Planning cities for pandemics: A review of urban and transport planning lessons from COVID-19

For the past years, the world has been facing one of the worst pandemics of modern times. The COVID-19 outbreak joined a long list of infectious diseases that turned pandemic, and it will most likely leave scars and change how we live, plan, and manage the urban space and its infrastructures. Many fields of science were called into action to mitigate the impacts of this pandemic, including spatial and transport planning. Given the large number of articles recently published in these research areas, it is time to carry out an overview of the knowledge produced, synthesising, systematising, and critically analysing it. This article aims to review how the urban layout, accessibility and mobility influence the spread of a virus in an urban environment and what solutions exist or have been proposed to create a more effective and less intrusive response to pandemics. This review is split into two avenues of research: spatial planning and transport planning, including the direct and indirect impact on the environment and sustainability. © 2023 ICE Publishing: All rights reserved.

Research Policy in Morocco and the Impact on National Development

The research policies in Morocco's private and public sectors illustrate the uneven progress made during the past 65 years or so, ever since its independence in 1956. Initially placed on the backburner as the nation struggled to train cadres capable of managing the challenges of nationhood, research picked up as the new universities strove to become internationally competitive. In fact, in spite of the efforts of the state during the last two decades to restructure, coordinate and mobilize national research initiatives, this domain today remains fragmented, and in need of an appropriate governance policy. Essential human and material resources are still lacking, even as the new Moroccan constitution of 2011 specifically mentions research as a national priority. Like most other countries, Moroccan research today faces three immediate challenges: the health crisis resulting from the pandemic spread of COVID-19, the transition to a green economy, and the fourth digital revolution and its impact on industry. Morocco boasts a number of research facilities, mostly placed within the 12 public universities, in addition to several laboratories in private and public/private partnership institutions. Autonomous national research structures, such as the Moroccan Foundation for Advanced Science, Innovation and Research (MAScIR), also contribute to scientific production. Public universities thus continue to dominate research output, whether as measured in indexed publications or in number of registered patents. In spite of its favorable position when compared to African francophone countries, Moroccan research production remains modest and its socio-economic impact (eg. employment opportunities) remains limited. The strategic research plan for 2025 sets policy measures meant to improve research governance, to integrate research activity and innovation with the needs of the economy, to reinforce technology innovation, especially in the automotive and aeronautical industries, and to further research in energy efficiency and alternative energy sources. In addition, the recent research initiatives launched in response to the COVID-19 pandemic by the National Center for Scientific Research (CNRST) and the Ministry of Higher Education have already started producing tangible results in pharmaceutical, biomedical and related industries. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Introduction to the Minitrack on Smart (City) and Data Streaming Application Development: Challenges and Experiences

Building Smart (City) Applications and data streaming have been fast evolving in the last couple of years with a breadth of topics with cities on the edge of the 4th industrial revolution. With COVID-19 starting to be better addressable and people returning to big cities and downtown areas, visions for urban utopia with focus on sustainability and communities arise again. The combination of Artifical Intelligence, Internet of Things and data streaming methods open up novel research areas with large transational potential and address topics such as smart transportation and standards such as Industry 4.0. This minitrack features the concepts and ideas of Smart Applications and data streaming applications, their implementations, especially from a software engineering point of view. Submissions to this minitrack include presentations of architectures, frameworks, platforms and infrastructures as well as success stories of implementations. © 2023 IEEE Computer Society. All rights reserved.

An End-to-End Knowledge Graph Based Question Answering Approach for COVID-19

Question Answering based on Knowledge Graph (KG) has emerged as a popular research area in general domain. However, few works focus on the COVID-19 kg-based question answering, which is very valuable for biomedical domain. In addition, existing question answering methods rely on knowledge embedding models to represent knowledge (i.e., entities and questions), but the relations between entities are neglected. In this paper, we construct a COVID-19 knowledge graph and propose an end-to-end knowledge graph question answering approach that can utilize relation information to improve the performance. Experimental result shows that the effectiveness of our approach on the COVID-19 knowledge graph question answering. Our code and data are available at https://github.com/CHNcreater/COVID-19-KGQA. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

An efficient lightweight encryption model with re-encryption scheme to create robust blockchain architecture for COVID-19 data

Handling electronic health records from the Internet of Medical Things is one of the most challenging research areas as it consists of sensitive information, which targets attackers. Also, dealing with modern healthcare systems is highly complex and expensive, requiring much secured storage space. However, blockchain technology can mitigate these problems through improved health record management. The proposed work develops a scalable, lightweight framework based on blockchain technology to improve COVID-19 data security, scalability and patient privacy. Initially, the COVID-19 related data records are hashed using the enhanced Merkle tree data structure. The hashed values are encrypted by lattice based cryptography with a Homomorphic proxy re-encryption scheme in which the input data are secured. After completing the encryption process, the blockchain uses inter planetary file system to store secured information. Finally, the Proof of Work concept is utilized to validate the security of the input COVID based data records. The proposed work's experimental setup is performed using the Python tool. The performance metrics like encryption time, re-encryption time, decryption time, overall processing time, and latency prove the efficacy of the proposed schemes. © 2022 John Wiley & Sons Ltd.

Student-centered Factors on Online Learning: A Systematic Literature Review

Along with its growth, one of the research areas to perfect online learning is student-centered factors that affect how online learning is going from the student's perspective. However, online learning still faces similar problems from the student perspective even until the COVID-19 pandemic. This fact confirms researchers still need to do more studies, or educators still need to learn more. So, this study aimed to help researchers and educators by providing the latest and relevant student-centered factors. This study reviewed 21 latest and relevant studies to achieve the objective. As a result, 12 types were discovered, with at least three factors in each factor type. Also, the impact of each factor on each other was given. Then, some factors were prioritized to solve the problems mentioned before. It'll be easier to improve the prioritized factors by taking steps back and improving the independent variables first. Hopefully, researchers can see a gap in those factors and start a study to solve it. Also, educators can learn at once and adjust some relevant factors to facilitate online learning as expected, especially during the COVID-19 pandemic. © 2022 IEEE.

Identification of covid-19 severity and associated genetic biomarkers based on scrna-SEQ data

Bio-marker identification for COVID-19 remains a vital research area to improve current and future pandemic responses. Innovative artificial intelligence and machine learning-based systems may leverage the large quantity and complexity of single cell sequencing data to quickly identify disease with high sensitivity. In this study, we developed a novel approach to classify patient COVID-19 infection severity using single-cell sequencing data derived from patient BronchoAlveolar Lavage Fluid (BALF) samples. We also identified key genetic biomarkers associated with COVID-19 infection severity. Feature importance scores from high performing COVID-19 classifiers were used to identify a set of novel genetic biomarkers that are predictive of COVID-19 infection severity. Treatment development and pandemic reaction may be greatly improved using our novel big-data approach. Our implementation is available on https://github.com/aekanshgoel/COVID-19-scRNAseq. © 2022 Owner/Author.

epiDAMIK 5.0: The 5th International Workshop on Epidemiology meets Data Mining and Knowledge Discovery

Similar to previous iterations, the epiDAMIK@KDD workshop is a forum to promote data driven approaches in epidemiology and public health research. Even after the devastating impact of COVID-19 pandemic, data driven approaches are not as widely studied in epidemiology, as they are in other spaces. We aim to promote and raise the profile of the emerging research area of data-driven and computational epidemiology, and create a venue for presenting state-of-the-art and in-progress results-in particular, results that would otherwise be difficult to present at a major data mining conference, including lessons learnt in the 'trenches'. The current COVID-19 pandemic has only showcased the urgency and importance of this area. Our target audience consists of data mining and machine learning researchers from both academia and industry who are interested in epidemiological and public-health applications of their work, and practitioners from the areas of mathematical epidemiology and public health. Homepage: https://epidamik.github.io/. © 2022 Owner/Author.

Assessing Deep Learning Approaches in Detecting Masked Facial Expressions

Automated facial expression recognition (FER) is an active research area due to its practical importance in a wide range of applications. In recent years, deep learning-based approaches have delivered promising performances in FER, leveraging the latest advances in computer vision. However, mask-wearing after the onset of the COVID-19 pandemic has posed challenges for the existing models when the salient features from the masked region are unavailable. This study investigates what effects facial masks will bring to expression detection using state-of-the-art deep learners. Specifically, we evaluate three deep neural networks in recognizing six emotional categories on masked facial images and compare the results to unmasked counterparts reported by prior studies. We based our work on the FER2013 dataset and augmented regular face images with artificial masks utilizing the Dlib and OpenCV libraries. Our experimental results indicate that deep learning models can be effective in recognizing some masked expressions (e.g., 'Happy', 'Surprise', and 'Neural') but fall short on the others (e.g., 'Angry', 'Fear', 'Sad'). Furthermore, with the presence of facial masks, angry faces are most likely to be misclassified as neural, and fear is the most challenging emotion to detect. © 2022 IEEE.

A Feature Extraction Method for scRNA-seq Processing and Its Application on COVID-19 Data Analysis

Single-cell RNA-sequencing (scRNA-seq) is a rapidly increasing research area in biomedical signal processing. However, the high complexity of single-cell data makes efficient and accurate analysis difficult. To improve the performance of single-cell RNA data processing, two single-cell features calculation method and corresponding dual-input neural network structures are proposed. In this feature extraction and fusion scheme, the features at the cluster level are extracted by hierarchical clustering and differential gene analysis, and the features at the cell level are extracted by the calculation of gene frequency and cross cell frequency. Our experiments on COVID-19 data demonstrate that the combined use of these two feature achieves great results and high robustness for classification tasks. © 2021 Journal of Beijing Institute of Technology

Virtual Safety Assistant: An Efficient Tool for Ensuring Safety During Covid-19 Pandemic

Using intelligent virtual assistants for controlling employee population in workspaces is a research area that remains unexplored. This paper presents a novel application of virtual humans to enforce Covid-19 safety measures in a corporate workplace. For this purpose, we develop a virtual assistant platform, Chloe, equipped with automatic temperature sensing, facial recognition, and dedicated chatbots to act as an initial filter for ensuring public health. Whilst providing an engaging user interaction experience, Chloe minimizes human to human contact, thus reducing the spread of infectious diseases. Chloe restricts the employee population within the office to government-approved safety norms. We experimented with Chloe as a virtual safety assistant in a company, where she interacted and screened the employees for Covid-19 symptoms. Participants filled an online survey to quantify Chloe’s performance in terms of interactivity, system latency, engagement, and accuracy, for which we received positive feedback. We performed statistical analysis on the survey results that reveal positive results and show effectiveness of Chloe in such applications. We detail system architecture, results and limitations. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Monitoring, Tracking and Diagnosing Dengue Fever using Smartphone, GPS and Machine Learning

Diagnosing infectious disease using smartphone and ML has emerged as popular research area. Many tropical nations including Pakistan are suffering from a viral disease i.e. Dengue. It can be recognized by its symptoms. Due to exhausted pressure of patients i.e. Covid-19 in hospitals and early monitoring, tracking and diagnosing of dengue epidemic is a real challenge to the authorities. Moreover, currently there does not exit any application to diagnose DF and SDF. Hence, we proposed a model, developed an android application, conducted pilot testing and apply ML. Whereas, WHO recommended symptoms of dengue are adopted. A pilot study is conducted on 80 participants. It revealed that the smartphone technology along with GPS on particular symptoms is helpful for early detection. Furthermore, the incorporation of GPS technology is useful for the surveillance during an epidemic or pandemic. Moreover, we also collected data of the last six-year dengue infection from hospitals for applying ML classification techniques using WEKA on clinical features of the patients. The results are compared in terms of Precision, Recall, F-measures and Accuracy to evaluate the performance of SMO, J48, Naïve Bayes, Random Forest and ZeroR classifiers. The performance of the Random Forest classifier has been achieved 98.8% using 10-folds cross-validation and 66% percentage split techniques. © 2021 IEEE.

A Comprehensive Review of Deep Learning-Based COVID-19 Detection Mechanisms Using CT Images

The diagnosis of COVID-19 has become a highly focused research area that captures researchers' attention worldwide. Although the results of RT-PCR have been regarded as the golden standard for diagnosing COVID-19, CT-based diagnostic systems also have their unique advantages, attracting numerous researchers continuously into the area of developing deep learning-based diagnostic systems that utilize CT images. This paper is committed to presenting a comprehensive review, including current dynamics, generalized framework and useful resources. To capture the pattern of the developed methods, this paper introduces a generalized framework containing two stages: segmentation and classification. Furthermore, various valuable online resources have also been collected to provide more datasets, existing implementations of diagnostic systems, and commonly adopted evaluation metrics to researchers that are new to this area for their better adaptation and contribution to this meaningful, life-changing field. © 2021 IEEE.

Fact-checking, fake news, propaganda, media bias, and the covid-19 infodemic

Social media have democratized content creation and have made it easy for anybody to spread information online. However, stripping traditional media from their gate-keeping role has left the public unprotected against biased, deceptive and disinformative content, which could now travel online at breaking-news speed and influence major public events. For example, during the COVID-19 pandemic, a new blending of medical and political disinformation has given rise to the first global infodemic. We offer an overview of the emerging and inter-connected research areas of fact-checking, disinformation, "fake news'', propaganda, and media bias detection. We explore the general fact-checking pipeline and important elements thereof such as check-worthiness estimation, spotting previously fact-checked claims, stance detection, source reliability estimation, detection of persuasion techniques, and detecting malicious users in social media. We also cover large-scale pre-trained language models, and the challenges and opportunities they offer for generating and for defending against neural fake news. Finally, we discuss the ongoing COVID-19 infodemic. © 2022 ACM.