reinforced concrete tie.

Contrary to the classical perfect or “no-slip” relation which supposes the same displacement between steel and concrete, the proposed model is able to reproduce both global (force-displacement curve) and local (crack openings) results.

The proposed approach, applicable to large-scale computations, represents a valuable alternative to the no-slip relation hypothesis to correctly capture the crack properties of reinforced concrete structures.

The purpose of this paper to present a practical and systematic approach to estimate the availability of a process plant using generalized stochastic Petri nets (GSPNs). The actual live problem at a fluid catalytic cracking unit (FCCU) of a refinery is used to demonstrate this approach.

A majority of models used for estimation of availability of a complex system are based on the assumptions that the failure of the system is associated with only a few states, and the system does not face different operating conditions, repair actions and common-cause failures. In reality, this is often not the case. Therefore, it is necessary to construct more sophisticated models without such assumptions. In this paper, an attempt has been made to model interaction of component failures, partial failures of components and common-cause failures.

The superiority of this approach over other modeling approaches such as fault tree and Markov analysis is demonstrated. The proposed GSPN is a promising tool that can be conveniently used to model and analyze any complex systems.

GSPN was used to model the reactor-regenerator section of FCCU, which is quite a large system, which shows the strength of modeling capability. The use of Petri nets (PNs) for modeling complex systems for the purpose of availability assessment is demonstrated in this paper. Sensitivity analysis was also carried out for various subsystem/components.

No similar work has been conducted for FCCU using GSPN as per literature incorporating different operating conditions and common-cause failures. The understanding and usage of PNs require a steep learning curve for the practitioners, and this paper provides an approach to estimate availability measures for the complex system.

The starting premise of this study is that women's empowerment is the goal for self-realization and that the support that comes from local tourism stakeholders represents an adequate base. In many rural areas, women have established self-help groups (SHGs), which facilitate the interaction with a wide range of stakeholders. The objective of this paper is to investigate the effects of SHGs on female entrepreneurship and self-employment in tourism.

To examine the research question, this study adopted a quantitative research that included a sample of 513 women in a less-advanced rural area in Serbia. For the data analysis, the generalized linear regression model (GLM) was used.

According to the results, self-employment is the leading goal of women's empowerment.

The main limitation in the research and the authors’ suggestion for future research is to increase the sample size of female respondents, so examination of their attitudes and role in the travel business in their local settings might reach higher significance. The second issue that the authors would like to point out is a highly local character of our study, so the future research should involve other rural areas in the country and from abroad (e.g. similar undeveloped countryside with noticeable, active women's role in local entrepreneurship).

The most important practical implications of this paper are twofold: (1) the results of the research have shown that the tourist potential of rural areas can be enhanced through local tourism stakeholders' support; (2) women without professional interest or jobs in rural areas, especially in the areas where the population is traditionally dominated by men (husband/brother/father), have a chance to earn and to be economically more independent. This research can affect future studies to investigate other aspects of empowerment depending on the difference of regions, from one side, and also alternative opportunities for tourism and local development in less-advanced rural areas, from another side.

The study analyzes the tourism potential of the rural areas (which are less advanced and mostly very poor in developing countries, such as Serbia). In this case, there are opportunities to increase employment, social inclusion of women, development of new tourism strategies, implementation of destination marketing, etc. Moreover, it contributes to future research in the field of stakeholders in tourism strategies.

To produce a coated fabric, a base fabric may be completely or partially coated with a polymer layer, which changes the properties of the new system relative to the base fabric. The purpose of this paper is to analyze the influence of the thermal transfer material and its shape on the deformability of knitted fabrics during the uniaxial extension and to determine the residual deformation of the thermoplastic transfer element of coated fabrics after unloading.

Knitted fabrics were partially and entirely coated with heat transfer material. For partial coating, square pieces of three different transfer materials were bonded on the middle of the specimen. They were solid, perforated with either nine circular holes or six rectangular holes. A heat seal press was used to laminate knitted fabrics. The samples were subjected to uniaxial tensile testing. The characteristics such as strain at maximum force, strain at break, and strain at low stress were measured. After stretching and relaxation of the specimens, the residual deformation of the heat transfer element was also investigated.

The results indicated that coating knitted fabrics with transfer material may decrease their stretchability. The experiments show that the decrease in stretchability and in the degree of residual deformation after stretching and relaxing depend on the knitted structure, the shape of the transfer element, and the degree to which the fabric is coated.

This study examines the influence of heat transfer material which may be not only entirely but also partially joined with knitted fabric layer on the deformability and shape stability of this system.

The purpose of this paper is to analyze and compare the effects of conventional and unconventional FDI on the host country in a more comprehensive and systematic way.

Both the OLI paradigm and the imbalance theory are linked to the diamond model in order to compare the effects of conventional and unconventional FDI on the host country. This methodology is then applied to the real world as a case study, FDI toward the Korean automobile industry.

Conventional FDI is often said to be more beneficial to the host country than the unconventional type. However, the actual effect of unconventional FDI is shown to be more positive with better management and is often larger than perceived. Therefore, unconventional FDI emerges as important as conventional FDI for sustainable economic development.

In general, unconventional FDI has often been criticized severely because of misperceptions derived from the dominance of conventional FDI on theoretical aspects, incomprehensive perspectives toward assessing the effects of FDI, and negative political views. Therefore, rigorous and holistic case study analyses based on solid analytical tools are needed in order to better understand the effects of unconventional FDI and to draw up effective and proper FDI promotion policies.

This paper provides a way to better understand the effect of unconventional FDI on the host country comprehensively and systematically by expanding and deepening existing theories. Based on this, the effects of conventional and unconventional FDI on the host country are compared theoretically and empirically, particularly with the case of the Korean automobile industry.

Cloud Computing (CC) technology is getting implemented rapidly in the educational sector to improve learning, research and other administrative process. As evident from the literature review, most of these implementations are happening in the western countries such as USA, UK, while the level of implementation of CC in developing countries such as India is rare. Moreover, implementing CC technology in the educational sector require various decisions to be made by the managers of the Information Technology (IT) department such as selecting suitable deployment model, vendor providing cloud service, etc. in their respective university or institute. The purpose of this paper is to attempt to address one such decision. Since, different types of CC deployment are available; selecting a suitable one plays a key role, as it might have an impact on the requirements of various stakeholders such as students, teachers, administrative staff (especially the staff members in the IT department), etc. apart from affecting the overall performance of the facilities such as a laboratory. Naturally, a proper decision by analysing multiple perspectives has to be made while carrying out such strategic initiatives by any educational institute.

A case study methodology has been chosen as the research methodology to discuss and demonstrate the above decision problem that was faced in real time by one of the educational institutes in India, offering high-quality management education. The IT managers of this institute were planning to switch over to CC technology for the computer laboratory and they have to make a decision of choosing suitable alternative CC deployment models such as private cloud (PRC), public cloud (PUC), community cloud (COC), hybrid cloud (HYC), etc. by analysing and comparing them based on various factors and perspectives such as elasticity, availability, scalability, etc. Since, multiple factors are involved in making such a strategic decision, the most commonly used Multi-Criteria Decision Making (MCDM) model – namely, the Analytic Hierarchy Process (AHP) is used as a decision support during the decision making process.

The team of decision makers, who were planning to implement CC in the case institute, found that PRC is best as they believed that it would provide adequate cost savings, apart from providing necessary security to maintain confidential information such as student's detail, grades, etc.

The results obtained are based on a single case study. Hence, they cannot be generalized for institutions across educational sector. However, the decision making situation and understanding its impact on the stakeholders of the educational institute can be common across various educational institute.

Using a real-life case study of an educational institute, this paper presented a strategic decision making situation, which needs to be considered by the IT managers of the educational institutes when they decide to switch over to CC technology. Various criteria to be considered during the decision making process was identified from the literature review were identified and enumerated. These factors would useful for the IT managers of the different educational institute and they can suitably add or delete these decision criteria as per their requirements and situation at hand. Moreover, the algorithm of AHP, which was used as a decision support, was presented in a step-by-step manner, which should be beneficial for the practitioners to apply the same for similar decision making situations.

It is believed that this paper would be the first to report on a strategic decision of choosing the deployment model for CC technology especially in the educational sector. Similarly, this paper would also contribute to the field of CC, as it lists out the decision criteria that are to be considered for making the above decision, which has not got adequate importance. Lastly, this paper is also unique in the realm of AHP because application for a decision problem in the field of CC especially in the educational sector is least reported.