Embedded system vehicle based on multi-sensor fusion

As intelligent driving vehicles came out of concept into people’s life, the combination of safe driving and artificial intelligence becomes the new direction of future transportation development. Autonomous driving technology is developing based on control algorithms and model recognitions. In this paper, a cloud-based interconnected multi-sensor fusion autonomous vehicle system is proposed that uses deep learning (YOLOv4) and improved ORB algorithms to identify pedestrians, vehicles, and various traffic signs. A cloud-based interactive system is built to enable vehicle owners to master the situation of their vehicles at any time. In order to meet multiple application of automatic driving vehicles, the environment perception technology of multi-sensor fusion processing has broadened the uses of automatic driving vehicles by being equipped with automatic speech recognition (ASR), vehicle following mode and road patrol mode. These functions enable automatic driving to be used in applications such as agricultural irrigation, road firefighting and contactless delivery under new coronavirus outbreaks. Finally, using the embedded system equipment, an intelligent car was built for experimental verification, and the overall recognition accuracy of the system was over 96%. Author

Mathematical Analysis of Fractal-Fractional Mathematical Model of COVID-19

In this work, we modified a dynamical system that addresses COVID-19 infection under a fractal-fractional-order derivative. The model investigates the psychological effects of the disease on humans. We establish global and local stability results for the model under the aforementioned derivative. Additionally, we compute the fundamental reproduction number, which helps predict the transmission of the disease in the community. Using the Carlos Castillo-Chavez method, we derive some adequate results about the bifurcation analysis of the proposed model. We also investigate sensitivity analysis to the given model using the criteria of Chitnis and his co-authors. Furthermore, we formulate the characterization of optimal control strategies by utilizing Pontryagin's maximum principle. We simulate the model for different fractal-fractional orders subject to various parameter values using Adam Bashforth's numerical method. All numerical findings are presented graphically. © 2023 by the authors.

Multi-objective Evolutionary Discretization of Gene Expression Profiles: Application to COVID-19 Severity Prediction

Machine learning models can use information from gene expressions in patients to efficiently predict the severity of symptoms for several diseases. Medical experts, however, still need to understand the reasoning behind the predictions before trusting them. In their day-to-day practice, physicians prefer using gene expression profiles, consisting of a discretized subset of all data from gene expressions: in these profiles, genes are typically reported as either over-expressed or under-expressed, using discretization thresholds computed on data from a healthy control group. A discretized profile allows medical experts to quickly categorize patients at a glance. Building on previous works related to the automatic discretization of patient profiles, we present a novel approach that frames the problem as a multi-objective optimization task: on the one hand, after discretization, the medical expert would prefer to have as few different profiles as possible, to be able to classify patients in an intuitive way;on the other hand, the loss of information has to be minimized. Loss of information can be estimated using the performance of a classifier trained on the discretized gene expression levels. We apply one common state-of-the-art evolutionary multi-objective algorithm, NSGA-II, to the discretization of a dataset of COVID-19 patients that developed either mild or severe symptoms. The results show not only that the solutions found by the approach dominate traditional discretization based on statistical analysis and are more generally valid than those obtained through single-objective optimization, but that the candidate Pareto-optimal solutions preserve the sense-making that practitioners find necessary to trust the results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

DIGITALIZATION OF PUBLIC CRIMINAL HEARINGS IN PERU DURING THE PANDEMIC: VIOLATION OF THE IMMEDIATE PRINCIPLE?

During the COVID 19 pandemic in Peru, the digitalization process of criminal public hearings increased, initiated in previous years by the introduction of ICTs in the work of legal institutions and professionals. This situation has given rise to debates on the application of the principle of immediacy, which traditionally governs criminal proceedings. The present work has the objective of analyzing the influence of digitalization on the principle of immediacy in criminal public hearings, held in Peru during the COVID 19 pandemic. To achieve this objective, a qualitative design study, descriptive and the use of theoretical level methods, to explain the use of ICTs and the digitization process, the general principles of law, with an emphasis on im-mediacy, and digitization in criminal public hearings. As a result, it is concluded that legislation and jurisprudence agree that the digitalization of public criminal hearings does not violate the principle of immediacy, but rather facilitates the interrelation of the parties, and the judge's appreciation of objective and subjective elements that guarantee his conviction. to dictate a fair and well-founded sentence in matters of facts and law. © 2023, University of Cienfuegos, Carlos Rafael Rodriguez. All rights reserved.

THE PRINCIPLE OF IMMEDIACY IN PORTUGUESE CIVIL PROCEDURE IN TIMES OF PANDEMIC: THE USE OF VIDEOCONFERENCING HEARINGS IN JUDICIAL PROCEEDINGS

The article aims to investigate how the use of videoconferencing hearings in Portuguese judicial proceedings, established by Law 1-A/2020 of March 19th, affected the principle of immediacy. To achieve it goals, this paper will analyze the meaning and definition of the mentioned principle. In addition to that, it will be highlighted the conduction of the hearings during the pandemic scenario. Last but not least, it will be evaluated whether or not there has been an effective violation of the principle. © 2022, Universidade do Estado do Rio de Janeiro. All rights reserved.

Mathematical modeling of COVID-19 and Omicron outbreak spread: Optimal control approach for intervention strategies

In Morocco, 966,777 confirmed cases and 14,851 confirmed deaths because of COVID-19 were recorded as of January 1, 2022. Recently, a new strain of COVID-19, the so-called Omicron variant, was reported in Morocco, which is considered to be more dangerous than the existing COVID-19 virus. To end this ongoing global COVID-19 pandemic and Omicron variant, there is an urgent need to implement multiple population-wide policies like vaccination, testing more people, and contact tracing. To forecast the pandemic's progress and put together a strategy to effectively contain it, we propose a new hybrid mathematical model that predicts the dynamics of COVID-19 in Morocco, considering the difference between COVID-19 and the Omicron variant, and investigate the impact of some control strategies on their spread. The proposed model monitors the dynamics of eight compartments, namely susceptible (S)$$ (S) $$, exposed (E)$$ (E) $$, infected with COVID-19 (I)$$ (I) $$, infected with Omicron (IO)$$ \left({I}_O\right) $$, hospitalized (H)$$ (H) $$, people in intensive care units (U)$$ (U) $$, quarantined (Q)$$ (Q) $$, and recovered (R)$$ (R) $$, collectively expressed as SEIIOHUQR$$ SEI{I}_O HUQR $$. We calculate the basic reproduction number Script capital R0$$ {\mathcal{R}}_0 $$, studying the local and global infection-free equilibrium stability, a sensitivity analysis is conducted to determine the robustness of model predictions to parameter values, and the sensitive parameters are estimated from the real data on the COVID-19 pandemic in Morocco. We incorporate two control variables that represent vaccination and diagnosis of infected individuals and we propose an optimal strategy for an awareness program that will help to decrease the rate of the virus' spread. Pontryagin's maximum principle is used to characterize the optimal controls, and the optimality system is solved by an iterative method. Finally, extensive numerical simulations are employed with and without controls to illustrate our results using MATLAB software. Our results reveal that achieving a reduction in the contact rate between uninfected and infected individuals by vaccinating and diagnosing the susceptible individuals, can effectively reduce the basic reproduction number and tends to decrease the intensity of epidemic peaks, spreading the maximal impact of an epidemic over an extended period of time. The model simulations demonstrate that the elimination of the ongoing SARS-COV-2 pandemic and its variant Omicron in Morocco is possible by implementing, at the start of the pandemic, a strategy that combines the two variables of control mentioned above. Our predictions are based on real data with reasonable assumptions.

CDC (Cindy and David's Conversations) Game: Advising President to Survive Pandemic.

Ongoing debates on anti-COVID19 policies have been focused on coexistence-with vs. zero-out (virus) strategies, which can be simplified as "always open (AO)" vs. "always closed (AC)." We postulate that a middle ground, dubbed LOHC (low-risk-open and high-risk-closed), is likely favorable, precluding obviously irrational HOLC (high-risk-open and low-risk-closed). From a meta-strategy perspective, these four policies cover the full spectrum of anti-pandemic policies. By emulating the reality of anti-pandemic policies today, the study aims to identify possible cognitive gaps and traps by harnessing the power of evolutionary game-theoretic analysis and simulations, which suggest that (i) AO and AC seems to be "high-probability" events (0.412-0.533); (ii) counter-intuitively, the middle ground-LOHC-seems to be small-probability event (0.053), possibly mirroring its wide adoptions but broad failures. Besides devising specific policies, an equally important challenge seems to deal with often hardly avoidable policy transitions along the process from emergence, epidemic, through pandemic, to endemic state.

Protection of Patients' Rights during a Pandemic

The coronavirus has brought the world unprecedented quarantine measures, border closures, air traffic closures, and restrictions on certain human rights as a result of which patient safety has become a major global health issue. Pursuant to international standards, there are inherent human rights that cannot be restricted under any circumstances. In the research, special attention is paid to the basic principles that must be adhered to when introducing temporary measures to restrict constitutional rights of human rights: the rule of law and the principle of proportionality The paper uses the following scientific methods of cognition: general dialectical method of cognition, systematization, problem-oriented, comparative-legal, special-legal, historical-legal, formal-legal analytical and scientific knowledge. On the basis of these, it can be deduced that as a result of scrutinizing this hitch, the article developed proposals in the field of protecting the rights of patients during a pandemic, as well as proposals for enriching the theoretical apparatus.

COVID-19 SIR model: Bifurcation analysis and optimal control

This paper proposes a SIR epidemic model with vital dynamics to control or eliminate the spread of the COVID-19 epidemic considering the constant population, saturated treatment, and direct-indirect transmission rate of the model. We demonstrate positivity, boundness and calculate the disease-free equilibrium point and basic reproduction number from the model. We use the Jacobian matrix and the Lyapunov function to analyze the local and global stability, respectively. It is observed that indirect infection increases the basic reproduction number and gives rise to multiple endemic diseases. We perform transcritical, forward, backward, and Hopf bifurcation analyses. We propose two control parameters (Use of face mask, hand sanitizer, social distancing, and vaccination) to minimize the spread of the coronavirus. We use Pontryagin's maximum principle to solve the optimal control problem and demonstrate the results numerically.

Optimal control analysis of Monkeypox disease with the impact of environmental transmission

Monkeypox is an emerging zoonotic viral disease resembling that of smallpox, although it is clinically less severe. Following the COVID-19 outbreak, monkeypox is an additional global health concern. The present study aims to formulate a novel mathematical model to examine various epidemiological aspects and to suggest optimized control strategies for the ongoing outbreak. The environmental viral concentration plays an important role in disease incidence. Therefore, in this study, we consider the impact of the environmental viral concentration on disease dynamics and control. The model is first constructed with constant control measures.The basic mathematical properties including equilibria, stability, and reproduction number of the monkeypox model are presented. Furthermore, using the nonlinear least square method, we estimate the model parameters from the actual cases reported in the USA during a recent outbreak in 2022. Normalized sensitivity analysis is performed to develop the optimal control problem. Based on the sensitivity indices of the model parameters, the model is reformulated by introducing six control variables. Based on theoretical and simulation results, we conclude that considering all suggested control measures simultaneously is the effective and optimal strategy to curtail the infection. We believe that the outcomes of this study will be helpful in understanding the dynamics and prevention of upcoming monkeypox outbreaks. © 2023 the Author(s), licensee AIMS Press.

Design principle and development of vaccine in COVID-19

In the contemporary world, the COVID-19 pandemic has seriously affected the health of people as well as economic development and social stability. Therefore, vaccination to prevent viral infection is an important scientific means to effectively control the epidemic. At present, many countries have developed more and more COVID-19 vaccines with different technology platforms at a record speed, and most of them have been approved for emergency use or be conditionally marketed. In this paper, we mainly reviewed and paid attention to the research progress and achievements related to 8 COVID-19 vaccine technology platforms, including inactivated vaccines, attenuated live vaccines, protein subunit vaccines, virus-like particle (VLP) vaccines, replicated virus vector-based vaccines, non-replicated virus vector-based vaccines, DNA vaccines, and mRNA vaccines. However, with the coming COVID-19 variants, the effectiveness and neutralization activity of the current vaccines, especially the omicron variant, are reduced. Therefore, for the improvement of vaccine effectiveness and safety, we can adopt measures such as developing and optimizing new vaccines such as immune adjuvants or nanoparticle vaccines, and changing inoculation methods such as mixed inoculation and changes in inoculation routes. Finally, we summarized a series of new problems and challenges arising from some COVID-19 vaccines and elaborated some views, hoping to provide some contributions for the development and design of effective strategies for future vaccine development to some extent. © 2023 SPIE.

COVID-Pass under Scrutiny. Nudging and Fundamental Rights

The requirement of the COVID-19 certificate by the regional Ministries of Health generalised in December 2021 to access leisure, catering and socio-healthcare establishments as an instrument to contain the pandemic and encourage vaccination limits, among others, the following fundamental rights: equality, physical integrity, privacy, freedom of movement and free enterprise. Given its novelty the literature on the topic is still in its infancy. This work analyses the legal basis of this instrument to establish such limitations, its proportionality and the constitutionality of the measure as a nudge to promote vaccination taking into account its real effectiveness and safety. The study concludes evaluating the general COVID-19 vaccination campaign's constitutionality.

COVID-19 Dataset Clustering based on K-Means and EM Algorithms

In this paper, a COVID-19 dataset is analyzed using a combination of K-Means and Expectation-Maximization (EM) algorithms to cluster the data. The purpose of this method is to gain insight into and interpret the various components of the data. The study focuses on tracking the evolution of confirmed, death, and recovered cases from March to October 2020, using a two-dimensional dataset approach. K-Means is used to group the data into three categories: "Confirmed-Recovered”, "Confirmed-Death”, and "Recovered-Death”, and each category is modeled using a bivariate Gaussian density. The optimal value for k, which represents the number of groups, is determined using the Elbow method. The results indicate that the clusters generated by K-Means provide limited information, whereas the EM algorithm reveals the correlation between "Confirmed-Recovered”, "Confirmed-Death”, and "Recovered-Death”. The advantages of using the EM algorithm include stability in computation and improved clustering through the Gaussian Mixture Model (GMM). © 2023,International Journal of Advanced Computer Science and Applications. All Rights Reserved.

Coating of favipiravir (FVP) on silver nanoparticles: First principle study

Silver nanoparticles, thanks to their antiviral and antibacterial properties, have great potential in a variety of applications, such as drug-delivery carriers. The coating properties of silver nanoparticles (size range of 1.6 nm) with a well-known drug, Favipirair, were investigated in this study using quantum mechanical and classical atomistic molecular dynamics simulation in order to use as the drug delivery to treat COVID-19 disease. The drug molecule's optimized structure, frequencies, charge distribution, and electrostatic potential maps were simulated using density functional theory (DFT) at the B3LYP/6–311++g(d,p) level of theory. The coating of AgNP with each of these drugs was then studied using molecular dynamics simulation. The interaction affinity obtained from MD results agrees with the DFT results on drug adsorption on the Ag(1 1 1) slab. © 2023

Intelligent predictions of Covid disease based on lung CT images using machine learning strategy.

Covid or Corona Virus, a term ruling the world from past two years and causes a huge destruction in all countries. One of the most important Covid disease identification method is Lung based Computed Tomography (CT) image scanning, in which it provides an effective disease identification means in clear manner. However, this Lung CT image based disease detection principles are complex to health care representatives and doctors to predict the Covid disease accurately. Several manual errors and medical flaws are raised day-by-day, so that a new systematic methodology is required to identify the Covid disease effectively with respect to machine learning principles. The machine learning principles are most popular to identify the respective disease efficiently as well as classify the disease in accurate manner without any time consumption. The infected portions of the chest are identified accurately and report to the respective person without any delay. In this paper, a new machine learning strategy is introduced called Hybrid Disease Detection Principle (HDDP), in which it is derived from the two classical machine learning algorithms called Convolutional Neural Network (CNN) and the AdaBoost Classifier. Both these algorithms are integrated together to produce a new strategy called HDDP, in which it process the lung CT image based on the machine learning factors such as pre-processing, feature extraction and classification. Based on these effective image processing strategies the proposed algorithm handles the CT images to predict the Covid disease and report to the respective user with proper accuracy ratio. This paper intends to provide effcient disease predictions as well as provide a sufficient support to medical people and patients in fine manner to assist them with modern classification algorithms.

Smart education in the prospective teachers' training

The article reveals the features of smart education as a leading concept in the development of professional training of future teachers. The main components of smart education, such as a smart student, smart pedagogy and smart environment were characterized. The main principles of smart education and the ideas that formed the basis of this concept of education (mobile access, formation of new knowledge, creation of a smart environment) were defined. The features of smart education were substantiated. The peculiarities of the implementation of smart education in the conditions of the COVID-19 pandemic and military events in Ukraine were revealed. The functions of the smart system (site management system) in the process of studying the disciplines of the pedagogical cycle, its content and technological components, and facilities of the smart complexes for students and teachers in the process of training future teachers were defined. The criteria of smart complexes (automation, sequencing, assessment, data collection in real time, self-organisation) were singled out. The distance learning systems for creating smart complexes in the process of training prospective teachers were considered. The results of students' survey as for using smart complexes in the educational process were analyzed. Due to the results, the advantages and disadvantages of using smart technologies in educational process were determined. The ways of further research work regarding the introduction of smart education into the educational process were outlined. © 2023 Copyright for this paper by its authors.

Considering the precautionary principle and its application to MRSA and SARS-CoV-2 as emerging novel pathogens of their time.

In the 1980s Contact Precautions were introduced as a precautionary measure to control the emerging threat of antimicrobial resistance in hospitals, particularly methicillin resistant Staphylococcus aureus (MRSA). Today, antimicrobial resistance remains a concerning global public health threat, and a focus for hospital patient safety priorities. In late 2019 a novel respiratory virus described as SARS-CoV-2, was reported. Just as MRSA had prompted control measures developed in the context of limited information and understanding of the pathogen, public health control measures against SARS-CoV-2 were promptly and strictly implemented. Whilst SARS-CoV-2 control measures were successful at containing the virus, numerous detrimental socio-economic and health impacts have led to a rebalancing of harms versus benefits and loosening of restrictions. Conversely, evidence collated over the past 50 years, suggests that Contact Precautions are not superior to well-applied standard infection prevention and control precautions in controlling MRSA acquisition in hospitals. Several harms associated with Contact Precautions, affecting patient safety, financial costs, and organisational culture, are described. However, rebalancing of hospital MRSA control policies has been slow to materialise. This commentary invites infection prevention and control policy makers to reflect and revise policies for the control of MRSA in hospitals so that harms do not outweigh benefits.

Analysis Pre and Post COVID-19 Pandemic Rorschach Test Data of Using EM Algorithms and GMM Models

The global spread of the COVID-19 virus has become one of the greatest challenges that humanity has faced in recent years. The unprecedented circumstances of forced isolation and uncertainty that it has imposed on us continue to impact our mental well-being, whether or not we have been directly affected by the virus. Over a period of nearly three years (2017-2020), data was collected from multiple administrations of the Rorschach test, one of the most renowned and extensively studied psychological tests. This study involved the clustering of data, collected through the RAP3 software, to analyze the distinctive trends in data recorded before and after the pandemic. This was achieved through the implementation of the well-established machine learning algorithm, Expectation-Maximization. The proposed solution effectively identifies the key variables that significantly influence the subject's score and provides a reliable solution. Additionally, the solution offers an intuitive visualization that can assist psychologists in accurately interpreting shifts in trends and response distributions within a large amount of data in the two periods. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

Anatomical and Vision-Guided Path Generation Method for Nasopharyngeal Swabs Sampling

Due to the global COVID-19 pandemic, there is a strong demand for pharyngeal swab sampling and nucleic acid testing. Research has shown that the positive rate of nasopharyngeal swabs is higher than that of oropharyngeal swabs. However, because of the high complexity and visual obscuring of the interior nasal cavity, it is impossible to obtain the sampling path information directly from the conventional imaging principle. Through the combination of anatomical geometry and spatial visual features, in this paper, we present a new approach to generate nasopharyngeal swabs sampling path. Firstly, this paper adopts an RGB-D camera to identify and locate the subject's facial landmarks. Secondly, the mid-sagittal plane of the subject's head is fitted according to these landmarks. At last, the path of the nasopharyngeal swab movement in the nasal cavity is determined by anatomical geometry features of the nose. In order to verify the validity of the method, the location accuracy of the facial landmarks and the fitting accuracy of mid-sagittal plane of the head are verified. Experiments demonstrate that this method provides a feasible solution with high efficiency, safety and accuracy. Besides, it can solve the problem that the nasopharyngeal robot cannot generate path based on traditional imaging principles. It also provides a key method for automatic and intelligent sampling of nasopharyngeal swabs, and it is of great clinical value to reduce the risk of cross-infection. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Model-Based Optimization of Vaccination Strategies in Different Phases of Pandemic Virus Spread

This paper aims at applying optimal control principles to investigate optimal vaccination strategies in different phases of a pandemic. Background of the study is that many countries have started their vaccination procedures against the COVID-19 disease in the beginning of 2021, but supply shortages for the vaccines prevented that everyone could be vaccinated immediately. At the beginning of 2022, in contrast, the vaccine supply was ample, but the effectiveness of different existing vaccines to avoid infection by new virus variants was in doubt, as well as the acceptance of booster doses decreased over time. To account for these effects, two formulations of optimization tasks based on different epidemic models are proposed in this paper. The solution of these tasks determines optimal distribution strategies for available vaccines, and optimized vaccination schemes to reduce the need of booster doses for later phase. Effectiveness of these strategies compared with other popular strategies (as applied in practice) is demonstrated through a series of simulations © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.