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Criteria to determine in which level of security forensic patients should receive treatment are currently non-existent in Belgium. Research regarding the assessment of security level is minimal and few instruments are available. The DUNDRUM toolkit is a structured clinical judgement instrument that can be used to provide support when determining security level. The purpose of this paper is to investigate the applicability and validity of the DUNDRUM-1 in Flanders.

The DUNDRUM-1 was scored for 50 male patients admitted at the forensic units in the public psychiatric hospital Rekem. Some files were rated by three researchers who were blind to participants’ security status, resulting in 33 double measurements.

Almost all files (96 per cent) contained enough information to score the DUNDRUM-1. Average DUNDRUM-1 final judgement scores were concordant with a medium security profile. No difference was found between the current security levels and the DUNDRUM-1 final judgement scores. Inter-rater reliability was excellent for the DUNDRUM-1 final judgement scores. On item level, all items had excellent to good inter-rater reliability with the exception of one item institutional behaviour which had an average inter-rater reliability.

The DUNDRUM-1 can be a useful tool in Flemish forensic settings. It has good psychometric properties. More research is needed to investigate the relationship between DUNDRUM-1 scores and security level decisions by the courts.

This is the first study that investigated the applicability of the DUNDRUM-1 in a Belgian setting, also a relative large number of repeated measurements were available to investigate the inter-rater reliability of the DUNDRUM-1.

The aim of this study is o examine the advantages and disadvantages of different existing scoring systems in the cybersecurity domain and their applicability to the AEC industry and to systematically apply a scoring system to determine scores for some of the most significant construction participants.

This study proposes a methodology that uses the Common Vulnerability Scoring System (CVSS) to calculate scores and the likelihood of occurrence based on communication frequencies to ultimately determine risk categories for different paths in a construction network. As a proof of concept, the proposed methodology is implemented in a construction network from a real project found in the literature.

Results show that the proposed methodology could provide valuable information to assist project participants to assess the overall cybersecurity vulnerability of construction and assist during the vulnerability-management processes. For example, a project owner can use this information to get a better understanding of what to do to limit its vulnerability, which will lead to the overall improvement of the security of the construction network.

It has to be noted that the scoring systems, the scores and categories adopted in the study need not necessarily be an exact representation of all the construction participants or networks. Therefore, caution should be exercised to avoid generalizing the results of this study.

The proposed methodology can provide valuable information and assist project participants to assess the overall cyber-vulnerability of construction projects and support the vulnerability-management processes. For example, a project owner can use this approach to get a better understanding of what to do to limit its cyber-vulnerability exposure, which will ultimately lead to the overall improvement of the construction network's security. This study will also help raise more awareness about the cybersecurity implications of the digitalization and automation of the AEC industry among practitioners and construction researchers.

Given the amount of digitized services and tools used in the AEC industry, cybersecurity is increasingly becoming critical for society in general. In some cases, (e.g. critical infrastructure) incidents could have significant economic and societal or public safety implications. Therefore, proper consideration and action from the AEC research community and industry are needed.

To the authors' knowledge, this is the first attempt to measure and assess the cybersecurity of individual participants and the construction network as a whole by using the Common Vulnerability Scoring System.

Trust is one of the main pillars of many communication and interaction domains. Computing is no exception. Fog computing (FC) has emerged as mitigation of several cloud computing limitations. However, selecting a trustworthy node from the fog network still presents serious challenges. This paper aims to propose an algorithm intended to mitigate the trust and the security issues related to selecting a node of a fog network.

The proposed model/algorithm is based on two main concepts, namely, machine learning using fuzzy neural networks (FNNs) and the weighted weakest link (WWL) algorithm. The crux of the proposed model is to be trained, validated and used to classify the fog nodes according to their trust scores. A total of 2,482 certified computing products, in addition to a set of nodes composed of multiple items, are used to train, validate and test the proposed model. A scenario including nodes composed of multiple computing items is designed for applying and evaluating the performance of the proposed model/algorithm.

The results show a well-performing trust model with an accuracy of 0.9996. Thus, the end-users of FC services adopting the proposed approach could be more confident when selecting elected fog nodes. The trained, validated and tested model was able to classify the nodes according to their trust level. The proposed model is a novel approach to fog nodes selection in a fog network.

Certainly, all data could be collected, however, some features are very difficult to have their scores. Available techniques such as regression analysis and the use of the experts have their own limitations. Experts might be subjective, even though the author used the fuzzy group decision-making model to mitigate the subjectivity effect. A methodical evaluation by specialized bodies such as the security certification process is paramount to mitigate these issues. The author recommends the repetition of the same study when data form such bodies is available.

The novel combination of FNN and WWL in a trust model mitigates uncertainty, subjectivity and enables the trust classification of complex FC nodes. Furthermore, the combination also allowed the classification of fog nodes composed of diverse computing items, which is not possible without the WWL. The proposed algorithm will provide the required intelligence for end-users (devices) to make sound decisions when requesting fog services.

Information security is a critical issue in all organizations. The success of information security in libraries depends, to a large extent, on the effective behavior of administrators, librarians, users and all human staff. Accordingly, this study aims to design a model for identifying human factors affecting information security in libraries.

This study is applied in terms of research objectives and is a survey in terms of data collection. Moreover, it goes under the rubric of structural equation modeling in terms of the relationship between variables. The statistical population consisted of 100 managers and librarians of academic and public libraries of Hamadan in Iran. A questionnaire was used for data collection. The face and content validity of the questionnaire were examined using the expert’s opinions in the field of Iranian libraries. Also, the reliability of the questionnaire was calculated through Cronbach’s alpha coefficient. Data were analyzed using SPSS 16 and Smart PLS 2.

The results showed that among the components of information security, the highest score was designated to self-esteem (4.11 ± 0.57) and level of skill (4.07 ± 0.59), whereas the lowest score belonged to the level of education (3.51 ± 0.74). Ranking human factors affecting information security showed that experience with Rank 1 had the most impact, whereas the level of skill with Rank 6 had the least impact on information security.

In this study, for the first time, a model was designed and tested for human factors affecting information security in libraries. Information security professionals, librarians and library and information science researchers can exploit this model in the future.

Ethiopia suffers from structural food insecurity due to its low food production, low purchasing power, and climatic shocks such as drought. Coffee is Ethiopia's primary source of foreign earnings, and 95% of it is produced by smallholder farmers. Coffee also provide better income to smallholder farmers than other crops. However, it is unclear how much smallholder coffee producers participate in cash savings and if their savings help them attain food security. In this study, the authors aim to assess the impact of cash savings on the food security of smallholder coffee farmers in Ethiopia.

Using cross-sectional quantitative data from 336 randomly selected households and qualitative data from the local community, this study examines the impact of cash savings on household food security. Logistic regression and propensity score matching models were used to analyze the impact of cash savings on households' food security.

About, 43.5% of coffee farmers did not participate in cash savings during the study period, while 50.6% had no access to credit. The major factors that limit households' likelihood of participating in cash saving were the gender of household head and family size. Approximately, 38.4 and 27.1% of coffee farmers were food poor based on calorie intake and consumption scores, respectively. Households' participation in saving increases their ability to meet dietary energy requirements and consume diverse foods.

To the knowledge of the author, empirical studies that examined the impact of cash saving on the food security status of smallholder farmers in the study area are limited. Therefore, this study brings original contribution and fills research gap on coffee farmers' cash saving and food security; that received little attention from previous researchers in Ethiopia.

The purpose of this chapter is to assess the food security situation in Bangladesh based on 2011/2012 Bangladesh Integrated Household Survey data using two commonly measured food security indicators: Food Consumption Score (FCS) and Household Hunger Scale (HHS).

The dependent variable in the model is a categorical variable representing different scales of food security as obtained from the FCS and HHS indicators. These categorical variables are explained by annual remittances received by the households; the demographic characteristics (age, gender, literacy level, and occupation) of the household head; and total monthly income from agricultural and non-agricultural wages using ordered probit regression models.

Results indicated that remittances play an important role in improving the food security of households. Other significant variables in the model were income earned outside of the farm, male-operated household, and literacy. Increasing income from other than the agricultural sector significantly raises the probability of a household being food secure.

The Government of Bangladesh should make the agriculture sector stronger at all levels of the value chain. Additionally, providing income generation opportunities for households outside of the farm can be used as a diversification measure to achieve food security within the country.

The purpose of this paper is to explore the differences in preferred supplier choice criteria between food purchasing agents who focus on supplier security and those that do not. Specifically, this research determines the relationship between purchasing agents’ supplier security preferences and their preferences for product quality, delivery reliability, price, and supplier location. The influence of international sourcing on demand for increased supplier security is also explored.

Choice-based conjoint analysis with hierarchical Bayes (HB) estimation and t-tests are used to assess and compare the utility food purchasing managers derive from different supplier attributes.

Purchasing managers that place a higher priority on security when choosing suppliers were willing to pay suppliers a higher price and receive lower levels of delivery reliability in return for higher security but placed less emphasis on suppliers’ product quality. Firms that source internationally do not have a significantly greater preference for advanced supplier security. However, purchasing managers that value supplier security were more likely to source internationally, potentially indicating that security allows for global sourcing by mitigating the increased vulnerability inherent to sourcing abroad.

This research was limited by its focus on the food industry and a relatively small sample size.

This work illustrated that food purchasing managers can be segmented by the emphasis they place on security. Food industry managers will find results useful in formulating their future service offerings with respect to security and other supplier choice criteria.

This is one of few works investigating security as a supplier choice criterion and utilizing HB estimation of choice-based conjoint data.

At the substantive level, there exists a gap in knowledge about the position of security risk management (i.e. SRM) during the terminals’ operations and management; particularly when there is potential for deliberate anti-security acts. Correspondingly, the purpose of this paper is a need for more practical research to find out the justification for the existence of the SRM and different techniques for its appropriate execution on these logistics infrastructures principally with due regard to the potential requirements in the near future.

Both qualitative and quantitative techniques are used in this study incorporating fuzzy set theory and risk assessment matrix to achieve the research objective.

A designed SRM framework tailored for Qalhat liquefied petroleum gas (LNG) terminal in Sultanate of Oman was established to manage the security threats which can be resulted from any probable terrorist attacks.

The limited numbers of experts for the purpose of the addressed SRM are causing challenges in data collection.

The pressures for enhanced attention to critical infrastructure security have fostered new challenges for petrochemical seaports and terminals (PSTs). These tendencies dictate to maintain comprehensive security regimens that can be integrated with national and international strategies to support the country’s security against terrorism.

The development of the security risk factor table model in the case of Qalhat LNG Terminal.

The study attempted to assess the food security status of urban agriculture households in Ghana and India. Also, the extent of urban agriculture participation and its effect on food security in Ghana and India were examined.

A total of 650 urban agriculture farmers were interviewed for this study in Ghana and India. Food security status of urban households was assessed by the use of the Household Food Insecurity Access Scale, whereas the determinants of the extent of urban agriculture and its effect on food security were analysed by the use of the heteroskedastic linear regression and the Seemingly Unrelated Regression models, respectively.

From the study on average, households in Ghana were mildly food insecure, but that of India was moderately food insecure. The results further revealed that various demographic, economic, institutional and health and nutrition factors differently influenced urban food security and urban agriculture. Also, the extent of urban agriculture participation positively influenced food security.

Several studies in Asia (India) and Africa (Ghana) on urban food security have been geographically limited to New Delhi, Mumbai and Greater Accra, with few studies in the Middle Belt of Ghana, and Bihar in India. Besides, there is a limited, rigorous, empirical study on the effect of the extent of UA on food security in Asia (India) and Africa (Ghana) individually and together. Moreover, we extend the frontiers of the methodological approach by applying the Seemingly Unrelated Regression (SUR) model to understand if the factors that affect food-security accessibility based on two food security accessibility tools are correlated.

This study aims to investigate the effects of firm- and governance-specific characteristics on digital trust (DT) and firm value. Firm-specific factors include return on assets (ROA), market-to-book ratio (M/B ratio), size and leverage, whilst governance-related factors comprise board size, percentage of female board members, board independence and institutional ownership. All listed US firms over the period of 2011–2016 were analysed in this study.

This study provides a novel method to empirically measure DT by combining multiple variables to create a combined DT score. The variables include security and privacy scores, security rankings and data breaches, amongst others. Subsequently, a linear regression was performed to evaluate the effect of firm- and governance-specific characteristics on DT, as well as the effect of DT on firm value.

By using signalling theory, this study finds significant evidence that a firm’s profitability (ROA) decreases whilst its size increases DT. This could be due to the fact that firms with lower DT monetise data more actively, decrease DT and increase short-term profitability. Significant evidence also shows that increasing DT leads to an increase in firm value.

Although numerous studies have been conducted on developing customers’ trust by incorporating corporate social responsibility to improve firm value, the literature remains still on its digital analogue. Therefore, this study extends the knowledge of corporate digital responsibility (CDR) by providing a novel method for calculating DT across industries as an antecedent of CDR. Specifically, it sheds light on how firms can enhance DT by utilising firm- and governance-level factors. This enhanced DT can subsequently increase firm value. The study provides important managerial implications by providing empirical evidence that cybersecurity investments increase firm value. This value increase is related to the rise in shareholder value amongst investors and the increase in the organisation’s consumer perceptions as the latter’s interests are better managed.