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A cross‐sectional survey of 300 male university students (18‐25 years) in the United Arab Emirates was carried out to study the relationship between obesity and some lifestyle factors. Obesity was determined using body mass index (weight/height²), with cut‐off of <25 and ≥25, to represent non‐obese and obese students, respectively. The overall prevalence of obesity was 35.7 per cent, which was higher than their counterpart female students. The risk of obesity was found to be greater among those who had a family history of obesity (relative risk, RR=1.88), watched television for more than four hours a day (RR=1.31), were smokers (RR=1.35), were not practising sport (RR=1.77) and had a car (RR=1.23). However, only family history of obesity was found to be statistically significant. The study suggests that social and lifestyle factors are important factors for the occurrence of obesity among male university students.

The aim of this paper is, first, to discuss how the risk perceptions of computer end‐users may be influenced by improving the process of risk communication by embedding symbols and graphics within information security messages. The second aim is to describe some pilot study research that the authors have conducted in an attempt to ascertain whether the embedding of symbols and graphics within information security messages achieves a shift in the risk perceptions of computer end‐users.

Two pilot studies were undertaken. The objective of each study was to establish whether the embedding of a relevant graphic relating to some known aspect of information security, when placed inside an information security message, would have any influence on the information security risk perceptions of any individual to whom the message was being communicated. In both studies, the method of eliciting a response from each participant involved the use of a type of semantic differential (SD) grid.

On completing an analysis of the responses to the SD grid survey for both studies, no statistically significant differences were detected between the groups with respect to any of the six relevant scales. Nevertheless, it seems that the differences were large enough for the present authors to be convinced that the SD measures used are an appropriate survey technique for future studies in a workplace environment.

The research subjects (i.e. survey participants) for both pilot studies were students of the University of South Australia. There are many ways in which information risk communication could be made more effective and this paper only attempts to show how graphics and symbols could be used to convey risk messages more effectively. This paper does not in any way attempt to provide any “silver‐bullet” solutions for management in terms of what they can do towards managing information risk.

The ultimate objective of this research is to subsequently advise management on how they can communicate information risk simply and more effectively to achieve the final outcome, i.e. the mitigation of actual risks.

It is believed that, if the effectiveness of the various forms of risk communication within an organisation can be increased, then the general perception of the risks to the information systems will be more realistic.

A zero-day vulnerability is a complimentary ticket to the attackers for gaining entry into the network. Thus, there is necessity to device appropriate threat detection systems and establish an innovative and safe solution that prevents unauthorised intrusions for defending various components of cybersecurity. We present a survey of recent Intrusion Detection Systems (IDS) in detecting zero-day vulnerabilities based on the following dimensions: types of cyber-attacks, datasets used and kinds of network detection systems.

Purpose: The study focuses on presenting an exhaustive review on the effectiveness of the recent IDS with respect to zero-day vulnerabilities.

Methodology: Systematic exploration was done at the IEEE, Elsevier, Springer, RAID, ESCORICS, Google Scholar, and other relevant platforms of studies published in English between 2015 and 2021 using keywords and combinations of relevant terms.

Findings: It is possible to train IDS for zero-day attacks. The existing IDS have strengths that make them capable of effective detection against zero-day attacks. However, they display certain limitations that reduce their credibility. Novel strategies like deep learning, machine learning, fuzzing technique, runtime verification technique, and Hidden Markov Models can be used to design IDS to detect malicious traffic.

Implication: This paper explored and highlighted the advantages and limitations of existing IDS enabling the selection of best possible IDS to protect the system. Moreover, the comparison between signature-based and anomaly-based IDS exemplifies that one viable approach to accurately detect the zero-day vulnerabilities would be the integration of hybrid mechanism.

The booming of the Internet of things (IoT) and artificial intelligence (AI) techniques contributes to knowledge adoption and management innovation for the healthcare industry. It is of great significance to transport the medical resources to required places in an efficient way. However, it is difficult to exactly discover matched transportation resources and deliver to its destination due to the heterogeneity. This paper studies the medical transportation resource discovery mechanism, leading to efficiency improvement and operational innovation.

To solve the transportation resource semantic discovery problem under the novel cloud environment, the ontology modelling approach is used for both transportation resources and tasks information modes. Besides, medical transportation resource discovery mechanism is proposed, and resource matching rules are designed including three stages: filtering reasoning, QoS-based matching and user preferences-based rank to satisfy personalized demands of users. Furthermore, description logic rules are built to express the developed matching rules.

An organizational transportation case is taken as an example to describe the medical transportation logistics resource semantic discovery process under cloud medical service scenario. Results derived from the proposed semantic discovery mechanism could assist operators to find the most suitable resources.

The case study validates the effectiveness of the developed transportation resource semantic discovery mechanism, contributing to knowledge management innovation for the medical logistics industry.

To improve task-resource matching accuracy under cloud scenario, this study develops a transportation resource semantic discovery procedure from the viewpoint of knowledge management. The novel knowledge management practice contributes to operational management of the cloud medical logistics service by introducing ontology modelling and creative management.

The paper aims to present a conceptual framework that helps in incorporating sustainability into software projects, highlights the importance of project sustainability and provides an extensive review of recent relevant contributions across various fields.

The authors carried out a systematic bibliographic search on relevant published materials to analyse links between the two disciplines (sustainability and software projects). Furthermore, content analysis was applied to the final selected publications to identify and classify relevant triple bottom line (TBL) aspects to develop the framework.

The inclusion of TBL-related aspects is the most efficient and effective method used to incorporate sustainability into projects. Most of the relevant contributions in the software literature have focussed on either project product or project process or on one or two dimensions of sustainability rather than the three dimensions of the TBL theory. This study contributes by proposing a conceptual framework that encompasses TBL-related aspects for incorporating sustainability into processes and products of software projects.

Validating the proposed framework empirically could be an interesting research issue. In addition, future works may focus on different types of industries, such as information systems, telecommunications and service sectors, which have seldom been studied in the literature.

Software companies – or other relevant organisations – may use the proposed framework as a measurement tool to evaluate the environmental and social impacts of their current products and project management practices. Consequently, these organisations may pay more attention to incorporating sustainability into their project management practices.

The proposed framework may contribute towards a more sustainable orientation by providing a unique combination of TBL-related aspects that gives academics and practitioners a better understanding of how software projects can be managed sustainably.

Software fault prediction is an important concept that can be applied at an early stage of the software life cycle. Effective prediction of faults may improve the reliability and testability of software systems. As service-oriented architecture (SOA)-based systems become more and more complex, the interaction between participating services increases frequently. The component services may generate enormous reports and fault information. Although considerable research has stressed on developing fault-proneness prediction models in service-oriented systems (SOS) using machine learning (ML) techniques, there has been little work on assessing how effective the source code metrics are for fault prediction. The paper aims to discuss this issue.

In this paper, the authors have proposed a fault prediction framework to investigate fault prediction in SOS using metrics of web services. The effectiveness of the model has been explored by applying six ML techniques, namely, Naïve Bayes, Artificial Networks (ANN), Adaptive Boosting (AdaBoost), decision tree, Random Forests and Support Vector Machine (SVM), along with five feature selection techniques to extract the essential metrics. The authors have explored accuracy, precision, recall, f-measure and receiver operating characteristic curves of the area under curve values as performance measures.

The experimental results show that the proposed system can classify the fault-proneness of web services, whether the service is faulty or non-faulty, as a binary-valued output automatically and effectively.

One possible threat to internal validity in the study is the unknown effects of undiscovered faults. Specifically, the authors have injected possible faults into the classes using Java C3.0 tool and only fixed faults are injected into the classes. However, considering the Java C3.0 community of development, testing and use, the authors can generalize that the undiscovered faults should be few and have less impact on the results presented in this study, and that the results may be limited to the investigated complexity metrics and the used ML techniques.

In the literature, only few studies have been observed to directly concentrate on metrics-based fault-proneness prediction of SOS using ML techniques. However, most of the contributions are regarding the fault prediction of the general systems rather than SOS. A majority of them have considered reliability, changeability, maintainability using a logging/history-based approach and mathematical modeling rather than fault prediction in SOS using metrics. Thus, the authors have extended the above contributions further by applying supervised ML techniques over web services metrics and measured their capability by employing fault injection methods.

To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International Roughness Index (IRI). Nonetheless, the behavior of those parameters throughout pavement life cycles is associated with high uncertainty, resulting from various interrelated factors that fluctuate over time. This study aims to propose the use of dynamic Bayesian belief networks for the development of time-series prediction models to probabilistically forecast road distress parameters.

While Bayesian belief network (BBN) has the merit of capturing uncertainty associated with variables in a domain, dynamic BBNs, in particular, are deemed ideal for forecasting road distress over time due to its Markovian and invariant transition probability properties. Four dynamic BBN models are developed to represent rutting, deflection, cracking and IRI, using pavement data collected from 32 major road sections in the United Arab Emirates between 2013 and 2019. Those models are based on several factors affecting pavement deterioration, which are classified into three categories traffic factors, environmental factors and road-specific factors.

The four developed performance prediction models achieved an overall precision and reliability rate of over 80%.

The proposed approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers in obtaining a realistic representation of expected future road conditions, where maintenance efforts could be prioritized and optimized.

(1) developing a dynamic and progressive software defect prediction model to successfully manage novel and huge amounts of software defect data and lessen the computational time. (2) to avoid the great diminish of static batch learning algorithms efficiency once the amount of data achieves a certain level.

This study explores the proficiency of the incremental classification based approach to elaborate anincremental software defect prediction system which helps recognizing and treating real-time software data streams.

The proposed method, as demonstrated by experimental results, is clearly competitive with the relevant two-class classifiers currently in use for software defect diagnosis. Detailed experimental findings clearly demonstrated the performance and efficiency of the suggested software defect detection approach: Incremental Discriminant-based Support Vector Machine (IDSVM) to differentiate between defective and non-defective objects.

To the best of our knowledge, this is the first a real-time prediction method that investigates incremental classification in software defect prediction research

The purpose of this paper is to find good values of onsite-offshore team strength; number of hours of communication between business users and onsite team and between onsite and offshore team so as to reduce project cost and improve schedule in a global software development (GSD) environment for software development project.

This study employs system dynamics simulation approach to study software project characteristics in both co-located and distributed development environments. The authors consulted 14 experts from Indian software outsourcing industry during our model construction and validation.

The study results show that there is a drop in overall team productivity in outsourcing environment by considering the offshore options. But the project cost can be reduced by employing the offshore team for coding and testing work only with minimal training for imparting business knowledge. The research results show that there is a potential to save project cost by being flexible in project schedule.

The implication of the study is that the project management team should be careful not to keep high percentage of manpower at offshore location in distributed software environment. A large offshore team can increase project cost and schedule due to higher training overhead, lower productivity and higher error proneness. In GSD, the management effort should be to keep requirement analysis and design work at onsite location and involves the offshore team in coding and testing work.

The software project manager can use the model results to divide the software team between onsite and offshore location during various phases of software development in distributed environment.

The study is novel as there is little attempt at finding the team distribution between onsite and offshore location in GSD environment.

The purpose of this paper is to examine the effect of the application of microorganisms of phosphate providers and organic matters for the growth of Arabica coffee seedlings in Bener Meriah Regency.

The experiments were performed inside the screen house using a random design of factorial group with six repetitions. The experimental treatments consisted of two factors. Factor I is the application of microorganisms of phosphate providers. Factor II is the application of organic matters (T. diversifolia and the coffee bean skins). The parameters observed were as follows: enhancement of plant height was observed every 30 days for 270 days, and a number of primary branch and dry weight of roots were observed 270 days after planting (DAP). The data obtained were analyzed by analyzing the variance at 5 per cent level; if the treatment had an effect, then the treatment was continued to Duncan’s multiple range test at 5 per cent.

The application of microorganisms of phosphate provider increases the height improvement, the number of primary branches and the dry weight of roots of Arabica coffee seedlings. The application of Listeria sp. microorganisms of phosphate provider gives the highest yield on the height increase, the number of primary branches and the dry weight of roots of Arabica coffee seedlings. The application of organic matters of coffee beans skin gives higher yield than Tithonia diversifolia in height and dry weight of roots of Arabica coffee seedlings. The interaction between the application of microorganisms of phosphate provider and organic matters increases the dry weight of roots of Arabica coffee seedlings. The interaction of Listeria sp.-skin of coffee beans gives the highest yield on dry weight of roots of Arabica coffee seedlings.

Several other studies have demonstrated that the application of microorganisms of phosphate providers using phosphate solubilizing bacteria (Fitriatin et al., 2014; Sembiring et al., 2017) and mycorrhizal use (Hart and Trevors, 2005; Rouphael et al., 2015) increased the growth and yield plant. No previous study comprehensively studied the application of microorganisms of phosphate providers and organic matters to improve the growth of Arabica coffee seedlings in Andisol in Bener Meriah Regency.