Cardiovascular imaging analysis is a useful tool for the diagnosis, treatment and monitoring of cardiovascular diseases. Imaging techniques allow non-invasive quantitative assessment of cardiac function, providing morphological, functional and dynamic information. Recent technological advances in ultrasound have made it possible to improve the quality of patient treatment, thanks to the use of modern image processing and analysis techniques. However, the acquisition of these dynamic three-dimensional (3D) images leads to the production of large volumes of data to process, from which cardiac structures must be extracted and analyzed during the cardiac cycle. Extraction, three-dimensional visualization, and qualification tools are currently used within the clinical routine, but unfortunately require significant interaction with the physician. These elements justify the development of new efficient and robust algorithms for structure extraction and cardiac motion estimation from three-dimensional images. As a result, making available to clinicians new means to accurately assess cardiac anatomy and function from three-dimensional images represents a definite advance in the investigation of a complete description of the heart from a single examination. The aim of this article is to show what advances have been made in 3D cardiac imaging by ultrasound and additionally to observe which areas have been studied under this imaging modality.

The COVID-19 pandemic had an adverse impact on the mental health of frontline workers including firefighters. To better understand this occurrence, this cross-sectional study evaluated the prevalence of depression, anxiety, and stress among 105 operational team and elite team firefighters in Kota Bharu, Kelantan State, Malaysia before and after the pandemic. The Depression, Anxiety and Stress Scale-21 (DASS-21), a validated self-reporting survey tool, was used to assess symptoms of depression, anxiety, and stress among the survey respondents. Findings revealed that firefighters had an increased level of anxiety and depression during the post-pandemic period compared to the pre-pandemic period. However, there was a decrease in the stress levels (20%) reported by study participants. Respondents belonging to the operational team had a higher reported level of depression, anxiety, and stress than those from the elite team. This may be attributed the operational team being more exposed to the risk of COVID-19 infection on account of their routine and more voluminous workload. The findings of this study suggest that firefighters, in general, are at an increased risk of mental health problems as a result of the COVID-19 pandemic. Knowing this, it is important to consider these findings when addressing the prevention and management of mental health among firefighters. This includes providing additional support and devoting more resources to those who are most at risk for experiencing symptoms of mental health such as firefighters performing functions aligned with that of an operational team.

In the Fourth Industrial Revolution (4IR) era, the rapid digitalisation of services poses both opportunities and challenges for the banking sector. This study addresses how adopting artificial intelligence (AI) and online and mobile banking advancements can influence customer satisfaction, particularly in Kaduna State, Nigeria. Despite significant investments in AI and digital banking technologies, banks often struggle to align these innovations with customer expectations and satisfaction. Using Structural Equation Modeling (SEM), this research investigates the impact of customer satisfaction with online banking (C_O) on AI integration (I_A) and mobile banking convenience (C_M). The SEM model reveals that customer satisfaction with online banking significantly influences AI integration (path coefficient of 0.40) and mobile banking convenience (path coefficient of 0.68). These results highlight a crucial problem: while technological advancements in banking are growing, their effectiveness is highly dependent on customer satisfaction with existing digital services. The study underscores the need for banks to prioritise enhancing online banking experiences as a strategic lever to improve AI integration and mobile banking convenience. Consequently, the research recommends that Nigerian banks develop comprehensive frameworks to evaluate and optimise their technology integration strategies, ensuring that technological innovations align with customer needs and expectations in the rapidly evolving digital landscape.

Despite Cameroon’s immense sand reserves, several enterprises continue to import standardized sands to investigate the properties of concretes and mortars and to guarantee the durability of built structures. The present work not only falls within the scope of import substitution but also aims to characterize and improve the properties of local sand (Sanaga) and compare them with those of imported standardized sand widely used in laboratories. Sanaga sand was treated with HCl and then characterized in the laboratory. The constituent minerals of Sanaga sand are quartz, albite, biotite, and kaolinite. The silica content (SiO₂) of this untreated sand is 93.48 wt.%. After treatment, it rose 97.5 wt.% for 0.5 M and 97.3 wt.% for 1 M HCl concentration. The sand is clean (ES, 97.67%–98.87%), with fineness moduli of 2.45, 2.48, and 2.63 for untreated sand and sand treated with HCl concentrations of 0.5 and 1 M respectively. The mechanical strengths (39.59–42.4 MPa) obtained on mortars made with untreated Sanaga sand are unsatisfactory compared with those obtained on mortars made with standardized sand and with the expected strengths. The HCl treatment used in this study significantly improved these strengths (41.12–52.36 MPa), resulting in strength deficiencies of less than 10% after 28 curing days compared with expected values. Thus, the treatment of Sanaga sand with a 0.5 M HCl concentration offers better results for use as standardized sand.

This study examines factors associated with an increasingly poor perception of the novel coronavirus in Africa using a designed electronic questionnaire to collect perception-based information from participants across Africa from twenty-one African countries (and from all five regions of Africa) between 1 and 25 February 2022. The study received 66.7% of responses from West Africa, 12.7% from Central Africa, 4.6% from Southern Africa, 15% from East Africa, and 1% from North Africa. The majority of the participants are Nigerians (56%), 14.1% are Cameroonians, 8.7% are Ghanaians, 9.3% are Kenyans, 2% are South Africans, 2.1% are DR-Congolese, 1.6% are Tanzanians, 1.2% are Rwandans, 0.4% are Burundians, and others are Botswana’s, Chadians, Comoros, Congolese, Gambians, Malawians, South Sudanese, Sierra Leoneans, Ugandans, Zambians, and Zimbabweans. All responses were coded on a five-point Likert scale. The study adopts descriptive statistics, principal component analysis, and binary logistic regression analysis for the data analysis. The descriptive analysis of the study shows that the level of ignorance or poor “perception” of COVID-19 in Africa is very high (87% of individuals sampled). It leads to skepticism towards complying with preventive measures as advised by the WHO and directed by the national government across Africa. We adopted logistic regression analysis to identify the factors associated with a poor perception of the virus in Africa. The study finds that religion (belief or faith) and media misinformation are the two leading significant causes of ignorance or poor “perception” of COVID-19 in Africa, with log odd of 0.4775 (resulting in 1.6120 odd ratios) and 1.3155 (resulting in 3.7265 odd ratios), respectively. The study concludes that if the poor attitude or perception towards complying with the preventive measures continues, COVID-19 cases in Africa may increase beyond the current spread.