Positive education is seen as a transformative methodological approach capable of improving the act of teaching and learning and, above all, essential for the development of students' personal skills and competences. However, few studies have been carried out on this subject in the field of lifelong and adult education. This study works with a sample of 399 people over 16 years of age and students of the Universidad Popular de Dos Hermanas in order to show the relationship between the Pygmalion effect and learned helplessness in the process of acquiring knowledge in adulthood. In this way, three tools were used: a questionnaire that showed teachers' perceptions of students' qualities and behaviour and two that provided information on self-concept, self-esteem, personal and social skills and other variables directly related to emotional intelligence and positive education. It shows how exposure to negative operational conditioning factors influences the psychosocial and socio-educational development of students in every possible way, while on the other hand, it indicates the importance of positive education to compensate for this phenomenology by improving the development and growth of those who study and participate in non-formal education. Likewise, the factorial interrelation of both positive and negative conditioning factors and their incidence on learning is shown; the importance of neutralising the negative components and strengthening the positive reinforcement and the role played by the community and education professionals as catalysts and behavioural modulators at any stage of learning and age group for the achievement of the objectives of the student and of education itself in a broad sense

The emergence of novel pathogens is a well-known epidemiological risk, however, the unexpected emergence of a truly novel coronavirus-mediated pandemic, SARS-nCOV2 (COVID-19), underscored the significance of understanding this contagion. The COVID-19 pandemic caused unprecedented social, economic, and educational disruptions on a scale never seen before. In addition to social protocols, the development of safe, effective, affordable COVID-19 vaccines was developed within months, the cornerstone to mitigating this pandemic. We present an overview of the evolution of the SARS-nCOV2 pandemic from a historical perspective and describe its biology and behavior, especially the immunological aspects of the disease. We further provide an overview of COVID-19 therapeutics, treatment, and vaccine development. It is critical to understand the transmission mechanism of the disease to control and mitigate its progression. We describe cohort studies to identify secondary and tertiary syndromes. The transmission characteristics help its diagnosis and detection. During the pandemic, a lot of emphasis was placed on personal protection equipment. It is now concluded that the virus particles spread by aerosol dispersion. While the recommended distance may not be sufficient, the use of personal protective equipment and social distancing may be helpful in close-quarters environments. Such protocols in conjunction with safe and effective vaccines and personal hygiene are among the safe practices. While we learn from our experience, this review provides a holistic view of COVID-19, so we are better prepared for a future pandemic. In addition to a wide-spectrum automated analytics system, we also suggest that the use of artificial intelligence in conjunction with data analytics can further reduces the risk of speculatively diagnosing agents incorrectly, to eliminate future pandemic, where the novelty can be the cloud-based presumptive diagnosis.

Deep Learning is developing interesting tools which are of great interest for inverse imaging applications. In this work, we consider a medical imaging reconstruction task from subsampled measurements, which is an active research field where Convolutional Neural Networks have already revealed their great potential. However, the commonly used architectures are very deep and, hence, prone to overfitting and unfeasible for clinical usages. Inspired by the ideas of the green-AI literature, we here propose a shallow neural network to perform an efficient Learned Post-Processing on images roughly reconstructed by the filtered backprojection algorithm. The results obtained on images from the training set and on unseen images, using both the non-expensive network and the widely used very deep ResUNet show that the proposed network computes images of comparable or higher quality in about one fourth of time.

Cameroon's national vaccination campaign was launched on April 12, 2021, amid a nationwide outbreak of COVID-19 with two types of vaccines. This study provides preliminary evidence of the level of coverage of the population and gives an early overview of the challenges, the achievements and the lessons learned. COVID-19 vaccine administration data were obtained from data of the Cameroon Ministry of Public Health. Descriptive statistical analyses were conducted. Thirty days after the introduction of COVID 19 vaccines, five percent of the target population was vaccinated. Women represented one third of the people vaccinated regardless of age and health conditions. Although AEFI reported were minor and scanty with both vaccines, the majority of the vaccinated did not come back for their second dose. There is a need to build confidence among eligible beneficiaries in order to expand the benefits of vaccination to control the current pandemic. The country is still far below the target which could be worrisome given that the uptake is slow and, the 391 200 doses of the AstraZeneca are going to expire in August 2021. This study offers insights into those early efforts as a contribution to significant discussions about upcoming approach to improve service delivery and uptake.

A drone performs comparable function to a laser scanner in the construction quality monitoring, following Scan-to-BIM process. Both technologies digitally capture the as-is environment into the computer and the data captured is transferred to a BIM world to create accurate as-built models. Although the laser scanner is the dominant method of the Scan-to-BIM process, a number of digital professionals point drawbacks of the method and present the drone is an alternative that can improve the drawbacks thereby leading to UAV-to-BIM process in parallel with the Scan-to-BIM. Korean construction industry plans to utilize the two technologies for monitoring construction operation quality in major public projects by 2025. While contractors need competent engineers to be competitive in the projects, the two technology applications are not so popular to the construction projects in Korea and very few experts skillful and knowledgeable of the technologies are available. Korean universities are requested to develop the curriculum of the technologies for the contractors. To be successful in progressing the curriculum, it is very essential to implement a preliminary study with the technologies minimizing the potential failure in operating the curriculum later on. This study performs empirical research on the technologies and identify valuable lessons beneficial to develop the UAV-to-BIM curriculum for the construction engineers.

Predicting change from multivariate time series has relevant applications ranging from medical to engineering fields. Multisensory stimulation therapy in patients with dementia aims to change the patient’s behavioral state. For example, patients who exhibit a baseline of agitation may be paced to change their behavioral state to relaxed. This study aims to predict changes in behavioral state from the analysis of the physiological and neurovegetative parameters to support the therapist during the stimulation session. In order to extract valuable indicators for predicting changes, both handcrafted and learned features were evaluated and compared. The handcrafted features were defined starting from the CATCH22 feature collection, while the learned ones were extracted using a Temporal Convolutional Network, and the behavioral state was predicted through Bidirectional Long Short-Term Memory Auto-Encoder, operating jointly. From the comparison with the state-of-the-art, the learned features-based approach exhibits superior performance with accuracy rates of up to 99.42% with a time window of 70 seconds and up to 98.44% with a time window of 10 seconds.