Competitive programming in computational thinking and problem solving education

Building practical programming competency requires a long-lasting journey of discovery, trial and error, learning and improvement. This article presents essential findings of a case study of a Python programming contest with an automatic judgement system for Competitive Programming training extending the learning experiences for students in an introductory course, computational thinking and problem-solving. The benefits and challenges are discussed. Due to the coronavirus disease 2019 (COVID-19) epidemic, a hybrid model of the contest was adopted, that is, some students participated in the contest on-site, while others participated remotely. To alleviate human effort in judging the submissions, the DOMjudge platform, a web-based automatic judgement system, has been deployed as an online automatic judgement system and contest management in competitive programming. The implementation roadmap and framework were provided. The contest problems and contestants' performances were discussed. Not many junior contestants could solve at least one problem(s), and competitive computing training should be offered if the students are keen on open competitions. An empirical study was conducted to evaluate the student feedback after the contest. Preliminary results revealed that the contest offering the chance to stimulate student learning interests could enhance their independent learning, innovative thinking and problem-solving skills, and could thus lead to the overall benefits of the learning experiences, which further encourage them to participate in future contests to improve their learning and therefore enhance their employability. Employers often treasure student experiences in competitive programming events, like association for computing machinery programming contests, Google Code Jam or Microsoft Imagine Cup. Sharp vision requiring skills to tackle unseen problems within a short period is also instrumental to students planning for graduate school. © 2023 Wiley Periodicals LLC.

Comparing airborne infectious aerosol exposures in sparsely occupied large spaces utilizing large-diameter ceiling fans

In sparsely occupied large industrial and commercial buildings, large-diameter ceiling fans1 (LDCFs) are commonly utilized for comfort cooling and destratification;however, a limited number of studies were conducted to guide the operation of these devices during the COVID-19 pandemic. This study conducted 223 parametrical computational-fluid-dynamics (CFD) simulations of LDCFs in the U.S. Department of Energy warehouse reference building to compare the impacts of fan operations, index-person, and worker-packing-line locations on airborne exposures to infectious aerosols under both summer and winter conditions. The steady-state airflow fields were modeled while transient exposures to particles of varying sizes (0.5–10 μm) were evaluated over an 8-h period. Both the airflow and aerosol models were validated by measurement data from the literature. It was found that it is preferable to create a breeze from LDCFs for increased airborne dilution into a sparsely occupied large warehouse, which is more similar to an outdoor scenario than a typical indoor scenario. Operation of fans at the highest feasible speed while maintaining thermal-comfort requirements consistently outperformed the other options in terms of airborne exposures. There is no substantial evidence that fan reversal is beneficial in the current large space of interest. Reversal flow direction to create upward flows at higher fan speeds generally reduced performance compared with downward flows, as there was less airflow through the fan blades at the same rotational speed. Reversing flow at lower fan speeds decreased airflow speeds and dilution in the space and, thus, increased whole-warehouse concentrations. © 2023 Elsevier Ltd

Effects of occupant behavior and ventilation on exposure to respiratory droplets in the indoor environment

To quantify the risk of the transmission of respiratory infections in indoor environments, we systematically assessed exposure to talking- and breathing-generated respiratory droplets in a generic indoor environment using computational fluid dynamic (CFD) simulations. The flow field in the indoor environment was obtained with SST k-ω model and Lagrangian method was used to predict droplet trajectories, where droplet evaporation was considered. Droplets can be categorized into small droplets (initial size ≤30 μm or ≤10 μm as droplet nuclei), medium droplets (30–80 μm) and large droplets (>100 μm) according to the exposure characteristics. Droplets up to 100 μm, particular the small ones, can contribute to both short-range and long-range airborne routes. For the face-to-face talking scenario, the intake fraction and deposition fractions of droplets on the face and facial mucosa of the susceptible were up to 4.96%, 2.14%, and 0.12%, respectively, indicating inhalation is the dominant route. The exposure risk from a talking infector decreases monotonically with the interpersonal distance, while that of nasal-breathing generated droplets maintains a relatively stable level within 1.0 m. Keeping an angle of 15° or above with the expiratory flow is efficient to reduce intake fractions to <0.37% for small droplets. Adjusting the orientation from face-to-face to face-to-back can reduce exposure to small droplets by approximately 88.0% during talking and 66.2% during breathing. A higher ventilation rate can reduce the risk of exposure to small droplets but may increase the risk of transmission via medium droplets by enhancing their evaporation rate. This study would serve as a fundamental research for epidemiologist, healthcare workers and the public in the purpose of infection control. © 2023 Elsevier Ltd

Optimal position of air purifiers in elevator cabins for the improvement of their ventilation effectiveness

Ventilation in confined spaces is essential to reduce the airborne transmission of viruses responsible for respiratory diseases such as COVID-19. Mechanical ventilation using purifiers is an interesting solution for elevator cabins to reduce the risk of infection and improve the air quality. In this work, the optimal position and blowing direction of these devices to maximize ventilation and minimize the residence time of the air inside two cabins (large and small) is studied. Special attention is devoted to idle periods when the cabin is not used by the passengers, in order to keep the cabin ambient safe and clean, avoiding that the trapped air in the cabin (after its use) could suppose a reservoir for contaminants. CFD numerical models of two typical cabin geometries, including the discretization of small slots and grilles for infiltration, have been developed. A full 3D URANS approach with a k-epsilon RNG turbulence model and a non-reactive scalar to compute the mean age of air (MAA) was employed. The CFD results have been also validated with experimental measurements from a home-made 1:4 small-scale mock-up. The optimal position of the purifier is on the larger sidewall of the cabins for a downward blowing direction (case 1 of the database). Flow rates in the range of 0.4–0.6 m3/min, depending on the size of the cabin, are sufficient to assure a correct ventilation. Upward blowing may be preferable only if interaction of the jet core with the ceiling or other flow deflecting elements are found. In general, the contribution of infiltrations (reaching values of up to 10%), and how these secondary flows interact with the main flow pattern driven by the purifier, is relevant and not considered previously in the literature. Though an optimal position can improve ventilation considerably, it has been proven that a good choice of the purification flow rate is more critical to ensure an adequate air renewal. © 2022 The Authors

Does intrinsic motivation mediate perceived artificial intelligence (AI) learning and computational thinking of students during the COVID-19 pandemic?

The concept of Artificial Intelligence (AI), born as the possibility of simulating the human brain's learning capabilities, quickly evolves into one of the educational technology concepts that provide tools for students to better themselves in a plethora of areas. Unlike the previous educational technology iterations, which are limited to instrumental use for providing platforms to build learning applications, AI has proposed a unique education laboratory by enabling students to explore an instrument that functions as a dynamic system of computational concepts. However, the extent of the implications of AI adaptation in modern education is yet to be explored. Motivated to fill the literature gap and to consider the emerging significance of AI in education, this paper aims to analyze the possible intertwined relationship between students' intrinsic motivation for learning Artificial Intelligence during the COVID-19 pandemic;the relationship between students' computational thinking and understanding of AI concepts;and the underlying dynamic relation, if existing, between AI and computational thinking building efforts. To investigate the mentioned relationships, the present empirical study employs mediation analysis based upon collected 137 survey data from Universidad Politécnica de Madrid students in the Institute for Educational Science and the School of Naval Architecture and Marine Engineering during the first quarter of 2022. Findings show that intrinsic motivation mediates the relationship between perceived Artificial Intelligence learning and computational thinking. Also, the research indicates that intrinsic motivation has a significant relationship with computational thinking and perceived Artificial Intelligence learning. © 2023

Free energy perturbation–based large-scale virtual screening for effective drug discovery against COVID-19

As a theoretically rigorous and accurate method, FEP-ABFE (Free Energy Perturbation-Absolute Binding Free Energy) calculations showed great potential in drug discovery, but its practical application was difficult due to high computational cost. To rapidly discover antiviral drugs targeting SARS-CoV-2 Mpro and TMPRSS2, we performed FEP-ABFE–based virtual screening for ∼12,000 protein-ligand binding systems on a new generation of Tianhe supercomputer. A task management tool was specifically developed for automating the whole process involving more than 500,000 MD tasks. In further experimental validation, 50 out of 98 tested compounds showed significant inhibitory activity towards Mpro, and one representative inhibitor, dipyridamole, showed remarkable outcomes in subsequent clinical trials. This work not only demonstrates the potential of FEP-ABFE in drug discovery but also provides an excellent starting point for further development of anti-SARS-CoV-2 drugs. Besides, ∼500 TB of data generated in this work will also accelerate the further development of FEP-related methods. © The Author(s) 2022.

CFD Analysis of Ventilation of Indian Railway 2 Tier AC Sleeper Coach

In light of the ongoing COVID-19 pandemic, it is important to analyse the ventilation system of an AC coach for safer as well as comfortable ride. In this study we have simulated the airflow, temperature distribution and velocity distribution inside the cabin, to find out the best layout for comfortable temperature as well as reduced chances of airborne infection. We have simulated various ventilation layouts of the 2 tier AC train coach of Indian Railways, to study the effect of the position of the inlet and outlet ports on the temperature and velocity distribution inside the cabin. CFD analysis was done using the Ansys Fluent solver employing the realizable k-ε model to solve the turbulence problem. Herein, a total of 12 layouts were simulated with 6 heated manikins sitting inside the cabin. The results of the study suggested that the temperature distribution inside the cabin changes significantly with a change in the inlet port position. Further, the layout with the above window and/or roof outlet has a relatively lower cabin temperature. This study forms the basis for further investigations to analyse the transmission of infection via cough droplets inside the cabin (unreported here). The results of this research are important for finding the optimum position of the inlet and outlet ports in AC coaches to enhance the overall thermal comfort and reduce infection transmission inside the cabin. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Computational design and experimental analysis of a novel visor for COVID-19 patients receiving high-flow nasal oxygen therapy

The Covid-19 global pandemic has reshaped the requirements of healthcare sectors worldwide. Following the exposure risks associated with Covid-19, this paper aims to design, optimise, and validate a wearable medical device that reduces the risk of transmission of contagious droplets from infected patients in a hospital setting. This study specifically focuses on those receiving high-flow nasal oxygen therapy. The design process consisted of optimising the geometry of the visor to ensure that the maximum possible percentage of harmful droplets exhaled by the patient can be successfully captured by a vacuum tube attached to the visor. This has been completed by deriving a number of concept designs and assessing their effectiveness, based on numerical analysis, computational fluid dynamics (CFD) simulations and experimental testing. The CFD results are validated using various experimental methods such as Schlieren imaging, particle measurement testing and laser sheet visualisation. Droplet capturing efficiency of the visor was measured through CFD and validated through experimental particle measurement testing. The results presented a 5% deviation between CFD and experimental results. Also, the modifications based on the validated CFD results improved the visor effectiveness by 47% and 38% for breathing and coughing events, respectively © 2022 The Author(s)

"As Reliable as a Kalashnikov Rifle”: How Sputnik News Promotes Russian Vaccine Technologies in the Turkish Twittersphere

Established in 2014, SputnikTR (a localized version of Sputnik News) is the most popular pro-Russian media outlet active in Turkey. The news content published by SputnikTR's Twitter account currently attracts the highest engagement rates among the international public broadcasters active in Turkey. SputnikTR's official Twitter account has more followers (1M) than Sputnik News English (326K). This article argues that SputnikTR's Twitter account is used to promote Russian vaccine technologies in Turkey. We believe that it is also a conduit for the dissemination of pro-Russian as well as anti-Western narratives to the Turkish online public. Using a computational methodology, we collected 2,782 vaccine-related tweets posted by SputnikTR's Twitter account between April 2019 and April 2021. We deployed framing as well as critical discourse analysis to study the contents of our dataset. Our findings suggest that SputnikTR uses (a) disinformation as well as misinformation in vaccine-related news and (b) unethical communication techniques to maximize engagement with content posted on Twitter. Our findings are significant insofar as they are the first documented instances of Russian propaganda efforts on Turkish Twitter. These efforts seem to be focused on promoting the Russian vaccine while encouraging public hesitancy toward Western vaccine technologies. © The Author(s) 2023.

Analysis and dynamical transmission of Covid-19 model by using Caputo-Fabrizio derivative

The SARS-CoV-2 pandemic is an urgent problem with unpredictable properties and is widespread worldwide through human interactions. This work aims to use Caputo-Fabrizio frac-tional operators to explore the complex action of the Covid-19 Omicron variant. A fixed-point the-orem and an iterative approach are used to prove the existence and singularity of the model's system of solutions. Laplace transform is used to generalize the fractional order model for stability and unique solution of the iterative scheme. A numerical scheme is also constructed by using an expo-nential law kernel for the computational and simulation of the Covid-19 Model. The graphs demon-strate that the fractional model of Covid-19 is accurate. In the sense of Caputo-Fabrizio, one can obtain trustworthy information about the model in either an integer or non-integer scenario. This sense also provides useful information about the model's complexity.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).

An agent-based model to assess citizens' acceptance of COVID-19 restrictions

Italy was the first European state affected by COVID-19. Despite many uncertainties, citizens chose to trust the authorities and their trust was pivotal. This research aims to investigate the contribution of Italian citizens' trust in Public Institutions and how it influenced the acceptance of the necessary counter measures. Applying linear regression to a dataset of 4260 Italian respondents, we modelled trust from its main cognitive components, with particular reference to competence and willingness. Therefore, exploiting agent-based modelling, we investigated how these components affected trust and how trust evolution influences the acceptance of these restrictive measures. Our analysis confirms the key role of competence and willingness as cognitive components of trust. Results also suggest that a generic attempt to raise the average trust, besides being challenging, may not be the best strategy to increase compliance. Furthermore, reasoning at category level is a fundamental to identify the best components on which to invest. © Operational Research Society 2021.

A Study of Emerging IoT Technologies for Handling Pandemic Challenges

Modern years, the Internet of Things (IoT) is mechanizing in abundant real-world functions such as smart transportation, smart business to build an individual life more accessible. IoT is the mainly used method in the previous decade in different functions. Deadly diseases always had severe effects unless they were well controlled. The latest knowledge with COVID-19 explains that by using a neat and speedy approach to deal with deadly diseases, avoid devastating of healthcare structures, and reduce the loss of valuable life. The elegant things are associated with wireless or wired communication, processing, computing, and monitoring dissimilar real-time situations. These things are varied and have low remembrance, less processing control. This article explains a summary of the system and the field of its function. The recent technology has supplied to manage previous closest diseases. From years ago, scientists, investigators, physicians, and healthcare specialists are using novel computer methods to resolve the mysteries of disease. The major objective is to study dissimilar innovation-based methods and methods that support handling deadly disease challenges that are further appropriate developments that can probably be utilized. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Data-driven scalable pipeline using national agent-based models for real-time pandemic response and decision support

This paper describes an integrated, data-driven operational pipeline based on national agent-based models to support federal and state-level pandemic planning and response. The pipeline consists of (i) an automatic semantic-aware scheduling method that coordinates jobs across two separate high performance computing systems;(ii) a data pipeline to collect, integrate and organize national and county-level disaggregated data for initialization and post-simulation analysis;(iii) a digital twin of national social contact networks made up of 288 Million individuals and 12.6 Billion time-varying interactions covering the US states and DC;(iv) an extension of a parallel agent-based simulation model to study epidemic dynamics and associated interventions. This pipeline can run 400 replicates of national runs in less than 33 h, and reduces the need for human intervention, resulting in faster turnaround times and higher reliability and accuracy of the results. Scientifically, the work has led to significant advances in real-time epidemic sciences. © The Author(s) 2022.

Computational Modeling of Reaction-Diffusion COVID-19 Model Having Isolated Compartment

Cases of COVID-19 and its variant omicron are raised all across the world. The most lethal form and effect of COVID-19 are the omicron version, which has been reported in tens of thousands of cases daily in numerous nations. Following WHO (World health organization) records on 30 December 2021, the cases of COVID-19 were found to be maximum for which boarding individuals were found 1,524,266, active, recovered, and discharge were found to be 82,402 and 34,258,778, respectively. While there were 160,989 active cases, 33,614,434 cured cases, 456,386 total deaths, and 605,885,769 total samples tested. So far, 1,438,322,742 individuals have been vaccinated. The coronavirus or COVID-19 is inciting panic for several reasons. It is a new virus that has affected the whole world. Scientists have introduced certain ways to prevent the virus. One can lower the danger of infection by reducing the contact rate with other persons. Avoiding crowded places and social events with many people reduces the chance of one being exposed to the virus. The deadly COVID-19 spreads speedily. It is thought that the upcoming waves of this pandemic will be even more dreadful. Mathematicians have presented several mathematical models to study the pandemic and predict future dangers. The need of the hour is to restrict the mobility to control the infection from spreading. Moreover, separating affected individuals from healthy people is essential to control the infection. We consider the COVID-19 model in which the population is divided into five compartments. The present model presents the population's diffusion effects on all susceptible, exposed, infected, isolated, and recovered compartments. The reproductive number, which has a key role in the infectious models, is discussed. The equilibrium points and their stability is presented. For numerical simulations, finite difference (FD) schemes like nonstandard finite difference (NSFD), forward in time central in space (FTCS), and Crank Nicolson (CN) schemes are implemented. Some core characteristics of schemes like stability and consistency are calculated. © 2023 Tech Science Press. All rights reserved.

Structural evolution of Delta lineage of SARS-CoV-2.

One of the main obstacles in prevention and treatment of COVID-19 is the rapid evolution of the SARS-CoV-2 Spike protein. Given that Spike is the main target of common treatments of COVID-19, mutations occurring at this virulent factor can affect the effectiveness of treatments. The B.1.617.2 lineage of SARS-CoV-2, being characterized by many Spike mutations inside and outside of its receptor-binding domain (RBD), shows high infectivity and relative resistance to existing cures. Here, utilizing a wide range of computational biology approaches, such as immunoinformatics, molecular dynamics (MD), analysis of intrinsically disordered regions (IDRs), protein-protein interaction analyses, residue scanning, and free energy calculations, we examine the structural and biological attributes of the B.1.617.2 Spike protein. Furthermore, the antibody design protocol of Rosetta was implemented for evaluation the stability and affinity improvement of the Bamlanivimab (LY-CoV55) antibody, which is not capable of interactions with the B.1.617.2 Spike. We observed that the detected mutations in the Spike of the B1.617.2 variant of concern can cause extensive structural changes compatible with the described variation in immunogenicity, secondary and tertiary structure, oligomerization potency, Furin cleavability, and drug targetability. Compared to the Spike of Wuhan lineage, the B.1.617.2 Spike is more stable and binds to the Angiotensin-converting enzyme 2 (ACE2) with higher affinity.

#COVIDisAirborne: AI-enabled multiscale computational microscopy of delta SARS-CoV-2 in a respiratory aerosol.

We seek to completely revise current models of airborne transmission of respiratory viruses by providing never-before-seen atomic-level views of the SARS-CoV-2 virus within a respiratory aerosol. Our work dramatically extends the capabilities of multiscale computational microscopy to address the significant gaps that exist in current experimental methods, which are limited in their ability to interrogate aerosols at the atomic/molecular level and thus obscure our understanding of airborne transmission. We demonstrate how our integrated data-driven platform provides a new way of exploring the composition, structure, and dynamics of aerosols and aerosolized viruses, while driving simulation method development along several important axes. We present a series of initial scientific discoveries for the SARS-CoV-2 Delta variant, noting that the full scientific impact of this work has yet to be realized.

Green adherent spectrophotometric determination of molnupiravir based on computational calculations; application to a recently FDA-approved pharmaceutical dosage form.

Molnupiravir is an oral antiviral drug developed to provide significant benefit in reducing hospitalizations or deaths in mild COVID-19. Integrated green computational spectrophotometric method was developed for the determination of molnupiravir. Theoretical calculations were performed to predict the best coupling agent for efficient diazo coupling of molnupiravir. The binding energy between molnupiravir and various phenolic coupling agents, α-naphthol, ß-naphthol, 8-hydroxyquinoline, resorcinol, and phloroglucinol, was measured using Gaussian 03 software based on the density functional theory method and the basis set B3LYP/6-31G(d). The results showed that the interaction between molnupiravir and 8-hydroxyquinoline was higher than that of other phenolic coupling agents. The method described was based on the formation of a red colored chromogen by the diazo coupling of molnupiravir with sodium nitrite in acidic medium to form a diazonium ion coupled with 8-hydroxyquinoline. The absorption spectra showed maximum sharp peaks at 515 nm. The reaction conditions were optimized. Beer's law was followed over the concentration range of 1-12 µg/ml molnupiravir. Job's continuous variation method was developed and the stoichiometric ratio of molnupiravir to 8-hydroxyquinoline was determined to be 1:1. The described method was successfully applied to the determination of molnupiravir in pure form and in pharmaceutical dosage form. The results showed that the proposed method has minimal environmental impact compared to previous HPLC method.

Fate of Exhaled Droplets From Breathing and Coughing in Supermarket Checkouts and Passenger Cars.

The global pandemic of COVID-19 has highlighted the importance of understanding the role that exhaled droplets play in virus transmission in community settings. Computational Fluid Dynamics (CFD) enables systematic examination of roles the exhaled droplets play in the spread of SARS-CoV-2 in indoor environments. This analysis uses published exhaled droplet size distributions combined with terminal aerosol droplet size based on measured peak concentrations for SARS-CoV-2 RNA in aerosols to simulate exhaled droplet dispersion, evaporation, and deposition in a supermarket checkout area and rideshare car where close proximity with other individuals is common. Using air inlet velocity of 2 m/s in the passenger car and ASHRAE recommendations for ventilation and comfort in the supermarket, simulations demonstrate that exhaled droplets <20 µm that contain the majority of viral RNA evaporated leaving residual droplet nuclei that remain aerosolized in the air. Subsequently ~ 70% of these droplet nuclei deposited in the supermarket and the car with the reminder vented from the space. The maximum surface deposition of droplet nuclei/m2 for speaking and coughing were 2 and 819, 18 and 1387 for supermarket and car respectively. Approximately 15% of the total exhaled droplets (aerodynamic diameters 20-700 µm) were deposited on surfaces in close proximity to the individual. Due to the non-linear distribution of viral RNA across droplet sizes, however, these larger exhaled droplets that deposit on surfaces have low viral content. Maximum surface deposition of viral RNA was 70 and 1.7 × 103 virions/m2 for speaking and 2.3 × 104 and 9.3 × 104 virions/m2 for coughing in the supermarket and car respectively while the initial airborne concentration of viral RNA was 7 × 106 copies per ml. Integrating the droplet size distributions with viral load distributions, this study helps explain the apparent importance of inhalation exposures compared to surface contact observed in the pandemic.

A Computational Approach for Predicting the Termination of COVID-19

In 2019, there was an epidemic to the human society, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus causes coronavirus disease 2019 (COVID-19). It is an uncertain disease encountered in society for which the technology and human society had not prepared before. COVID-19 first spread over the Wuhan city of China. Since, the past two years of time-span, it has affected the citizen's life culture and expectancy. Now, most of the population are concern about when will be COVID-19 terminate. Basically, this paper aims to analyze the COVID-19 data with features as total confirmed cases, death rate, and vaccination rate around the world-wide region. On analyzing the data, with the help of Machine Learning (ML) algorithms, we estimate the termination of COVID-19. The rapid expansion of the COVID-19 epidemic has compelled the need for technology in this field. © 2022 IEEE.