Digital Imaging System for the Detection of Diabetic Retinopathy

COVID-19 since its appearance caused serious problems to the health sector due to the increase in infected and deceased people by directly affecting their respiratory system, making it a primordial disease that led all countries to fight this virus, generating that other diseases go to the background such as diabetes mellitus, which is a disease caused by the neglect of people's lifestyles, that has been increasing over time and that has no cure but can be prevented by controlling your blood glucose level, this disease causes diabetic retinopathy in people that with the advance of it can cause loss of sight. In addition, to detect its stage the ophthalmologist relies on his experience, occupying a lot of time and being prone to make mistakes about the patient. In view of this problem, in this article a digital image processing system was performed for the detection of diabetic retinopathy and classified according to the characteristics obtained from the features by analyzing the fundus of the eye automatically and determining the stage in which the patient is. Through the development of this system, it was determined that it works in the best way, visualizing an efficiency of 95.78% in the detection of exudates, and an efficiency of 97.14% in the detection of hemorrhages and blood vessels, resulting in a reliable and safe system to detect diabetic retinopathy early in diabetic patients. © 2023 IEEE.

Comparative Analysis of Linear Regression and Logistic Regression for the COVID-19 Detection from CT-Scans

The new emerging Coronavirus disease (COVID-19) is a pandemic disease due to its enormous infectious capability. Generally affecting the lungs, COVID-19 engenders fever, dry cough, and tiredness. However, some patients may not show symptoms. An imaging test, such as a chest X-ray or a chest CT scan, is therefore requested for reliable detection of this pneumonia type. Despite the decreasing trends both in the new and death reported cases, there is an extent need for quick, accurate, and inexpensive new methods for diagnosis. In this framework, we propose two machine learning (ML) algorithms: linear regression and logistic regression for effective COVID-19 detection in the abdominal Computed Tomography (CT) dataset. The ML methods proposed in this paper, effectively classify the data into COVID-19 and normal classes without recourse to image preprocessing or analysis. The effectiveness of these algorithms was shown through the use of the performance measures: accuracy, precision, recall, and F1-score. The best classification accuracy was obtained as 96% with logistic regression using the saga solver with no added penalty against 95.3% with linear regression. As for precision, recall, and F1-score the value of 0.89 was reached by logistic regression for all these metrics, as well as the value of 0.87 by linear regression. © 2022 IEEE.

Online Availability of a Robot Therapy for Children with Autism

In this paper we will present a case in which a robot therapy for children with autism was transferred from clinic to home conditions. The developed application enables the children to continue with the interventions in home conditions. This proved especially important in the COVID-19 pandemic. The application also allows monitoring of the child's activities, through which the therapist can later analyze the patient's behavior and offer appropriate therapy. The application shows reliable results and gives promise to develop beyond the user case we are considering. © 2023 IEEE.

Insights on the methodology for indoor microbial aerosol studies

Covid-19 has highlighted the need for reliable methods for airborne microbe control. Different microbes are suitable for different purposes, and the microbes are sensitive to collection methods used. We identified three safe-to-use microbes suitable for airborne microbial studies: MS2-bacteriophage virus, Staphylococcus simulans and Bacillus atrophaeus bacterial spores. We found that the sensitive microbes (MS2 and S. simulans) survive better, when collected directly in a liquid media. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Decision making under deep uncertainty for pandemic policy planning.

Policymakers around the world were generally unprepared for the global COVID-19 pandemic. As a result, the virus has led to millions of cases and hundreds of thousands of deaths. Theoretically, the number of cases and deaths did not have to happen (as demonstrated by the results in a few countries). In this pandemic, as in other great disasters, policymakers are confronted with what policy analysts call Decision Making under Deep Uncertainty (DMDU). Deep uncertainty requires policies that are not based on 'predict and act' but on 'prepare, monitor, and adapt', enabling policy adaptations over time as events occur and knowledge is gained. We discuss the potential of a DMDU-approach for pandemic decisionmaking.

Network based Learning Platform Application Model for Enhancement of Realtime Working Systems

Learning platforms have become an integral part of the education system these days. Especially after the COVID era, education has become more allied with self-paced and remote learning and learning platforms have made it boom exponentially. Improper construction and implementation of such platforms can lead to huge risks for the users and the company. Data security is not taken much care of while building such platforms;instead, concentration is given to fancy front-end pages and attractive interfaces. This may not be good at all times. Data is one of the most powerful resources and can have a very big impact if misused. This paper proposes a networking-based approach to implementing such platform systems in a safe and organized way. Implementation using networking concepts gives a better hand in managing permissions, access rights, and security in all data-related transfers and communications. In terms of online gaming and real-time video communication, User Datagram Protocol (UDP) is often used because it is faster than Transmission Control Protocol (TCP) and is well-suited for real-time applications that cannot tolerate delays. UDP is a connectionless protocol, which means it doesn't retransmit lost data and therefore has lower overhead, making it a good choice for real-time applications like video conferencing and online gaming. Examples of such applications include Skype, Google Meet, Zoom, and Facetime. Based on these existing applications, this work introduces UDP in the field of Learning Platform Applications and builds a model on top of which real-time applications can be constructed. The proposed system makes use of UDP for all its requests, responses, and file transfers. The protocol itself is not very reliable, but the addition of provisions for acknowledgements in all requests and responses makes this system overcome transfer uncertainty. Implementation using networking concepts improves the speed, security, privacy, and customization abilities of the proposed system. © 2023 IEEE.

Applications and Services

The Institute of Electrical and Electronic Engineers (IEEE) Future Networks International Network Generations Roadmap (INGR) Applications and Services Working Group developed a Transdisciplinary Framework that is sustainable, structured, flexible, adaptable, and scalable framework that extends across end-to-end ecosystems, and caters to different stages of priorities, resources, and technologies. The framework may be used by academic stakeholders for new research topics of interest, industry stakeholders to develop solutions for roadmap identified opportunities while minimizing negative risks, and government stakeholders for governance and policy development. The 2022 edition provides additional details on the Applications and Services Transdisciplinary Framework from Smart Cities, developed in the 1st edition, and was extended towards Smart Communities that include both urban and non-urban areas in the 2021 edition. This edition of the IEEE INGR Application and Services roadmap chapter includes: •Applications and Services Framework: a dynamic sustainable framework for applications and services that extends across end-to-end ecosystems, and caters to the priorities, resources, and technologies for local urban and non-urban areas. ○ Ecosystem of Ecosystems: intra-ecosystem and inter-ecosystem alignments for agriculture, education, electrical power, health care, media and entertainment, public safety, transportation, and water distribution and wastewater treatment ecosystems. ○ Network of Networks: Future networks components (access, service delivery, operations and service management, and network extensions), use case categories and network operations enhancements. ○ Governance Function of Functions: strategic and governance related functions to support local area objectives that include economic development, quality of life, stakeholder attraction and retention, and policy development. •Transdisciplinary Framework Scenarios and Use Cases: smart cities, smart regions, and pandemic planning scenarios The Applications and Services working group will extend the reach and depth of this framework to add new ecosystems and enhance the existing ecosystems already addressed for future INGR editions. © 2022 IEEE.

Covid-19 Identification through Chest X-ray Images using Deep Learning Classifiers

In the context of the nidovirales order, the coronavirus (Covid-19) is a virus family i.e. extracted from Ribonucleic Acid (RNA) viruses. The pandemic ensued due to it has already infected 9,716,060 people across the globe and is still causing problems with mutations of concern. Because of the immense number of infected patients, and the resulting deficiency of testing kits in hospitals;a rapid, reliable, and automatic detection system is in extreme need to curb the numbers. SARS-Cov-2 is an influenza kind of virus that can be detected using imaging techniques. It is important to distinguish between Covid-19 (caused by SARS-Cov-2) disease against pneumonia disease infected patients and healthy person's chest x-ray scans respectively. Advanced computational techniques like ML (machine learning) and DL (deep learning) had proven to be extremely useful in image processing, especially for the processing of medical images. In this work, 2906 images were taken from the publically available datasets. Various transfer learning-based DL models are applied to these images. Resulting that the ML-based classifiers effectively categorizing the input images (normal/Covid-19/pneumonia). The model achieves 96.3% accuracy with the VGG19 model and Logistic Regression (LR) classifier. This model proves to be highly convenient in treating this pandemic disease Covid-19. © 2022 IEEE.

Reliability-Oriented Route Generation Algorithm for Multimodal Transport: A Perspective from Supply Chain Reliability Enhancement

Sudden infectious diseases and other malignant events cause excessive costs in the supply chain, particularly in the transportation sector. This issue, along with the uncertainty of the development of global epidemics and the frequency of extreme natural disaster events, continues to provoke discussion and reflection. However, transport systems involve interactions between different modes, which are further complicated by the reliable coupling of multiple modes. Therefore, for the vital subsystem of the supply chain-multimodal transport, in this paper, a heuristic algorithm considering node topology and transport characteristics in a multimodal transport network (MTN): the Reliability Oriented Routing Algorithm (RORA), is proposed based on the super-network and improved k-shell (IKS) algorithm. An empirical case based on the Yangtze River Delta region of China demonstrates that RORA enables a 16% reduction in the boundary value for route failure and a reduction of about 60.58% in the route cost increase compared to the typical cost-optimal algorithm, which means that RORA results in a more reliable routing solution. The analysis of network reliability also shows that the IKS values of the nodes are positively correlated with the reliability of the MTN, and nodes with different modes may have different transport reliabilities (highest for highways and lowest for inland waterways). These findings inform a reliability-based scheme and network design for multimodal transportation. Practical Applications: Recently, the COVID-19 epidemic and the frequency of natural disasters such as floods have prompted scholars to consider transport reliability. Therefore, efficient and reliable cargo transportation solutions are crucial for the sustainable development of multimodal transport in a country or region. In this paper, a new algorithm is designed to obtain a reliability-oriented optimal routing scheme for multimodal transport. Using actual data from the Yangtze River Delta region of China as an example for experimental analysis, we obtain that: (1) the proposed algorithm is superior in terms of efficiency, accuracy, and route reliability, which means that the new algorithm can quickly find more reliable routing solutions in the event of urban transport infrastructure failures;and (2) highway hubs have the greatest transport reliability. Conversely, inland waterway hubs are the least reliable. The influence of national highways and railways on the multimodal transport system is unbalanced. These findings provide decision support to transport policymakers on reliability. For example, transport investments should be focused on building large infrastructure and increasing transport capacity, strengthening the connectivity of inland waterway hubs to hubs with higher transport advantages, and leveraging the role of large hubs. © 2023 American Society of Civil Engineers.

Cross-cultural validation of 'Students' knowledge and use of digital technology during the COVID-19 pandemic' questionnaire to the Peruvian context

To obtain useful, valid, and reliable results, it is essential to carry out a cross-cultural adaptation process when using measurement instruments developed in other cultures, contexts, and populations with a different language from the original one. An instrumental study was conducted to determine the validity and reliability of the 'Students' knowledge and use of digital technology during the COVID-19 pandemic' questionnaire, developed in another context and different culture from the original one. The participants were 139 students of the Communications program of a private higher education institute in Lima, selected by non-probabilistic convenience sampling. The study presents a proposal consisting of eight factors and 56 items, as opposed to the original structure composed of seven factors and 77 items. The analyses show that the instrument is valid and reliable among the population under study. © 2022 IEEE.

The Relationship of Public Health with Indicators of the Road Transport System

The paper presents a comparative analysis of the transport system of Russia by 12 indicators in accordance with the incidence of respiratory organs according to Rosstat data in 2019 and 2020. Machine learning methods have been applied, namely, data analysis was carried out using 9 available classification methods collected in the Data Master Azforus (DMA) program. In this program "Autoclassing” was carried out, which runs nine available methods on the same training sample. The conducted studies have demonstrated the effectiveness of using machine learning methods to identify patterns linking the health status of the population, including respiratory morbidity, with indicators of the transport system. In the course of the work, a high statistical significance of differences between classes of regions of the Russian Federation, which differ in the dynamics of Covid-19, was obtained by the most important indicators of transport system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

SanJeeVni: Secure UAV-Envisioned Massive Vaccine Distribution for COVID-19 Underlying 6G Network.

Recently, unmanned aerial vehicles (UAVs) are deployed in Novel Coronavirus Disease-2019 (COVID-19) vaccine distribution process. To address issues of fake vaccine distribution, real-time massive UAV monitoring and control at nodal centers (NCs), the authors propose SanJeeVni, a blockchain (BC)-assisted UAV vaccine distribution at the backdrop of sixth-generation (6G) enhanced ultra-reliable low latency communication (6G-eRLLC) communication. The scheme considers user registration, vaccine request, and distribution through a public Solana BC setup, which assures a scalable transaction rate. Based on vaccine requests at production setups, UAV swarms are triggered with vaccine delivery to NCs. An intelligent edge offloading scheme is proposed to support UAV coordinates and routing path setups. The scheme is compared against fifth-generation (5G) uRLLC communication. In the simulation, we achieve and 86% improvement in service latency, 12.2% energy reduction of UAV with 76.25% more UAV coverage in 6G-eRLLC, and a significant improvement of [Formula: see text]% in storage cost against the Ethereum network, which indicates the scheme efficacy in practical setups.

S-PDB: Analysis and Classification of SARS-CoV-2 Spike Protein Structures

This paper proposes a novel and efficient method, called S-PDB, for the analysis and classification of Spike (S) protein structures of SARS-CoV-2 and other viruses/organisms in the Protein Data Bank (PDB). The method first finds and identifies protein structures in PDB that are similar to a protein structure of interest (SARS-CoV-2 S) via a protein structure comparison tool. The amino acid (AA) sequences of identified protein structures, downloaded from PDB, and their aligned amino acids (AAA) and secondary structure elements (ASSE), that are stored in three separate datasets, are then used for the reliable detection/classification of SARS-CoV-2 S protein structures. Three classifiers are used and their performance is compared by using six evaluation metrics. Obtained results show that two classifiers for text data (Multinomial Naive Bayes and Stochastic Gradient Descent) performed better and achieved high accuracy on the dataset that contains AAA of protein structures compared to the datasets for AA and ASSE, respectively. © 2022 IEEE.

Acceptance and commitment therapy in a psychiatric day hospital-A longitudinal naturalistic effectiveness trial.

Objectives: Despite the transdiagnostic approach and the good cross-professional applicability, only few studies have examined the effects of Acceptance and Commitment Therapy (ACT) in a naturalistic clinic setting. This study aims to help closing this gap by investigating the effects of ACT in a psychiatric day hospital during COVID pandemic. It was investigated whether psychopathological symptomology decreased, and quality of life and general functioning improved with the treatment. Additionally, longitudinal effects were tested. Methods: Participants in this follow-up-design were 92 patients (64.1% female) of a psychiatric day hospital. Survey data of clinical symptoms, quality of life and global functioning were assessed at three time points (with admission, discharge, and 3 months after treatment). Differences between time points were tested using two-sided paired samples t-tests. Additionally, the reliability of change index (RCI) was calculated. Results: From pre-treatment to post-treatment, symptomology decreased significantly (d = 0.82-0.99, p < 0.001), and global functioning as well as quality of life increased significantly (d = 0.42-1.19, p < 0.001). The effects remained relatively stable, with no significant change between post-treatment and follow-up. The difference between pre-treatment and follow-up was significant for clinical symptoms, physical and psychological wellbeing, and global quality of life (d = 0.43-0.76, p < 0.007). Conclusion: The significant and sustained improvement in all measures indicates that patients are benefiting from the treatment. Since the trial was neither randomized nor controlled, effects have to be interpreted with caution. Possible influences of the pandemic are discussed. Clinical trial registration: http://www.drks.de/DRKS00029992, identifier DRKS00029992.

Evaluation of Bag-of-Words Classifiers for COVID-19 Detection from Cough Recordings

The paper assesses the efficiency of bag-of-words classifiers for reliable detection of Covid-19 from cough recordings. The effect of using two distinct encoding strategies and variable codebook dimensions is evaluated in terms of Area Under Curve (AUC), accuracy, sensitivity, and specificity. Three distinct feature extraction procedures are tested, followed by a Support Vector Machine (SVM) classifier. Experiments conducted on two cough recordings datasets indicate that sparse encoding yields best performances, showing robustness against feature type and codebook dimension. © 2022 IEEE.

THE SOCIAL VULNERABILITIES OF SCIENCE AND THE COVID-19 PANDEMIC CRISIS

According to the traditional image of science, if its achievements are reliable, then they will be communicated successfully and the public will trust in their applicability to solve practical problems. The new perspective on science as "socially robust knowledge” (Gibbons, 1999) is based on two other necessary conditions of knowledge production, namely, transparency and public participation. But the recent Covid-19 pandemic crisis has shown that the institutional weaknesses of the relationship between science and society generates an equally endemic mistrust. Should we go back to "hero-ic science” and the ‘"magic of science” to regain trust? Or the pandemic crisis just high-lighted that the death of expertise (Nichols, 2017) is inevitable in the public space?. © 2022, Polish Academy of Sciences - Institute of Philosophy and Sociology. All rights reserved.

A meta heuristic approach for reliable capacitated facility joint inventory-location problem with round-trip transportation under imperfect information of disruption in a Fuzzy environment

In today’s systems and networks, disruption is inevitable. Designing a reliable system to overcome probable facility disruptions plays a crucial role in planning and management. This article proposes a reliable capacitated facility joint inventory-location problem where location-independent disruption may occur in facilities. The system tries to satisfy customer’s demands and considers penalty costs for unmet customer demand. The article aims to minimize total costs such as establishing inventory, uncovered demand’s penalty, and transportation costs. While many articles in this area only use exact methods to solve the problem, this article uses a metaheuristic algorithm, the red deer algorithm, and the exact methods. Various numerical examples have shown the outstanding performance of the red deer algorithm compared to exact methods. Sensitivity analyses show the impacts of various parameters on the objective function and the optimal facility layouts. Lastly, managerial insights will be proposed based on sensitivity analysis.

Detection of COVID-19 from X-RAY Images using Artificial Intelligence (AI)

Corona virus (COVID-19) is an infectious disease. Several millions of people worldwide suffer from this disease. The signs of progress of virus infection are more severe damage to lungs and causes to organs failure, death. X-rays are readily available and an excellent alternative method to x-ray imaging in the diagnosis of covid-19 and very crucial role play to recognizing this disease and recovery with hospitalization. The goal of this revise is to expand a reliable method for detecting COVID-19 from digital chest X-ray pictures using well-before deep-learning algorithms while optimizing detection performance. To train and verify, the transfer learning (TL) approach was utilized with the aid of picture extension. Current would be hugely beneficial in this pandemic because the illness severity and the necessity for prevention methods are at odds with available resources. © 2022 IEEE.

Paving the way for precise diagnostics of antimicrobial resistant bacteria.

The antimicrobial resistance (AMR) crisis from bacterial pathogens is frequently emerging and rapidly disseminated during the sustained antimicrobial exposure in human-dominated communities, posing a compelling threat as one of the biggest challenges in humans. The frequent incidences of some common but untreatable infections unfold the public health catastrophe that antimicrobial-resistant pathogens have outpaced the available countermeasures, now explicitly amplified during the COVID-19 pandemic. Nowadays, biotechnology and machine learning advancements help create more fundamental knowledge of distinct spatiotemporal dynamics in AMR bacterial adaptation and evolutionary processes. Integrated with reliable diagnostic tools and powerful analytic approaches, a collaborative and systematic surveillance platform with high accuracy and predictability should be established and implemented, which is not just for an effective controlling strategy on AMR but also for protecting the longevity of valuable antimicrobials currently and in the future.

AIBLOCK: Blockchain based Lightweight Framework for Serverless Computing using AI

Artificial intelligence (AI)-based studies have been carried out recently for the early detection of COVID-19. The goal is to prevent the spread of the disease and the number of fatal cases. In AI-based COVID-19 diagnostic studies, the integrity of the data is critical to obtain reliable results. In this paper, we propose a Blockchain-based framework called AIBLOCK, to offer the data integrity required for applications such as Industry 4.0, healthcare, and online banking. In addition, the proposed framework is integrated with Google Cloud Platform (GCP)-Cloud Functions, a serverless computing platform that automatically manages resources by offering dynamic scalability. The performance of five different machine learning models is evaluated and compared in terms of Accuracy, Precision, Recall, F-Score and Area under the curve (AUC). The experimental results show that decision trees gives the best results in terms of accuracy (98.4 %). Further, it has been identified that utilization of Blockchain technology can increase the load on memory. © 2022 IEEE.