Photoredox-Catalyzed Plastic Waste Conversion: Nonselective Degradation versus Selective Synthesis

Plastic waste pollution is becoming one of the most pressing environmental crises due to the large-scale production without satisfactory recycling schemes, especially with the outbreak of the COVID-19 pandemic in recent years. Upcycling of plastic waste into valuable chemicals powered by solar energy presents a substantially untapped opportunity to turn waste into treasure. In this review, the fundamental principles from plastic nonselective degradation to selective synthesis are first clarified. Then, we aim to outline the representative recent advances in photoredox-based catalytic plastic waste conversion. Particular emphasis is placed on the valorization of plastic waste regarding nonselective degradation versus selective synthesis. Finally, we present challenges and individual insights for further exploration of the plastic waste conversion domain. It is anticipated that this timely and critical review would provide an instructive direction and foresight on the selective conversion of plastics to value-added chemical feedstocks, thus stimulating the development of a circular and sustainable plastic economy in the coming decades. © 2023 American Chemical Society.

Cycling in an ‘ordinary city': A practice theory approach to supporting a modal shift

There has long been evidence of the benefit of a modal shift toward cycling can bring to meeting several pressing urban challenges including ill-health, climate change, and poor air quality. In the wake of COVID-19, policy-makers have identified a modal shift toward cycling as part of the solution to mobility challenges introduced by social distancing measures. However, beyond exemplar areas, cycling has been largely characterized by a stubbornly-low modal share. In this paper, we use the ‘ordinary city'–in cycling terms–of Liverpool as a case study to understand this. We apply practice theory in doing so, finding the provision of materials for cycling is the key factor in supporting a modal shift. Not only do they provide the means to support the practice of cycling in the city, but they also have a key role in shaping individuals perceptions of, and the skills required to cycle. We then reflect upon the utility of practice theory in understanding the patterns of everyday life, finding it was particularly well suited in understanding the interactions between different factors which influence modal choice. We go on to identify practical challenges in its application within our analysis raising questions around an inconsistent analysis of influential factors including ‘driver behavior' and ‘political commitment'. We suggest how this might be overcome, through the isolation of such factors within a category of ‘action of others', this we argue means the findings in this paper have broad relevance to researchers and policy-makers alike. © 2021 The Author(s). Published with license by Taylor and Francis Group, LLC.

Cybersecurity Assessment: World and Ukrainian Experience

During the coronavirus pandemic, digital technology has opened up new possibilities and helped mitigate and circumvent many of the pandemic's limitations. For many countries and companies, cybersecurity has become one of the most pressing issues in modern life. Assessment of the Global Cybersecurity Index, the National Cybersecurity Index, and the complementary Digital Development Level give different results for the same countries. We provide an analysis of countries' efficiency in the context of cybersecurity. In the example of Ukraine, a detailed analysis of cybersecurity indices was conducted. The main cybersecurity vulnerabilities were identified: technical mechanisms and capabilities to combat spam, use of the cloud for cybersecurity purposes, mechanisms to protect children online, single point of contact for international coordination, and so on. © 2022 IEEE.

The role of sustainability assessment tools in realizing bioenergy and bioproduct systems

The pressing global challenges, including global warming and climate change, the Russia-Ukraine war, and the Covid-19 pandemic, all are indicative of the necessity of a transition from fossil-based systems toward bioenergy and bioproduct to ensure our plans for sustainable development. Such a transition, however, should be thoroughly engineered, considering the sustainability of the different elements of these systems. Advanced sustainability tools are instrumental in realizing this important objective. The present work critically reviews these tools, including techno-economic, life cycle assessment, emergy, energy, and exergy analyses, within the context of the bioenergy and bioproduct systems. The principles behind these methods are briefly explained, and then their pros and cons in designing, analyzing, and optimizing bioenergy and bioproduct systems are highlighted. Overall, it can be concluded that despite the promises held by these tools, they cannot be regarded as perfect solutions to address all the issues involved in realizing bioenergy and bioproduct systems, and integration of these tools can provide more reliable and accurate results than single approaches. © 2022 BRTeam. All rights reserved.

The Battlefront of Combating Misinformation and Coping with Media Bias

Misinformation is a pressing issue in modern society. It arouses a mixture of anger, distrust, confusion, and anxiety that cause damage on our daily life judgments and public policy decisions. While recent studies have explored various fake news detection and media bias detection techniques in attempts to tackle the problem, there remain many ongoing challenges yet to be addressed, as can be witnessed from the plethora of untrue and harmful content present during the COVID-19 pandemic, which gave rise to the first social-media infodemic, and the international crises of late. In this tutorial, we provide researchers and practitioners with a systematic overview of the frontier in fighting misinformation. Specifically, we dive into the important research questions of how to (i) develop a robust fake news detection system that not only fact-checks information pieces provable by background knowledge, but also reason about the consistency and the reliability of subtle details about emerging events;(ii) uncover the bias and the agenda of news sources to better characterize misinformation;as well as (iii) correct false information and mitigate news biases, while allowing diverse opinions to be expressed. Participants will learn about recent trends, representative deep neural network language and multimedia models, ready-to-use resources, remaining challenges, future research directions, and exciting opportunities to help make the world a better place, with safer and more harmonic information sharing. © 2022 Owner/Author.

POINT-OF-CARE ISOTHERMAL NUCLEIC ACID AMPLIFICATION PLATFORM FOR COVID-19 DIAGNOSTICS IN RESOURCE-LIMITED SETTINGS

The demand for scalable, rapid and sensitive COVID-19 diagnostics is particularly pressing at present to help contain the spread of infection and prevent overwhelming the capacity of health systems. While high-income countries have managed to rapidly expand diagnostic capacities, such is not the case in resource-limited settings of low- to medium-income countries. We report the development of an integrated modular centrifugal microfluidic platform costing less than 250 USD to perform loop-mediated isothermal amplification (LAMP) of viral RNA directly from heat-inactivated nasopharyngeal swab samples. The platform was validated with a panel of 131 nasopharyngeal swab samples collected from symptomatic COVID-19 patients. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

Patient- and Ventilator-Specific Modeling to Drive the Use and Development of 3D Printed Devices for Rapid Ventilator Splitting During the COVID-19 Pandemic

In the early days of the COVID-19 pandemic, there was a pressing need for an expansion of the ventilator capacity in response to the COVID19 pandemic. Reserved for dire situations, ventilator splitting is complex, and has previously been limited to patients with similar pulmonary compliances and tidal volume requirements. To address this need, we developed a system to enable rapid and efficacious splitting between two or more patients with varying lung compliances and tidal volume requirements. We present here a computational framework to both drive device design and inform patient-specific device tuning. By creating a patient- and ventilator-specific airflow model, we were able to identify pressure-controlled splitting as preferable to volume-controlled as well create a simulation-guided framework to identify the optimal airflow resistor for a given patient pairing. In this work, we present the computational model, validation of the model against benchtop test lungs and standard-of-care ventilators, and the methods that enabled simulation of over 200 million patient scenarios using 800,000 compute hours in a 72 h period. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Aerial Drone use for Sustainable Development in India-A Content Blog Analysis

India urgently requires technological solutions to resolve its pressing sustainability issues. The study, therefore, analyzed published blog content to explore how drones were being used to promote the United Nations sustainable development goals in India. Blog entries were retrieved through the Google search engine and screened to select 35 blogs, which contained sustainable drone applications and drone operation policies in India. Blogs published from 2016 to 2021 were analyzed through qualitative content analysis using a phenomenological approach. The study identified four themes and ten sub-themes related to drone use for sustainability in India. Themes included drone applications for sustainable management of natural resources, drone use for sustainable agriculture, drone implementation for other sustainability purposes, such as biodiversity conservation, and rules and regulations for drone use that enhance sustainability. The study recommends commercializing India's drone industry to promote research on finding solutions to sustainability issues in various sectors. © 2022 IEEE.

A Privacy Calculus Model for Contact Tracing Apps: Analyzing the German Corona-Warn-App

The SARS-CoV-2 pandemic is a pressing societal issue today. The German government promotes a contact tracing app named Corona-Warn-App (CWA), aiming to change citizens’ health behavior during the pandemic by raising awareness about potential infections and enable infection chain tracking. Technical implementations, citizens’ perceptions, and public debates around apps differ between countries, i.e., in Germany there has been a huge discussion on potential privacy issues of the app. Thus, we analyze effects of privacy concerns regarding the CWA, perceived CWA benefits, and trust in the German healthcare system to answer why citizens use the CWA. We use a sample with 1,752 actual users and non-users and find support for the privacy calculus theory, i.e., individuals weigh privacy concerns and benefits in their use decision. Thus, citizens’ privacy perceptions about health technologies (e.g., shaped by public debates) are crucial as they can hinder adoption and negatively affect future fights against pandemics. © 2022, IFIP International Federation for Information Processing.

Exploring the Dynamic Capabilities of Businesses to Cope with COVID-19 Challenges

The pandemic caused by COVID-19 triggered a severe disruption in businesses worldwide, with pressing changes aimed business survival and striving. In this context, the pace of the digital transformation of business was tremendously accelerated, launching managers and employees into a new world of digital challenges. In this work, we explore the dynamic capabilities of businesses to cope with the COVID-19 contingencies, focusing on survival and success measures, dynamic digital capabilities and emerging management skills. We interviewed 24 business managers using semi-structured interviews and mixed-methods research to reveal how these businesses have coped during the second wave of COVID-19 in the country. Our results show that little to no governmental and financial support was received, and overall negative impact on the business potential to succeed at several levels, with little exceptions. Most businesses reveal having dynamic digital capabilities in place and ability to take advantage of their digital presence to foster business survival and growth. These capabilities are, however, heavily built on what we believe to be core emerging business management skills, as they are associated with several areas of expertise in the domain of digital communication. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Data acquisition based COVID-19 Spread Prediction Analysis

The most pressing global concern right now is Covid-19. Covid-19 affects the health, daily activities and movement of people, disrupts the global economy, damages the tourist sector, and constitutes a significant threat to global health. Finding a vaccine in a short amount of time is a success that leads to a quicker return to normalcy. Following the intricate developments of Covid-19, it is also vital to foresee the scenario early in order to aid in the construction of improved health facilities, take legislative measures, and avoid economic losses, particularly human losses. The Arima model is used in this article to forecast Covid-19 in India. Arima is well suited to forecasting data using two time-ordered data points. In this paper, data acquired by Indian states from January 1, 2020 to November 8, 2021 are used. © 2022 IEEE.

A Journey into Hybrid Meetings: A story of a team's journey into hybridizing their meeting rooms, the outcomes, and lessons learned along the way

Once the economic shutdown of the COVID-19 pandemic reversed and people started returning to work at the University of Wisconsin-Madison, there was a pressing need for meeting spaces that support hybrid meetings. Since there was little expertise on hybridizing meetings on campus and a surge of fully remote workers, local technicians had to gain expertise quickly to implement workable solutions. Implementing various web conferencing systems and solutions in meeting rooms, performing rigorous testing, and rapid iteration provided necessary learning opportunities to build competencies in local support technicians. Additionally, the inevitable and unpredictable shift in meeting culture had to be closely observed and managed. As a result, the entire project involved a lean approach paired with cross-organizational collaboration and organizational change management. © 2022 ACM.

Sentiment analysis of messages on Twitter related to COVID-19 using deep learning approach

The widespread situation of the Coronavirus-19 (COVID-19) pandemic is a tangible and pressing concern. Many changes in terms lifestyle are necessary to reduce the chance of infection. While citizens have gone through different emotions, they would share their thought and interactions on social media, especially on Twitter. COVID-19 related messages can imply social emotion. This study performs sentiment analysis on tweets and annotated them into six classes of positive and negative feelings consist of anger, disgust, fear, sadness, joy, and surprise. We analyzed both textual information and historical data. We collected 120, 642 unique tweets datasets between 1 January 2020 and 30 June 2021. We compared the performance of five neural network models which are multi-layer perceptron, RNN, LSTM, Bidirectional LSTM, and GRU with several metrics consists of accuracy, F1 score, precision, and recall. The results show that LSTM model has the highest accuracy score at 77.4% while GRU has the best F1 score at 77.13%. These models could be used to monitor the movement of negative emotions. In addition, we provide interesting insights from sentiment analysis with tweet data and historical reported of infected cases, and vaccination data. © 2021 IEEE.

Towards Zero Trust: The Design and Implementation of a Secure End-Point Device for Remote Working

COVID-19 pandemic and lockdowns forced employees across the world to work from home. Remote working has become a necessity rather than a choice. However, in order to meet this increasing demand, the most pressing security concerns of organizations should be addressed. In this paper, we present the design and implementation of ProGun, an end-point device (a USB dongle) for remote working. We present the hardware/software co-design of ProGun, by which most security risks due to lack of physical protection could be mitigated. We also discuss choices we made among many alternatives for user authentication and their security and usability implications in a remote working environment. © 2021 IEEE.

Work in Progress: Post-Pandemic Opportunities to Re-Engineer Engineering Education: A Pragmatic-Futurist Framework

In the context of the COVID-19 pandemic, education has stepped up some of its long-overdue transformations. Higher education (HE) and Engineering Education (E.E.), particularly, is facing a potential crisis. Engineering schools and professional societies are dealing with several pressing problems that potentially threaten their survival. Although E.E. leaders are bound to focus on short-term survival, sustainable growth and development in the long term must also feature in the agenda. In this work, the context of disruption in the education domain is revisited through a literature review, related threats and opportunities are identified, and a strategic way forward is investigated in those lights from Engineering Education's perspective to inform a pragmatic futurist's perspective. A needs-driven innovation model (why-what-how approach) is pursued to present the study where the shift in mindset, changes in infrastructure, and leveraging digital technologies emerged as the central concepts. Each of those broad implementation categories encompassed various subsequent initiatives such as a life-long learner's mindset, a focus on how-to-learn, a strong emphasis on professional skill development, industry-academia alliances, a reflective broadening of engineers' considerations, and extensive opportunities for multi-disciplinary collaboration. To this end, we propose a pragmatic futuristic framework for accessible access to affordable, relevant, and personalized education for learners, faculty, and institutions from all diverse backgrounds. The new framework encourages fresh relationships among the key actors in the context of new modalities for the transfer and co-creation of knowledge, requirements, and possibilities for change in operational models and tapping into the boundary-breaking opportunities fostered by digital ways of teaching and learning. This study aims to provide a future-proof pathway for the engineering education ecosystem to better equip it for solving real-world problems with a multi-disciplinary approach to create new value for society. In the process, the study also sheds light on relevant new research avenues. © American Society for Engineering Education, 2021

On the Generation of Medical Dialogs for COVID-19

Under the pandemic of COVID-19, people experiencing COVID19-related symptoms have a pressing need to consult doctors. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this problem, we aim to develop a medical dialog system that can provide COVID19-related consultations. We collected two dialog datasets - CovidDialog - (in English and Chinese respectively) containing conversations between doctors and patients about COVID-19. While the largest of their kind, these two datasets are still relatively small compared with generaldomain dialog datasets. Training complex dialog generation models on small datasets bears high risk of overfitting. To alleviate overfitting, we develop a multi-task learning approach, which regularizes the data-deficient dialog generation task with a masked token prediction task. Experiments on the CovidDialog datasets demonstrate the effectiveness of our approach. We perform both human evaluation and automatic evaluation of dialogs generated by our method. Results show that the generated responses are promising in being doctorlike, relevant to conversation history, clinically informative and correct. © 2021 Association for Computational Linguistics.

Towards Approximating Population-Level Mental Health in Thailand Using Large-Scale Social Media Data

Mental health is one of the pressing issues during the COVID-19 pandemic. Psychological distress can be caused directly by the pandemic itself, such as fear of contracting the disease, or by stress from losing jobs due to the disruption of economic activities. In addition, many government measures such as lockdown, unemployment aids, subsidies, or vaccination policy also affect population mood, sentiments, and mental health. This paper utilizes deep-learning-based techniques to extract sentiment, mood, and psychological signals from social media messages and use such aggregate signals to trace population-level mental health. To validate the accuracy of our proposed methods, we cross-check our results with the actual mental illness cases reported by Thailand’s Department of Mental Health and found a high correlation between the predicted mental health signals and the actual mental illness cases. Finally, we discuss potential applications that could be implemented using our proposed methods as building blocks. © 2021, Springer Nature Switzerland AG.