Safety Perception Evaluation of Civil Aviation Based on Weibo Posts in China: An Enhanced Large-Scale Group Decision-Making Framework

The massive spread of COVID-19 and the crash of China Eastern Airlines MU5735 have negatively impacted the public's perception of civil aviation safety, which further affects the progress of the civil aviation industry and economic growth. The aim of research is to investigate the public's perception of China's civil aviation safety and give the authorities corresponding suggestions. First, we use online comment collection and sentiment analysis techniques to construct a novel evaluation index system reflecting the public's greatest concern for civil aviation safety. Then, we propose two novel large-scale group decision-making (LSGDM) models for aggregating evaluation: (1) K-means clustering with a novel distance measure for evaluators combined with unsupervised K-means clustering in two-stage, (2) unsupervised K-means clustering for evaluators combined with unsupervised K-means clustering for processing evaluation in two-stage. Finally, we compare the characteristics of different models and use the average of the two models as the final evaluation results. © 2023, The Author(s) under exclusive licence to Taiwan Fuzzy Systems Association.

Generalizable Segmentation of COVID-19 Infection From Multi-Site Tomography Scans: A Federated Learning Framework

COVID-19-like pandemics are a major threat to the global health system that causes a lot of deaths across ages. Large-scale medical images (i.e., X-rays, computed tomography (CT)) dataset is favored to the accuracy of deep learning (DL) in the screening of COVID-19-like pneumonia. The cost, time, and efforts for acquiring and annotating, for instance, large CT datasets make it impossible to obtain large numbers of samples from a single institution. The research attentions have been moved toward sharing medical images from numerous medical institutions. However, owing to the necessity to preserve the privacy of the data of a patient, it is challenging to build a centralized dataset from many institutions, especially during the pandemic. More. The difference in the data acquisition process from one institution to another brings another challenge known as distribution heterogeneity. This paper presents a novel federated learning framework, called Federated Multi-Site COVID-19 (FEDMSCOV), for efficient, generalizable, and privacy-preserved segmentation of COVID-19 infection from multi-site data. In FEDMSCOV, a novel is local drift smoothing (LDS) module encodes the input from feature space to frequency space, aiming to suppress the modules that are not conducive to generalization. Given the smoothed local updated, FEDMSCOV presents a novel Mixture-of-Expert (MoE) scheme to resolve global shift in parameters. An adapted differential privacy method is applied to design and protect the privacy of local updates during the training. Experimental evaluation on a large-scale multi-institutional COVID-19 dataset demonstrated the efficiency of the proposed framework over competing learning approaches with statistical significance. IEEE

The Fallout of Catastrophic Technogenic Emissions of Toxic Gases Can Negatively Affect Covid-19 Clinical Course.

The coronavirus D-19 (Covid-19) pandemic has shaken almost every country in the world: as we stand, 6,3 million deaths from the infection have already been recorded, 167,000 and 380,000 of which are in Italy and the Russian Federation, respectively. In the first wave of the pandemic, Italy suffered an abnormally high death toll. A detailed analysis of available epidemiological data suggests that that rate was shockingly high in the Northern regions and in Lombardy, in particular, whilst in the southern region the situation was less dire. This inexplicably high mortality rate in conditions of a very well-developed health care system such as the one in Lombardy - recognized as one of the best in Italy - certainly cries for a convincing explanation. In 1976, the small city of Seveso, Lombardy, experienced a release of dioxin into the atmosphere after a massive technogenic accident. The immediate effects of the industrial disaster did not become apparent until a surge in the number of tumors in the affected population in the subsequent years. In this paper, we endeavor to prove our hypothesis that the release of dioxin was a negative cofactor that contributed to a worsening of the clinical course of COVID-19 in Lombardy.

The Concept of Granular Representation of the Information Potential of Variables

With the advent of research into Granular Computing, in particular information granules, the way of thinking about data has changed gradually. Researchers and practitioners do not consider only their specific properties, but also try to look at the data in a more general way, closer to the way people think. This kind of knowledge representation is expressed particularly in approaches based on linguistic modeling or fuzzy techniques such as fuzzy clustering, but also newer approaches related to the explanation of how artificial intelligence works on these data (so-called explainable artificial intelligence). Therefore, especially important from the point of view of the methodology of data research is an attempt to understand their potential as information granules. Such a kind of approach to data presentation and analysis may introduce considerations of a higher, more general level of abstraction, while at the same time reliably describing the network of relationships between the data and the observed information granules. In this study, we tackle this topic with particular emphasis on the problem of choosing a predictive model. In a series of numerical experiments based on both artificially generated data, ecological data on changes in bird arrival dates in the context of climate change, and COVID-19 infections data we demonstrate the effectiveness of the proposed approach built with a novel application of information potential granules.

A Comprehensive Analysis of the Impact of Selecting the Training Set Elements on the Correctness of Classification for Highly Variable Ecological Data

Classification of objects in empirical data, especially in biological sciences, is a very complex process and has been a big challenge for researchers who do not specialize in data analysis. Therefore, in this study, we present a comprehensive summary of selected classifiers operating on both exact and fuzzy numbers. The results of performance of specific classifiers are compared on the example of a unique set of empirical data on changes in the behavior of animals in response to environmental factors. This is one of the key challenges in ecological research and it is strictly related to ecosystem changes caused by climate change. Nowadays, changes in behavior are a very popular topic of research because as a result of the COVID-19 pandemic and lower activity of people (lockdown effect). Therefore, various unusual reactions of wild animals were found around the world. A detailed compilation of research results, shortcomings, and strengths of various classification methods may be a compendium of knowledge for biologists and other practitioners as well as researchers working with empirical data.

A Granular Computing-Driving Hesitant Fuzzy Linguistic Method for Supporting Large-Scale Group Decision Making

Considering the conditions that: 1) same linguistic term means different things for different people;2) flexible semantics cannot be represented by original linguistic term;and 3) some semantics given by decision makers are possible to be changed during the consistency improving process, we bring some flexibility and personality into hesitant fuzzy linguistic preference matrix structures by allowing the linguistic preference matrices to be granular rather than numeric, providing a new characterization of linguistic preference matrices. Inspired by the thought of granular computing, this article proposes a new hesitant fuzzy linguistic method to deal with issues when a lot of decision makers provide hesitant and uncertain preference information in the decision-making process. First, we design a multiplicative consistency index and calculate its thresholds corresponding to different dimensions of preference matrix by the Monte Carlo experiment. Then, we construct a hesitant fuzzy linguistic model with granularity level, so as to recharacterize original assessment information and improve the consistency of preference matrices as far as possible. Considering the features of some large-scale group decision-making situation, where the decision makers have little opportunity to take part in multiple consensus reaching processes, hesitant fuzzy linguistic fuzzy C-means clustering algorithm is developed to integrate the assessment information given by decision makers. Finally, the final decision-making results are derived. An illustrative example of assessing psychological situation of some COVID-19 infected persons clarifies the reasonability of the proposed method. Finally, we complete some comparative studies and simulation experiments to demonstrate the method's validity and advantages. IEEE

1,000,000 cases of COVID-19 outside of China: The date predicted by a simple heuristic.

We forecast 1,000,000 COVID-19 cases outside of China by March 31st, 2020 based on a heuristic and WHO situation reports. We do not model the COVID-19 pandemic; we model only the number of cases. The proposed heuristic is based on a simple observation that the plot of the given data is well approximated by an exponential curve. The exponential curve is used for forecasting the growth of new cases. It has been tested for the last situation report of the last day. Its accuracy has been 1.29% for the last day added and predicted by the 57 previous WHO situation reports (the date 18 March 2020). Prediction, forecast, pandemic, COVID-19, coronavirus, exponential growth curve parameter, heuristic, epidemiology, extrapolation, abductive reasoning, WHO situation report.