Predicting the climate impact of aviation for en-route emissions: the algorithmic climate change function submodel ACCF 1.0 of EMAC 2.53

Using climate-optimized flight trajectories is one essential measure to reduce aviation's climate impact. Detailed knowledge of temporal and spatial climate sensitivity for aviation emissions in the atmosphere is required to realize such a climate mitigation measure. The algorithmic Climate Change Functions (aCCFs) represent the basis for such purposes. This paper presents the first version of the Algorithmic Climate Change Function submodel (ACCF 1.0) within the European Centre HAMburg general circulation model (ECHAM) and Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model framework. In the ACCF 1.0, we implement a set of aCCFs (version 1.0) to estimate the average temperature response over 20 years (ATR20) resulting from aviation CO2 emissions and non-CO2 impacts, such as NOx emissions (via ozone production and methane destruction), water vapour emissions, and contrail cirrus. While the aCCF concept has been introduced in previous research, here, we publish a consistent set of aCCF formulas in terms of fuel scenario, metric, and efficacy for the first time. In particular, this paper elaborates on contrail aCCF development, which has not been published before. ACCF 1.0 uses the simulated atmospheric conditions at the emission location as input to calculate the ATR20 per unit of fuel burned, per NOx emitted, or per flown kilometre.In this research, we perform quality checks of the ACCF 1.0 outputs in two aspects. Firstly, we compare climatological values calculated by ACCF 1.0 to previous studies. The comparison confirms that in the Northern Hemisphere between 150–300 hPa altitude (flight corridor), the vertical and latitudinal structure of NOx-induced ozone and H2O effects are well represented by the ACCF model output. The NOx-induced methane effects increase towards lower altitudes and higher latitudes, which behaves differently from the existing literature. For contrail cirrus, the climatological pattern of the ACCF model output corresponds with the literature, except that contrail-cirrus aCCF generates values at low altitudes near polar regions, which is caused by the conditions set up for contrail formation. Secondly, we evaluate the reduction of NOx-induced ozone effects through trajectory optimization, employing the tagging chemistry approach (contribution approach to tag species according to their emission categories and to inherit these tags to other species during the subsequent chemical reactions). The simulation results show that climate-optimized trajectories reduce the radiative forcing contribution from aviation NOx-induced ozone compared to cost-optimized trajectories. Finally, we couple the ACCF 1.0 to the air traffic simulation submodel AirTraf version 2.0 and demonstrate the variability of the flight trajectories when the efficacy of individual effects is considered. Based on the 1 d simulation results of a subset of European flights, the total ATR20 of the climate-optimized flights is significantly lower (roughly 50 % less) than that of the cost-optimized flights, with the most considerable contribution from contrail cirrus. The CO2 contribution observed in this study is low compared with the non-CO2 effects, which requires further diagnosis.

Assessment Challenges Encountered by Teachers in the Implementation of Printed Modular Distance Learning

The COVID-19 pandemic brought about unprecedented challenges in the field of education. Printed Modular Distance Learning (PMDL) was one of the most challenging modes of instruction in basic education, and the assessment stage posed specific difficulties for secondary school teachers in the Schools Division of Puerto Princesa City during the School Year 2020-2021. This research aimed to explore the real-life experiences of 15 secondary school teachers who taught via PMDL. Using a phenomenological study, the researcher conducted individual interviews with the teachers, who were chosen through purposive sampling. The study found that teachers encountered various challenges during the implementation of PMDL, with specific difficulties arising during the assessment stage. One of the significant problems reported by the participants was the submission of unanswered or incomplete modules by some students. Additionally, some learners had modules that were answered by their parents or other family members, while others copied answers directly from the internet or answer keys. The lack of names on modules and illegible handwriting also added to the challenges experienced by the teachers. Furthermore, the sheer volume of modules to be checked was a significant challenge for the participants. To ensure the quality and validity of module-based assessments, it is recommended that guidelines be provided to learners, monitoring and feedback mechanisms be put in place, and policies that prevent cheating be implemented. Educators should also have workload management strategies to ensure timely grading and feedback. [ FROM AUTHOR] Copyright of International Journal of Multidisciplinary: Applied Business & Education Research is the property of Future Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Problems and responses of schools in the COVID-19 pandemic: The case of Mountain Province

This study explored the problems faced by the schools in Mountain Province, Philippines with the implementation of distance education during the pandemic and looked into the schools' good practices to mitigate the problems. 26 schools participated in this study. Analysis of the data gathered highlighted the serious problems in Philippine education. The problems faced by the schools during the pandemic are similar to those faced by developing countries, however, the problems and responses faced by the schools in Mountain Province are specific to their context. The common problems identified during this pandemic are: difficulty in distributing and retrieving modules, unavailability of equipment and gadgets, poor internet access, poor quality of learning materials, the unpreparedness of parents taking on the role of teachers, teachers' lack of training on the modality of learning, lack of technological knowledge, a one-size-fits-all approach to instruction, an inadequate learning management plan for blended learning and the decreased income of private schools. The good practices implemented by the schools to address some of their problems are: home visits, tutorials, Parent-Teacher Association (PTA) involvement, remediation classes, a seminar on stress management and mechanisms for blended learning. There is a need for the school administration to provide learning management plans which should include short and long-term goals and professional learning initiatives like training to improve teachers' technological pedagogical knowledge. © 2023 by the author.

Design of Workstation for Remote Workers with Work Environment Consideration

The COVID-19 pandemic that began in 2020 has caused various phenomena in some fields of work, especially the phenomenon of remote working. The phenomenon of remote working is possible through the advancement of information and communication technology (ICT). The good impact on the environment, especially air pollution decreasing due to the lack of commuting, makes the phenomenon of remote working need to be enhanced so that the positive impact on the environment increases. This exploratory research found that the work environment factors in the workstation design, including physical ergonomics, lighting, noise, etc., greatly affect the productivity of remote workers. This workstation design is a solution for remote workers during and post-pandemic.

A framework for manufacturing system reconfiguration and optimisation utilising digital twins and modular artificial intelligence

Digital twins and artificial intelligence have shown promise for improving the robustness, responsiveness, and productivity of industrial systems. However, traditional digital twin approaches are often only employed to augment single, static systems to optimise a particular process. This article presents a paradigm for combining digital twins and modular artificial intelligence algorithms to dynamically reconfigure manufacturing systems, including the layout, process parameters, and operation times of numerous assets to allow system decision -making in response to changing customer or market needs. A knowledge graph has been used as the enabler for this system-level decision-making. A simulation environment has been constructed to replicate the manufacturing process, with the example here of an industrial robotic manufacturing cell. The simulation environment is connected to a data pipeline and an application programming interface to assist the integration of multiple artificial intelligence methods. These methods are used to improve system decision-making and optimise the configuration of a manufacturing system to maximise user-selectable key performance indicators. In contrast to previous research, this framework incorporates artificial intelligence for decision -making and production line optimisation to provide a framework that can be used for a wide variety of manufacturing applications. The framework has been applied and validated in a real use case, with the automatic reconfiguration resulting in a process time improvement of approximately 10%.

Evaluation of Temporary COVID-19 Testing Sites at Brussels Airport, Belgium: Users' Comfort and Privacy

This paper explored the temporary facilities created during the pandemic for use as COVID-19 test centers at Zaventem Airport, the main airport for the city of Brussels, Belgium. The possibilities of modular construction technology and the impact of the building envelope on users' perception of indoor space comfort and privacy were closely observed. The three main problem statements were as follows: (1) the COVID-19 crisis indirectly influenced the creation of temporary modular buildings with glass envelopes;(2) the modular envelope is adaptable to the existing context;and (3) the envelope meets the needs of the users. This study's objectives were to identify the critical factors for users' well-being in temporary modular buildings, and to conduct factor comparisons among different users and between different building-enclosure systems. The study data were collected through a subjective assessment using a SWOT analysis and a survey questionnaire. The glass facade affected the users' acceptance levels of comfort and privacy. The main results show that the users were satisfied with the level of comfort and dissatisfied with the level of privacy. This information can be used to identify areas for improvement and provide useful feedback to designers and engineers about specific design features and operating strategies for temporary modular emergency healthcare buildings.

COVID-19 Precautionary Measures and Practices for Delivering Modular Distance Learning

The purpose of this descriptive study is to investigate the safety precautions and delivery methods for the modular distance learning modality during the COVID-19 pandemic. This empirical study employed a quantitative approach and descriptive research design to address the research problem and questions. The respondents for primary data collection were the junior high school teachers who were identified through a referral sampling technique. The frequency count, percentage, ranking, and Chi-square test for homogeneity and independence were the statistical tools employed in the study. The alpha threshold for all inferential statistics was set at 0.05. The outcomes showed that the safety precautions in the modular distance learning delivery provided equitable and inclusive access to favorable learning environments. Teachers' evaluations of the methods employed in the modular distance learning program revealed that parents were urged to play a significant role as home facilitators. The data analysis revealed that there were significant differences in how the modular distance learning was carried out. According to the findings, there is no conclusive link between the prevention measures and the delivery of the distance learning mode. To strengthen this investigation, additional research is required with a larger focus and new factors. © 2023 World Journal of English Language.

Rapid construction of modular buildings for emergencies: a case study from Hong Kong, China

A new wave of the Covid-19 pandemic struck Hong Kong in February 2022. It led to construction of a temporary 1000-bed hospital and 10 000-bed isolation and treatment facility on an island site in just 51 days using factory-made modules. To achieve such rapid construction, module assembly was carried out at a separate site between the factories and site. Several new modular construction technologies were also developed, including adjustable base supports, large-span roof modules, universal safety barriers and an intelligent cloud platform for construction management. But to enable sustainable construction of such emergency buildings in future, further studies on demolition, recycling and relocation of modular buildings need to be carried out in the post-pandemic era.

Modular Integration Based on Semi public Space Body Disinfection Device

The global novel coronavirus pneumonia has brought great impact on people's daily life, and has also strengthened the importance of health care and public health in various countries. This design is a whole body disinfection device designed for public space and semi public space. It uses ergonomics, modular design and other concepts to develop an epidemic prevention and control product that is easy to assemble, move and keep, intuitive to use and fast to run. Such epidemic prevention and control products are relatively rare in the market. This design aims to help China and even the world improve the public health and safety of public space and semi public space with large population flow and density in the post epidemic era, and to quickly assemble, emergency and carry out epidemic prevention and control in case of large-scale respiratory tract infectious diseases in the future, To avoid the spread of the virus in the dense population, to achieve early detection, early prevention. © 2022 IEEE.

Teachers' Perceived Usefulness on the Utilization of Mobile-Learning Approach in Teaching High School Biology: A Case from the Philippines

Providing quality learning in science during the onset of the COVID-19 pandemic became one of the quandaries of most secondary schools in the Philippines, especially in remote areas. Likewise, high school teachers at Occidental Mindoro State College (OMSC) despite the prominence of technological learning platforms, have still resorted to the use of printed modules as primary learning modality due to the emergence of digital gap among the learners. Thus, the possibility of addressing the gap through the utilization of m-learning approach in teaching science on a distance-learning set-up was explored. Using a developed mobile-learning modular app (MoLMA) by the researcher, the approach was utilized in one of the Grade-7 Science classes of the institution. Following a descriptive-comparative mixed-method research design, the perceived usefulness of m-learning approach was gathered based on the pre-and post-perception responses of the teacher-users before and after the intervention. A focus group discussion was conducted therein after to gather an in-depth response. Results revealed high positive feedback in the utilization of m-learning before and after the intervention. Though both yielded positive responses, t-test results showed a difference between the areas of instructional delivery, enhancement of learning, and flexibility and convenience. Apart from promoting ubiquitous and personalized learning experience, results of FGD showed its cost- and time-efficiency in teaching biology subjects compared to the modular. Technologically speaking, results showed that m-learning is one of the most adaptive and accessible ways of utilizing technology-enabled learning in science at the onset of pandemic. It is recommended that the effectiveness of the approach in increasing students' academic performance shall be also tested. © ICCE 2022.All rights reserved.

A new prediction approach of the COVID-19 virus pandemic behavior with a hybrid ensemble modular nonlinear autoregressive neural network.

We describe in this paper an approach for predicting the COVID-19 time series in the world using a hybrid ensemble modular neural network, which combines nonlinear autoregressive neural networks. At the level of the modular neural network, which is formed with several modules (ensembles in this case), the modules are designed to be efficient predictors for each country. In this case, an integrator is used to combine the outputs of the modules, in this way achieving the goal of predicting a set of countries. At the level of the ensembles, forming a part of the modular network, these are constituted by a set of modules, which are nonlinear autoregressive neural networks that are designed to be efficient predictors under particular conditions for each country. In each ensemble, the results of the modules are combined with an aggregator to achieve a better and improved result for the ensemble. Publicly available datasets of coronavirus cases around the globe from the last months have been used in the analysis. Interesting conclusions have been obtained that could be helpful in deciding the best strategies in dealing with this virus for countries in their fight against the coronavirus pandemic. In addition, the proposed approach could be helpful in proposing strategies for similar countries.

The Future of Epidemic and Pandemic Vaccines to Serve Global Public Health Needs.

This Review initiates a wide-ranging discussion over 2023 by selecting and exploring core themes to be investigated more deeply in papers submitted to the Vaccines Special Issue on the "Future of Epidemic and Pandemic Vaccines to Serve Global Public Health Needs". To tackle the SARS-CoV-2 pandemic, an acceleration of vaccine development across different technology platforms resulted in the emergency use authorization of multiple vaccines in less than a year. Despite this record speed, many limitations surfaced including unequal access to products and technologies, regulatory hurdles, restrictions on the flow of intellectual property needed to develop and manufacture vaccines, clinical trials challenges, development of vaccines that did not curtail or prevent transmission, unsustainable strategies for dealing with variants, and the distorted allocation of funding to favour dominant companies in affluent countries. Key to future epidemic and pandemic responses will be sustainable, global-public-health-driven vaccine development and manufacturing based on equitable access to platform technologies, decentralised and localised innovation, and multiple developers and manufacturers, especially in low- and middle-income countries (LMICs). There is talk of flexible, modular pandemic preparedness, of technology access pools based on non-exclusive global licensing agreements in exchange for fair compensation, of WHO-supported vaccine technology transfer hubs and spokes, and of the creation of vaccine prototypes ready for phase I/II trials, etc. However, all these concepts face extraordinary challenges shaped by current commercial incentives, the unwillingness of pharmaceutical companies and governments to share intellectual property and know-how, the precariousness of building capacity based solely on COVID-19 vaccines, the focus on large-scale manufacturing capacity rather than small-scale rapid-response innovation to stop outbreaks when and where they occur, and the inability of many resource-limited countries to afford next-generation vaccines for their national vaccine programmes. Once the current high subsidies are gone and interest has waned, sustaining vaccine innovation and manufacturing capability in interpandemic periods will require equitable access to vaccine innovation and manufacturing capabilities in all regions of the world based on many vaccines, not just "pandemic vaccines". Public and philanthropic investments will need to leverage enforceable commitments to share vaccines and critical technology so that countries everywhere can establish and scale up vaccine development and manufacturing capability. This will only happen if we question all prior assumptions and learn the lessons offered by the current pandemic. We invite submissions to the special issue, which we hope will help guide the world towards a global vaccine research, development, and manufacturing ecosystem that better balances and integrates scientific, clinical trial, regulatory, and commercial interests and puts global public health needs first.

A Bayesian approach to estimating COVID-19 incidence and infection fatality rates.

Naive estimates of incidence and infection fatality rates (IFR) of coronavirus disease 2019 suffer from a variety of biases, many of which relate to preferential testing. This has motivated epidemiologists from around the globe to conduct serosurveys that measure the immunity of individuals by testing for the presence of SARS-CoV-2 antibodies in the blood. These quantitative measures (titer values) are then used as a proxy for previous or current infection. However, statistical methods that use this data to its full potential have yet to be developed. Previous researchers have discretized these continuous values, discarding potentially useful information. In this article, we demonstrate how multivariate mixture models can be used in combination with post-stratification to estimate cumulative incidence and IFR in an approximate Bayesian framework without discretization. In doing so, we account for uncertainty from both the estimated number of infections and incomplete deaths data to provide estimates of IFR. This method is demonstrated using data from the Action to Beat Coronavirus erosurvey in Canada.

An Autonomous Modular Mobility Paradigm

In the United States, public transit vehicles have a very low average load factor (10.1-12.4%), resulting in an excessive waste of seat capacity and poor fuel economy per passenger mile served. This problem is gravely exacerbated by the COVID-19 pandemic, which at its peak had caused more than 70% reduction in transit ridership nationwide. On the other hand, the rapid uptake of e-commerce, also accelerated by the pandemic, has put tremendous pressure on last-mile delivery. A co-modality system that integrates transit services with last-mile logistics offers a promising solution to better utilization/sharing of vehicle capacity and supporting infrastructure. Here we show such a system may be implemented based on Autonomous Modular Vehicle Technology (AMVT). At the core of AMVT is the ability to operate a fleet of modular autonomous vehicles or pods that can be moved, stationed, joined, and separated in real time. Coupling modularity with autonomy is poised to enable co-modality and beyond. We describe an AMVT bimodality system that provides integrated public transit and last-mile logistics services with a fleet of pods and discuss relevant research challenges and opportunities, research approaches, and real-world adoption issues. © 2009-2012 IEEE.

Situation Awareness Based Smart Contract for Modular Construction

Modular construction has been implemented to achieve shorter project duration, lower cost, and higher productivity for construction projects. This option is especially helpful to reduce on-site activities and interaction under and after COVID impact. However, additional planning and support in engineering, procurement, and delivery are required to facilitate modular construction. Unreliable prefabrication and delivery can deteriorate subsequent activity productivity and overall project performance. This research aims to develop an automatic incentive—penalty enforcement system for modular construction based on the situation awareness of delivery tracking. The research selected a high-rise residential project in Singapore as a case study. The project used modular construction for making and installing 120 Prefabricated Bathroom Units. Based on the empirical data of delivery, on-site lifting, and installation, we built STROBOSCOPE simulation models to understand the impact on productivity and schedule from five scenarios at various delivery reliability levels of the Prefabricated Bathroom Units. Smart Contract rules were developed based on the impact. A Blockchain platform was established so that once a real-time delivery is identified and the information is entered into the Smart Contract, the associated incentive or penalty can be triggered instantly. The Smart Contract based incentive—penalty enforcement system will be beneficial for construction projects to monitor and track modular delivery, motivate reliable supply, reduce payment disputes, and improve productivity. © 2023, Canadian Society for Civil Engineering.

Assembly process based on supply chain management of prefabricated houses using BIM

The assembly of modular containers using building information modelling (BIM) technologies was studied. The purpose of this study was to analyse the literature on prefabricated (prefab) houses and explore the concept of creating a digital prototype of a building based on Huoshenshan hospital using Autodesk Revit software. This hospital was constructed to treat Covid-19 patients in early 2020. The article describes the methodology of installing modular containers and assembly structures using BIM technologies for rapid construction. The results of this study showed that building object implementation depends directly on a proper model with a step-by-step mechanism for installation. Due to the supply of prefab structures at the construction site, both initial project cost and project time can be reduced. Prefab house technology demonstrated the high efficiency of using BIM technology in the assembly of Huoshenshan hospital, which was constructed in 10 days. The need for information modelling data exchange with modern technology and systems, which allows the team to become acquainted with the project before installation work starts at the construction site, is investigated.

Face Mask Recognition Using Modular, Wavelet and Modular-Wavelet PCA

The world is currently under the grip of the COVID-19 pandemic. The only possible escape from this pandemic is wearing a mask. Mask checks are being done in most public places. This project brings out a good technique for recognizing a mask on a face. Principal component analysis (PCA) is a dimensionality reduction method which is used in image and signal processing. It uses only the principal components of a dataset and ignores rest of the components. In this paper, three different pre-processing methods (modular, wavelet and a combination of both) have been performed on an image dataset. The resultant data has been processed through PCA. Through comparison of the processed data vectors, the similarity between images has been established. Also, a comparison between different PCA techniques is developed through recognition of distorted faces. Initially, a comparison between different PCA techniques is developed through recognition of distorted faces. Then, a comparison is done between the Modular PCA, Wavelet PCA and Modular-Wavelet PCA techniques by observing the performance for mask recognition. Finally, a conclusion is drawn as to which method is the best in terms of mask recognition. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Epidemic dynamics in census-calibrated modular contact network.

Network-based models are apt for understanding epidemic dynamics due to their inherent ability to model the heterogeneity of interactions in the contemporary world of intense human connectivity. We propose a framework to create a wire-frame that mimics the social contact network of the population in a geography by lacing it with demographic information. The framework results in a modular network with small-world topology that accommodates density variations and emulates human interactions in family, social, and work spaces. When loaded with suitable economic, social, and urban data shaping patterns of human connectance, the network emerges as a potent decision-making instrument for urban planners, demographers, and social scientists. We employ synthetic networks to experiment in a controlled environment and study the impact of zoning, density variations, and population mobility on the epidemic variables using a variant of the SEIR model. Our results reveal that these demographic factors have a characteristic influence on social contact patterns, manifesting as distinct epidemic dynamics. Subsequently, we present a real-world COVID-19 case study for three Indian states by creating corresponding surrogate social contact networks using available census data. The case study validates that the demography-laced modular contact network reduces errors in the estimates of epidemic variables.

Call Up Approach: An Intervention Program Driving Effects on Learners‟ Performance in the Core Subject Areas

This study examined the effectiveness of CALL UP Approach, Teachers‟ Calibrating action for Lifelong Learning to Upstage Pandemic, on pupils‟ performance specifically in English, Mathematics and Science. CALL UP Approach is an intervention strategy formulated by the teacher and employed during the Modular Distance Learning;a modality implemented during the occurrence of Covid 19 pandemic. The quasi-experimental method of research was employed to investigate the effectiveness of CALL UP Approach to improve learners‟ performance in English, Math and Science. This pre-posttest design makes use of two group of learners with almost similar characteristics. The learners were divided into two groups to compose the control and experimental groups. The control group were subjected to the usual treatment. Meanwhile, the experimental group were subjected to CALL UP Approach composed of home visits, small group community teaching, scaffolding and feedbacking. The results yielded significant difference on the mean gain score between the control and experimental groups implying that the CALL UP Approach is more effective in improving the pupil‟s academic performance in English, Math and Science than the previously available learning model of delivery. The Call Up Approach as an intervention program is found effective to improve the learner‟s academic performance as revealed in this study. Copyright for this article is retained by the author(s), with first publication rights granted to the journal.