Health Literacy and Digital Communication

The SARS-COV-2 pandemic has placed the entire planet under a global health threat, but it has also provided a golden opportunity for us to make the digital transition. With the successive confinements and restrictions on circulation and communication to which we were subjected, we had to look for other models of relationships, work, and learning. In a few months, the Portuguese went from an essentially playful and informative use of information and communication technologies (ICT) to an intensive work, academic, and communicational use.

Comparative Analysis of Steering Angle Prediction For Automated Object Using Deep Neural Network

Deep learning's rapid gains in automation are making it more popular in a variety of complex jobs. The self-driving object is an emerging technology that has the potential to transform the entire planet. The steering control of an automated item is critical to ensuring a safe and secure voyage. Consequently, in this study, we developed a methodology for predicting the steering angle only by looking at the front images of a vehicle. In addition, we used an Internet of Things-based system for collecting front images and steering angles. A Raspberry Pi (RP) camera is used in conjunction with a Raspberry Pi (RP) processing unit to capture images from vehicles, and the RP processing unit is used to collect the angles associated with each image. Apart from that, we've made use of deep learning-based algorithms such as VGG16, ResNet-152, DenseNet-201, and Nvidia's models, all of which were trained using labeled training data. Our models are End-to-End CNN models, which do not require extracting elements from data such as roads, lanes, or other objects before predicting steering angle. As a result of our comparative investigation, we can conclude that the Nvidia model's performance was satisfactory, with a Mean Squared Error (MSE) value of 0. But the Nvidia model outperforms the other pre-trained models, even though other models work well.<br>

Comparative Analysis of Steering Angle Prediction For Automated Object Using Deep Neural Network

Deep learning's rapid gains in automation are making it more popular in a variety of complex jobs. The self-driving object is an emerging technology that has the potential to transform the entire planet. The steering control of an automated item is critical to ensuring a safe and secure voyage. Consequently, in this study, we developed a methodology for predicting the steering angle only by looking at the front images of a vehicle. In addition, we used an Internet of Things-based system for collecting front images and steering angles. A Raspberry Pi (RP) camera is used in conjunction with a Raspberry Pi (RP) processing unit to capture images from vehicles, and the RP processing unit is used to collect the angles associated with each image. Apart from that, we've made use of deep learning-based algorithms such as VGG16, ResNet-152, DenseNet-201, and Nvidia's models, all of which were trained using labeled training data. Our models are End-to-End CNN models, which do not require extracting elements from data such as roads, lanes, or other objects before predicting steering angle. As a result of our comparative investigation, we can conclude that the Nvidia model's performance was satisfactory, with a Mean Squared Error (MSE) value of 0. But the Nvidia model outperforms the other pre-trained models, even though other models work well.<br>

Comparative Analysis of Steering Angle Prediction For Automated Object Using Deep Neural Network

Deep learning's rapid gains in automation are making it more popular in a variety of complex jobs. The self-driving object is an emerging technology that has the potential to transform the entire planet. The steering control of an automated item is critical to ensuring a safe and secure voyage. Consequently, in this study, we developed a methodology for predicting the steering angle only by looking at the front images of a vehicle. In addition, we used an Internet of Things-based system for collecting front images and steering angles. A Raspberry Pi (RP) camera is used in conjunction with a Raspberry Pi (RP) processing unit to capture images from vehicles, and the RP processing unit is used to collect the angles associated with each image. Apart from that, we've made use of deep learning-based algorithms such as VGG16, ResNet-152, DenseNet-201, and Nvidia's models, all of which were trained using labeled training data. Our models are End-to-End CNN models, which do not require extracting elements from data such as roads, lanes, or other objects before predicting steering angle. As a result of our comparative investigation, we can conclude that the Nvidia model's performance was satisfactory, with a Mean Squared Error (MSE) value of 0. But the Nvidia model outperforms the other pre-trained models, even though other models work well.<br>

Globalization, Greed and Glocal Ecology: A Psychological Perspective

Evidently, a globalized society causes global environmental crises. Undoubtedly, survival of human life on the planet Earth is threatened. Is there any connection between globalization, environmental crises and psychological manifestations? What are the psychological perspectives linking the ecological damages from local to the global scale? This article explores such intricate relationships and discusses the implications. The underlying principal cause is human’s unending greed to acquire maximum materials and power to control the planet and entire humanity. The greed is believed to be a bottomless pit which exhausts the person in an endless effort to satisfy the need without ever reaching satisfaction. The greedy people are supposed to have biological, psychological and sociological drives. Evidently, global destruction of the ecosystems and natural environment are directly or indirectly linked to unprecedented chronic human greed and self-indulgence. Undoubtedly, unencumbered chronic greed of a few elite institutions led by top capitalists has put the entire planet in havoc and infiltrated widespread sufferings at the global scale. Conclusively, psychological basis of environmental problems has a sociological and socio-historical scope within the frame of globalization. Psychological account of the environmental crisis is explained subsequently in this article followed by a case study of deforestation of Carpathian Mountains staged by a greedy Austrian man.

Trust in a Pandemic: Save Cannot Be Changed. What Does the Region Feel?

Trust as a system-forming factor of social relations during crisis situations is tested for strength. This was most pronounced when studying the state of trust in the context of the coronavirus pandemic, which has engulfed almost the entire planet. The article examines the situation with trust, using the example of a regional community. The source of empirical data was the results of several surveys of the population of the Vologda Oblast conducted by researchers of the VolRC RAS for the period from2016 to 2020. in the course of the implementation of scientific projects on grants from the RFBR The data obtained on the basis of comparable methods in the period before the pandemic and at the end of its second wave are analyzed. The purpose of the article is to carry out a comparative analysis of the available statistical data and to identify the changes that have occurred in the level and structure of trust in the region’s population. The paper concludes that in the context of a pandemic, the existing level of generalized trust, the existing structure and its radius have generally remained. At the same time, certain changes were revealed in the sphere of abstract trust or, to use the terminology of E. Giddens, trust in “anonymous others”. The changes concerned primarily the priorities in choosing the addressees of trust. The observed changes reflected the pain points of society during the crisis. Trust in doctors and teachers, expert scientists has fallen. The sharp decline in the level of trust in them can be seen as a kind of expression of protest against the difficult situation in education and health care, which has developed as a result of the pandemic and self-isolation. Trust in government institutions remains stable, which to a certain extent symbolize the stability of social structures and take responsibility in the current situation.

Sustaining the Arctic in Order to Sustain the Global Climate System

The unraveling of the Arctic is bad enough for the Arctic itself, but it will have enormous consequences for the entire planet since the Arctic is a crucial component of the global climate system. Current policies do not provide much hope to prevent these harms. We have committed the earth to too much warming to take a step-by-step approach. We have entered a period of history when planetary management has become unavoidable and must move forward on many fronts simultaneously. Key components of a multiprong approach include decarbonization, focus on short-lived climate forcers, greenhouse gas removal, adaptation, Arctic interventions, and solar climate intervention. This article discusses the last option, which may be the only means of cooling the earth quickly enough to save Arctic ice and permafrost. Scientific research is essential to better understand its feasibility, effectiveness, and safety. However, research is not enough; we need to be ready to respond right away if Arctic or global temperatures need to be lowered quickly. This means we need significant technology research and development so that solar climate intervention technologies are deployment-ready in the relatively near future, perhaps in a decade or two, and could be used should the need arise and should research show that they are effective and safe.

HMGB1-TIM3-HO1: A New Pathway of Inflammation in Skin of SARS-CoV-2 Patients? A Retrospective Pilot Study

The SARS-CoV-2 pandemic has completely disrupted the health systems of the entire planet. From the earliest months, it became increasingly clear that in addition to affecting the upper airways and lungs, there were other organs that could be affected. Among these, the skin became a real “sentinel signal” to be able to even suspect COVID-19. Background: this study deals with a little-explored issue for now: the study of skin immunopathology in SARS-CoV-2 positive subjects ascertained using the most reliable methods available. Methods: we used skin biopsy samples from SARS-CoV-2 positive and negative patients, studying morphology (Hematoxylin-Eosin), T lymphocyte population (CD4 and CD8), three markers such as HMGB-1, TIM-3 and HO-1 by immunohistochemistry. Results: although the presence of the CD4 and CD8 T population did not differ statistically significantly, we found greater activation and release of HMGB-1 in skin samples from SARS-CoV-2 positive patients, greater immunolabeling for TIM-3 at the level of CD4 and CD8 and a reduced expression of Heme oxygenase 1. Conclusions: these results support the possibility that there is immune deregulation in SARS-CoV-2 positive patients who develop skin manifestations of various kinds.

Purpuric Skin Rash in a Patient Undergoing Pfizer-BioNTech COVID-19 Vaccination: Histological Evaluation and Perspectives

The COVID-19 pandemic has affected the entire planet, and within about a year and a half, has led to 174,502,686 confirmed cases of COVID-19 worldwide, with 3,770,361 deaths. Although it is now clear that SARS-CoV-2 can affect various different organs, including the lungs, brain, skin, vessels, placenta and others, less is yet known about adverse reactions from vaccines, although more and more reports are starting to emerge. Among the adverse events, we focused particularly on skin rashes. In this short report, we describe the case of a patient vaccinated with Comirnaty, who developed a purpuric rash resistant to oral steroid therapy after 2 weeks. To date, this is one of the very few cases in which skin biopsy was performed to better characterize the histopathological picture of this rash. Finally, we conduct a literature review of the cases of rashes from SARS-CoV-2 vaccines described in the literature, with the aim of laying foundations for future, larger case studies.

COVID-19 AND TOURISM

With the outbreak of the novel coronavirus (COVID-19), the whole world has been in a total shutdown. It came out as a challenge to just not some specific countries of continents, but to the entire planet. With no preparation for such type of pandemic and the time and effort required to come up with a fully functional and tested vaccine, the planet went down on its knees. Non pharmaceutical methods were the first aid for the outbreak. Unprecedented country wide lockdowns, stay at home decisions, closure of global as well as local travel, closure of public gatherings greatly disrupted the world economy. International travel bans were the first to be practiced which roughly affected 100% of the world population. With restrictions on public gatherings and movement of people, tourism came to a stand-still in March-April 2020. By this time, COVID-19 had reached most of the countries and the virus was spreading like wild fire.