Antiviral Effects of Fructans

Infectious diseases of viral origin have never received so much interest globally since the emergence of the COVID-19 pandemic disease. In contrast to bacterial infections, antibiotic treatments do not have any effect on viral infections, requiring alternative solutions to reduce the impact of viral spread on animal populations. More important than curing, preventing viral replication before disease development is probably the best strategy to minimalize the negative effects of viruses on a global scale. Fructans, known to stimulate the immune system (by either interacting directly or indirectly with the immune system), may be interesting candidates as part of this broader prevention strategy. This chapter discusses the potential antiviral properties of fructans in relation to their well-described immunomodulating, antioxidant and prebiotic attributes, as well as a possible role as protein binders which may disturb the proper function of viral proteins, and thus reduce the infection ability of certain viral strains. © 2023 Elsevier Inc. All rights reserved.

A Low-Cost Wireless Body Area Network for Human Activity Recognition in Healthy Life and Medical Applications

Moved by the necessity, also related to the ongoing COVID-19 pandemic, of the design of innovative solutions in the context of digital health, and digital medicine, Wireless Body Area Networks (WBANs) are more and more emerging as a central system for the implementation of solutions for well-being and healthcare. In fact, by elaborating the data collected by a WBAN, advanced classification models can accurately extract health-related parameters, thus allowing, as examples, the implementations of applications for fitness tracking, monitoring of vital signs, diagnosis, and analysis of the evolution of diseases, and, in general, monitoring of human activities and behaviours. Unfortunately, commercially available WBANs present some technological and economic drawbacks from the point of view, respectively, of data fusion and labelling, and cost of the adopted devices. To overcome existing issues, in this paper, we present the architecture of a low-cost WBAN, which is built upon accessible off-the-shelf wearable devices and an Android application. Then, we report its technical evaluation concerning resource consumption. Finally, we demonstrate its versatility and accuracy in both medical and well-being application scenarios. Author

Understanding occupancy pattern of university libraries in the post-pandemic era

Understanding indoor occupancy patterns is crucial for energy model calibration, efficient operations of fresh air systems, and COVID-19 exposure risk assessment. University libraries, as one of centers of campus life, due to the high mobility and "foot-voting” nature of them, i.e., occupants pick seats in the micro-environments they prefer, provide a non-intrusive opportunity to carry out post-occupancy evaluations. We conducted a long-term online monitoring of occupancy in libraries of a university in China by web-crawling the online seat reservation system, based on which, we constructed two sets of databases consisting of around 70 million records of nearly 3, 000 seats in 4 library sections, with seat-level resolution and sampling frequency up to every 10 seconds. The informative data set depicts not only the overall spatio-temporal occupancy patterns, but also nuances hidden within seats and visits. The daily flow of the main libraries exceeded two visits per seat. Half of the visitors stayed at the libraries for 3-6 hours during a single occupancy. Semester schedules and campus accessibility together influence students' decisions on when and which library to go, while even within the same zone, some seats were always more popular than their neighbours. "Semi-isolation” is one of the candidate attractive features proposed to understand the underlying patterns. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Statistical correlation coefficients for single-valued neutrosophic sets and their applications in medical diagnosis

The concept of single-valued neutrosophic sets (SVNSs) is considered as an attractive tool for dealing with highly ambiguous and uncertain information. The correlation coefficient of SVNSs acts as an important measure in the single-valued neutrosophic set theory and it has been applied in various fields, such as the pattern recognition, medical diagnosis, decision-making and also clustering analysis. To alleviate the weakness of the existing correlation coefficients, a novel statistical correlation coefficient is put forward to measure the degree of correlation between two SVNSs. This statistical correlation coefficient is developed based on the variance and covariance of SVNSs and its value is between −1 and 1. When solving the multicriteria decision making problems, the criteria show different weight values. To consider the weight information of multiple criteria, the weighted statistical correlation coefficient is developed for SVNSs. Afterwards, two numerical examples are given to show the effectiveness of the proposed statistical correlation coefficient in the pattern recognition, which can accurately classify unknown patterns into known patterns. Finally, the feasibility and practicability of the proposed correlation coefficient formula are illustrated by a practical multiple attribute decision making problem of traditional Chinese medicine diagnosis. The comparative results show that the proposed correlation coefficient formula is rational and effective. © 2023 the Author(s), licensee AIMS Press.

A Study on Information Disorders on Social Networks during the Chilean Social Outbreak and COVID-19 Pandemic

Information disorders on social media can have a significant impact on citizens' participation in democratic processes. To better understand the spread of false and inaccurate information online, this research analyzed data from Twitter, Facebook, and Instagram. The data were collected and verified by professional fact-checkers in Chile between October 2019 and October 2021, a period marked by political and health crises. The study found that false information spreads faster and reaches more users than true information on Twitter and Facebook. Instagram, on the other hand, seemed to be less affected by this phenomenon. False information was also more likely to be shared by users with lower reading comprehension skills. True information, on the other hand, tended to be less verbose and generate less interest among audiences. This research provides valuable insights into the characteristics of misinformation and how it spreads online. By recognizing the patterns of how false information diffuses and how users interact with it, we can identify the circumstances in which false and inaccurate messages are prone to becoming widespread. This knowledge can help us to develop strategies to counter the spread of misinformation and protect the integrity of democratic processes.

Automatic Diagnosis of COVID-19 Based on Machine Learning

The lives and health of individuals are significantly threatened by the extremely infectious and dangerous Corona Virus Disease 2019 (COVID-19). For the containment of the epidemic, quick and precise COVID-19 detection and diagnosis are essential. Currently, artificial diagnosis based on medical imaging and nucleic acid detection are the major approaches used for COVID-19 detection and diagnosis. However, nucleic acid detection takes a long time and requires a dedicated test box, while manual diagnosis based on medical images relies too much on professional knowledge, and analysis takes a long time, and it is difficult to find hidden lesions. Thanks to the rapid development of pattern recognition algorithms, building a COVID-19 diagnostic model based on machine learning and clinical symptoms has become a feasible rapid detection solution. In this paper, support vector machines and random forest algorithms are used to build a COVID-19 diagnostic model, respectively. Based on the quantitative comparison of the performance of the two methods, the future development trends in this field are discussed. © 2023 IEEE.

UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

Biomedical image segmentation (BIS) task is challenging due to the variations in organ types, position, shape, size, scale, orientation, and image contrast. Conventional methods lack accurate and automated designs. Artificial intelligence (AI)-based UNet has recently dominated BIS. This is the first review of its kind that microscopically addressed UNet types by complexity, stratification of UNet by its components, addressing UNet in vascular vs. non-vascular framework, the key to segmentation challenge vs. UNet-based architecture, and finally interfacing the three facets of AI, the pruning, the explainable AI (XAI), and the AI-bias. PRISMA was used to select 267 UNet-based studies. Five classes were identified and labeled as conventional UNet, superior UNet, attention-channel UNet, hybrid UNet, and ensemble UNet. We discovered 81 variations of UNet by considering six kinds of components, namely encoder, decoder, skip connection, bridge network, loss function, and their combination. Vascular vs. non-vascular UNet architecture was compared. AP(ai)Bias 2.0-UNet was identified in these UNet classes based on (i) attributes of UNet architecture and its performance, (ii) explainable AI (XAI), and, (iii) pruning (compression). Five bias methods such as (i) ranking, (ii) radial, (iii) regional area, (iv) PROBAST, and (v) ROBINS-I were applied and compared using a Venn diagram. Vascular and non-vascular UNet systems dominated with sUNet classes with attention. Most of the studies suffered from a low interest in XAI and pruning strategies. None of the UNet models qualified to be bias-free. There is a need to move from paper-to-practice paradigms for clinical evaluation and settings.

The Language of Smell: Code Failure

In today's world filled with complex signs and symbols, visual and auditory channels are the most intensive in semiotic terms. The language of smell, associated with the most ancient reactions, is usually considered as secondary and supplementary, and its possibilities for conveying meanings are limited to simple recognition. However, experts have been using the alphabet of smells to convey emotional messages from ancient times to date. The assessment of the role of odors in the modern world became possible due to the Covid-19 pandemic which often involved the loss, change or intensification of the sense of smell. In the course of the study 250 cases were considered, representing the stories associated with the disease and deviations in the perception of odors. The loss of the perception of unpleasant odors makes it impossible to learn about the dangers which cannot be perceived visually like in ancient times (spoiled food, poisoned air, etc.). Phantom interpretation of odors is often unpleasant: people can identify the smells of burning, ammonia, acetone, decomposition, feces, and others, and sometimes the excessiveness of an ordinary smell is unpleasant as well. The change of sign recognition can cause serious consequences for people. Phantom unpleasant odors can result in changes in eating habits and cause problems in communication. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Development and Application of a Specialized Set of Engineering Tools to Enhance Enterprise and Supply Chain Resilience

Complexity, dynamism, sudden changes, and disruptive events (COVID-19, Ukraine war, etc.) have become the norm in the current business world. Companies and their related supply chains are trying to adapt to a business reality fed by disruptive events to try to guarantee their survival in the long term. It is essential to highlight that some disruptive events are more predictable than others. However, even for the non-predictable events, early symptoms will facilitate their detection. Thus, it is critical to provide quantitative tools to identify patterns and warn companies to activate resilience plans and preventive actions. These tools should include features such as multivariate analysis for pattern recognition, disruptive events prediction, and prioritization of the preventive actions related to each disruptive event to support companies in enhancing their resilience capacity. In addition, the entire organization must be committed and convinced of the benefits that improved resilience will bring. For this reason, it is also critical to develop mechanisms to make workers aware of the importance of being resilient and promote the implementation of the resilience dimension in their quality systems, which is an opportunity for an organization to get formally certified in this area. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Spatial-Temporal Evolution Patterns and Influencing Factors of Hotels in Yellow River Basin from 2012 to 2022

Governmental attention towards the high-quality development of the Yellow River basin has brought new development opportunities for the hotel industry. This study aims to reveal the spatial-temporal evolution patterns and influencing factors of hotels in the Yellow River Basin from 2012 to 2022, based on economic, social, and physical geographic data of 190,000 hotels in the Yellow River flowing. With the help of a GIS technology system, the spatial-temporal evolution patterns of all hotels, star hotels, and ordinary hotels were explored, respectively. Then, the significant influencing factors of these patterns were revealed by using geographic detector and Person correlation analysis. The following conclusions were drawn: (1) the overall scale of the hotel industry in the Yellow River Basin expanded year by year, achieving rapid growth from 2016, and fluctuating around 2020 due to the impact of the novel coronavirus epidemic;the overall spatial distribution had significant regional differences, showing the structural characteristics of "southeast more, northwest less”;(2) there was a great difference in the degree of spatial autocorrelation agglomeration among prefecture-level cities, and the degree of agglomeration of both the hotel industry as a whole and general hotels decreased year by year, showing a random distribution in 2022;star hotels were always distributed randomly. Additionally, a strong synergistic correlation was shown between the number of ordinary hotels and the number of star hotels in local space;(3) overall, the development of the hotel industry was significantly affected by seven factors: structural force, macro force, ecological force, internal power, consumption power, intermediary power, and external power. There were differences in the forces acting on different types of hotels, which gives a pattern recognition in-depth.

Sustainable Artificial Intelligence-Based Twitter Sentiment Analysis on COVID-19 Pandemic

The COVID-19 outbreak is a disastrous event that has elevated many psychological problems such as lack of employment and depression given abrupt social changes. Simultaneously, psychologists and social scientists have drawn considerable attention towards understanding how people express their sentiments and emotions during the pandemic. With the rise in COVID-19 cases with strict lockdowns, people expressed their opinions publicly on social networking platforms. This provides a deeper knowledge of human psychology at the time of disastrous events. By applying user-produced content on social networking platforms such as Twitter, the sentiments and views of people are analyzed to assist in introducing awareness campaigns and health intervention policies. The modern evolution of artificial intelligence (AI) and natural language processing (NLP) mechanisms has revealed remarkable performance in sentimental analysis (SA). This study develops a new Marine Predator Optimization with Natural Language Processing for Twitter Sentiment Analysis (MPONLP-TSA) for the COVID-19 Pandemic. The presented MPONLP-TSA model is focused on the recognition of sentiments that exist in the Twitter data during the COVID-19 pandemic. The presented MPONLP-TSA technique undergoes data preprocessing to convert the data into a useful format. Furthermore, the BERT model is used to derive word vectors. To detect and classify sentiments, a bidirectional recurrent neural network (BiRNN) model is utilized. Finally, the MPO algorithm is exploited for optimal hyperparameter tuning process, and it assists in enhancing the overall classification performance. The experimental validation of the MPONLP-TSA approach can be tested by utilizing the COVID-19 tweets dataset from the Kaggle repository. A wide comparable study reported a better outcome of the MPONLP-TSA method over current approaches.

Identifying, exploring, and interpreting time series shapes in multivariate time intervals

We introduce a concept of episode referring to a time interval in the development of a dynamic phenomenon that is characterized by multiple time-variant attributes. A data structure representing a single episode is a multivariate time series. To analyse collections of episodes, we propose an approach that is based on recognition of particular patterns in the temporal variation of the variables within episodes. Each episode is thus represented by a combination of patterns. Using this representation, we apply visual analytics techniques to fulfil a set of analysis tasks, such as investigation of the temporal distribution of the patterns, frequencies of transitions between the patterns in episode sequences, and co-occurrences of patterns of different variables within same episodes. We demonstrate our approach on two examples using real-world data, namely, dynamics of human mobility indicators during the COVID-19 pandemic and characteristics of football team movements during episodes of ball turnover. © 2023 The Author(s)

Transcriptome in Human Mycoses

Mycoses are infectious diseases caused by fungi, which incidence has increased in recent decades due to the increasing number of immunocompromised patients and improved diagnostic tests. As eukaryotes, fungi share many similarities with human cells, making it difficult to design drugs without side effects. Commercially available drugs act on a limited number of targets and have been reported fungal resistance to commonly used antifungal drugs. Therefore, elucidating the pathogenesis of fungal infections, the fungal strategies to overcome the hostile environment of the host, and the action of antifungal drugs is essential for developing new therapeutic approaches and diagnostic tests. Large-scale transcriptional analyses using microarrays and RNA sequencing (RNA-seq), combined with improvements in molecular biology techniques, have improved the study of fungal pathogenicity. Such techniques have provided insights into the infective process by identifying molecular strategies used by the host and pathogen during the course of human mycoses. This chapter will explore the latest discoveries regarding the transcriptome of major human fungal pathogens. Further we will highlight genes essential for host–pathogen interactions, immune response, invasion, infection, antifungal drug response, and resistance. Finally, we will discuss their importance to the discovery of new molecular targets for antifungal drugs. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2014, 2022.

A Chaotic-Based Interactive Autodidactic School Algorithm for Data Clustering Problems and Its Application on COVID-19 Disease Detection

In many disciplines, including pattern recognition, data mining, machine learning, image analysis, and bioinformatics, data clustering is a common analytical tool for data statistics. The majority of conventional clustering techniques are slow to converge and frequently get stuck in local optima. In this regard, population-based meta-heuristic algorithms are used to overcome the problem of getting trapped in local optima and increase the convergence speed. An asymmetric approach to clustering the asymmetric self-organizing map is proposed in this paper. The Interactive Autodidactic School (IAS) is one of these population-based metaheuristic and asymmetry algorithms used to solve the clustering problem. The chaotic IAS algorithm also increases exploitation and generates a better population. In the proposed model, ten different chaotic maps and the intra-cluster summation fitness function have been used to improve the results of the IAS. According to the simulation findings, the IAS based on the Chebyshev chaotic function outperformed other chaotic IAS iterations and other metaheuristic algorithms. The efficacy of the proposed model is finally highlighted by comparing its performance with optimization algorithms in terms of fitness function and convergence rate. This algorithm can be used in different engineering problems as well. Moreover, the Binary IAS (BIAS) detects coronavirus disease 2019 (COVID-19). The results demonstrate that the accuracy of BIAS for the COVID-19 dataset is 96.25%.

Innate immune pathway modulator screen identifies STING pathway activation as a strategy to inhibit multiple families of arbo and respiratory viruses.

RNA viruses continue to remain a threat for potential pandemics due to their rapid evolution. Potentiating host antiviral pathways to prevent or limit viral infections is a promising strategy. Thus, by testing a library of innate immune agonists targeting pathogen recognition receptors, we observe that Toll-like receptor 3 (TLR3), stimulator of interferon genes (STING), TLR8, and Dectin-1 ligands inhibit arboviruses, Chikungunya virus (CHIKV), West Nile virus, and Zika virus to varying degrees. STING agonists (cAIMP, diABZI, and 2',3'-cGAMP) and Dectin-1 agonist scleroglucan demonstrate the most potent, broad-spectrum antiviral function. Furthermore, STING agonists inhibit severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and enterovirus-D68 (EV-D68) infection in cardiomyocytes. Transcriptome analysis reveals that cAIMP treatment rescue cells from CHIKV-induced dysregulation of cell repair, immune, and metabolic pathways. In addition, cAIMP provides protection against CHIKV in a chronic CHIKV-arthritis mouse model. Our study describes innate immune signaling circuits crucial for RNA virus replication and identifies broad-spectrum antivirals effective against multiple families of pandemic potential RNA viruses.

Nonlinear distance measures under the framework of Pythagorean fuzzy sets with applications in problems of pattern recognition, medical diagnosis, and COVID-19 medicine selection.

Background: The concept of Pythagorean fuzzy sets (PFSs) is an utmost valuable mathematical framework, which handles the ambiguity generally arising in decision-making problems. Three parameters, namely membership degree, non-membership degree, and indeterminate (hesitancy) degree, characterize a PFS, where the sum of the square of each of the parameters equals one. PFSs have the unique ability to handle indeterminate or inconsistent information at ease, and which demonstrates its wider scope of applicability over intuitionistic fuzzy sets. Results: In the present article, we opt to define two nonlinear distances, namely generalized chordal distance and non-Archimedean chordal distance for PFSs. Most of the established measures possess linearity, and we cannot incorporate them to approximate the nonlinear nature of information as it might lead to counter-intuitive results. Moreover, the concept of non-Archimedean normed space theory plays a significant role in numerous research domains. The proficiency of our proposed measures to overcome the impediments of the existing measures is demonstrated utilizing twelve different sets of fuzzy numbers, supported by a diligent comparative analysis. Numerical examples of pattern recognition and medical diagnosis have been considered where we depict the validity and applicability of our newly constructed distances. In addition, we also demonstrate a problem of suitable medicine selection for COVID-19 so that the transmission rate of the prevailing viral pandemic could be minimized and more lives could be saved. Conclusions: Although the issues concerning the COVID-19 pandemic are very much challenging, yet it is the current need of the hour to save the human race. Furthermore, the justifiable structure of our proposed distances and also their feasible nature suggest that their applications are not only limited to some specific research domains, but decision-makers from other spheres as well shall hugely benefit from them and possibly come up with some further extensions of the ideas.

A Systematic Literature Review on Human Ear Biometrics: Approaches, Algorithms, and Trend in the Last Decade

Biometric technology is fast gaining pace as a veritable developmental tool. So far, biometric procedures have been predominantly used to ensure identity and ear recognition techniques continue to provide very robust research prospects. This paper proposes to identify and review present techniques for ear biometrics using certain parameters: machine learning methods, and procedures and provide directions for future research. Ten databases were accessed, including ACM, Wiley, IEEE, Springer, Emerald, Elsevier, Sage, MIT, Taylor & Francis, and Science Direct, and 1121 publications were retrieved. In order to obtain relevant materials, some articles were excused using certain criteria such as abstract eligibility, duplicity, and uncertainty (indeterminate method). As a result, 73 papers were selected for in-depth assessment and significance. A quantitative analysis was carried out on the identified works using search strategies: source, technique, datasets, status, and architecture. A Quantitative Analysis (QA) of feature extraction methods was carried out on the selected studies with a geometric approach indicating the highest value at 36%, followed by the local method at 27%. Several architectures, such as Convolutional Neural Network, restricted Boltzmann machine, auto-encoder, deep belief network, and other unspecified architectures, showed 38%, 28%, 21%, 5%, and 4%, respectively. Essentially, this survey also provides the various status of existing methods used in classifying related studies. A taxonomy of the current methodologies of ear recognition system was presented along with a publicly available occlussion and pose sensitive black ear image dataset of 970 images. The study concludes with the need for researchers to consider improvements in the speed and security of available feature extraction algorithms.

Facial Affect Recognition in Depression Using Human Avatars

This research assesses facial emotion recognition in depressed patients using a novel dynamic virtual face (DVF) collection. The participant sample comprised 54 stable depressed patients against 54 healthy controls. The experiment entailed a non-immersive virtual reality task of recognizing emotions with DVFs representing the six basic emotions. Depressed patients exhibited a deficit in facial affect recognition in comparison to healthy controls. The average recognition score for healthy controls was 88.19%, while the score was 75.17% for the depression group. Gender and educational level showed no influence on the recognition rates in depressed patients. As for age, the worst results were found in older patients as compared to other cohorts. The average recognition rate for the younger group was 84.18%, 78.63% for the middle-aged group, and 61.97% for the older group, with average reaction times of 4.00 s, 4.07 s, and 6.04 s, respectively.

Forced telecommuting during the COVID-19 lockdown: the impact on corporate culture in Spain and Kazakhstan

PurposeThis paper studied organizational culture in two different countries during the COVID-19 lockdown, a stressful social and labor context that obliged entire working populations to telecommute from home. We considered how people have coped with this new scenario, bearing in mind that one of the most relevant aspects of organizational culture and climate is the face-to-face interactions that take place in offices. With telework, that important physical relationship disappears and, since body language has its own grammar, work-related messages logically become open to misunderstanding between leaders and subordinates, as well as among peers.Design/methodology/approachAn anonymous questionnaire (in Spanish and Russian) was distributed through the LinkedIn social media platform. The study intended to capture responses from white-collar professionals with managerial profiles, including those occupying high and medium-level positions, consultants, section directors, and project managers across different industries in both the countries. We collected 142 responses from Spain and 115 from Kazakhstan, with a total of 257 valid responses. Principal component's analysis (PCA), to obtain factorial axis was applied. We then performed a factor analysis of those principal components using Coheris Analytics SPAD 9.1.FindingsThe first finding herein points to the fact that the same experience had different consequences in these two different places, which can be traced back to national-cultural values. Spain and Kazakhstan share some common values and, at the same time, are culturally opposite. People fear uncertainty and one of the best ways to avoid this feeling is to provide them with technical and emotional support to manage a situation. During the COVID-19 lockdown, professionals from both countries expected their bosses to be assertive, driven, attentive and encouraging. And it seems they got just that. Secondly, a robust structure is mandatory for feeling secure: workers reported devoting more hours to telecommuting at home and even felt that their jobs were invading their personal lives, but they handled it because they knew to whom they should report. Procedures, rules, and methods were clear enough to avoid uncertainty. They even invented new rituals, patterns and practices that helped to reinforce their sense of belonging to the team. On top of this, in their responses, they noted that leaders acted consistently, even admirably, during lockdown and, for this reason, they gained their subordinates' respect.Research limitations/implicationsResponses from female participants more than doubled those from males in this sample. Women are assumed to prefer flexible working conditions so that they can better take care of children and/or elderly or dependent persons, but this could just be a long-standing bias. On the other hand, the incorporation of women into professional life has feminized work environments, translating into more concern for workers' personal circumstances and more awareness of the human relationships therein. Thus, independent of the country studied, gender is another factor to consider for future research.Practical implicationsThis article proposes further exploratory study of how organizational contexts are affected by unexpected, informal and even radical changes, as well as of organizations' ability to manage said changes by looking to their cultural values.Originality/valueFacing a common enemy— the coronavirus— seems to have made workers more positive and less prone to complaining. Workers have been resolute and have tried their best not only in their individual work, but also with their co-workers and teams. The data suggests that, even when analyzing two diverse countries in terms of their cultural historical, and sociological contexts, companies' reactions impacted their employees somewhat similarly and engendered similar responses. At the same time, the reactions of Spanish and Kazakhstani professionals vary on certain aspects, and, surprisingly, converge in terms of avoiding uncertainty, w ich suggests a conservative reaction in both countries. This study concludes that structure (clarity of procedures, norms, patterns) and leaders' recognition of their employees' efforts to overcome uncertainty were of utmost importance.

The Language of Smell: Code Failure

In today's world filled with complex signs and symbols, visual and auditory channels are the most intensive in semiotic terms. The language of smell, associated with the most ancient reactions, is usually considered as secondary and supplementary, and its possibilities for conveying meanings are limited to simple recognition. However, experts have been using the alphabet of smells to convey emotional messages from ancient times to date. The assessment of the role of odors in the modern world became possible due to the Covid-19 pandemic which often involved the loss, change or intensification of the sense of smell. In the course of the study 250 cases were considered, representing the stories associated with the disease and deviations in the perception of odors. The loss of the perception of unpleasant odors makes it impossible to learn about the dangers which cannot be perceived visually like in ancient times (spoiled food, poisoned air, etc.). Phantom interpretation of odors is often unpleasant: people can identify the smells of burning, ammonia, acetone, decomposition, feces, and others, and sometimes the excessiveness of an ordinary smell is unpleasant as well. The change of sign recognition can cause serious consequences for people. Phantom unpleasant odors can result in changes in eating habits and cause problems in communication. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.