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The current (and largely unforeseen) COVID-19 pandemic highlights the value of scenario analysis as a complementary exercise to standard, extrapolative prediction. In this chapter, we review our main findings for geopolitical scenario analysis in general, and for Antarctic geopolitical futures in particular. We conclude that the Antarctic Treaty promotes effective governance of a region described in the Madrid Protocol as ‘a natural reserve devoted to peace and science'. We hope to have shown that a classical geopolitical lens is important and relevant to the study of Antarctic futures. On the specific topic of militarisation, we identified some key driving forces for change and equilibrium. How well the ATS responds to these driving forces will turn on its resilience as a governance system. By this, we mean ‘a capacity to prepare for, to respond to, or bounce back from problems or perturbations and disturbances, which cannot necessarily be predicted or foreseen in advance' (Chandler and Coaffee 2017). As we have seen, scenarios are useful in this zone beyond standard prediction—provided they are plausible, rigorous, and robust. It is our hope that like-minded Parties and researchers might collaborate in scenario work, to contribute to the resilience of the ATS in the challenging years ahead. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Resumo A mudanca climatica ja e vista como uma das maiores ameacas a biodiversidade no seculo XXI. Poucos estudos direcionam a atencAo para seus efeitos em comunidades inteiras de hotspots ameacados. Neste trabalho, combinamos a modelagem de nicho climatico (ENM) com um futuro cenario climatico de emissoes de gases de efeito estufa para estudar as futuras mudancas na diversidade alfa e beta das aves do bioma Cerrado brasileiro, um hotspot da biodiversidade com alta velocidade de mudanca climatica e expansAo agricola. Esperavamos que o sul do Cerrado (altamente modificado) apresentasse a maioria das mudancas negativas. Em geral, encontramos resultados heterogeneos para mudancas na riqueza de especies, na diversidade beta taxonomica e funcional espacial e temporal, e na diferenciacAo ecologica media. Analisamos 1301 aves, 1115 Menos Preocupantes, 83 Quase Ameacadas, 63 Vulneraveis, 33 Em Perigo, cinco Criticamente em Perigo e duas Extintas na natureza. Ao contrario de um estudo anterior sobre mamiferos do Cerrado, espera-se que a riqueza de especies aumente no norte do Cerrado, onde a homogeneizacAo das comunidades (diminuicAo da rotatividade espacial) tambem deve ocorrer especialmente atraves de invasoes locais. Mostramos que a homogeneizacAo biotica crescente (similaridade entre as comunidades) ocorrera em dois grupos biologicos, mas atraves de subprocessos diferentes: extincoes locais para mamiferos e invasoes locais para aves. Acoes distintas de manejo da conservacAo devem ser direcionadas dependendo dos resultados das analises de diversidade alfa e beta espacial e temporal, por exemplo, controlando invasoes de especies no norte do Cerrado. Tambem mostramos prioridades em nivel de especies para as aves do Cerrado. Os estudos de conservacAo devem continuar estudando o Cerrado no Brasil mesmo durante a pandemia de covid, pois a situacAo ambiental no pais nAo e boa e os incentivos para estudos cientificos sAo quase inexistentes. Tambem consideramos que o norte do Cerrado poderia ser visto como um refugio potencial para outros grupos de organismos (morcegos, borboletas, sapos etc.). Portanto, e crucial que os tomadores de decisAo tomem medidas ambiciosas de conservacAo.
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The Flipped Classroom methodology encourages students to interact with content in multiple ways and professors, who provide active learning strategies to create a super engaging group space that can extend beyond the classroom walls. The purpose of the study was to generate learning scenarios that ensure the good performance of students to achieve the skills in two programming courses at a private university in Peru, making its implementation sustainable over the years. The educational proposal presented in this research made use of the flipped classroom methodology and the Discord platform as an agile means of communication. The results are very encouraging because it allowed students to participate in their own learning in an active and self-directed way so that they self-regulate their own learning path individually and in groups;based on flipped classroom and successfully deployed on the Discord platform. © 2023 IEEE.
ABSTRACT
Accurately estimating land-use demand is essential for urban models to predict the evolution of urban spatial morphology. Due to the uncertainties inherent in socioeconomic development, the accurate forecasting of urban land-use demand remains a daunting challenge. The present study proposes a modeling framework to determine the scaling relationship between the population and urban area and simulates the spatiotemporal dynamics of land use and land cover (LULC). An allometric scaling (AS) law and a Markov (MK) chain are used to predict variations in LULC. Random forest (RF) and cellular automata (CA) serve to calibrate the transition rules of change in LULC and realize its micro-spatial allocation (MKCA(RF-AS)). Furthermore, this research uses several shared socioeconomic pathways (SSPs) as scenario storylines. The MKCA(RF-AS) model is used to predict changes in LULC under various SSP scenarios in Jinjiang City, China, from 2020 to 2065. The results show that the figure of merit (FoM) and the urban FoM of the MKCA(RF-AS) model improve by 3.72% and 4.06%, respectively, compared with the MKCA(ANN) model during the 2005-2010 simulation period. For a 6.28% discrepancy between the predicted urban land-use demand and the actual urban land-use demand over the period 2005-2010, the urban FoM degrades by 21.42%. The growth of the permanent urban population and urban area in Jinjiang City follows an allometric scaling law with an exponent of 0.933 for the period 2005-2020, and the relative residual and R-2 are 0.0076 and 0.9994, respectively. From 2020 to 2065, the urban land demand estimated by the Markov model is 19.4% greater than the urban area predicted under scenario SSP5. At the township scale, the different SSP scenarios produce significantly different spatial distributions of urban expansion rates. By coupling random forest and allometric scaling, the MKCA(RF-AS) model substantially improves the simulation of urban land use.
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Nowadays, developing the best policy mix to manage national industrial development is an open question. The inherent complexity and competitive circumstances increased the risk of failure and challenged the development of national programs, especially in the case of developing economies. To address such a complex world, this study proposes a novel scenario-based economic complexity analyzing methodology to overcome future uncertainties. The method contains two independent research lines. The first one evaluates and prioritizes industrial development by referring to the opportunity gain and product complexity indexes (a historical and current analysis). The other is to develop future scenarios through an expert-based process to uncover the most plausible futures (future-oriented evaluation for uncertainty behaviors). Then, results merged to increase a robust policy to guarantee development success and reduce failure risks. Iran has been selected as the case study since the country is facing a very uncertain future both from political and economic perspectives. Results revealed that the priority is to focus on the opportunity gain of products instead of product complexity since the country is facing international sanctions, limited investment capacities, and the potential of global challenges in the era of globalization, similar to the world faced during COVID-19 pandemics. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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Inevitable and constant change is challenging school systems worldwide, and COVID-19 has further intensified the debate on the future. This article examines the possible futures of Finnish comprehensive schools through three scenarios generated by analysing data from a Delphi panel of 30 Finnish experts in the field of education. This study contributes to two major intertwining debates: first, who or what determines the content and goals of the curriculum. The study's theoretical framework builds on the curriculum as a social practice model, which views curriculum work as interwoven layers and sites of practice. Another topical debate concerns the tension between powerful knowledge and competences in the curricula. This is explored through Young and Muller's model of three types of knowledge: knowledge of power, tacit knowledge and powerful knowledge. The results show that Finnish comprehensive schools have various substantially divergent trends. In the three scenarios, the role of the teacher as a curriculum maker varies from non-existent to a strong interpreter. International policy flows can be transferred to schools to varying degrees. The three types of knowledge included in Young and Muller's model can be recognised in the three scenarios. Competences can be identified as learning outcomes in all scenarios, but the intensity varies. Scenarios are not predictions of the future or policy recommendations but an efficient tool for provoking strategic debate, generating new visionary thinking and considering the need for system-wide change in education.
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The epidemiological crisis of 2020-2021 has revealed a number of imbalances and "bottlenecks" that have developed in the Russian healthcare system over the past 20 years as a result of a policy of limiting development to breakthroughs in individual areas accompanied by optimization of the sector. It became evident that one of the most acute problems is interregional disparity in terms of personnel and resource availability in the healthcare system, which determines the system's ability to respond to challenges and shocks. Solving these problems requires a comprehensive approach: simply increasing the sector's financing is not sufficient and must be accompanied by structural changes, in particular, modifying the education system and training new highly qualified personnel, creating an effective system of territorial distribution of personnel, and radically increasing the availability of high-end equipment, i.e., a transition to a new model of healthcare.
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Video conferencing has become more common than ever due to the COVID-19 pandemic, which makes high-resolution video transmission a pressing issue. Although semantic video conferencing (SVC) has achieved a great success to improve the transmission efficiency by only transmitting some keypoints to represent changed expressions, its performance can still be improved by adapting to varying channel scenarios, which is lack of study when designing the whole SVC in the end-to-end manner. In this paper, we first establish a standard SVC-OFDM system. Then, the receiver part of the SVC is added with an adaptive network called Switch-SVC for varying channels and improve the accuracy of the received keypoints. Some parameters in Switch-SVC are trained online so that the receiver can adapt to the current environment. Simulation results show that the proposed method can greatly improve the keypoint reconstruction performance compared to the traditional SVC-OFDM receiver without online training.
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Among the problems caused by the pandemic of the new coronavirus (SARS-COV-2), besides the irreparable loss of loved ones and the damage to global health caused by the disease, the restrictions imposed and the economic losses incurred by them stood out. Given the sudden changes imposed on the routine of society and companies, many businesses went bankrupt, while the other survivors needed to adapt quickly, resulting in a routine based on home office, ecommerce and distance learning. The educational sector was strongly affected by these restrictions, as well as the assets linked to it, as highlighted by the cumulative annual drop of 22% of the IFIX (index of Real Estate Investment Funds), witnessed by investors during the arrival and spread of the pandemic in Brazilian territory. Thus, the work was based on the prospective analysis of scenarios through the Momentum method, producing three possible future scenarios for the recovery of the educational REITs (Pessimistic, Optimistic, and Trend), with the help of three financial planning specialists. At the end of this study, it was possible to configure the scenarios: "The recovery of education REITs" as optimistic scenario, "Challenges of education REITs" as trend scenario, and "The crisis of education REITs" as Pessimistic scenario.
ABSTRACT
E-Learning has shown to be an important resource, particularly in recent times due to the limitations in the Sars-CoV-2 pandemic. Several ways to deliver lessons through the Internet were used but both instructors and students complained about visual outputs. An evaluation of the most proficient techniques to create video-based lessons is highly relevant and critical. Seventy-eight students participated to 30 h of university online courses delivered through MS Teams, in which OBS (Open Broadcaster Software) Studio was used to create the lessons. The software allowed merging: a) MS Powerpoint slides, b) the instructor through a webcam, c) pictures of background sceneries. After the end of the courses, students filled in a questionnaire evaluating pictures taken from different e-learning sceneries. The OBS-based situation obtained the best evaluation in all measures (fruition, attention keeping and promotion of learning) and the highest rank when participants were asked to compare all the sceneries. These results confirm that students prefer reality-based sceneries, in which the most informative aspects (face, body and voice of the instructor, and the slides used for the lesson) are all present. Beside other obvious factors related to the quality of teaching, e-learning should also definitely consider visual features. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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This chapter discusses lessons from the Covid-19 crisis, based on the history of the disease in France and distribution throughout the world. The Covid-19 crisis raises many questions, in addition to those addressed in the deciphering of the epidemic. In addition to the pre-positioning of the epidemic control system, for which the best organization must be found, the tools for analyzing the emergence that have just been presented can be optimized through predictive modeling, propagation scenarios and the study of the consequences of anti-epidemic measures. While no one appears "especially guilty" of the occurrence of the Covid-19 crisis, it is highly unfortunate that real-time epidemic threat analysis systems, whose annual cost can be estimated at 1/10,000th the cost of the epidemic, were not used to contain severe acute respiratory syndrome coronavirus 2. © ISTE Ltd 2022.
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In a multifractal paradigm of motion, nonlinear behaviors of biological structures (virus systems) of Schrödinger-type regimes at various scale resolutions are analyzed. Then, in the stationary case of these regimes, the functionality of a hidden symmetry of SL(2R) type implies, through a Riccati-type gauge, different synchronization modes among these virus systems. Moreover, assuming that the nonmanifest chaos is not present, specific patterns corresponding to the dynamics in the virus systems can be highlighted. In such a framework, utilizing the methods of artificial intelligence, it is proved that, based on the dynamics of certain patterns, the modifications of the acoustic field can constitute a method of COVID-19 detection. The foundation of the use of artificial intelligence in such a situation is fundamental through the following. The harmonic mapping from the usual measurement space to the matter induces a variational principle, based on which both chaos scenarios and pattern dynamics can be studied. When assimilated to a hyperbolic space, based on which the variational principle works, the initial conditions space permits the generation of a virtual database, based on which the real behaviors of viruses can be shown through a group isomorphism of SL(2R) type. © 2023 Elsevier Inc. All rights reserved.
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Digital health change management projects have a high rate of failure which limits the realization of their potential benefits. While there are many change management models, there is limited evidence of one model being effective in all circumstances. We propose a framework for building on an organizations preferred change management model and adapting it based on the change desired and the organization. We use three change management scenarios (small, large, and rapid) from radiology to explore the application of the framework. Radiology was chosen to illustrate the framework because it has been digital longer than many medical specialties. Given the high number of upgrades and new digital platforms in Radiology, it could also serve as a testing ground for such a framework. © 2023 "IISE”.
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The health crisis caused by COVID-19 compelled university teachers to adapt their learning scenarios to new technology-mediated contexts. This paper analyses teaching and learning experiences, strategies and lessons learned during the lockdown period at the Faculty of Education of the Universitat Autònoma de Barcelona (N=29 teachers, 227 students). The results reveal that participants experienced difficulties (lack of literacy in online pedagogies and work overload among lecturers;privation of physical presence and fluent communication among students). Teachers acquired knowledge around digital technologies and are predisposed to learn about innovative teaching methods supported by technologies. Students are dissatisfied with the learning experience, although they value the opportunities for flexible learning and saving time on commuting. Teaching strategies were less innovative and active than usual, and usually involved a combination of synchronous time for lectures and resolving problems, and self-study. Nevertheless, students valued more traditional teaching strategies (i.e. combinations of lectures and tutoring). The paper concludes that the teachers' view of the use of digital technologies has improved, although training is needed to make effective use of such technologies for active learning and innovative approaches to teaching. © 2023 Universitat Autonoma de Barcelona. All rights reserved.
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The base reactivity of the mRNA sequence has a significant impact on the effectiveness of the mRNA vaccine in fighting against the pandemic of COVID-19. The annotation of mRNA sequence reactivity value is a time-consuming and labor-intensive work, which belongs to the private digital assets of each medical institution. It is not practical to train a predictive model by pooling private data from various parties. Fortunately, federated learning techniques can serve to collaboratively train a predictive model among medical institutions while preserving respective digital assets. However, due to the scarcity of data from each participant, conventional sequential prediction mod-els often fail to perform well. To overcome such a challenge, we propose a reactivity value prediction model based on both the self-attention and the convolutional attention mechanisms only requiring a small dataset of labeled samples. Inspired by BERT, we first train a self-attention feature extraction model through self-supervision using both labeled and unlabeled mRNA samples. In this way, the information of mRNA in the semantic space is deeply mined. Then, a convolutional attention block follows the self-attention block, to extract the attention matrix from the base-pair probability matrix and adjacency matrix. By doing so, the attention matrix can compensate for the insensitivity of the self-attention mechanism to the spatial information of mRNA. By using the Open Vaccine RNA database, experiments show that our prediction model for unseen mRNA has a better performance than other state-of-the-art deep learning models that are used to process gene sequences. Further ablation experiments demonstrate that the existence of the dual attention mechanism reduces the risk of overfitting, resulting in an excellent generalization capability of our model. © 2022 IEEE.
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Against the backdrop of the ongoing COVID-19 pandemic, We propose FMRS-CFR (Face mask recognition system-Centerface Resnet), a mask recognition system for epidemic prevention and control based on multi-algorithm fusion to adapt to multi-scenario applications. In this work, Centerface face key point detection and Resnet50 classification model were used. Built a system that maintains multi-adaptation with the dynamics of external scenarios and ported the system to the Atlas 200 Developer Kit, And quantitative evaluation of videos in more than a dozen different scenarios. Experimental results show that the FMRS-CFR system can achieve a recognition accuracy rate of 99.88%, which greatly improves the recognition rate of not wearing a mask or wearing the correct one to a certain extent, and achieves the purpose of effectively assisting epidemic prevention and control. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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This paper projects machine learning as a valuable tool for the restriction of the Covid-19 pandemic escalation in the global scenario. The proposed system involves detection of masked or unmasked people and a temperature sensing system for ensuring Covid-19 appropriate protocol is followed to allow only healthy person(s) in public/crowded places. The integration of Arduino Uno and MLX90614 non-contact temperature sensor, along with a MobileNetV2 machine learning model, is performed for complete execution. The system will classify a person as a masked or unmasked individual using ML techniques and detect their body temperature. If the individual meets the appropriate requirements, the system will enable them to access via the gate, which will be controlled by a servo motor in conjunction with a temperature sensor module. © 2022 IEEE.
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Underrepresented students face many significant challenges in their education. In particular, they often have a harder time than their peers from majority groups in building long-term high-quality study groups. This challenge is exacerbated in remote-learning scenarios, where students are unable to meet face-to-face and must rely on pre-existing networks for social support. We present a scalable system that removes structural obstacles faced by underrepresented students and supports all students in building inclusive and flexible study groups. One of our main goals is to make the traditionally informal and unstructured process of finding study groups for homework more equitable by providing a uniform but lightweight structure. We aim to provide students from underrepresented groups an experience that is similar in quality to that of students from majority groups. Our process is unique in that it allows students the opportunity to request group reassignments during the semester if they wish. Unlike other collaboration tools our system is not mandatory and does not use peer-evaluation. We trialed our approach in a 1000+ student introductory Engineering and Computer Science course that was conducted entirely online during the COVID-19 pandemic. We find that students from underrepresented backgrounds were more likely to ask for group-matching support compared to students from majority groups. At the same time, underrepresented students that we matched into study groups had group experiences that were comparable to students we matched from majority groups. B-range students in high-comfort and high-quality groups had improved learning outcomes. © 2023 Owner/Author.
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The Covid-19 pandemic continues as a major global threat. Like all sectors, agriculture is adversely affected by this pandemic as a twin crisis in the context of health and economy. It is predicted that the losses in agricultural production can be compensated by biotechnological methods. The aim and subject of this study is to draw a framework for post-pandemic sustainable agro-bioeconomy and to develop a series of utopian and dystopian scenarios for this. The reason for the number of scenarios to be determined as 19 here is that the outbreak originated in 2019. Ten of the scenarios are utopian (increase in biotech added value and employment;strengthening the supply chain with biotechnology;green budgeting;agro-bioeconomic diplomacy;agro-bioeconomic citizenship;collective food sector;the need for less child labor;weakening of neoliberalism with bioeconomy;agro-bioeconomic tourism) and nine of them as dystopic (new corona tax;reverse migration waves;pandession;negative externalities;asymmetric information and alternative costs;climate change;abundance paradox;globesity;coronomy). The validity and sustainability of each of these scenarios depends on specific conditions. Utopias, unlike dystopias, can be expected to come true more and more rapidly in order to humanity to prosper on the basis of equality and justice in the future. In this expectation, sustainable agro-bioeconomy production will have a substantial contribution. © 2023 Elsevier Inc. All rights reserved.
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Teacher training/education is problematic in every country, and it was puzzling across historical epochs. There were questions and preoccupations about the perfect teacher or how to educate a better teacher for our children, although the quest for such a teacher was not always scientifical or sustained by proof. But starting with the early twentieth century the search for the better teacher became scientific and serios. Many theories have been developed. Starting from some of them, this paper, propose an innovative approach for the best adaptation of students to labour market. Our domain is teacher education in Business and Economics. The aim is to put together the labour market organizations (LMO), in our case, trainers form a commercial bank, with higher education (HE) teachers to construct joint activities from which students (the future teacher of Economics) become better critical thinkers. The reason is that in recent years, critical thinking (CT) was listed among the most desired skills for twenty-first century jobs. Hence, our project is aiming to enhance this skill in student-teachers and bring labour market in the university to create a more adapted curriculum to job needs. In the following we shall present a curriculum for Pedagogy of Economics and concrete examples of blended, work-based learning scenarios in an online synchronous environment (a condition determined by the Covid-19 pandemic). © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.