Improved method for COVID-19 Classification of Complex-Architecture CNN from Chest CT volumes using Orthogonal Ensemble Networks

This paper introduces the improved method for the COVID-19 classification based on computed tomography (CT) volumes using a combination of a complex-architecture convolutional neural network (CNN) and orthogonal ensemble networks (OEN). The novel coronavirus disease reported in 2019 (COVID-19) is still spreading worldwide. Early and accurate diagnosis of COVID-19 is required in such a situation, and the CT scan is an essential examination. Various computer-aided diagnosis (CAD) methods have been developed to assist and accelerate doctors' diagnoses. Although one of the effective methods is ensemble learning, existing methods combine some major models which do not specialize in COVID-19. In this study, we attempted to improve the performance of a CNN for the COVID-19 classification based on chest CT volumes. The CNN model specializes in feature extraction from anisotropic chest CT volumes. We adopt the OEN, an ensemble learning method considering inter-model diversity, to boost its feature extraction ability. For the experiment, We used chest CT volumes of 1283 cases acquired in multiple medical institutions in Japan. The classification result on 257 test cases indicated that the combination could improve the classification performance. © 2023 SPIE.

Analysis of mobility network metrics in the spread of COVID-19 in the Metropolitan Region of Vale do Paraíba and Litoral Norte (RMVPLN)

In 2019, a pandemic of the so-called new coronavirus (SARS-COV-II) began, which causes the disease COVID-19. In a short time after the first case appeared, hundreds of countries began to register new cases every day. Mapping and analyzing the flow of people, regardless of the mode of transport, can help us to understand and prevent several phenomena that can affect our society in different ways. Graphs are complex networks made up of points and edges. The (geo)graphs are graphs with known spatial location and, in the case of our study, the edges represent the flow between them. The (geo)graphs proved to be a promising tool for such analyses. In the study region, municipalities that first registered their COVID-19 cases are also municipalities that have the highest mobility indices analyzed: degree, betweenness and weight of edges. © 2022 National Institute for Space Research, INPE. All rights reserved.

Traffic-driven SIR epidemic spread dynamics on scale-free networks

Traffic flow affects the transmission and distribution of pathogens. The large-scale traffic flow that emerges with the rapid development of global economic integration plays a significant role in the epidemic spread. In order to more accurately indicate the time characteristics of the traffic-driven epidemic spread, new parameters are added to represent the change of the infection rate parameter over time on the traffic-driven Susceptible-Infected-Recovered (SIR) epidemic spread model. Based on the collected epidemic data in Hebei Province, a linear regression method is performed to estimate the infection rate parameter and an improved traffic-driven SIR epidemic spread dynamics model is established. The impact of different link-closure rules, traffic flow and average degree on the epidemic spread is studied. The maximum instantaneous number of infected nodes and the maximum number of ever infected nodes are obtained through simulation. Compared to the simulation results of the links being closed between large-degree nodes, closing the links between small-degree nodes can effectively inhibit the epidemic spread. In addition, reducing traffic flow and increasing the average degree of the network can also slow the epidemic outbreak. The study provides the practical scientific basis for epidemic prevention departments to conduct traffic control during epidemic outbreaks.

Smart Methods to Deal with COVID-19 at University-Level Institutions Using Social Network Analysis Techniques

The current global health crisis is a consequence of the pandemic caused by COVID-19. It has impacted the lives of people from all factions of society. The re-emergence of new variants is threatening the world, which urges the development of new methods to prevent rapid spread. Places with more extensive social dealings, such as offices, organizations, and educational institutes, have a greater tendency to escalate the viral spread. This research focuses on developing a strategy to find out the key transmitters of the virus, particularly at educational institutes. The reason for considering educational institutions is the severity of the educational needs and the high risk of rapid spread. Educational institutions offer an environment where students come from different regions and communicate with each other at close distances. To slow down the virus's spread rate, a method is proposed in this paper that differs from vaccinating the entire population or complete lockdown. In the present research, we identified a few key spreaders, which can be isolated and can slow down the transmission rate of the contagion. The present study creates a student communication network, and virus transmission is modeled over the predicted network. Using student-to-student communication data, three distinct networks are generated to analyze the roles of nodes responsible for the spread of this contagion. Intra-class and inter-class networks are generated, and the contagion spread was observed on them. Using social network strategies, we can decrease the maximum number of infections from 200 to 70 individuals, with contagion lasting in the network for 60 days.

Competitive Epidemic Spreading Over Networks

In this letter, we consider an epidemic model for two competitive viruses spreading over a metapopulation network, termed the 'bivirus model' for convenience. The dynamics are described by a networked continuous-time dynamical system, with each node representing a population and edges representing infection pathways for the viruses. We survey existing results on the bivirus model beginning with the nature of the equilibria, including whether they are isolated, and where they exist within the state space with the corresponding interpretation in the context of epidemics. We identify key convergence results, including the conclusion that for generic system parameters, global convergence occurs for almost all initial conditions. Conditions relating to the stability properties of various equilibria are also presented. In presenting these results, we also recall some of the key tools and theories used to secure them. We conclude by discussing the various open problems, ranging from control and network optimization, to further characterization of equilibria, and finally extensions such as modeling three or more viruses.

Simulation Analysis of Infectious Disease Trend Based on Improved SEIR Model

Scholars have carried out a lot of research in the field of using data processing methods to analyze the evolution characteristics and development trends of infectious diseases. The research on data model method is more in-depth, that is, according to the specific characteristics of infectious diseases, suitable data models are designed and combined with different parameters to analyze infectious diseases, mainly including infectious disease data models based on statistical theory or dynamic theory. The former is mostly used in the case of insufficient initial data. Local analysis is carried out by means of a priori or assumptions to achieve global prediction. The latter mainly includes SIR model, complex network model, and cellular automata model. SIR model is the most in-depth research. Scholars have constructed or optimized Si model, SIS model, SEIR model, IR model, and other derivative models based on SIR model in combination with the characteristics of viruses. In this paper, the data source is Wuhan epidemic information released by Health Commission of Hubei Province. Combined with the specific characteristics of COVID-19, the traditional dynamic propagation model is optimized, and an improved SEIR model is constructed. The results of the improved SEIR model are in good agreement with the actual epidemic trend in Wuhan. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Effective multi-step ahead container throughput forecasting under the complex context

Accurate and effective container throughput forecasting plays an essential role in economic dispatch and port operations, especially in the complex and uncertain context of the global Covid-19 pandemic. In light of this, this research proposes an effective multi-step ahead forecasting model called EWT-TCN-KMSE. Specifically, we initially use the empirical wavelet transform (EWT) to decompose the original container throughput series into multiple components with varying frequencies. Subsequently, the state-of-the-art temporal convolutional network is utilized to predict the decomposed components individually, during which an improved loss function that combines mean square error (MSE) and kernel trick is employed. Eventually, the deduced prediction results can be obtained by integrating the predicted values of each component. In particular, this research introduces the MIMO (multi-input and multi-output) strategy to conduct multi-step ahead container throughput forecasting. Based on the experiments in Shanghai port and Ningbo-Zhoushan port, it can be found that the proposed model shows its superiority over benchmark models in terms of accuracy, stability, and significance in container throughput forecasting. Therefore, our proposed model can assist port operators in their daily management and decision making. © 2023 John Wiley & Sons Ltd.

Capturing complexity over space and time via deep learning: An application to real-time delay prediction in railways

Predictive analytics is an increasingly popular tool for enhancing decision-making processes but is in many business settings based on rule-based models. These rule-based models reach their limits in complex settings. This study compares the performance of a rule-based system with a customised LSTM encoder-decoder deep learning model for predicting train delays. For this, we use a purposefully built real-world dataset on railway transportation, where trains' interdependence over the network makes delay prediction more difficult. Results show that the deep learning model, which incorporates rich spatiotemporal interdependency information in real-time, outperforms the rule-based system by 18%, with the difference increasing to above 23% with higher complexity. The study also dissects the performance difference across different settings: dense versus rural areas, peak versus off-peak hours, low versus high delay, and before versus during the COVID-19 pandemic. The deep learning model is implemented as a proof of concept for decision support within Belgium's railway infrastructure company Infrabel. © 2023 Elsevier B.V.

Design of an Intelligent Educational Evaluation System Using Deep Learning

Nowadays, online education has been a more general demand in context of COVID-19 epidemic. The intelligent educational evaluation systems assisted by intelligent techniques are in urgent demand. To deal with this issue, this paper introduces the strong information processing ability of deep learning, and proposes the design of an intelligent educational evaluation system using deep learning. Inside the algorithm part, the low-complexity offset minimal sum (OMS) is selected as the front-end processor of deep neural network, so as to reduce following computational complexity in deep neural network. And the deep neural network is adopted as the major calculation backbone. In this paper, our OMS deep neural network parameters are 23 and 57 compared with other parameters, which can save about 59.64% of the network parameters, and the training time is 11270 s and 25000 s respectively, which saves the training time 54.92%. It can be also reflected from experiments that the proposal further improves the performance of unbalanced data classification in this problem scenario. © 2013 IEEE.

Predicting COVID-19 Case Counts using Twitter Image Data

A crucial task with diseases, such as COVID-19, is accurate forecasting of cases for early detection of spikes, which allows policymakers to adjust local restrictions. The use of face masks to prevent disease spread among the general population has become widespread due to the COVID-19 pandemic. While predictive models for COVID-19 case counts exist, capturing localized information about mask usage has the potential to improve prediction accuracy. In this paper, we develop time series models that utilize Twitter image data for COVID-19 case count prediction. A crucial part of such a model is the accurate detection of face mask presence in Twitter images, which we train a convolutional neural network (CNN) to perform. While multiple datasets exist to train CNNs for face mask detection, existing datasets do not adequately represent the complexity nor the diversity in social media images. To address this and create a sufficiently accurate CNN for use with social media images, we also present a new social media face mask image dataset designed for the training of CNNs to detect the presence of face masks in complex real-world images, such as social media images. The presented dataset consists of approximately 120k images and attempts to more adequately account for diversity in ethnicity, mask type, and physical orientation of individuals in images than existing datasets. We demonstrate the effectiveness of both the CNN model for face mask detection and the resulting time series model trained on data obtained from applying the CNN model to historical twitter data, illustrating that data on the presence of masks in social media images can increase predictive accuracy of time series models for COVID-19 case counts. © 2022 IEEE.

An effective method for epidemic suppression by edge removing in complex network

Since the birth of human beings, the spreading of epidemics such as COVID-19 affects our lives heavily and the related studies have become hot topics. All the countries are trying to develop effective prevention and control measures. As a discipline that can simulate the transmission process, complex networks have been applied to epidemic suppression, in which the common approaches are designed to remove the important edges and nodes for controlling the spread of infection. However, the naive removal of nodes and edges in the complex network of the epidemic would be practically infeasible or incur huge costs. With the focus on the effect of epidemic suppression, the existing methods ignore the network connectivity, leading to two serious problems. On the one hand, when we remove nodes, the edges connected to the nodes are also removed, which makes the node is isolated and the connectivity is quickly reduced. On the other hand, although removing edges is less detrimental to network connectivity than removing nodes, existing methods still cause great damage to the network performance in reality. Here, we propose a method to measure edge importance that can protect network connectivity while suppressing epidemic. In the real-world, our method can not only lower the government's spending on epidemic suppression but also persist the economic growth and protect the livelihood of the people to some extent. The proposed method promises to be an effective tool to maintain the functionality of networks while controlling the spread of diseases, for example, diseases spread through contact networks. Copyright © 2023 Liang, Cui and Zhu.

Dielectric Spectroscopy: Revealing the True Colors of Biological Matter

Accurate characterization of biological matter, for example, in tissue, cells, and biological fluids, is of high importance. For example, early and correct detection of abnormalities, such as cancer, is essential as it enables early and effective type-specific treatment, which is crucial for mortality reduction [1]. Moreover, it is imperative to investigate the effectiveness and toxicity of pharmaceutical treatments before administration in clinical practice [2]. However, biological matter characterization still faces many challenges. State-of-the-art imaging and characterization methods have drawbacks, such as the requirement to attach difficult-to-find and costly labels to the biological target (e.g., COVID-19 rapid tests), expensive equipment (e.g., magnetic resonance imaging), low accuracy (e.g., ultrasound), use of ionizing radiation (e.g., X-rays), and invasiveness [3]. The characterization of biological matter using microwave (μW), millimeter-wave (mmW), and terahertz (THz) spectroscopy is a promising alternative: it is label-free, does not require ionizing radiation, and can be noninvasive. Moreover, there is a significant difference in how different biological materials absorb, reflect, and transmit electromagnetic (EM) waves [4] that is due to the difference in their dielectric properties. The dielectric properties are described by the frequency-dependent material parameter called the complex permittivity f, which expresses how the material responds to an external oscillating electric field. The complex permittivity of a material determines how the material absorbs, reflects, and transmits EM waves at different frequencies (Figure 1). Since each biological material's permittivity spectrum is different, it acts as an EM fingerprint. A material's complex permittivity can be calculated from the reflection and transmission of EM waves through the material, described by the S-parameters, which can be measured using a vector network analyzer (VNA) transmitting and receiving EM waves over a range of frequencies. The amplitude and phase of the transmitted and reflected EM waves at different frequencies are influenced by different underlying biological effects at different scales. That causes the entire spectrum to provide information from the supracellular to the molecular and even atomic scale. © 2000-2012 IEEE.

Relationship Between the Formation of Post-Covid Residential Complexes and the Architecture of Soviet Commune Houses

The article explores the connection between the principles shaping the architecture of contemporary Russian residential complexes and Soviet commune houses of the 1920s and 1930s. Today, the social picture has changed, society lives under the conditions of "new normality”. As a result of social upheaval, people develop new ideas about the world and their way of life is transformed. Modern residential complexes begin to be formed according to the principle of ‘self-sufficiency', as a network of interconnected spaces which ensure the satisfaction of all the needs of the residents within the space of the residential complex. Soviet commune houses were formed on a similar principle, providing for the satisfaction of domestic needs within a residential unit. Considering the modern period and the period of Soviet history in the 1920s and 1930s, the author relates the ongoing global changes. Two factors, social and epidemiological, which had the strongest influence on the development of housing typologies of the two periods, are analyzed. By comparing the contents of the two typologies of housing in the considered periods, a direct analogy of functional spaces can be traced. The patterns which were laid down in the planning of residential space in commune houses are updated under the conditions of modernity and repeated in the solutions of new residential complexes. Modern architecture is revealed by the authors through a reinterpretation of the experience of past generations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Hierarchical Multi-agent Model for Reinforced Medical Resource Allocation with Imperfect Information

With the advent of the COVID-19 pandemic, the shortage in medical resources became increasingly more evident. Therefore, efficient strategies for medical resource allocation are urgently needed. However, conventional rule-based methods employed by public health experts have limited capability in dealing with the complex and dynamic pandemic-spreading situation. In addition, model-based optimization methods such as dynamic programming (DP) fail to work since we cannot obtain a precise model in real-world situations most of the time. Model-free reinforcement learning (RL) is a powerful tool for decision-making;however, three key challenges exist in solving this problem via RL: (1) complex situations and countless choices for decision-making in the real world;(2) imperfect information due to the latency of pandemic spreading;and (3) limitations on conducting experiments in the real world since we cannot set up pandemic outbreaks arbitrarily. In this article, we propose a hierarchical RL framework with several specially designed components. We design a decomposed action space with a corresponding training algorithm to deal with the countless choices, ensuring efficient and real-time strategies. We design a recurrent neural network-based framework to utilize the imperfect information obtained from the environment. We also design a multi-agent voting method, which modifies the decision-making process considering the randomness during model training and, thus, improves the performance. We build a pandemic-spreading simulator based on real-world data, serving as the experimental platform. We then conduct extensive experiments. The results show that our method outperforms all baselines, which reduces infections and deaths by 14.25% on average without the multi-agent voting method and up to 15.44% with it. © 2022 Association for Computing Machinery.

Research on CT image classification algorithm of COVID-19 based on improved ResNet

The classification of COVID-19 and other viral pneumonias will help doctors to diagnose new coronary patients more accurately and quickly. Aiming at the classification problem of CT in patients with COVID-19, this paper proposes a CT image classification method based on an improved ResNet50 network based on the traditional convolutional neural network classification model. This paper uses the multiscale feature fusion strategy, combined with the improved attention mechanism to obtain the correlation coefficient between the internal feature points of the feature map, and finally achieves the effect of enhancing the representation ability of the feature map. Through the analysis and comparison of the technical principle, classification accuracy, and other parameters, it shows that the improved algorithm has better adaptive ability and classification ability. Through experiments, the improved ResNet50 classification model has a certain improvement in accuracy, time complexity, and spatial complexity compared with the traditional classification model, and the accuracy rate can reach 90.1 %. © 2022 Copyright for this paper by its authors.

Research on the Spatiotemporal Evolution of China's Intercity Network Structure from the Perspective of the Relationship between Listed Companies and Customers

The connection between enterprises is an important part of urban connection. Strengthening the analysis of urban functional network based on the connection between enterprises is of great significance to enrich the theoretical research of urban network. Based on the trade relationship data between listed companies and their top five customers from 2010 to 2020, this paper constructs China's urban network, and analyzes the spatio- temporal evolution characteristics of urban network based on the perspective of trade links between enterprises. The research shows that: ① From 2010 to 2020, the urban network scale shows the characteristics of first rising and then falling, and the overall network density is low, ranging from 0.014 to 0.018. The center of gravity of the network presents the trend of "S" - shaped spatial trajectory change and overall southward movement.This feature is consistent with the trend of China's economic center moving southward in recent years. The overall spatial structure of the network changes from coastal to "T" - shaped structure. This feature is consistent with the "T" strategy of China's land development. ② The network traffic is concentrated in a few node cities. The total amount of capital in and out of the top 20 cities accounts for 71.9% of the total capital flow. Beijing and Shanghai are the absolute core of the network. The provincial capitals or sub provincial cities such as Hangzhou, Wuhan, Shenzhen and Guangzhou assume the function of regional centers. Foshan, Qiqihar, Nantong and other manufacturing developed cities are important nodes. It indicates that trade links are more likely to occur in cities with high administrative levels or developed industries. ③ The Pearl River Delta has the highest network density, which is between 0.324 and 0.334. The Yangtze River Delta has the highest total trade flow, which is 78.35 billion yuan. Although the networking level of urban agglomeration in the middle reaches of the Yangtze River and Chengdu Chongqing urban agglomeration is relatively low, they have become an important force to promote the evolution of network structure. ④ The COVID-19 has had a significant impact on the trade flow and network structure of the overall network. The network associations have been further divided and reorganized. The Guangzhou Shenzhen associations have been significantly strengthened. It shows that Guangzhou and Shenzhen have a strong combination effect. The Shanghai associations have been significantly weakened. The research results have a certain reference value for promoting the construction of domestic big cycle and unified big market. © 2023 Journal of Geo-Information Science. All rights reserved.

Household behavior and vulnerability to acute malnutrition in Kenya.

Anticipating those most at-risk of being acutely malnourished significantly shapes decisions that pertain to resource allocation and intervention in times of food crises. Yet, the assumption that household behavior in times of crisis is homogeneous-that households share the same capacity to adapt to external shocks-ostensibly prevails. This assumption fails to explain why, in a given geographical context, some households remain more vulnerable to acute malnutrition relative to others, and why a given risk factor may have a differential effect across households? In an effort to explore how variation in household behavior influences vulnerability to malnutrition, we use a unique household dataset that spans 23 Kenyan counties from 2016 to 2020 to seed, calibrate, and validate an evidence-driven computational model. We use the model to conduct a series of counterfactual experiments on the relationship between household adaptive capacity and vulnerability to acute malnutrition. Our findings suggest that households are differently impacted by given risk factors, with the most vulnerable households typically being the least adaptive. These findings further underscore the salience of household adaptive capacity, in particular, that adaption is less effective for economic vis-à-vis climate shocks. By making explicit the link between patterns of household behavior and vulnerability in the short- to medium-term, we underscore the need for famine early warning to better account for variation in household-level behavior.

Characterizing data patterns with core–periphery network modeling

Traditional classification techniques usually classify data samples according to the physical organization, such as similarity, distance, and distribution, of the data features, which lack a general and explicit mechanism to represent data classes with semantic data patterns. Therefore, the incorporation of data pattern formation in classification is still a challenge problem. Meanwhile, data classification techniques can only work well when data features present high level of similarity in the feature space within each class. Such a hypothesis is not always satisfied, since, in real-world applications, we frequently encounter the following situation: On one hand, the data samples of some classes (usually representing the normal cases) present well defined patterns;on the other hand, the data features of other classes (usually representing abnormal classes) present large variance, i.e., low similarity within each class. Such a situation makes data classification a difficult task. In this paper, we present a novel solution to deal with the above mentioned problems based on the mesostructure of a complex network, built from the original data set. Specifically, we construct a core–periphery network from the training data set in such way that the normal class is represented by the core sub-network and the abnormal class is characterized by the peripheral sub-network. The testing data sample is classified to the core class if it gets a high coreness value;otherwise, it is classified to the periphery class. The proposed method is tested on an artificial data set and then applied to classify x-ray images for COVID-19 diagnosis, which presents high classification precision. In this way, we introduce a novel method to describe data pattern of the data "without pattern” through a network approach, contributing to the general solution of classification. © 2022 Elsevier B.V.

Bifurcation analysis of the Microscopic Markov Chain Approach to contact-based epidemic spreading in networks

The dynamics of many epidemic compartmental models for infectious diseases that spread in a single host population present a second-order phase transition. This transition occurs as a function of the infectivity parameter, from the absence of infected individuals to an endemic state. Here, we study this transition, from the perspective of dynamical systems, for a discrete-time compartmental epidemic model known as Microscopic Markov Chain Approach, whose applicability for forecasting future scenarios of epidemic spreading has been proved very useful during the COVID-19 pandemic. We show that there is an endemic state which is stable and a global attractor and that its existence is a consequence of a transcritical bifurcation. This mathematical analysis grounds the results of the model in practical applications. © 2022 Elsevier Ltd

Commodity supply risk assessment of China's copper industrial chain: The perspective of trade network

With the rapid development of China's new energy industry, the consumption demand for copper resources is increasing. As a key raw material, copper resources are becoming increasingly important. Taking the demand for copper commodities in China's new energy development as the research background and the international trade environment and pattern of copper supply as the research perspective, this paper makes an overall assessment of the commodity supply risk of China's copper industrial chain from 2010 to 2021 using the complex network and the newly established three-dimensional risk assessment model and finally reaches the following conclusions. The supply risk of commodities in China's copper industrial chain has been rising continuously since 2019 after experiencing fluctuating development in the early stage and a continuous decline in recent years, and there may be a trend of continuing to rise. The supply risk of China's copper industrial chain was gradually reduced from upstream to midstream and downstream, and the supply risk of copper smelting was more severe. The disruption potential risk of China's copper industrial chain was relatively low, and the international import market structure of copper commodities was relatively reasonable. The supply risk characteristics of each link in China's copper industrial chain were different. Due to the influence of import dependence, the copper mining industry had a high risk of trade exposure. However, the smelting and copper processing industries had certain limitations in production management, operation management and technology research and development, and their ability to withstand risks was weak. In addition, the impact of the domestic COVID-19 epidemic ha caused a high industrial chain vulnerability risk. © 2023 Elsevier Ltd