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Seasonal influenza epidemics accounted for significant morbidity and mortality loads worldwide despite the availability of a safe vaccine as an efficient tool against severity of the disease. However, the uptake of the latter was sub-optimal. This study aims to identify predictors and barriers related to seasonal influenza vaccine uptake in the Kingdom of Bahrain.

A cross-sectional study enrolled 502 individuals attending primary healthcare centers in Bahrain for ambulatory care between July and August 2022. The data were collected using an interviews-based questionnaire which included questions on demographic data, knowledge and attitudes and practices toward influenza vaccine. The authors identified the barriers as well as the determinants of the vaccine uptake and its recommendation to others.

The mean age of participants was 35.07 years (SD = 13.9). Most of the respondents were Bahraini (86.5%) and 53.4 % were females. The results revealed that 34.1% have previous information about the influenza vaccine and 36.9% versus 69.9% are willing to receive the vaccine or advice it to others, respectively. Determinants of vaccine uptake were identified.

This study confirmed a sub-optimal influenza vaccine acceptance in the general community of Bahrain despite a global access in primary care. Health professionals need to be more proactive in mobilizing the community and particularly females toward influenza vaccination.

Several types of vaccines were manufactured by different companies to control and stop the spread of COVID-19. This study aimed to identify the postvaccination side effects of the three different vaccines (Pfizer, AstraZeneca and Sinopharm) among the Iraqi population in Baghdad, Iraq.

A prospective cross-sectional study was conducted in Baghdad, Iraq from May 2021 to March 2022. An online-based questionnaire was used to collect the data through social media, i.e. WhatsApp, Messenger and Google Classroom. A total of 737 vaccinated participants using a snowball sampling methodology were used in this study.

Among the study population, 328 (44.50%) were males and 409 (55.50%) were females. The highest age group that participated was 18–30 years (79.10%) followed by 31–40 years (12.10%), 41–50 years (4.20%), 51–60 years (2.40%) and 60 = years (2.20%). However, 58.8% of the participants received Pfizer-BioNTech, 23.7% received the AstraZeneca-Oxford vaccine and 17.5% received Sinopharm. Out of the total participants, 56.60% showed postvaccination side-effects such as fever, headache, fatigue and dizziness, while 33% showed no side-effects and 10.40% were not sure. Pfizer-BioNTech and AstraZeneca-Oxford vaccines were the most vaccines prevalent of side-effects.

The majority of the side reactions associated with the AstraZeneca and Pfizer vaccines were manageable and self-limiting, including fever, fatigue, headache, joint pain and dizziness, compared to the Sinopharm vaccines, which reported lower postside effects.

This paper aims to investigate the impact of halal awareness on the relationship between halal supply chain knowledge and purchase intention for halal meat products in the millennial generation.

The quantitative approach with the respondents of 177 millennial generations in Indonesia is selected to understand the relationships between variables. Structural equation model-partial least square is used to analyze the relationship between variables.

The findings of this study found that the purchase intention of halal products in the millennial generation is influenced by several factors such as halal supply chain knowledge, halal certification and logo and religious beliefs. However, the results of this study also show that concern for halal products failed to moderate the relationship between these three main variables on the purchase intention of halal products.

This study provides insights into the concern that strengthens the relationship between the main variables on the intention to purchase halal meat products for the millennial generation.

Considering the world population, an additional 415.1 billion m² of built floor will be needed by 2050, which could worsen the environmental impact of the construction industry that is responsible for one-third of global Carbon Emissions (CEs). Thus, the current construction practices need to be upgraded toward eco-friendly technologies. Building Information Modeling (BIM) proved a significant potential to enhance Building and Infrastructure (B&I) ecological performances. However, no previous study has evaluated the nexus between BIM and B&I CEs. This study aims to fill this gap by disclosing the research evolution and metrics and key concepts and tools associated with this nexus.

A mixed-method design was adopted based on scientometric and scoping reviews of 52 consistent peer-reviewed papers collected from 3 large scientific databases.

This study presented six research metrics and revealed that the nexus between BIM and CEs is a contemporary topic that involves seven main research themes. Moreover, it cast light on six key associated concepts: Life Cycle Assessment; Boundary limits; Building Life Cycle CE (BLCCE); Responsible sources for BLCCE; Green and integrated BIM; and sustainable buildings and related rating systems. Furthermore, it identified 56 nexus-related Information and Communication Technologies tools and 17 CE-coefficient databases and discussed their consistency.

This study will fill the knowledge gap by providing scholars, practitioners and decision-makers with a good grasp of the nexus between CEs and BIM and paving the path toward further research, strategies and technological solutions to decrease CEs of B&I sectors and their impacts on the climate change.

Several factors influence OSC adoption, but extant literature did not articulate the dominant barriers or drivers influencing adoption. Therefore, this research has not only ventured into analyzing the core influencing factors but has also employed one of the best-known predictive means, Machine Learning, to identify the most influencing OSC adoption factors.

The research approach is deductive in nature, focusing on finding out the most critical factors through literature review and reinforcing — the factors through a 5- point Likert scale survey questionnaire. The responses received were tested for reliability before being run through Machine Learning algorithms to determine the most influencing OSC factors within the Nigerian Construction Industry (NCI).

The research outcome identifies seven (7) best-performing algorithms for predicting OSC adoption: Decision Tree, Random Forest, K-Nearest Neighbour, Extra-Trees, AdaBoost, Support Vector Machine and Artificial Neural Network. It also reported finance, awareness, use of Building Information Modeling (BIM) and belief in OSC as the main influencing factors.

Data were primarily collected among the NCI professionals/workers and the whole exercise was Nigeria region-based. The research outcome, however, provides a foundation for OSC adoption potential within Nigeria, Africa and beyond.

The research concluded that with detailed attention paid to the identified factors, OSC usage could find its footing in Nigeria and, consequently, Africa. The models can also serve as a template for other regions where OSC adoption is being considered.

The research establishes the most effective algorithms for the prediction of OSC adoption possibilities as well as critical influencing factors to successfully adopting OSC within the NCI as a means to surmount its housing shortage.