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The purpose of this study is to formulate a new class of vehicle routing problem with an objective to minimise the total cost of raw material collection and derive a new approach to solve optimization problems. This study can help to select the optimum number of suppliers based on cost.

To model the raw material vehicle routing problem, a mixed integer linear programming (MILP) problem is formulated. An interesting phenomenon added to the proposed problem is that there is no compulsion to visit all suppliers. To guarantee the demand of semiconductor industry, all visited suppliers should reach a given raw material capacity requirement. To solve the proposed model, the authors developed a novel hybrid approach that is a combination of block and edge recombination approaches. To avoid bias, the authors compare the results of the proposed methodology with other known approaches, such as genetic algorithms (GAs) and ant colony optimisation (ACO).

The findings indicate that the proposed model can be useful in industries, where multiple suppliers are used. The proposed hybrid approach provides a better sequence of suppliers compared to other heuristic techniques.

The data used in the proposed model is generated based on previous literature. The problem derives from the assumption that semiconductor industries use a variety of raw materials.

This study provides a new model and approach that can help practitioners and policymakers select suppliers based on their logistics costs.

This study provides two important contributions in the context of the supply chain. First, it provides a new variant of the vehicle routing problem in consideration of raw material collection; and second, it provides a new approach to solving optimisation problems.

This study analyzes the effect of the techniques of active teaching and learning as a way of delivery on the outcomes of quality learning. Focusing on the courses of architectural science taught in a nontraditional method using various active learning strategies, the study takes the case study of the course Building Illumination and Acoustics (BIA) delivered in the academic year 2019–2020 at the University of Sharjah (UoS)'s Architectural Engineering Department (AED).

Utilizing both quantitative and qualitative research approaches, the study applied a case study and survey as methods. A questionnaire was designed and performed to assess the level of students' satisfaction with the implemented active teaching method.

The vibrant learning setting made the students actively engaged and more motivated and enthusiastic. The active learning practices used, including employing senses as in sight and hearing, reasoning rationally and intuitively, reflecting and acting, working steadily and in fits and starts, creating mathematical models, visualizing and memorizing and drawing analogies, were efficient in boosting their ability to comprehend theoretical concepts more effectively. The delivery style effectively enhances quality learning when various active techniques are used pedagogically beyond being merely a utilitarian instrument to prepare novice students of architectural engineering to fulfill practical challenges.

This article focuses specifically on a theoretical, scientific non-studio course in a particular program of architectural engineering in a particular semester before the dramatic changes in styles of teaching delivery that happened due to the COVID-19 pandemic. Future research could further highlight its results by comparing them to statistical evidence of the development of the course, especially for the duration of online teaching during the pandemic and the hybrid teaching period after it.

This article contributes to the development of teaching and learning of architectural engineering in the local Emirati context by putting original theories of teaching into practice. This paper further contributes to the field of architectural pedagogy in terms of the effect of active learning in the architecture field in the non-studio courses in higher education in the United Arab Emirates.

Students pursuing different professional courses at the higher education level during 2021–2022 saw the first-time occurrence of a pandemic in the form of coronavirus disease 2019 (COVID-19), and their mental health was affected. Many works are available in the literature to assess mental health severity. However, it is necessary to identify the affected students early for effective treatment.

Predictive analytics, a part of machine learning (ML), helps with early identification based on mental health severity levels to aid clinical psychologists. As a case study, engineering and medical course students were comparatively analysed in this work as they have rich course content and a stricter evaluation process than other streams. The methodology includes an online survey that obtains demographic details, academic qualifications, family details, etc. and anxiety and depression questions using the Hospital Anxiety and Depression Scale (HADS). The responses acquired through social media networks are analysed using ML algorithms – support vector machines (SVMs) (robust handling of health information) and J48 decision tree (DT) (interpretability/comprehensibility). Also, random forest is used to identify the predictors for anxiety and depression.

The results show that the support vector classifier produces outperforming results with classification accuracy of 100%, 1.0 precision and 1.0 recall, followed by the J48 DT classifier with 96%. It was found that medical students are affected by anxiety and depression marginally more when compared with engineering students.

The entire work is dependent on the social media-displayed online questionnaire, and the participants were not met in person. This indicates that the response rate could not be evaluated appropriately. Due to the medical restrictions imposed by COVID-19, which remain in effect in 2022, this is the only method found to collect primary data from college students. Additionally, students self-selected themselves to participate in this survey, which raises the possibility of selection bias.

The responses acquired through social media networks are analysed using ML algorithms. This will be a big support for understanding the mental issues of the students due to COVID-19 and can taking appropriate actions to rectify them. This will improve the quality of the learning process in higher education in Oman.

Furthermore, this study aims to provide recommendations for mental health screening as a regular practice in educational institutions to identify undetected students.

Comparing the mental health issues of two professional course students is the novelty of this work. This is needed because both studies require practical learning, long hours of work, etc.

Consumers are assumed to engage in external information search only after exhausting their internal information sources. Guided by the accessibility/diagnosticity and ease-of-retrieval frameworks, and the elaboration likelihood model, the current study investigates this phenomenon.

To test the relationships between internal information accessibility/diagnosticity and the importance of external search, and the moderating role of involvement in these relationships, 308 responses were collected on Amazon MTurk. Then, structural equation modeling was employed to analyze the data.

The analyses showed that while accessibility and diagnosticity of internal information have an impact on external information search, involvement with the product class has a consequential moderating effect on these relationships. In particular, in the low-involvement group, only the diagnosticity of internal information had a negative effect on external information search. On the contrary, in the high-involvement group, only accessibility of internal information had a negative effect.

These findings highlight the possibility of drawing erroneous conclusions resulting from not incorporating involvement, in conjunction with information accessibility and diagnosticity, in the study of the consumer external information search behavior.

The findings also imply that if practitioners aim to prime consumers to engage in external information search, they need to take into account that the effects of internal information's accessibility and diagnosticity on consumers' external search behavior may be different depending on their levels of involvement.

This study's results showed that without considering the moderating effect of involvement, spurious conclusions may be made about the relationships between accessibility and diagnosticity of internal and external information importance. This finding may explain the discrepancy between the accessibility/diagnosticity and ease-of-retrieval frameworks, thus enriching the literature.