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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.

Nowadays, there is a high demand for online services and applications. However, there is a challenge to keep these applications secured by applying different methods rather than using the traditional approaches such as passwords and usernames. Keystroke dynamics is one of the alternative authentication methods that provide high level of security in which the used keyboard plays an important role in the recognition accuracy. To guarantee the robustness of a system in different practical situations, there is a need to examine how much the performance of the system is affected by changing the keyboard layout. This paper aims to investigate the impact of using different keyboards on the recognition accuracy for Arabic free-text typing.

To evaluate how much the performance of the system is affected by changing the keyboard layout, an experimental study is conducted by using two different keyboards which are a Mac’s keyboard and an HP’s keyboard.

By using the Mac’s keyboard, the results showed that the false rejection rate (FRR) was 0.20, whilst the false acceptance rate (FAR) was 0.44. However, these values have changed when using the HP’s keyboard where the FRR was equal to 0.08 and the FAR was equal to 0.60.

The number of participants in the experiment, as the authors were targeting much more participants.

These results showed for the first time the impact of the keyboards on the system’s performance regarding the recognition accuracy when using Arabic free-text.

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.

Construction cost management is one of the important processes that should be achieved effectively and accurately for successful project delivery. Modern-day construction cost management demands a high level of spatial skills. Augmented reality (AR) can potentially increase the stakeholders’ spatial skills as a supportive technology to traditional cost management tools and techniques. AR is a breakthrough technology that could considerably ease execution in various industries, but AR applicability in cost management has not been studied extensively. Thus, this study aims to explore the use of AR in construction cost management tools and techniques.

Data were collected using a qualitative approach consisting of two rounds of the Delphi technique. A total of 22 experts in the construction and information technology fields were interviewed using a purposive sampling technique. The manual content analysis helped analyse data.

The study identified AR features with the potential to increase the usage of cost management tools and techniques. AR can enable spatial skills (abilities, thinking and tasks) in most cost management tools and techniques. However, technical, cultural and technical and cultural barriers obstruct the use of AR in the construction industry.

The usage of AR in construction cost management tools and techniques has not been examined in detail until now. Thus, the study was developed to meet the industry needs and fill the literature gap to investigate the potential use of AR in construction cost management tools and techniques.

Over the past few decades, the higher education landscape underwent dramatic changes, causing strain on higher education institutions (HEI) worldwide. The academic debate is filled with very well-argued accounts of these developments. Advances in information and communication technology (ICT) add an additional layer to this already complex reality. Against this backdrop, it is necessary that the question of managing and administering HEI today be rethought. Emerging technologies such as artificial intelligence (AI) and cognitive computing, augmented, virtual and mixed reality (R+), Internet of Things (IoT), blockchain, bear the promise to effectively assisting HEI administrators in navigating their institutions through the period of profound change. This chapter offers a brief account of that.

This study aims to evaluate the perceived effectiveness of residential treatment which follows the therapeutic community (TC) model for Sri Lankan individuals with substance use disorder (SUD).

A descriptive cross-sectional study was carried out in two selected residential treatment centers which follow the TC model as a rehabilitation approach. All the clients of the selected residential treatment centers were invited to take part in the study and data were collected from 75 consented male participants using an interviewer-administered questionnaire. The perceived effectiveness of the residential treatment was assessed using the Treatment Effectiveness Assessment instrument. Data were analyzed using descriptive statistics.

Most of the participants were Sinhalese (98.7%, n = 74) with a mean age of 27.0 ± 6.0 years. The average duration in which participants stayed at selected centers was 8.0 ± 4.0 months. Most of the participants reported that the encouragement during the program was sufficient (89.3%, n = 67), psychological support was satisfactory (89.3%, n = 67), freedom (69.5%, n = 52) and facilities were adequate (76.0%, n = 57) within the program. A higher proportion of participants said that the counselors and program guides were friendly (80.0%, n = 60), and participants were allowed to communicate with their family with restrictions (92.0%, n = 69). The mean perceived effectiveness score (score range 1–40) was 34.0 ± 7.0 and the four domain scores (score range 1–10) were substance use (9.0 ± 2.0), health (8.0 ± 2.0), lifestyle (9.0 ± 2.0) and community (9.0 ± 2.0). The majority (88.0%, n = 66) of the participants perceived a high level of treatment effectiveness.

There is a paucity of empirical evidence on effectiveness of TC model for individuals with SUD in South Asian countries including Sri Lanka. The findings of this study highlight that the participants perceived higher effectiveness of the residential treatment which follows the TC approach for Sri Lankan individuals with SUD.

The proliferation of lean principles in the construction industry is offset by the enduring uncertainty among industry stakeholders regarding their respective roles in lean implementation. This uncertainty is further compounded by the scarcity of empirical investigations in this area. Consequently, this study undertakes the task of bridging this knowledge gap by identifying the critical roles of lean learners and their indispensable contributions to achieving successful lean implementation.

A qualitative exploratory approach informed by an interpretivism perspective was adopted. The case study strategy was employed to gather data from three contracting organisations that had implemented lean practices. Empirical data was collected through in-depth semi-structured interviews with fifteen industry experts and complemented by document reviews. To analyse the data, a code-based content analysis approach was employed using NVivo software, while Power BI software was utilised to develop a comprehensive force-directed graph visualisation.

The research findings substantiated nine lean learners and unveiled a set of seventy-three roles associated with them. The force-directed graph facilitated the identification of lean learners and their connections to the emerged roles. Notably, the graph highlighted the pivotal role played by project managers and internal lean trainers in ensuring the success of lean implementation, surpassing the contributions of other lean learners.

The implications of findings extend to industry professionals seeking to establish a robust lean learning framework to expedite lean implementation within the construction sector. This study not only provides a comprehensive definition of lean learners’ roles but also transcends specific construction types, making it a significant catalyst for global impact.

Quality management barriers have been discovered in construction small- and medium-sized enterprises (SMEs), determining their long-term survival. Despite the recognition of Lean Six Sigma (LSS) as a valuable quality management technique for addressing the barriers faced by SMEs, LSS implementation within the construction SME context is alarmingly low. Therefore, this study aims to investigate the barriers for implementing LSS within construction SMEs and to determine the most effective strategies for overcoming these barriers.

A quantitative research approach was used, and data was collected in two stages: a questionnaire survey with 44 construction professionals and an expert opinion survey with 12 LSS specialists. The collected data was then analysed using the fuzzy TOPSIS method, achieving a higher degree of sensitivity.

The findings revealed the 15 most significant LSS barriers that need to be addressed. In addition, the ten most important strategies to be implemented in overcoming the identified barriers before LSS implementation were discovered and thematised, most notably the hiring of LSS specialists for project monitoring and the formation of a committee for strategic planning through LSS.

Previous research on LSS examined barriers and strategies for SMEs in general, but to the best of the authors’ knowledge, this study is the first of its kind, focusing especially on the construction SME context and involving the unique fuzzy TOPSIS approach.

This study examines the influence of the ownership structure of banks on investors' behavior by dissecting the investors' response to news regarding performance indicators in private and government-owned banks.

The event study methodology is used for the analysis. The data for 35 banks (out of 38), listed on the National Stock Exchange (NSE) for a duration of 230 months (January 2001 to February 2020) is collected. A set of cross-sectional regression analyses is done to identify variables influencing the returns under differential circumstances.

Private banks seem to display a sharper response to negative changes in earnings, while government-owned banks show a more robust reaction to a positive change. The contrast is seen in the variables, having a bearing on the abnormal returns After controlling for a set of factors, the regression analysis shows the ownership structure may not matter on abnormal returns (on event day), the factors such as a change in quarterly earnings, firm-size and three-year average-sales growth influence the positive and negative changes in abnormal returns of government banks, and predictability for private banks is found to be poor regarding selected indicators.

The study evaluates the role of ownership structure on the heterogeneity in investors' responses to the financial performance of banks, thereby assisting in designing strategies to ensure the optimal outcome around the quarterly earnings announcements.