Constructive Approach Research Articles

A comprehensive research of deep learning approaches for High Definition map construction in autonomous driving

In the realm of autonomous driving, High Definition Maps (HD maps) are indispensable for safe and precise navigation. Traditional HD map construction methods involving point cloud capture and SLAM have proven effective but labor-intensive. This paper addresses the growing interest in leveraging deep learning techniques to streamline HD map creation. This paper presents a systematic exploration of deep learning methodologies for HD map construction. It categorizes these approaches into two core components: Feature Extraction and Feature Decoding. Feature Extraction involves the transformation of input data, comprising images and LiDAR point clouds, into Bird's Eye View (BEV) representations. Feature Decoding is dissected into rasterized map objectives and vector map objectives. Detailed analysis is conducted on prominent methodologies. The paper provides a nuanced evaluation of these deep learning techniques, highlighting their respective strengths and limitations. Factors such as precision, computational efficiency, and the preservation of fine-grained details are considered when selecting the most suitable method. This comprehensive review summarizes and prospects the research in related fields.

SIGNS OF CONSTRUCTIVE JOURNALISM IN THE UKRAINIAN MEDIA

The article explores the question of implementing constructive journalism as a new media practice in the Ukrainian media space. Today, the use of the constructive approach by traditional media indicates an attempt to change the way information is presented, emphasize positive aspects, and contribute to the creation of solution-oriented materials that do not solely focus on problems and conflicts. For Ukrainian media outlets, constructive journalism is a new media practice to which they are adapting, but there are already observable efforts to create content that promotes positive dynamics and problem resolution, rather than just identification. The spread of the constructive approach in the Ukrainian media space, which is filled with demands for solutions to issues arising from societal crises and ongoing conflicts, will contribute to the formation of a worldview based on mutual aid, support, and understanding. Therefore, the analysis of the features of constructive journalism embodied in materials from Ukrainian media is essential for studying the potential of this new direction.

Öğretmenlerin Yapılandırmacı Yaklaşımı Uygulamaya Yönelik Öz-yeterlik İnançlarının İncelenmesi

Yapılandırmacı yaklaşımın uygulanmasında en önemli rollerden birini öğretmenler üstlenmektedir. Öğretmenlerin bu rollerini yerine getirirken dikkate alınacak özelliklerinden biri ise öz-yeterlik inançlarıdır. Öz-yeterlik inancının öğrenme ortamlarındaki uygulamalara etkisinin öneminden hareketle, araştırmada öğretmenlerin yapılandırmacı yaklaşımı uygulamaya yönelik öz-yeterlik inançlarının düzeylerini belirlemek amaçlanmıştır. Araştırmada ayrıca öğretmenlerin oluşturdukları metaforlar da incelenmiştir. Karma desende tasarlanan araştırmanın evrenini, İstanbul ilindeki Anadolu liselerinde çalışan öğretmenler oluşturmaktadır. Örneklemin seçiminde tabakalı örnekleme yöntemi kullanılmıştır. Araştırmada, evrende yer alan okullar, Merkezi Sınav Puanı yüzdelik dilimlerine göre homojen alt gruplara ayrılmıştır. Belirlenen alt tabakalardan örneklemler seçkisiz örnekleme yöntemi ile seçilerek örneklemin temsil gücü garanti altına alınmaya çalışılmıştır. Araştırmanın nitel boyutunda da aynı grup ile çalışılmıştır. Araştırmanın nitel boyutundaki çalışma grubu da, nicel boyut kapsamında örnekleme alınan öğretmenlerden oluşmaktadır. Araştırmanın nicel kısmında veriler, Öğretmenlerin Yapılandırmacı Yaklaşımı Uygulamaya Yönelik Öz-Yeterlik İnanç Ölçeği (YÖİÖ) ile toplanmıştır. Nitel kısımda ise öğretmenlerin yapılandırmacı anlayışa ilişkin sahip oldukları metaforlar belirlenerek üst ve alt öz-yeterlik düzeylerine göre sınıflandırılmıştır. Nicel verilerin analizinde çoklu varyans analizi (MANOVA) kullanılmış; nitel veriler ise içerik analizi yöntemiyle çözümlenmiştir. Sonuçlar, öğretmenlerin yapılandırmacı yaklaşımı uygulamaya yönelik öz-yeterlik inanç düzeylerinin yüksek olduğunu göstermiştir. Öğretmenlerin ölçek puanları kıdem değişkenine göre anlamlı şekilde farklılaşırken, branş ve eğitim düzeyi değişkenlerine göre anlamlı farklılık göstermemiştir. Araştırmanın nitel boyutunda, 10 farklı kategori altında üretilen toplam 73 geçerli metafor incelenmiştir. Öz-yeterlik düzeyleri açısından alt ve üst grupta yer alan öğretmenlerin oluşturdukları metaforlara ilişkin kategoriler arasında bir farklılık gözlenmemiştir. Araştırmadan elde edilen sonuçlar alan yazın ışığında tartışılarak araştırmacı ve uygulayıcılara yönelik öneriler geliştirilmiştir.

A novel object-oriented Bayesian network on risk assessment of sea lanes of communication

The maritime transportation system heavily depends on sea lanes of communication. Unfortunately, the surge in shipping activities has also resulted in an increase in maritime incidents, encompassing both conventional accidents and piracy-related events. This paper presents a novel object-oriented Bayesian network framework for assessing the risk of traditional maritime accidents and piracy-related incidents. Furthermore, an enhanced machine-learning-based Tree augmented naïve learning approach is proposed to address the correlation relationships among the influential factors and the distinctions between mathematical and practical meanings. The paper also introduces mutual information theory and an improved scenario construction approach to identify the key influential factors on maritime risks and unveil the non-linear relationships among them. The findings indicate that both the degraded natural environment and social environment contribute to the risk levels of maritime accidents. Furthermore, the type of ship is recognized as the most significant factor contributing to unsafe conditions. The research results will offer valuable insights to the maritime industry, specifically aiding ship owners, ship crew, and shipping companies in understanding the key influential factors in multiple types of maritime accidents and assessing potential risks.

The Implementation of Constructive Approach to Improve Students' Problem Solving Ability and Self Concept Ability

The ability to solve mathematical problems is very important because it is one of the abilities that belong to the ability to think at a high level, therefore it must be trained. One of the factors that can support this is self-concept in doing something and especially in learning mathematics, but this is inversely proportional to the field which stated that self-concept and mathematical problem-solving abilities were still relatively low, the panelists were interested in conducting a research entitled the application of a constructivist approach to improve mathematical problem-solving abilities and self-concept of junior high school students, this study used the experimental method using experimental design and schematized by researchers therefore the researcher chose Krida Utama Middle School Jl. Gedong Lima No. V, Kertamulya, Kec. Padalarang, of course, with class divisions, namely an experimental class and a control class of 30 students. To measure mathematical problem-solving abilities and self-concept towards mathematics, the researcher makes a design or learning that is by the approach and makes measurement tools clear and accurate to see the results of constructivism learning, in the final posttest results from the experimental class obtains a Ngain value of 0.5968 in the learning category the experimental class has a pretty good category, and from the results of distributing the Self Concept questionnaire to learning mathematics using the constructivism approach it gets a percentage score of 77.71%, this indicates that learning using the constructivism approach can have a good effect on students' mathematical problem-solving abilities and concepts students towards learning mathematics.

Pretreatment of shrimp shells with an acidic deep eutectic solvent system for chitin extraction and its enhanced performance as a carrier for immobilized lipase

Existing methods for chitin extraction usually produce substantial waste, which poses ecological hazards. Natural deep eutectic solvent (NADES) offers a promising one-step pretreatment alternative, replacing the resource-intensive demineralization (DM) and deproteinization (DP) process. Hence, in this study, the influence of various acidic NADES, on achieving a simplified one-step DM and DP in the chitin extraction process was investigated. The study yielded chitin with 87.73 % purity, and microstructural analysis showed that NADES pretreatment minimally affected chitin quality without deacetylation. In addition, chitin extracted using choline chloride-oxalic acid as a carrier displayed excellent performance in the immobilization of Geobacillus thermocatenulatus lipase 2 (GTL2) because of obvious Ca2+ activation effect. This process contributed to enhancement of immobilized enzyme activity. The immobilized GTL2 showed excellent hydrolytic capabilities, with its highest activity reaching 547.80 ± 20.62 U/mg, significantly better than the five commercial lipases that exhibited <40 % of the enzyme activity. Furthermore, the hydrolytic capacity of immobilized GTL2 was notably high for 4-nitrophenyl butyrate, measuring 935.47 ± 51.60 U/mg. This study provided a constructive approach for the one-step pretreatment of shrimp shells with organic acid-based NADES to isolate and purify chitin and its potential application as an immobilized carrier to enhance enzyme activity.

CodeKGC: Code Language Model for Generative Knowledge Graph Construction

Current generative knowledge graph construction approaches usually fail to capture structural knowledge by simply flattening natural language into serialized texts or a specification language. However, large generative language model trained on structured data such as code has demonstrated impressive capability in understanding natural language for structural prediction and reasoning tasks. Intuitively, we address the task of generative knowledge graph construction with code language model: given a code-format natural language input, the target is to generate triples which can be represented as code completion tasks. Specifically, we develop schema-aware prompts that effectively utilize the semantic structure within the knowledge graph. As code inherently possesses structure, such as class and function definitions, it serves as a useful model for prior semantic structural knowledge. Furthermore, we employ a rationale-enhanced generation method to boost the performance. Rationales provide intermediate steps, thereby improving knowledge extraction abilities. Experimental results indicate that the proposed approach can obtain better performance on benchmark datasets compared with baselines. 1

Promoting sustainable agri-food systems through sustainability and responsible marketing: The case of peruvian companies at international trade shows

For companies in the agri-food sector, sustainability marketing represents a strategic tool, insofar as it enables companies to achieve sustainability in their production and commercial processes, while, at the same time, exerting a fundamental influence on their reputation. This study raises the following research questions: How do agri-food exporting companies view sustainability marketing and its practical application? To which aspects of their sustainable marketing system do such companies attach greater or lesser importance? Using a constructive research approach, a study was carried out by conducting interviews with experts in the sector, as well as with managers from agrifood export companies. On the basis of such interviews, an analytical framework as well as an accessible management tool comprising 17 variables (grouped into 6 sets) are proposed to support the management of sustainability marketing by companies in the agri-food sector. The results indicate that companies prioritized strategies linked to the certification of their production systems and their quality, as well as logistical capacity, environmental certifications, and commitment to climate change and fairtrade principles. Among the aspects they attach less importance, the ones related to distribution systems stand out. These results serve as a basis to support the managers of agri-food exporting companies in developing more sustainable agri-food systems through the appropriate and comprehensive use of sustainability marketing. A recommendation further arises for companies to align their sustainability marketing strategies with their market positioning, in order to support both their reputation and their competitive advantages.

Optimizing Mental Health Detection in Indian Armed Forces Personnel through Feature Engineering Driven Dataset Reduction, Addressing Suicide, Depression, and Stress

Within the realm of machine learning, the construction of high-quality datasets stands as a crucial factor profoundly influencing model performance. This research aims to furnish a comprehensive guide for enhancing the accuracy and efficiency of dataset construction. It achieves this by integrating multi-variate reduction techniques and innovative feature engineering strategies, implemented within the Python programming ecosystem. As the landscape of datasets becomes increasingly diverse and complex, the imperative to optimize precision grows more critical. This study explores the judicious application of dimensionality reduction methods, such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), alongside various feature selection approaches to strategically streamline datasets while preserving vital information. In conjunction with these reduction techniques, the research introduces novel feature engineering methods to amplify the discriminative power of remaining features, thereby enriching the dataset's representational capacity. The exploration spans a spectrum of multi-variate reduction techniques and delves into feature engineering methodologies, including polynomial feature creation, interaction term generation, and domain-specific transformation functions. Practical implementations of these techniques are demonstrated through Python, showcasing their applicability across diverse domains. Empirical evaluations on real-world datasets underscore the efficacy of the proposed methodology, revealing superior accuracy and efficiency compared to conventional dataset construction approaches. The insights derived from this research contribute significantly to the broader discourse in machine learning, presenting a generic yet potent framework for enhancing precision in datasets. Beyond deepening our understanding of multi-variate reduction and feature engineering, the findings offer a practical guide for researchers and practitioners seeking to optimize precision in various machine learning applications.

Multicyclic Norias: A First-Transition Approach to Extreme Values of the Currents

For continuous-time Markov chains we prove that, depending on the notion of effective affinity F, the probability of an edge current to ever become negative is either 1 if F<0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$F< 0$$\\end{document} else ∼exp-F\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim \\exp - F$$\\end{document}. The result generalizes a “noria” formula to multicyclic networks. We give operational insights on the effective affinity and compare several estimators, arguing that stopping problems may be more accurate in assessing the nonequilibrium nature of a system according to a local observer. Finally we elaborate on the similarity with the Boltzmann formula. The results are based on a constructive first-transition approach.

Toward a theory of Yangians

We review the main ideas underlying the emerging theory of Yangians—the new type of hidden symmetry in string-inspired models. Their classification by quivers is a far-going generalization of simple Lie algebras classification by Dynkin diagrams. However, this is still a kind of project, while a more constructive approach goes through toric Calabi-Yau spaces, related supersymmetric systems, and the Duistermaat-Heckman or equivariant integrals between the fixed points in the Atiyah-Drinfeld-Hitchin-Manin (ADHM)-like moduli spaces. These fixed points are classified by crystals (Young-type diagrams), and Yangian generators describe “instanton” transitions between them. Detailed examples will be presented elsewhere. Published by the American Physical Society 2024

Influence of Prefabrication on Job Satisfaction in The Construction Industry

New construction approaches, such as prefabrication, have been increasingly recognised as a strategy to boost productivity and support the industry’s sustainability and growth. Advancing existing understanding on the benefits of prefabrication to promote its adoption, this research aims to assess job satisfaction and work conditions of construction practitioners involved in prefabrication and assess its impacts on their job satisfaction, represented by job demands, job support, physical work environments, and job perception. Quantitative data were collected from 37 practitioners exposed to prefabrication and 30 practitioners involved in conventional construction. The findings show that prefabrication can lead to higher job satisfaction and better work conditions than conventional construction. Compared with conventional construction, practitioners working in prefabricated projects experienced improved workload distribution, better physical surroundings, and positive job perception. This research, therefore, provides new insights into the benefits of prefabrication and its potential to promote better mental wellbeing and higher productivity.

Fast reconstruction of boiler numerical physical field based on proper orthogonal decomposition and conditional deep convolutional generative adversarial networks

ABSTRACT Obtaining real-time physical field data inside the boiler furnace is crucial for combustion diagnostics and optimization. In this work, we propose a fast reconstruction technique for the physical fields in a tangentially-fired coal boiler. The method combines the Proper Orthogonal Decomposition (POD) and Conditional Deep Convolutional Generative Adversarial Networks (cDCGAN) based on the boiler physical field obtained by computational fluid dynamics (CFD). Firstly, CFD simulations are performed using data from the boiler Distributed Control System (DCS). After verifying the CFD results, we expanded the simulation to 105 operational condition cases sample sets. Then, the physical fields of the sample set undergo POD to determine the appropriate number of POD bases, thereby ensuring the accuracy of the reconstruction. Finally, the cDCGAN method is employed to establish the correlation between the POD bases and the operational conditions of the boiler. Results show that the POD-cDCGAN method achieves rapid and accurate reconstruction of temperature and velocity fields, with mean relative error (MRE) below 1.5% and root relative squared error (RRSE) below 5.7%. The reconstruction time is reduced to 10.57s, significantly faster than direct CFD methods. The POD-cDCGAN method offers an efficient approach for online operational diagnosis and digital twin construction of boiler equipment.

The Multifaceted Work of Christ: Herman Bavinck’s Doctrine of Atonement as Both Corrective and Retrieval

Abstract This essay examines Herman Bavinck’s doctrine of atonement within his treatment of Christ’s humiliation in volume three of his Reformed Dogmatics. Through an investigation of Bavinck’s critiques of key nineteenth century theologians and of Bavinck’s own constructive account of the atonement, I show that Bavinck intended his theology of Christ’s work to serve as a corrective to the dominant approach to the theology of the atonement in his day. Against what he considered one-sided views of Christ’s work, I argue that Bavinck sought to offer an account which emulates Scripture in its multifaceted character. This theology further sought to retrieve and build upon historical Christian theology, most notably that of Anselm and especially the Protestant Reformers, in its focus on Christ’s vicarious satisfaction as the central (though not only) concern of his redeeming action. This exploration of Bavinck’s doctrine of the atonement not only deepens our understanding of Bavinck’s theological method but has relevance for broader studies of nineteenth and early twentieth century conceptualizations of Christ’s person and work and shows Bavinck’s attractiveness for contemporary constructive approaches to the atonement.

Stock price crash prediction based on multimodal data machine learning models

This study introduces multimodal data machine learning framework to predict stock crashes. It encapsulates market data, graph data cultivated from industry affiliations through node2vec, and text data derived from sentiment analysis. The LightGBM is utilized, marking an improvement by 7.13% over preceding studies, achieving 75.85% balanced accuracy. An innovative long-short portfolio construction approach is articulated, demonstrating the practical significance of the predictions, with a 4.75% portfolio return in 2022 — a 27.26% advancement over the CSI 300. This endeavour in leveraging multimodal data machine learning for stock crash prediction offers a promising performance, serving as a valuable reference for investors.

3D-printed PCL framework assembling ECM-inspired multi-layer mineralized GO-Col-HAp microscaffold for in situ mandibular bone regeneration

BackgroundIn recent years, natural bone extracellular matrix (ECM)-inspired materials have found widespread application as scaffolds for bone tissue engineering. However, the challenge of creating scaffolds that mimic natural bone ECM’s mechanical strength and hierarchical nano-micro-macro structures remains. The purposes of this study were to introduce an innovative bone ECM-inspired scaffold that integrates a 3D-printed framework with hydroxyapatite (HAp) mineralized graphene oxide-collagen (GO-Col) microscaffolds and find its application in the repair of mandibular bone defects.MethodsInitially, a 3D-printed polycaprolactone (PCL) scaffold was designed with cubic disks and square pores to mimic the macrostructure of bone ECM. Subsequently, we developed multi-layer mineralized GO-Col-HAp microscaffolds (MLM GCH) to simulate natural bone ECM's nano- and microstructural features. Systematic in vitro and in vivo experiments were introduced to evaluate the ECM-inspired structure of the scaffold and to explore its effect on cell proliferation and its ability to repair rat bone defects.ResultsThe resultant MLM GCH/PCL composite scaffolds exhibited robust mechanical strength and ample assembly space. Moreover, the ECM-inspired MLM GCH microscaffolds displayed favorable attributes such as water absorption and retention and demonstrated promising cell adsorption, proliferation, and osteogenic differentiation in vitro. The MLM GCH/PCL composite scaffolds exhibited successful bone regeneration within mandibular bone defects in vivo.ConclusionsThis study presents a well-conceived strategy for fabricating ECM-inspired scaffolds by integrating 3D-printed PCL frameworks with multilayer mineralized porous microscaffolds, enhancing cell proliferation, osteogenic differentiation, and bone regeneration. This construction approach holds the potential for extension to various other biomaterial types.Graphical

A solution-based oxidation–reduction approach for spontaneous construction of nanowire architectures on copper metals

Copper (Cu) is a critically important functional material with widespread applications in catalysis, battery technology, and sensing. The oxidation–reduction approach serves as an efficient means to modify Cu metals, enabling the creation of nanowire architectures that expand their specific surface area and facilitate the engineering of catalytic active sites. However, the crucial issue is that the reduction process typically requires high-temperature treatment in an H2 atmosphere, which is laborious and demanding. Here using the dimethylamine methyl borane (C2H10BN) as an efficient reduction reagent, we report a facile solution-based oxidation–reduction approach at room temperature for the direct formation of the nanowire architectures on commercial Cu foams (CF), resulting in a unique three-dimensional (3D) hierarchically skeleton architecture. Moreover, as a typical application, cobalt hydroxide (Co(OH)2) is electrodeposited on the CF with nanowires (CFNW) to form a CFNW/Co(OH)2 electrode for oxygen evolution reaction (OER). The obtained results indicate the nanowire structure significantly increases the contact area between Co(OH)2 and CFNW substrate, resulting in an outstanding OER performance with an overpotential of 175 mV at 10 mA cm−2 in 1 M KOH. Importantly, this study presents a straightforward modification approach applicable to Cu metals, enabling large-scale production of advanced 3D skeleton Cu materials with diverse applications.

ANALYSIS OF TEACHERS’ WRITTEN FEEDBACK ON WRITING SKILLS OF THIRD-YEAR ENGLISH-MAJORED STUDENTS AT A VIETNAMESE UNIVERSITY

In the realm of higher education, the acquisition of proficient writing skills is deemed a cornerstone of academic success, particularly for students majoring in English. However, developing writing skills is a complex and challenging process that requires effective instruction and feedback. Central to this journey is the role of teachers, who, through their written feedback, become instrumental guides in shaping and refining students’ writing abilities. This study, therefore, aims to investigate the perceptions, preferences, and attitudes of 56 third-year English-majored students at a university in Vietnam regarding teachers' written feedback on essays. The results show that students are engaged in essay writing and receive regular or frequent feedback. However, they have mixed emotions about feedback, ranging from eagerness to improve to feeling lost. They also have diverse preferences for feedback types, such as error correction, explanation, suggestion, etc. Most students agree that feedback helps their writing skills and motivates them to revise, but some also face challenges such as discouragement and uselessness. The study recommends a balanced and constructive feedback approach that considers students’ varied needs and addresses their challenges, which can enhance the feedback experience for students.

CHALLENGES AND BARRIERS FOR UNMANNED AERIAL VEHICLE (UAV) IMPLEMENTATION IN MALAYSIAN INFRASTRUCTURE PROJECTS

The usage of Unmanned Aerial Vehicles (UAV) in infrastructure projects has inclined drastic recently due to its benefits. However, there is a slowness in transition from conventional construction approach to new innovation. Besides, there are lots of issues in current practices such as cost overrun, dangerous, delay of time completion and lack of an overall comprehensive scientific system. Furthermore, although there was high level of awareness on the advantages of using UAVs, the Malaysian construction industry are still reluctant for the changes. In response, to overcome issues that the construction industry faces, such as infrastructure projects thus, this study aims to develop a conceptual framework for promoting the applications of UAV in Malaysia infrastructure projects. The methods adopted include explanatory sequential research methods. For instance, questionnaire surveys and interviews are used to collect quantitative and qualitative data, respectively. Besides that, a refined conceptual framework of the challenges and barriers of UAV adoption in Malaysia infrastructure projects were mainly contributed by the restrictive regulatory environment and industry point of view. For example, the privacy issues and guidelines on autonomous UAV and the industry lack of confidence in the adoption of UAV and education on the adoption of the UAV. In addition, it offers some solutions for the issues that have been revealed in the preliminary conceptual framework for the challenges on UAV adoption in Malaysia infrastructure projects. For instance, amaze the contractor with the final data from the UAV. Lastly, the identify solution served as a reference for other Malaysian construction stakeholders that have not used UAVs before. The recommendations for future scholarly research include the roles of UAV technology with other technologies such as Artificial Intelligent (AI) or integration of data with other software.

KONSTRUKSI SOSIAL OBAT HERBAL PASCA PANDEMI COVID-19

When COVID-19 hit the world, people had reconstructed their assumptions about the importance of health. To reduce morbidity and mortality, they needed various alternative actions through various kinds of drug use, both medical drugs and traditional medicines, both during and after the pandemic. Nigella Sativa herbal medicine is one of the traditional medicine options. Nigella Sativa referred to black cumin, is among the plants that the Prophet used to make herbal medication. Prophetic Hadiths show the benefits and uses of black cumin. In Indonesia, we can find the use of Habbatussauda, which can be processed and packaged in various products. This study employs a qualitative technique in field research and a social construction approach. To learn more about the advantages of habbatussauda herbs for boosting the body's immunity, it is important to discuss this issue with social construction. People consider this herbal plant important because of its potentially active anti-inflammatory properties that can fight viral and bacterial infections both during the COVID-19 epidemic and afterwards.