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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.

Artificial intelligence (AI) tools, namely ChatGPT, Copilot (Bing) or Gemini (Bard) chatbots, have brought very rapid repercussions at education level in general, especially in higher education. Several challenges are being presented to institutions, teachers and students, highlighting the way teachers teach and students learn, and significant transformations are expected in the short term due to the availability of this type of tools. This chapter aims to analyse and discuss how the new generative AI tools, which are accessible to all, can influence teachers' instructional methods and transform the format and the ways students learn. What are the main challenges, limitations and changes that we can foresee, considering the dangers and constraints of these new tools, regarding the teaching and learning processes of young adults in higher education. A case study was selected, using an exploratory questionnaire survey of undergraduate students, which was analysed using descriptive statistics. This study demonstrated that teachers, in student perspectives, are still far integrating IA in their pedagogical strategies, mainly focussing in the dangers and limitations and little in the potential. The aim was to contribute to assess how these new working tools can be an instrument that enhances critical thinking and active and transformative learning of young adults, when used with the minimisation of risks and errors. Furthermore, it is intended to reflect on appropriate AI educational policies as well as on the development of competences and digital literacy in the training of higher education students as global citizens.

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 purpose of this study is to investigate the effectiveness of foreign aid in terms of trade in Sri Lanka. This study aims to analyze whether foreign aid from the Western countries to Sri Lanka promotes exports to the donor countries and encourages imports from Asian countries.

The results derived from the analysis suggest that there is no significant relationship between aid and trade in the long run whereas foreign aid marginally causes only the imports in the short run. Overall, the study finds that foreign aid does not significantly affect trade in Sri Lanka.

The auto regressive distributed lag bounds testing approach is used to examine the long- and short-run relations between foreign aid and trade using the annual time series data during the period from 1977 to 2019.

Sri Lanka has been one of the aid recipients among Asian countries since 1960. At the same time, Sri Lanka has been experiencing trade deficit since its independence and has to find all the ways to improve the trade. Because foreign aid has promoted trade in a numerous countries, it is high time for Sri Lanka to negotiate with the donor countries and agencies to design aid for trade promotion.

The nexus between the trade–aid link of Sri Lanka with Western and Asian country’s perspectives has not so far been investigated. The findings of this study would be a new knowledge added to the literature on the trade–aid link and be useful to the policymakers for decision-making and future researchers to cope up with further analysis.

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.