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<span lang="EN-US">As many countries experience the emergence of new waves of Covid-19, many governments around the world have reminded their citizens of the need for an engaging intervention that could improve compliance with Covid-19 safe behaviors using the media general public or social media. In the face of the serious threat of Covid-19, immunity issues are currently the subject of various research and studies. A promising approach is to use video game culture to educate and train citizens to healthily adopt eating habits to strengthen the immune system. The objective of this study is to develop a prototype of a serious game (SG) on how to strengthen the immune defenses in order to be able to fight a coronavirus infection and to constitute an anti-virus barrier. After defining the learning objectives by interviewing the stakeholders, we searched the scientific literature to establish the relevant theoretical bases. The learning contents have been validated by biology teachers. The learning mechanisms were then determined based on the learning objectives. The obtained experimental results show that 92% of the participants in the study have appreciated the quality of the scenario and the way in which the concept of interaction between the different game elements was presented.</span>

In this study, machine learning is examined in relation to commercial machine learning's resilience to the COVID-19 pandemic-related crisis. Two approaches are used to assess the pandemic's impact on machine learning risk, as well as a method to prioritize sectors according to the crisis's potential negative consequences. I conducted the study to determine Santander machine learning's resilience. The data mining area offers prospects for COVID-19's future. A total of 13 machine learning demos were selected for its organization. The Hellweg strategy and the technique for order preference by similarity to ideal solution (TOPSIS) technique were utilized as direct request strategies. Parametric assessment of machine learning versatility in business was based on capital sufficiency, liquidity proportion, market benefits, and share in an arrangement of openings with a perceived disability, and affectability of machine learning's credit portfolio to monetary hazard. As a result of the COVID-19 pandemic, these enterprises were ranked according to their threat. Based on the findings of the research, machine learning worked the best for the pandemic. Meanwhile, machine learning suffered the most during the downturn. It can be seen, for example, in conversations about the impact of the pandemic on developing business sector soundness and managing financial framework solidity risk.

The economy of a country depends on various industries such as manufacturing, agriculture, construction, communication, energy, healthcare, and tourism. All these industries contribute a lot to the economic growth. Thus, the tourism industry plays an essential part in contributing to the economic sector in a country. The tourism industry faces many challenges, which include the most recent pandemic and the spread of the COVID-19 virus. Therefore, this research studied the trend of international tourist arrivals in Malaysia and their arrivals during two periods, before and after the COVID-19 pandemic. The main goal of this research is to assist in the preparation of recovery from various parties, such as tourists, tourism managers, and the government. In this research, the data obtained were from Tourism Malaysia for monthly international arrivals from January 2000 until March 2022. The findings of this research were based on a time frame study that highlights before and after of the COVID-19 pandemic, where the pandemic happened in March 2020, as Malaysia enforces its lockdown, and the tourism industry is most affected. The trend pattern observed shows a drastic decline in 2020 after the pandemic declaration. The impact of the COVID-19 pandemic was measured by using a paired sample test using Wilcoxon signed-rank test. It was conducted on the number of international tourists from before and after March 2020, the start of lockdown in Malaysia. The findings of this study showed that the COVID-19 pandemic has significantly impacted tourist arrivals in Malaysia.

As technology continues to advance, governments around the world have implemented health protocols to minimize direct contact between individuals and objects, in response to the ongoing COVID-19 outbreak. To address this need, a touchless parking portal was designed using a microcontroller-based and internet of things (IoT) -based system, with the Arduino UNO microcontroller device serving as the core component. The system employs an ultrasonic sensor HC-SR04 and passive infrared (PIR) to detect vehicles as they arrive at the portal area, in addition to requiring an ESP32-CAM camera, servo motor, light-emitting diode (LED), I2C 16x2 liquid crystal display (LCD), push button, universal serial bus (USB) to transistor-transistor logic (TTL) converter, power supply, and portal bar. The system builder software was developed using Arduino integrated development environment (IDE), Android, and Blynk. The authors conducted thorough testing and analysis of the system, concluding that its overall performance reaches 100%. Nevertheless, despite the extensive experimentation conducted, there remains a possibility that certain factors could still affect the results. Therefore, caution is advised when interpreting the outcomes of this experiment.

Covid-19, Klimakrise, Big Tech, algorithmic bias, #MeToo: Wissen, Wissenschaft und Technologie hat sich in den vergangenen Jahren in einem Maße politisiert wie letztmals um 1980. Damals waren die Themen noch andere: Waldsterben, Ozonloch, Atomkatastrophen, Genmanipulation, Automatisierung. Mit der Kritik an der bestehenden Wissensordnung entstand innerhalb von sozialen Bewegungen, bald aber auch in Politik, Wirtschaft und der offiziellen Wissenschaft das Bedürfnis nach alternativen Formen von Wissen: »Gegenwissen«. Was war dieses Gegenwissen? Wo war es erfolgreich? Wo scheiterte es? Und warum ist es heute wieder aktuell? Diesen Fragen widmet sich der erste Band von cache, der die Recherchen von zwölf Wissenschafts- und Technikhistoriker*innen aus der Schweiz, Deutschland und Österreich miteinander verschaltet. cache 01 ist ab Mitte Juli 2020 sukzessive online verfügbar und wächst dort weiter; das Buch ist im Oktober 2020 erschienen.

Covid-19, Klimakrise, Big Tech, algorithmic bias, #MeToo: Wissen, Wissenschaft und Technologie hat sich in den vergangenen Jahren in einem Maße politisiert wie letztmals um 1980. Damals waren die Themen noch andere: Waldsterben, Ozonloch, Atomkatastrophen, Genmanipulation, Automatisierung. Mit der Kritik an der bestehenden Wissensordnung entstand innerhalb von sozialen Bewegungen, bald aber auch in Politik, Wirtschaft und der offiziellen Wissenschaft das Bedürfnis nach alternativen Formen von Wissen: »Gegenwissen«. Was war dieses Gegenwissen? Wo war es erfolgreich? Wo scheiterte es? Und warum ist es heute wieder aktuell? Diesen Fragen widmet sich der erste Band von cache, der die Recherchen von zwölf Wissenschafts- und Technikhistoriker*innen aus der Schweiz, Deutschland und Österreich miteinander verschaltet. cache 01 ist ab Mitte Juli 2020 sukzessive online verfügbar und wächst dort weiter; das Buch ist im Oktober 2020 erschienen.

The unexpected closure of educational institutions as a result of the emergence of COVID-19 prompted the authorities to suggest adopting alternatives to traditional learning methods. E-learning is an innovative approach for delivering electronically mediated, well-designed, learner-centred interactive learning environments by utilizing internet and digital technologies with respect to instructional design principles. This paper presents the implementation and prototyping of an innovative web-based e-learning platform for chemistry course known as Al-Chemy. Al-Chemy was developed for foundation students at Tahmidi Centre, Universiti Sains Islam Malaysia. The rapid application development (RAD) methods have been used in developing Al-Chemy through website wix.com. Al-Chemy was structured with interactive notes, animation, virtual experiment, quizzes, and games. The combination of these activities helps students in learning basic and advanced concepts of chemistry.

Urban greening has benefits for both human and environmental health. However, urban greening might also have negative effects as the abundance of wild rats, which can host and spread a great diversity of zoonotic pathogens, increases with urban greenness. Studies on the effect of urban greening on rat-borne zoonotic pathogens are currently unavailable. Therefore, we investigated how urban greenness is associated with rat-borne zoonotic pathogen prevalence and diversity, and translated this to human disease hazard. We screened 412 wild rats (Rattus norvegicus and Rattus rattus) from three cities in the Netherlands for 18 different zoonotic pathogens: Bartonella spp., Leptospira spp., Borrelia spp., Rickettsia spp., Anaplasma phagocytophilum, Neoehrlichia mikurensis, Spiroplasma spp., Streptobacillus moniliformis, Coxiella burnetii, Salmonella spp., methicillin-resistant Staphylococcus aureus (MRSA), extended-spectrum beta-lactamase (ESBL)/AmpC-producing Escherichia coli, rat hepatitis E virus (ratHEV), Seoul orthohantavirus, Cowpox virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Toxoplasma gondii and Babesia spp. We modelled the relationships between pathogen prevalence and diversity and urban greenness. We detected 13 different zoonotic pathogens. Rats from greener urban areas had a significantly higher prevalence of Bartonella spp. and Borrelia spp., and a significantly lower prevalence of ESBL/AmpC-producing E. coli and ratHEV. Rat age was positively correlated with pathogen diversity while greenness was not related to pathogen diversity. Additionally, Bartonella spp. occurrence was positively correlated with that of Leptospira spp., Borrelia spp. and Rickettsia spp., and Borrelia spp. occurrence was also positively correlated with that of Rickettsia spp. Our results show an increased rat-borne zoonotic disease hazard in greener urban areas, which for most pathogens was driven by the increase in rat abundance rather than pathogen prevalence. This highlights the importance of keeping rat densities low and investigating the effects of urban greening on the exposure to zoonotic pathogens in order to make informed decisions and to take appropriate countermeasures preventing zoonotic diseases.

Instantial variation (IV) refers to variation that is due not to population differences or errors, but rather to within-subject variation, that is the intrinsic and characteristic patterns of variation pertaining to a given instance or the measurement process. Although taking into account IV is critical for the proper analysis of the results, this source of uncertainty and its impact on robustness have so far been neglected in Machine Learning (ML). To fill this gap, we look at how IV affects ML performance and generalization, and how its impact can be mitigated. Specifically, we provide a methodological contribution to formalize the problem of IV in the statistical learning framework. To prove the relevance of our contribution, we focus on one of the most critical domains, healthcare, and take individual (analytical and biological) variation as a specific kind of IV; in this domain, we use one of the largest real-world laboratory medicine datasets for the task of COVID-19 detection, to show that: (1) common state-of-the-art ML models are severely impacted by the presence of IV in data; and (2) advanced learning strategies, based on data augmentation and soft computing methods (data imprecisiation), and proper study designs can be effective at improving robustness to IV. Our findings demonstrate the critical relevance of correctly accounting for IV to enable safe deployment of ML in real-world settings.