Semi-Supervised Modified-UNet for Lung Infection Image Segmentation

Automatic lung infection segmentation in computed tomography (CT) scans can offer great assistance in radiological diagnosis by improving accuracy and reducing time required for diagnosis. The biggest challenges for deep learning (DL) models in segmenting infection region are the high variances in infection characteristics, fuzzy boundaries between infected and normal tissues, and the troubles in getting large number of annotated data for training. To resolve such issues, we propose a Modified U-Net (Mod-UNet) model with minor architectural changes and significant modifications in the training process of vanilla 2D UNet. As part of these modifications, we updated the loss function, optimization function, and regularization methods, added a learning rate scheduler and applied advanced data augmentation techniques. Segmentation results on two Covid-19 Lung CT segmentation datasets show that the performance of Mod-UNet is considerably better than the baseline U-Net. Furthermore, to mitigate the issue of lack of annotated data, the Mod-UNet is used in a semi-supervised framework (Semi-Mod-UNet) which works on a random sampling approach to progressively enlarge the training dataset from a large pool of unannotated CT slices. Exhaustive experiments on the two Covid-19 CT segmentation datasets and on a real lung CT volume show that the Mod-UNet and Semi-Mod-UNet significantly outperform other state-of-theart approaches in automated lung infection segmentation. IEEE

Analyzing the effects of quarantine, isolation, and vaccination on the spread of COVID-19 via a mathematical model

The main COVID-19 control strategies presently practiced are maintaining social distancing, quarantin-ing suspected exposures, and isolating infectious people. In this paper, a deterministic compartmental mathematical model is proposed considering these three control strategies. Based on the proposed model the effect of vaccination on the suppression of the disease is discussed. Critical vaccination rate and vaccinated population size relevant to disease suppression are determined based on the proposed mathematical model. Different forms of the most used key term in infectious disease modelling, reproduction number, are determined relevant to the proposed model. Sensitivity analysis of the reproduction numbers is done to identify model parameters mostly affecting the spread of the disease. Based on the reproduction number of the model disease controlling parameter regions are determined and graphical representations of those parameter regions are presented. Based on the results of the proposed mathematical model, it is observed that earlier implementation of the vaccination process is helpful to better control the disease. However, it takes considerable time to invent successful vaccinations for newly out-breaking diseases like COVID-19. Therefore, it took considerable time to start the vaccination process for COVID-19. It is observed that after starting a vaccination process at a particular rate it should continue until the vaccinated population reaches a critical size. © 2023, National Science Foundation. All rights reserved.

Populism and Its Definitions: Interpretations and Perspectives of a Multifaceted Political Model

This chapter deals with some of the many definitions of populism, starting with the first event in which a community of researchers came together for this purpose: the 1967 London Conference ‘To Define Populism'. The text follows the evolution of the central themes to the present day and explores, in particular, the tools produced by populism studies that help to understand two contemporary challenges: first, the emergence of new forms of populism fragmented into antagonistic groups during the SarsCoV2 Pandemic, but linked to broader authoritarian visions, and second, the new reflection on the principle of nationality and international solidarity that arose after the invasion of Ukraine by the Russian Federation. Both are challenges to the principles, intelligence and strength of democracies. This text focuses on two aspects in particular: first, the logic with which to construct definitions, so as to avoid errors of setting (unclear and ill-defined choice of subject to be studied), elaboration (conceptual stretching) and evaluation (researcher bias), and second, the understanding of the different identities with which the ‘people' presents itself and acts historically: populace, mob, civil society and revolutionary people;they cannot be confused within the same ‘populism'. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

CAUSES OF DEATH IN TURKEY: HOW THE INCREASE IN THE BURDEN OF COMMUNICABLE DISEASES VARY BY SEX AND AGE?

Causes of death statistics are essential tools for public health, but Turkey lags in the number of studies on causes and trends of death. This study measures causes and trends of death in Turkey for the 2013-2019 period, with special emphasis on the increase in communicable diseases (CDs). This study has a representative research design based on the national population and cause of death registration systems. Causes of death with International Classification of Diseases, Tenth Revision (ICD-10) codes were grouped and garbage codes were determined and redistributed. To understand how the increase in the burden of CDs vary by sex and age, modal age at death, age-specific death rates, probability of eventual death, years of life lost (YLL) due to three main causes of death were calculated by using discrete absorbing Markov chain model. According to results, modal age at death among male population shifted to older ages, the share of respiratory infectious diseases and other infectious and parasitic diseases increased rapidly between 2013 and 2019, just before the onset of COVID-19 pandemic. Overall, our results suggest that burden of CDs increased for both sexes, and elderly male population was among the most effected group. Since non-communicable diseases were still the leading causes of death, increasing rate of CDs may create an extra burden on health system.Alternate abstract: Ölüm nedeni istatistikleri, halk sağlığı için çok önemli araçlardır, ancak Türkiye ölüm nedenleri ve eğilimlerine ilişkin yapılan çalışmalarda geride kalmaktadır. Bu çalışma, bulaşıcı hastalıklardaki (BH'lerdeki) artışa özel bir vurgu yaparak, 2013-2019 döneminde Türkiye'deki ölüm nedenlerini ve eğilimlerini değerlendirmektedir. Çalışma, ulusal nüfus ve ölüm nedeni kayıt sistemlerine dayalı temsili araştırma tasarımına sahiptir. Uluslararası Hastalık Sınıflandırması Onuncu Revizyon (UHS-10) kodlarına sahip tüm ölüm nedenleri gruplandırılmış ve çöp kodlar belirlenerek ölüm nedenleri içinde yeniden dağıtılmıştır. BH yükündeki artışın cinsiyete ve yaşa göre nasıl değiştiğini anlamak için ayrık Markov zinciri modellemesi kullanılmış ve en fazla ölümün meydana geldiği yaş, üç ana ölüm nedenine göre yaşa özel ölüm oranları, ölüm olasılıkları ve kaybedilen yaşam yılları hesaplanmıştır. Çalışmanın sonuçlarına göre, erkek nüfusta en fazla ölümün meydana geldiği yaş daha ileri yaşlara kaymış;her iki cinsiyette de 2013-2019 yılları arasında- COVID-19 pandemisinin başlamasından hemen önce- solunum yolu enfeksiyon hastalıkları ile diğer bulaşıcı ve parazit hastalıkların payı hızla artmıştır. Genel olarak, sonuçlarımız her iki cinsiyet için de BH yükünün arttığını ve yaşlı erkek nüfusunun en çok etkilenen grup arasında olduğunu göstermektedir. Bulaşıcı olmayan hastalıklar hala önde gelen ölüm nedenleri olduğundan, artan BH oranları sağlık sistemi üzerinde fazladan bir yük oluşturabilir.

A Rolling-Based Multipeak Learning Model for COVID-19 Pandemic Predicting

The COVID-19 pandemic has led to a dramatic loss of human life and the global economy, and presents an unprecedented challenge to public health management for all countries around the world. Access to an accurate epidemic prediction model plays a crucial role in epidemic prevention, infection scale control, and medical resource allocation. In this paper, we first propose a multipeak SEIYAQURD model by using the multipeak learning algorithm to predict the COVID-19 epidemic. The model separates the total population according to characteristics of COVID-19 and can capture trend changes in the epidemic. Then, the fitting period technique and the rolling prediction strategy are proposed to improve the prediction accuracy. Numerical experiments based on the data of COVID-19 in the United States are performed to demonstrate the effectiveness of our proposed method by comparing with two benchmark methods from the literature in two cases, one has a smooth trend and the other has a significant changing trend. © 2022 IEEE.

Nonlinear Modeling of COVID-19 and the Significance of Testing: The Case of Kazakhstan

It has been more than two years since the world faced a global pandemic of COVID-19, which affected the global economy negatively and took many human lives. This paper considers the extended susceptible-exposed-infectious-recovered (SEIR) model and finds out whether it is effective for the government of Kazakhstan to conduct massive free PCR testing of the exposed population. To this end, we constructed a new mathematical model and the government cost function that incorporates the hospital cost for the COVID-19 treatment and the cost of PCR testing. To address the above-mentioned objectives, we constructed nonlinear differential equations for our epidemic model and numerically solved them. Furthermore, the government's cost was modeled as a function that depends on the rate of PCR tests. The findings of the numerical analysis show that the government's cost is minimized if the exposed individuals were tested for the disease as often as possible. Moreover, testing both susceptible and exposed individuals is not beneficial in terms of the economic cost. © 2022 IEEE.

COVID-19 Impact on a Business modelfor Sustainable supply chain innovation

Industry in Morocco is facing one of the hardest crises in its history due to the COVID-19 pandemic. Companies are managing several changes and reforms into their business model based on Industry 4.0 transformation, mainly on their supply chain. Two objectives are targeted: enhance innovation inside the supply chain processes and strengthen supply chain resilience and sustainability in front of this crisis, and also the relative environment's changes. Our paper investigates the CO VID-19 pandemic impact on the main factors empowering this innovation and sustainability during 2020 and 2021. It evaluates a business model considering six constructs with the mediation of industry 4.0 technological systems in achieving sustainable supply chain innovation. The evaluation is based on data analysis using SEM P LS method to provide the model validation and reliability. © 2022 IEEE.

Fine-Grained Prediction and Control of Covid-19 Pandemic in a City: Application to Post-Initial Stages

Predicting the evolution of the Covid-19 pandemic during its early phases was relatively easy as its dynamics were governed by few influencing factors that included a single dominant virus variant and the demographic characteristics of a given area. Several models based on a wide variety of techniques were developed for this purpose. Their prediction accuracy started deteriorating as the number of influencing factors and their interrelationships grew over time. With the pandemic evolving in a highly heterogeneous way across individual countries, states, and even individual cities, there emerged a need for a contextual and fine-grained understanding of the pandemic to come up with effective means of pandemic control. This paper presents a fine-grained model for predicting and controlling Covid-19 in a large city. Our approach borrows ideas from complex adaptive system-of-systems paradigm and adopts a concept of agent as the core modeling ion. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Radiation estimation and crop growth trajectory reconstruction by novel algorithms improve MOD16 evapotranspiration predictability for global multi-site paddy rice ecosystems

• MOD16 products indicated significant underestimations in all paddy rice ET observations. • R n estimation in overcast conditions and LAI reconstruction were two key causes. • Daily R n estimations under all-sky conditions by a global cloudy index algorithm were improved by 40.6%. • Daily LAI dynamics estimated by the LTDG_PhenoS algorithm were improved by 818.7%. • Daily ET estimations were improved by 68.7%. Reliable estimations in evapotranspiration (ET) of paddy rice ecosystems by satellite products are critical because of their important roles in regional hydrological processes and climate change. However, the NASA MODIS ET products (MOD16A2) and its derivatives do not have good correlations with all global paddy rice ET observations. In this research, MOD16 model sensitivity analyses and parameter optimization strategies were conducted in order to solve the problem. Results suggested that underestimation of daily net radiation (R n) in overcast conditions and less satisfactory reconstruction of field-scale leaf area index (LAI) growth trajectory from the start date of field flooding and transplanting (FFTD) to the end of growing seasons by MODIS coarse vegetation index were identified as two major causes. A Light and Temperature-Driven Growth model and a Phenology-based LAI temporal Smoothing method fusion algorithm (LTDG_PhenoS) and an improved R n estimation method were introducted and evaluated in paddy rice fields in South Korea, Japan, China, Philippines, India, Spain, Italy, and the USA from 2002 to 2019. The LTDG_PhenoS algorithm considers Landsat and MODIS EVI observations and meteorological data as input variables and 30-m LAI daily time series as outcomes. Introducing the global cloudy index algorithm resulted in improved estimations of daily R n under all-sky conditions, with a significant decrease of root mean square error (RMSE) from 1.87 to 1.11 MJ m−2 day−1. The LTDG_PhenoS algorithm well reconstructed crop LAI growth dynamics from the FFTD to the end of rice growing seasons, with a substantial decline of RMSE from 1.49 to 0.27 m2/m−2. The FFTD estimations by the LTDG_PhenoS algorithm had an R2 of 0.97 and a small RMSE of less than 12-days. Daily ET rates estimated by novel algorithms had a substantial decline in RMSE from 2.88 to 0.90 mm day−1. [ FROM AUTHOR] Copyright of Journal of Hydrology is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)