A Robust Automated Framework for Classification of CT Covid-19 Images Using MSI-ResNet

Nowadays, the COVID-19 virus disease is spreading rampantly. There are some testing tools and kits available for diagnosing the virus, but it is in a limited count. To diagnose the presence of disease from radiological images, automated COVID-19 diagnosis techniques are needed. The enhancement of AI (Artificial Intelligence) has been focused in previous research, which uses X-ray images for detecting COVID-19. The most common symptoms of COVID-19 are fever, dry cough and sore throat. These symptoms may lead to an increase in the rigorous type of pneumonia with a severe barrier. Since medical imaging is not suggested recently in Canada for critical COVID-19 diagnosis, computer-aided systems are implemented for the early identification of COVID-19, which aids in noticing the disease progression and thus decreases the death rate. Here, a deep learning-based automated method for the extraction of features and classification is enhanced for the detection of COVID-19 from the images of computer tomography (CT). The suggested method functions on the basis of three main processes: data preprocessing, the extraction of features and classification. This approach integrates the union of deep features with the help of Inception 14 and VGG-16 models. At last, a classifier of Multi-scale Improved ResNet (MSI-ResNet) is developed to detect and classify the CT images into unique labels of class. With the support of available open-source COVID-CT datasets that consists of 760 CT pictures, the investigational validation of the suggested method is estimated. The experimental results reveal that the proposed approach offers greater performance with high specificity, accuracy and sensitivity. © 2023 CRL Publishing. All rights reserved.

Perspectives of non-expert users on cyber security and privacy: An analysis of online discussions on twitter

Many researchers have studied non-expert users' perspectives of cyber security and privacy aspects of computing devices at home, but their studies are mostly small-scale empirical studies based on online surveys and interviews and limited to one or a few specific types of devices, such as smart speakers. This paper reports our work on an online social media analysis of a large-scale Twitter dataset, covering cyber security and privacy aspects of many different types of computing devices discussed by non-expert users in the real world. We developed two new machine learning based classifiers to automatically create the Twitter dataset with 435,207 tweets posted by 337,604 non-expert users in January and February of 2019, 2020 and 2021. We analyzed the dataset using both quantitative (topic modeling and sentiment analysis) and qualitative analysis methods, leading to various previously unknown findings. For instance, we observed a sharp (more than doubled) increase of non-expert users' tweets on cyber security and privacy during the pandemic in 2021, compare to in the pre-COVID years (2019 and 2020). Our analysis revealed a diverse range of topics discussed by non-expert users, including VPNs, Wi-Fi, smartphones, laptops, smart home devices, financial security, help-seeking, and roles of different stakeholders. Overall negative sentiment was observed across almost all topics in all the three years. Our results indicate the multi-faceted nature of non-expert users' perspectives on cyber security and privacy and call for more holistic, comprehensive and nuanced research on their perspectives. © 2022

Automation and redistribution of work: the impact of social distancing on live TV production

The TV industry has long been under pressure to adapt its workflows to use advanced Internet technologies. It also must face competition from social media, video blogs, and livestreaming platforms, which are enabled by lightweight production tools and new distribution channels. The social-distancing regulations introduced due to the COVID-19 pandemic added to the list of challenging adaptations. One of the remaining bastions of legacy TV production is the live broadcast of sporting events and news. These production practices rely on tight collaboration in small spaces, such as control rooms and outside broadcast vans. This paper focuses on current socio-technical changes, especially those changes and adaptations in collaborative practices and workflows in TV production. Some changes necessary during the pandemic may be imposed, temporary adjustments to the ongoing situation, but some might induce permanent changes in key work practices in TV production. Further, these imposed changes are aligned with already ongoing changes in the industry, which are now being accelerated. We characterize the changes along two main dimensions: redistribution of work and automation. © 2021 The Author(s). Published with license by Taylor & Francis Group, LLC.

Towards gestured-based technologies for human-centred Smart Factories

Despite the increasing degree of automation in industry, manual or semi-automated are commonly and inevitable for complex assembly tasks. The transformation to smart processes in manufacturing leads to a higher deployment of data-driven approaches to support the worker. Upcoming technologies in this context are oftentimes based on the gesture-recognition, − monitoring or–control. This contribution systematically reviews gesture or motion capturing technologies and the utilization of gesture data in the ergonomic assessment, gesture-based robot control strategies as well as the identification of COVID-19 symptoms. Subsequently, two applications are presented in detail. First, a holistic human-centric optimization method for line-balancing using a novel indicator–ErgoTakt–derived by motion capturing. ErgoTakt improves the legacy takt-time and helps to find an optimum between the ergonomic evaluation of an assembly station and the takt-time balancing. An optimization algorithm is developed to find the best-fitting solution by minimizing a function of the ergonomic RULA-score and the cycle time of each assembly workstation with respect to the workers' ability. The second application is gesture-based robot-control. A cloud-based approach utilizing a generally accessible hand-tracking model embedded in a low-code IoT programming environment is shown. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Privacy-preserving COVID-19 contact tracing solution based on blockchain

The COVID-19 pandemic has severely affected daily life and caused a great loss to the global economy. Due to the very urgent need for identifying close contacts of confirmed patients in the current situation, the development of automated contact tracing app for smart devices has attracted more attention all over the world. Compared with expensive manual tracing approach, automated contact tracing apps can offer fast and precise tracing service, however, over-pursing high efficiency would lead to the privacy-leaking issue for app users. By combing with the benign properties (e.g., anonymity, decentralization, and traceability) of blockchain, we propose an efficient privacy-preserving solution in automated tracing scenario. Our main technique is a combination of non-interactive zero-knowledge proof and multi-signature with public key aggregation. By means of aggregating multiple signatures from different contacts at the mutual commitment phase, we only need fewer zero-knowledge proofs to complete the task of identifying contacts. It inherently leads to the benefits of saving storage and consuming less time for running verification algorithm on blockchain. Furthermore, we perform an experimental comparison by timing the execution of signature verification with and without aggregate signature, respectively. It shows that our solution can actually preserve the full-fledged privacy protection property with a lower computational cost. © 2022

Ship carbon dioxide emission estimation in coastal domestic emission control areas using high spatial-temporal resolution data: A China case

It is necessary to accurately calculate ship carbon emissions for shipping suitability. The state-of-the-art approaches could arguably not be able to estimate ship carbon emissions accurately due to the uncertainties of Ship Technical Specification Database (STSD) and the geographical and temporal breakpoints in Automatic Identification System (AIS) data, hence requiring a new methodology to be developed to address such defects and further improve the accuracy of emission estimation. Firstly, a novel STSD iterative repair model is proposed based on the random forest algorithm by the incorporation of13 ship technical parameters. The repair model is scalable and can substantially improve the quality of STSD. Secondly, a new ship AIS trajectory segmentation algorithm based on ST-DBSCAN is developed, which effectively eliminates the impact of geographical and temporal AIS breakpoints on emission estimation. It can accurately identify the ships' berthing and anchoring trajectories and reasonably segment the trajectories. Finally, based on this proposed framework, the ship carbon dioxide emissions within the scope of domestic emission control areas (DECA) along the coast of China are estimated. The experiment results indicate that the proposed STSD repair model is highly credible due to the significant connections between ship technical parameters. In addition, the emission analysis shows that, within the scope of China's DECA, the berthing period of ships is longer owing to the joint effects of coastal operation features and the strict quarantine measures under the COVID-19 pandemic, which highlights the emissions produced by ship auxiliary engines and boilers. The carbon intensity of most coastal provinces in China is relatively high, reflecting the urgent demand for the transformation and updates of the economic development models. Based on the theoretical models and results, this study recommends a five-stage decarbonization scheme for China's DECA to advance its decarbonization process. © 2022 Elsevier Ltd

Automated multi-class classification of lung diseases from CXR-images using pre-trained convolutional neural networks

According to the World Health Organization (WHO), Pneumonia, COVID-19, Tuberculosis, and Pneumothorax are the leading death causes in the world. Coughing, sneezing, fever, and shortness of breath are common symptoms. To detect them, several tests such as molecular tests (RT-PCR), antigen tests, Monteux tuberculin skin test (TST), and complete blood count (CBC) tests are needed. But these are time-consuming processes and have an error rate of 20% and a sensitivity of 80%. So, radiographic tests like computed tomography (CT) and an X-ray are used to identify lung diseases with the help of a physician. But the risk of these lung diseases' diagnoses overlapping features in chest radiographs is a worry with chest X-ray or CT-scan images. To accurately classify one of four diseases with healthy images demands the automation of such a process. There is no method for identifying and categorizing these lung diseases. As a result, we were encouraged to use eight pre-trained convolutional neural networks (CNN) to classify various lung diseases into COVID-19, pneumonia, pneumothorax, tuberculosis, and normal images from the chest X-ray image dataset. This classification process is divided into two phases. In the training phase, the CNNs are trained with the Adam optimizer with a maximum epoch of 30 and a mini-batch size of 32. In the classification phase, these trained networks are used to classify diseases. In both phases, the dataset is color preprocessed, resized, and undergoes data augmentation. For this, we used eight pre-trained CNNs: Alexnet,Darknet-19, Darknet-53, Densenet-201, Googlenet, InceptionResnetV2, MobilenetV2, and Resnet-18. Finally, we concluded that the best one to classify these diseases. Among these networks, Densenet-201, achieved the highest accuracy of 97.2%, 94.28% of sensitivity, and 97.92% of specificity for K=5. For K=10, it achieved 97.49% of accuracy, 95.57% of sensitivity, and 97.96% of specificity and for K=15, achieved 97.01% of accuracy, 96.71% of sensitivity, and 97.17% of specificity. Hence, the proposed method outperformed the existing state-of-the-art methods. Finally, our proposed research could aid clinicians in making quick conclusions concerning lung problems so that treatment can proceed. © 2022 Elsevier Ltd

The collateralized personality: creditability and resistance in the age of automated credit-scoring and lending

This paper explores the force of automation and its contradictions and resistances within (and beyond) the financial sector, with a specific focus on computational practices of credit-scoring and lending. It examines the operations and promotional discourses of fintech start-ups LendUp.com and Elevate.com that offer small loans to the sub-prime consumers in exchange for access to their online social media and mobile data, and Zest AI and LenddoEFL that sell automated decision-making tools to verify identity and assess risk. Reviewing their disciplinary reputational demands and impacts on users and communities, especially women and people of colour, the paper argues that the automated reimagination of credit and creditability disavows the formative design of its AI and redefines moral imperatives about character to align with the interests of digital capitalism. The economic, social and cultural crises precipitated by the Covid-19 pandemic have only underscored the internal contradictions of these developments, and a variety of debt resistance initiatives have emerged, aligned with broader movements for social, economic, and climate justice around the globe. Cooperative lending circles such as the Mission Asset Fund, activist groups like #NotMyDebt, and Debt Collective, a radical debt abolition movement, are examples of collective attempts to rehumanize credit and debt and resist the appropriative practices of contemporary digital finance capitalism in general. Running the gamut from accommodationist to entirely radical, these experiments in mutual aid, debt refusal, and community-building provide us with roadmaps for challenging capitalism and re-thinking credit, debt, power, and personhood within and beyond the current crises. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

COVID-19 Associated Multisystem Inflammatory Syndrome in Adults (MIS-A): How Aware Are We?

Introduction: Multisystem inflammatory syndrome-in adults (MIS-A), which occurs as a post-acute syndrome associated with COVID-19, is rarely seen and does not come to mind as in pediatric patients. Therefore, it was aimed to raise awareness that MIS-A, which can occur with different clinical presentations and findings, should be considered in the differential diagnosis with this study. Materials and Methods: Of 4984 patients found positive SARS-COV-2 reverse transcriptase-polymerase chain reaction test (RT-PCR) between March 24, 2020 and June 14, 2021, the files of 370 patients hospitalized within 2-12 weeks after their PCR positivity were investigated retrospectively. Analysis results of six cases meeting the MIS-A criteria defined by the American Center for Disease Control and Prevention were evaluated. The results were analyzed using the SPSS program. Results: Six cases meeting the MIS-A criteria were evaluated. Mean age of the six patients was 44.7 ± 10.2 years. Fever (≥38.0°C) was detected in all patients. Gastrointestinal and cardiovascular systems were the two most commonly involved systems. Two patients had nonpurulent conjunctivitis and mucocutaneous involvement. In laboratory examination, all patients had high inflammatory indicators. Although no patient was diagnosed with MIS-A, it was found that all patients received corticosteroid therapy. Finally, none of the patients were mortal. Conclusion: We think that MIS-A should be included in the differential diagnosis in patients with increased inflammation and coagulation parameters along with multisystemic symptoms. However, we claim that it would be fitting to develop a warning system over hospital automation for cases with appropriate clinical and laboratory data to reduce the possibility of skipping the diagnosis due to the emergence of MIS-A with various clinical and system involvements.

Smart Hospitality: Goodbye Virus!

One of the main impacts of technologies in the tourism sector is traveler empowerment. Technology has allowed travelers to be a lot more informed and to take a proactive role in organizing their own trip. Now, tourists are much more demanding, have several options to choose, and have more bargaining power. Tourist has also become not just a mere visitor, but a content generator sharing their experience with other travelers. This research intends to identify the adoption of technologies in the hotel and restaurant sector and secondly to identify smart technologies that can help to eradicate the virus and normalize the hospitality business. The research question is: How COVID-19 could be a trigger for the adoption of new technologies in the hospitality sector? The objectives are: (i) to identify new technologies already adopted in the hotel and restaurant areas, (ii) to pinpoint how new technologies could help in health and cleanness protocols, and (iii) to categorize changes in operational areas after the consecutive lockdowns. To achieve these goals, several case studies were collected to show and explain best practices for the future. The results are expected to help identify outcomes to build a survey to apply to hospitality professionals. Also, those outcomes will be useful in improving professionals' tools, resources, and procedures to help to eradicate the COVID-19 virus. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A review on the progress and research directions of ocean engineering

This paper reviews research in ocean engineering over the last 50+ years with the aim to (I) understand the technological challenges and evolution in the field, (II) investigate whether ocean engineering studies meet present global demands, (III) explore new scientific/engineering tools that may suggest pragmatic solutions to problems, and (IV) identify research and management gaps, and the way forward. Six major research divisions are identified, namely (I) Ocean Hydrodynamics, (II) Risk Assessment and Safety, (III) Ocean Climate and Geophysics: Data and Models, (IV) Control and Automation in the Ocean, (V) Structural Engineering and Manufacturing for the Ocean, and (VI) Ocean Renewable Energy. As much as practically possible research sub-divisions of the field are also identified. It is highlighted that research topics dealing with ocean renewable energy, control and path tracking of ships, as well as computational modelling of wave-induced motions are growing. Updating and forecasting energy resources, developing computational methods for wave generation, and introducing novel methods for the optimised control of energy converters are highlighted as the potential research opportunities. Ongoing studies follow the global needs for environmentally friendly renewable energies, though engineering-based studies often tend to overlook the longer-term potential influence of climate change. Development and exploitation of computational engineering methods with focus on continuum mechanics problems remain relevant. Notwithstanding this, machine learning methods are attracting the attention of researchers. Analysis of COVID-19 transmission onboard is rarely conducted, and 3D printing-based studies still need more attention from researchers.

AutoMID: A Novel Framework For Automated Computer Aided Diagnosis Of Medical Images

Machine Learning, a subtype of AI, enables computers to mimic human behavior without explicit programming. Machine learning models aren't used very often in diagnostic imaging because there isn't enough knowledge and resources to do so. Hence, this study aims to apply automated machine learning to the diagnosis of medical images to make machine learning more accessible to non-experts. In this study, a dataset containing 2313 images each of covid-19, pneumonia and normal chest x-rays were selected and divided into testing, training, and validation datasets. The AutoGluon library was used to train and produce a model that would classify an input image and infer the probable diagnosis from the diseases it was trained upon. This study can prove that applying hyperparameter optimization and neural architecture search is able to produce high accuracy models for medical image diagnosis. © 2022 Association for Computing Machinery.

A Method for Flexible Definition and Automatic Implementation of Laboratory Environment in Online Training Platforms

Cloud infrastructure enables individuals, organizations, and enterprises to offer scalable and elastic resources to support business operations remotely. The demand for digital transformation encourages communities and technical professionals to adopt cloud computing and automation platforms for facilitating their resource capacity, including operating systems, networks, and applications. Of cloud-based applications for social good, virtual education platforms play an important role to re-duce the cost and effort for trainees and trainers during practical courses, especially in the context of pandemics such as Covid-19. Nonetheless, the task of setting up practical environments with virtual machines, network elements, and software programs is the burden of the system that hosts many training courses with numerous trainees or resources. Hence, this research provides the mechanism for defining and automatically implementing the hands-on laboratory environments for information technology (IT) training. Specifically, we design and implement a concurrent scheme and local repository for deploying multiple environments with high performance in large virtual classrooms. The total time to finish environmental settings for learners is kept stable to meet the satisfaction of users in case of the remarkable growth in the number of environments and trainees. © 2022 IEEE.

Present status of microfluidic PCR chip in nucleic acid detection and future perspective

Polymerase chain reaction (PCR) amplifies specific fragment of DNA molecules and has been extensively applied in fields of pathogens and gene mutation detection, food safety and clinical diagnosis which on the other hand, holds the drawbacks of large size instrument, high heat dissipation etc. It has been demonstrated that microfluidics technique coupling with PCR reaction exhibits characteristics of integration, automatization, miniaturization, and portability. Meanwhile, various designed fabrication of microchip could contribute to diverse applications. In this review, we summarized major works about a variety of microfluidic chips equipped with several kinds of PCR techniques (PCR, RT-PCR, mPCR, dPCR) and detection methods like fluorescence, electrochemistry, and electrophoresis detection. The development and application of PCR-based microfluidic chip in pathogen and gene mutation detection, diseases prevention and diagnosis, DNA hybridization and low-volume sample treatment were also discussed. Copyright © 2022 Elsevier B.V.

Skeptical Communities: The Participatory Dynamics of Political Fandom

Depuis le début de la pandemie de COVID-19, nous avons observé la maniere dont les microcélébrités politiques de la sphere informationnelle québécoise font circuler et répondent â la désinformation afin de creer et de maintenir une communauté. Les possibilités offertes par les plateformes encourageant le développement et le maintien de relations multidirectionnelles qui compliquent les modeles uni directi önnel s ďinfluence ou de manipulation. Nous abordons ces processus épistémologiques a travers le prisme de la propagande participative (Wanless & Berk, 2017, 2019) et du fandom politique (Reinhard et al., 2021). En analysant le contenu et des commentaires de videos affichées sur YouTube par des influenceurs québécois contre le masque, la vaccination et les mesures sanitaires, nous étudions une construction identitaire en opposition aux sources officielles : les membres de la communauté sont des éveillés (éveillés â la vérité), par opposition aux endormis (ceux qui sont endormis ou manipules). Grâce â des analyses qualitatives, cette étude met en lumiére la maniere dont le travail des micro-influenceurs cree des opportunités pour la formation ďune identite communautaire basée sur un affect negatif et une posture épistémologique de scepticisme.

The Governance and Disclosure of IFRS 9 Economic Scenarios

Extraordinary economic conditions during the COVID-19 pandemic caused many IFRS 9 impairment models to produce unreliable results. Severe market reactions, resulting from unprecedented events, prompted swift action from the regulatory authorities to maintain the financial system's stability. Banks managed the uncertainty and volatility in the models with expert overlays, increasing the risk of biased outcomes. This study examines new ways of enhancing the governance and transparency of the IFRS 9 economic scenarios within banks and suggests additional financial disclosures. Benchmarking is proposed as a useful tool to evaluate the IFRS 9 economic scenarios and ensure effective challenge as part of a model risk governance framework. Archimedean copulas are used to generate objective economic benchmarks. Ideas around benchmarking are illustrated for a set of South African economic variables, and the outcomes are compared to the IFRS 9 scenarios published by the six biggest South African banks in their annual financial statements during the pandemic.

Platform urbanism in a pandemic: Dark stores, ghost kitchens, and the logistical-urban frontier

As demand for e-commerce surged during the COVID-19 pandemic, investors began pouring billions into start-ups promising to accelerate digitization and automation in small-margin, winner-take-all sectors, such as retail, grocery, and dining. I examine two business models that feature prominently in this swell of financial optimism: dark stores and ghost kitchens. Both sacrifice consumer-facing real estate to create logistical spaces for online order fulfillment, and both are predicted to become permanent fixtures of the post-pandemic economic landscape. However, few have commented on the consequences of this future-in-the-making or who is likely to suffer them. The essay therefore anticipates how "going dark” may impact consumers, workers, and urban geographies. I argue that going dark represents a new threshold in the spatial materialities and financial imaginary of platform urbanism, what I call the logistical-urban frontier. I theorize how this frontier threatens historically disenfranchised urban communities, and I conclude the essay with a reflection on the conflicted temporalities of logistical speculation.

Conceptual design and simulation study of an autonomous indoor medical waste collection robot

Solid waste management is one of the critical challenges seen everywhere, and the coronavirus disease (COVID-19) pandemic has only worsened the problems in the safe disposal of infectious waste. This paper outlines a design for a mobile robot that will intelligently identify, grasp, and collect a group of medical waste items using a six-degree of freedom (DoF) arm, You Only Look Once (YOLO) neural network, and a grasping algorithm. Various designs are generated before running simulations on the selected virtual model using Robot Operating System (ROS) and Gazebo simulator. A lidar sensor is also used to map the robot's surroundings and navigate autonomously. The robot has good scope for waste collection in medical facilities, where it can help create a safer environment.

THE EUROPEAN WAVE OF DIGITAL TRANSFORMATION AND THE WORKFORCE MIGRATION

This article presents the current European context regarding the extent of the labour migration phenomenon, along with the evolution of the digitalization process of the Union, using as a reference point the dynamics of the Digital Economy and Society Index in economically developed and emerging European states. It is based on the examination of the specialized academic literature and the official reports of the institutions on the evolution of the European labour market and digitalization. It tries to capture the direction and the way of labour force migration. A strong DESI level is the equivalent of a complete digital infrastructure, of a level of self-sufficient individual digital skills, and a high-performance digital integration of companies. Also, it is equivalent to the loss of obsolete jobs and the birth of new ones, for which a high level of human resources skills and knowledge is necessary. An elevated digital state can no longer be the equivalent of the destination of GIG workers from economic branches characterized by a workforce equipped with only essential digital skills.

Forecasting energy consumption demand of customers in smart grid using Temporal Fusion Transformer (TFT)

Energy consumption prediction has always remained a concern for researchers because of the rapid growth of the human population and customers joining smart grids network for smart home facilities. Recently, the spread of COVID-19 has dramatically increased energy consumption in the residential sector. Hence, it is essential to produce energy per the residential customers' requirements, improve economic efficiency, and reduce production costs. The previously published papers in the literature have considered the overall energy consumption prediction, making it difficult for production companies to produce energy per customers' future demand. Using the proposed study, production companies can accurately have energy per their customers' needs by forecasting future energy consumption demands. Scientists and researchers are trying to minimize energy consumption by applying different optimization and prediction techniques;hence this study proposed a daily, weekly, and monthly energy consumption prediction model using Temporal Fusion Transformer (TFT). This study relies on a TFT model for energy forecasting, which considers both primary and valuable data sources and batch training techniques. The model's performance has been related to the Long Short-Term Memory (LSTM), LSTM interpretable, and Temporal Convolutional Network (TCN) models. The model's performance has remained better than the other algorithms, with mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE) of 4.09, 2.02, and 1.50. Further, the overall symmetric mean absolute percentage error (sMAPE) of LSTM, LSTM interpretable, TCN, and proposed TFT remained at 29.78%, 31.10%, 36.42%, and 26.46%, respectively. The sMAPE of the TFT has proved that the model has performed better than the other deep learning models. © 2023 The Author(s)