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The main purpose of this study was to evaluate students’ perception of blended learning instructional design in a Higher Education Institution (HEI) in the Sultanate of Oman. A course in a bachelor degree was offered using blended learning instructional mode, and students’ feedback was obtained on the impact of blended learning. The study was conducted in Ibra College of Technology, one of the seven colleges under the Ministry of Manpower in the Sultanate of Oman. A course in Business Strategy and HR Management was offered during Summer 2016 using a combination of traditional and online instructional design. A structured questionnaire was administered at the end of the semester to solicit feedback from students. The results of the study highlighted a positive attitude among students towards blended learning courses. The research stressed the requirements for proper infrastructure, training and development initiatives for staff and students, shift to a more focused practical assessment methods to measure the graduate attributes. Student engagement, learner autonomy, connection of learning to real life environments and flexibility all appeared to benefit from the blended learning course.

This paper aims to examine the hypotheses of main international theories (realism, liberalism and constructivism) and the development of these theories toward the behavior of foreign policy of small states in the developing world. The theories of international relations, especially the realistic theory, face a theoretical debate and a fundamental criticism. The hypotheses of these theories are not able to explain the external behavior of some small states, especially those in the developing world such as Qatar. In particular, these small states do not have the elements of physical power through which they can play this role. However, they are based on the internal determinants (such as political leadership and the variable of perception) and non-physical dimensions of power to play an effective and influential external role.

This topic sheds light on the hypotheses of theories of main international relations, which explain the behavior of foreign policy of small states. This is due to the increased number of such states after the disintegration of Soviet Union, the practice of some countries an effective foreign role and the transformation of the concept of power from the hard power to soft power, and then to smart power

The theories of international relations, especially the realistic theory, face a theoretical debate and a fundamental criticism. The hypotheses of these theories are not able to explain the external behavior of some small states, especially those in the developing world such as Qatar. In particular, these small states do not have the elements of physical power through which they can play this role. However, they are based on the internal determinants (such as political leadership and the variable of perception) and non-physical dimensions of power to play an effective and influential external role.

The importance of the study comes from its interest in small countries in general and the Qatar situation in particular. The small country emerged as a player independent of the Gulf Cooperation Council, unlike what prevailed before, which led to the discussion of a regional role for Qatar despite its small power compared to the strength and size of other factions in the region such as Turkey, Israel and Iran.

Diagnosing brain tumors is a process that demands a significant amount of time and is heavily dependent on the proficiency and accumulated knowledge of radiologists. Over the traditional methods, deep learning approaches have gained popularity in automating the diagnosis of brain tumors, offering the potential for more accurate and efficient results. Notably, attention-based models have emerged as an advanced, dynamically refining and amplifying model feature to further elevate diagnostic capabilities. However, the specific impact of using channel, spatial or combined attention methods of the convolutional block attention module (CBAM) for brain tumor classification has not been fully investigated.

To selectively emphasize relevant features while suppressing noise, ResNet50 coupled with the CBAM (ResNet50-CBAM) was used for the classification of brain tumors in this research.

The ResNet50-CBAM outperformed existing deep learning classification methods like convolutional neural network (CNN), ResNet-CBAM achieved a superior performance of 99.43%, 99.01%, 98.7% and 99.25% in accuracy, recall, precision and AUC, respectively, when compared to the existing classification methods using the same dataset.

Since ResNet-CBAM fusion can capture the spatial context while enhancing feature representation, it can be integrated into the brain classification software platforms for physicians toward enhanced clinical decision-making and improved brain tumor classification.

This research has not been published anywhere else.

This study aims to identify the location of the micropyle, the role of the micropyle in seed germination and the association between the micropyle size and seed weight of grass peas.

First, the micropyle was identified by cutting the seed in half and observing the seeds under the electron microscope. Second, the micropyle was covered by lanolin to block water imbibition. The rate of imbibition and germination was then observed. Lastly, micropyle sizes of various grass pea genotypes were identified by capturing seed images under a light microscope and converting the sizes to mm² using computer software (ImageJ).

The location of micropyle was located nearby the hilum, similar to soybean seeds. Seed imbibition was significantly lower in lanolin application (<87%) than in the control (>124%) after 24 hours of submergence. Germination was a day delay for lanolin application on the micropyle compared to lanolin application on the non-micropyle. The germination delay resulted in a significantly lower germination percentage at <57% on the micropyle lanolin application than at >79% on the non-micropyle lanolin application after 10 days of sowing. There is no correlation between the micropyle size and seed weight.

These findings add information on the location and the role of the micropyle for grass pea seed germination.

This paper addresses the under-explored collateral damage of economic sanctions, shedding light on the disproportionate harm endured by the most vulnerable segments of societies, which at the same time lack political influence to effect the ruling government into change. The primary objective is to review the literature on humanitarian repercussions associated with sanctions, concluding if they really are a comparably human way of international interference.

Employing a systematic literature review, adhering to the PRISMA approach, and incorporating key term definitions and clear selection criteria, this review analyses 52 studies sourced from Scopus and EconBiz.

The surveyed literature reveals profound adverse impacts of sanctions on health, economic well-being, inequality, and education. Critical gaps in the literature such as disproportional focus on extreme cases like Iran and Iraq, scarce literature on effects on education and inequality, and predominantly inadequate control groups are identified, limiting the generalizability of existing findings.

This paper is the first systematic and replicable review of the literature on the effects of sanctions using a capabilistic approach to define poverty. Highlighting gaps in the current research landscape underscores the limited generalizability of reviewed results. Providing a well-structured summary of existing literature, this work serves as a foundation for future research.

The purpose of this study was to investigate trust within human-AI teams. Trust is an essential mechanism for team success and effective human-AI collaboration.

In an online experiment, the authors investigated whether trust perceptions and behaviours are different when introducing a new AI teammate than when introducing a new human teammate. A between-subjects design was used. A total of 127 subjects were presented with a hypothetical team scenario and randomly assigned to one of two conditions: new AI or new human teammate.

As expected, perceived trustworthiness of the new team member and affective interpersonal trust were lower for an AI teammate than for a human teammate. No differences were found in cognitive interpersonal trust and trust behaviours. The findings suggest that humans can rationally trust an AI teammate when its competence and reliability are presumed, but the emotional aspect seems to be more difficult to develop.

This study contributes to human–AI teamwork research by connecting trust research in human-only teams with trust insights in human–AI collaborations through an integration of the existing literature on teamwork and on trust in intelligent technologies with the first empirical findings on trust towards AI teammates.

Milk and yoghurt provide essential sources of nutrition throughout the life cycle in the Irish diet. Health claims on dairy product labels were popular in the 1980s, and since the introduction of the nutrition and health claims (NHC) Regulation (EC) No 1924/2006 in 2007, many new regulated claims have been used to communicate product benefits. Meanwhile, COVID-19 and the Farm to Fork strategy have heightened consumer awareness of health issues. All of these factors have contributed to a change in our food environment and interest in health. In addition, the European Commission is working to introduce a legislative proposal on nutrient profiles (NP) to restrict the use of NHC on foods that are high in salt, fat or sugar. This qualitative study aims to research knowledge on adults’ attitudes, perceptions and behaviour towards NHC on dairy products.

The study used a thematic analysis using transcripts from a series of discussion groups, attended by adults (n = 24). The participants also completed an introductory questionnaire.

The study noted positive attitudes towards dairy protein. This attitude was common across age, gender or life stage. There were misperceptions regarding yoghurt composition and health benefits. There were negative perceptions of low-fat nutrition claims on yoghurt, which led to a preference for full-fat dairy products. This requires further insight and research.

Participants from a wider socio-demographic group could have broadened the research limits of this project.

These findings will interest policymakers, regulators, dieticians and the food industry.

As nowadays the knowledge economy puts a strong emphasis on the universities’ role in the present economy, the recent challenge focuses on the interrelations between entrepreneurship culture and academic engagement. This study aims to investigate the new role that universities are assuming as entrepreneurial entities and gather information taking place internal university stakeholders and students’ perception on entrepreneurship education. The research hypothesis stands entrepreneurship is mainly supposed as being professionally and educationally active rather than setting up a company.

The present study carries out a study on the perception of entrepreneurship education conducted among students of the University of Economics in Krakow in the winter semester of the academic year 2017/2018. The selected target group meets the criteria of the different national country origin of the respondents. Another criterion for selecting the target group was diversity in the field of students’ academic interests.

The results of the study give a clear view of the still valid confirmation of the growing academic role in terms of entrepreneurship culture development that appears necessary to address the demand for global competitiveness. In particular, it is possible to categorize two groups of people, moderate and strong supporters of recognition that entrepreneurship is not only about starting a company but also at the same time that it is an expression of its own dynamic and entrepreneurial attitudes.

As the importance of entrepreneurship in the context of an entrepreneurial university is rising and the definition of entrepreneurship goes beyond its understanding of starting a business, universities and academic engagement can and have to better address and focus their planning of the courses and their contents.

The study sheds some light and gives some interesting perspectives on the issue of different levels of entrepreneurship education expectations against different levels at which this education should be provided. In addition, it is in line with the EU entrepreneurship competence framework (EntreComp) aimed at defining tools to improve the entrepreneurial capacity and culture of EU citizens and organizations by means of consensus among stakeholders and by establishing a bond between education and study.

Literature reveals ongoing debates around the role of business analysts in agile software development (ASD) teams. This can be attributed, in part, to a knowledge gap concerning how business analysts contribute to overall team capabilities, particularly those which are essential in enabling teams to respond to fast-paced environmental changes. The purpose of this study was to address this gap by investigating how business analysts (BAs) contribute to the dynamic capabilities of ASD teams.

Through a deductive approach, this study adapted and applied a research model based on the team dynamic capabilities (DC) theory to explore the contributions of BAs in agile teams. The study was executed using a qualitative, single case study research strategy directed at an ASD team in the financial services industry. Moreover, data were collected through face-to-face, semi-structured interviews; a focus group; non-participant observation and physical artefacts review. The thematic analysis technique was used to analyse the data.

The study contributes to teams DC theory through four theoretical propositions centred on the role of BAs. The proposition highlights how BAs relationship management, tacit knowledge sharing, task mental models and transactive memory are key contributors of ASD teams' DC. The study also found that BAs contribute to ASD teams' ability to embrace agile principles 2, 4, 6 and 12. This study can inform the design of capacity development programmes for individual team members and BAs and thus help managers curate teams that will best promote DC.

This study can inform the design of capacity development programmes for individual team members and BAs and thus help managers curate teams that will best promote DC.

This study builds on the relatively few studies which focus on DC within software development (SD) teams and ASD project teams. Moreover, the study explores how an individual (i.e. a BA) can contribute to the DC of a team.

This paper aims to develop predictive methods, based on recurrent neural networks, useful to support facility managers in building maintenance tasks, by collecting information coming from a computerized maintenance management system (CMMS).

This study applies data-driven and text-mining approaches to a CMMS data set comprising more than 14,500 end-users’ requests for corrective maintenance actions, collected over 14 months. Unidirectional long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) recurrent neural networks are trained to predict the priority of each maintenance request and the related technical staff assignment. The data set is also used to depict an overview of corrective maintenance needs and related performances and to verify the most relevant elements in the building and how the current facility management (FM) relates to the requests.

The study shows that LSTM and Bi-LSTM recurrent neural networks can properly recognize the words contained in the requests, thus correctly and automatically assigning the priority and predicting the technical staff to assign for each end-user’s maintenance request. The obtained global accuracy is very high, reaching 93.3% for priority identification and 96.7% for technical staff assignment. Results also show the main critical building elements for maintenance requests and the related intervention timings.

This work shows that LSTM and Bi-LSTM recurrent neural networks can automate the assignment process of end-users’ maintenance requests if trained with historical CMMS data. Results are promising; however, the trained LSTM and Bi-LSTM RNN can be applied only to different hospitals adopting similar categorization.

The data-driven and text-mining approaches can be integrated into the CMMS to support corrective maintenance management by facilities management contractors, i.e. to properly and timely identify the actions to be carried out and the technical staff to assign.

The improvement of the maintenance of the health-care system is a key component of improving health service delivery. This work shows how to reduce health-care service interruptions due to maintenance needs through machine learning methods.

This study develops original methods and tools easily integrable into IT workflow systems (i.e. CMMS) in the FM field.