GPU libraries speed performance analysis for RCWA simulation matrix operations

Rigorous Coupled Wave Analysis (RCWA) method is highly efficient for the simulation of diffraction efficiency and field distribution patterns in periodic structures and textured optoelectronic devices. GPU has been increasingly used in complex scientific problems such as climate simulation and the latest Covid-19 spread model. In this paper, we break down the RCWA simulation problem to key computational steps (eigensystem solution, matrix inversion/multiplication) and investigate speed performance provided by optimized linear algebra GPU libraries in comparison to multithreaded Intel MKL CPU library running on IRIDIS 5 supercomputer (1 NVIDIA v100 GPU and 40 Intel Xeon Gold 6138 cores CPU). Our work shows that GPU outperforms CPU significantly for all required steps. Eigensystem solution becomes 60% faster, Matrix inversion improves with size achieving 8x faster for large matrixes. Most significantly, matrix multiplication becomes 40x faster for small and 5x faster for large matrix sizes. © 2023 SPIE.

A multiconsistent computational methodology to resolve a diffusive epidemiological system with effects of migration, vaccination and quarantine.

BACKGROUND: We provide a compartmental model for the transmission of some contagious illnesses in a population. The model is based on partial differential equations, and takes into account seven sub-populations which are, concretely, susceptible, exposed, infected (asymptomatic or symptomatic), quarantined, recovered and vaccinated individuals along with migration. The goal is to propose and analyze an efficient computer method which resembles the dynamical properties of the epidemiological model. MATERIALS AND METHODS: A non-local approach is utilized for finding approximate solutions for the mathematical model. To that end, a non-standard finite-difference technique is introduced. The finite-difference scheme is a linearly implicit model which may be rewritten using a suitable matrix. Under suitable circumstances, the matrices representing the methodology are M-matrices. RESULTS: Analytically, the local asymptotic stability of the constant solutions is investigated and the next generation matrix technique is employed to calculate the reproduction number. Computationally, the dynamical consistency of the method and the numerical efficiency are investigated rigorously. The method is thoroughly examined for its convergence, stability, and consistency. CONCLUSIONS: The theoretical analysis of the method shows that it is able to maintain the positivity of its solutions and identify equilibria. The method's local asymptotic stability properties are similar to those of the continuous system. The analysis concludes that the numerical model is convergent, stable and consistent, with linear order of convergence in the temporal domain and quadratic order of convergence in the spatial variables. A computer implementation is used to confirm the mathematical properties, and it confirms the ability in our scheme to preserve positivity, and identify equilibrium solutions and their local asymptotic stability.

Quantifying social contact patterns in Minnesota during stay-at-home social distancing order.

SARS-CoV-2 is primarily transmitted through person-to-person contacts. It is important to collect information on age-specific contact patterns because SARS-CoV-2 susceptibility, transmission, and morbidity vary by age. To reduce the risk of infection, social distancing measures have been implemented. Social contact data, which identify who has contact with whom especially by age and place are needed to identify high-risk groups and serve to inform the design of non-pharmaceutical interventions. We estimated and used negative binomial regression to compare the number of daily contacts during the first round (April-May 2020) of the Minnesota Social Contact Study, based on respondent's age, gender, race/ethnicity, region, and other demographic characteristics. We used information on the age and location of contacts to generate age-structured contact matrices. Finally, we compared the age-structured contact matrices during the stay-at-home order to pre-pandemic matrices. During the state-wide stay-home order, the mean daily number of contacts was 5.7. We found significant variation in contacts by age, gender, race, and region. Adults between 40 and 50 years had the highest number of contacts. The way race/ethnicity was coded influenced patterns between groups. Respondents living in Black households (which includes many White respondents living in inter-racial households with black family members) had 2.7 more contacts than respondents in White households; we did not find this same pattern when we focused on individual's reported race/ethnicity. Asian or Pacific Islander respondents or in API households had approximately the same number of contacts as respondents in White households. Respondents in Hispanic households had approximately two fewer contacts compared to White households, likewise Hispanic respondents had three fewer contacts than White respondents. Most contacts were with other individuals in the same age group. Compared to the pre-pandemic period, the biggest declines occurred in contacts between children, and contacts between those over 60 with those below 60.

The Impact of Restricting Community Activities on COVID-19 Transmission: A Case Study in Sumatra Island, Indonesia

Sumatra Island is the third largest island with the second largest population in Indonesia which has the following eight provinces: Aceh, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Bengkulu and Lampung. The connectivity of these eight provinces in the economic field is very strong. This encourages high mobility between these provinces. During this Covid-19 pandemic, the high mobility between provinces affects the level of spread of Covid-19 on the island of Sumatra. The central government ordered local governments to implement a community activity restriction program called PPKM. In this article, a study is conducted on the impact of the PKKM program on the spread of Covid 19 on the island of Sumatra, Indonesia. The spread of Covid-19 is modeled using the Susceptible-Infected-Recovered-Death (SIRD) model which considers the mobility factor of the population. The model parameters were estimated using Approximate Bayesian Computation (ABC). The results of the study using this model show that the application of PKKM in several provinces in Sumatra can reduce the level of spread of COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The Security Algorithm ES-BR22-001 Used to Secure the Health Data

The data age information is considerably more significant in open life, since individuals' well-being information just concluded regardless of whether COVID-19 impacted, and furthermore connected with all medical problems information. These information used to examine and anticipate the medical problems information by Machine Learning Algorithm, and afterward anticipated information need greater security. In this way, we applied the current strategy ChaCha technique and that strategy zeroed in as it were "encryption execution” so security is less. In this paper, to apply the new ES-BR22-001 strategy, this technique has 7 stages. The 1st stage is finding the K value. The 2nd stage is applying the K value in Eq. (1). The 3rd stage is finding the Sk values by using Eq. (1). The 4th stage is applying the Sk values in the sparse matrix. The 5th stage is sparse matrix values are converted into single line. The 6th stage is pairing all the values. The final stage is all paired values will be applied in the matrix. The new ES-BR22-001 method provides security and performance is good while compared to ChaCha method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Comparative analysis of the service quality in homestay accommodation

Purpose - The paper focuses on guest satisfaction and service process improvement, considering the importance of quality elements as an important factor for differentiation in the market. The purpose of this article is to identify the differences in the attitudes of domestic and foreign guests who have stayed in homestay accommodation. As part of the research, an importance-performance analysis was conducted to compare the importance of and satisfaction with quality elements among domestic and foreign guests. Design - Primary research was conducted in September 2020 in 10 counties of the Republic of Croatia. A total of 168 valid questionnaires were collected. The sample consists of guests from 17 countries who were accommodated in homestay accommodation. Methodology - For this article, importance-performance analysis (IPA) was performed and the importance of, and satisfaction with, each quality element was presented in an IPA matrix. By analysing the position of the quality elements in the matrix, it is possible to identify future strategies and improvements that need to be implemented to meet the needs of guests. Approach - The study analyses 20 quality elements in two IPA matrices, one for domestic guests, and one for foreign guests. Each matrix has four quadrants: "Concentrate here", "Keep up the good work", "Low priority" ", and "Possible overkill". Findings - The results show that there are differences between domestic and foreign guests in their perceptions of satisfaction with and importance of quality elements. The results of the conducted IPA provide guidelines for improving certain areas in the service delivery process in accordance with guest preferences. The research has several limitations, mainly the sample size and the fact that the study was conducted during the COVID-19 pandemic. Originality of the research - The results show the peculiarities of guests staying in homestay accommodation and highlight the differences in perception depending on the origin of the guests. The findings provide clear guidance for practitioners by identifying elements of service quality that are important to domestic and international guests. This provides the basis for creating services tailored to guests' needs and expectations.

Informative Initialization and Kernel Selection Improves t-SNE for Biological Sequences

The t-distributed stochastic neighbor embedding (t-SNE) is a method for interpreting high dimensional (HD) data by mapping each point to a low dimensional (LD) space (usually two-dimensional). It seeks to retain the structure of the data. An important component of the t-SNE algorithm is the initialization procedure, which begins with the random initialization of an LD vector. Points in this initial vector are then updated to minimize the loss function (the KL divergence) iteratively using gradient descent. This leads comparable points to attract one another while pushing dissimilar points apart. We believe that, by default, these algorithms should employ some form of informative initialization. Another essential component of the t-SNE is using a kernel matrix, a similarity matrix comprising the pairwise distances among the sequences. For t-SNE-based visualization, the Gaussian kernel is employed by default in the literature. However, we show that kernel selection can also play a crucial role in the performance of t-SNE.In this work, we assess the performance of t-SNE with various alternative initialization methods and kernels, using four different sets, out of which three are biological sequences (nucleotide, protein, etc.) datasets obtained from various sources, such as the well-known GISAID database for sequences of the SARS-CoV-2 virus. We perform subjective and objective assessments of these alternatives. We use the resulting t-SNE plots and k-ary neighborhood agreement (k-ANA) to evaluate and compare the proposed methods with the baselines. We show that by using different techniques, such as informed initialization and kernel matrix selection, that t-SNE performs significantly better. Moreover, we show that t-SNE also takes fewer iterations to converge faster with more intelligent initialization. © 2022 IEEE.

A Numerical Investigation Based on Exponential Collocation Method for Nonlinear SITR Model of COVID-19

In this work, the exponential approximation is used for the numerical simulation of a nonlinear SITR model as a system of differential equations that shows the dynamics of the new coronavirus (COVID-19). The SITR mathematical model is divided into four classes using fractal parameters for COVID-19 dynamics, namely, susceptible (S), infected (I), treatment (T), and recovered (R). The main idea of the presented method is based on the matrix representations of the exponential functions and their derivatives using collocation points. To indicate the usefulness of this method, we employ it in some cases. For error analysis of the method, the residual of the solutions is reviewed. The reported examples show that the method is reasonably efficient and accurate. © 2023 Tech Science Press. All rights reserved.

Extracting Self-Reported COVID-19 Symptom Tweets and Twitter Movement Mobility Origin/Destination Matrices to Inform Disease Models

The emergence of the novel coronavirus (COVID-19) generated a need to quickly and accurately assemble up-to-date information related to its spread. In this research article, we propose two methods in which Twitter is useful when modelling the spread of COVID-19: (1) machine learning algorithms trained in English, Spanish, German, Portuguese and Italian are used to identify symptomatic individuals derived from Twitter. Using the geo-location attached to each tweet, we map users to a geographic location to produce a time-series of potential symptomatic individuals. We calibrate an extended SEIRD epidemiological model with combinations of low-latency data feeds, including the symptomatic tweets, with death data and infer the parameters of the model. We then evaluate the usefulness of the data feeds when making predictions of daily deaths in 50 US States, 16 Latin American countries, 2 European countries and 7 NHS (National Health Service) regions in the UK. We show that using symptomatic tweets can result in a 6% and 17% increase in mean squared error accuracy, on average, when predicting COVID-19 deaths in US States and the rest of the world, respectively, compared to using solely death data. (2) Origin/destination (O/D) matrices, for movements between seven NHS regions, are constructed by determining when a user has tweeted twice in a 24 h period in two different locations. We show that increasing and decreasing a social connectivity parameter within an SIR model affects the rate of spread of a disease. © 2023 by the authors.

Grayscale Image Statistical Attributes Effectively Distinguish the Severity of Lung Abnormalities in CT Scan Slices of COVID-19 Patients.

Grayscale statistical attributes analysed for 513 extract images taken from pulmonary computed tomography (CT) scan slices of 57 individuals (49 confirmed COVID-19 positive; eight confirmed COVID-19 negative) are able to accurately predict a visual score (VS from 0 to 4) used by a clinician to assess the severity of lung abnormalities in the patients. Some of these attributes can be used graphically to distinguish useful but overlapping distributions for the VS classes. Using machine and deep learning (ML/DL) algorithms with twelve grayscale image attributes as inputs enables the VS classes to be accurately distinguished. A convolutional neural network achieves this with better than 96% accuracy (only 18 images misclassified out of 513) on a supervised learning basis. Analysis of confusion matrices enables the VS prediction performance of ML/DL algorithms to be explored in detail. Those matrices demonstrate that the best performing ML/DL algorithms successfully distinguish between VS classes 0 and 1, which clinicians cannot readily do with the naked eye. Just five image grayscale attributes can also be used to generate an algorithmically defined scoring system (AS) that can also graphically distinguish the degree of pulmonary impacts in the dataset evaluated. The AS classification illustrated involves less overlap between its classes than the VS system and could be exploited as an automated expert system. The best-performing ML/DL models are able to predict the AS classes with better than 99% accuracy using twelve grayscale attributes as inputs. The decision tree and random forest algorithms accomplish that distinction with just one classification error in the 513 images tested.

Superiority of UV222 radiation by in situ aquatic electrode KrCl excimer in disinfecting waterborne pathogens: Mechanism and efficacy.

Microbial safety in water has always been the focus of attention, especially during the COVID-19 pandemic. Development of green, efficient and safe disinfection technology is the key to control the spread of pathogenic microorganisms. Here, an in situ aquatic electrode KrCl excimer radiation with main emission wavelength 222 nm (UV222) was designed and used to disinfect model waterborne virus and bacteria, i.e. phage MS2, E. coli and S. aureus. High inactivation efficacy and diversity of inactivation mechanisms of UV222 were proved by comparision with those of commercial UV254. UV222 could totally inactivate MS2, E. coli and S. aureus with initial concentrations of ∼107 PFU or CFU mL-1 within 20, 15, and 36 mJ/cm2, respectively. The UV dose required by UV254 to inactivate the same logarithmic pathogenic microorganism is at least twice that of UV222. The protein, genomic and cell membrane irreparable damage contributed to the microbial inactivation by UV222, but UV254 only act on nucleic acid of the target microorganisms. We found that UV222 damage nucleic acid with almost the same or even higher efficacy with UV254. In addition, free base damage of UV222 in similar ways with UV254(dimer and hydrate). But due to the quantum yield of free base degradation of UV222 was greater than UV254, the photolysis rates of UV222 to A, G, C and U four bases were 11.5, 1.2, 3.2 and 1 times as those of UV254, respectively. Excellent disinfection performance in UV222 irradiation was also achieved in real water matrices (WWTP and Lake). In addition, it was proved that coexisting HCO3- or HPO42 - in real and synthetic water matrices can produce • OH to promote UV222 disinfection. This study provided novel insight into the UV222 disinfection process and demonstrated its possibility to take place of the conventional ultraviolet mercury lamp in water purification.

Modeling of Factors Impacting Remote Workforce During COVID-19

The way an organization operates has a pattern to it. A knowledge-based way of understanding these patterns and implementing according to them retains the competitive advantage of the organizations. Thus, identifying factors is important because, if successful, it results in shared intellectual capital. Changing the core of the pattern upon which the organization works creates several problems in retaining an organization's competitiveness. This research focuses on identifying the elements which have a significant influence on an organization's operations due to the remote working of employees during situations like the COVID-19 pandemic. Further, the relationships of factors among each other have been explored from the available research. Based on the study of various organizations it has been found that not much work has been done to identify such factors even though several organizations have suddenly opted for their workforce to work remotely due to the COVID-19 pandemic. This has resulted in lost productivity and opportunities, organizational dis-balances, and a slower rate of development. The generated model may help organizations to understand the weak notes of remote working and implement structural changes accordingly to improve the productivity in remote working and tackle the productivity and opportunity loss due to remote workforce. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Assessment of SARS-CoV-2 Stability in human and environmental matrices, and potential hazards.

In the context of the ongoing pandemic of COVID-19, SARS-CoV-2 was detected in human excreta and environmental matrices. The occurrence of SARS-CoV-2 in environmental compartments raises questions on its fate and stability in these matrices and its potential to spread in the exposed communities. This review focused on the stability of the SARS-CoV-2 in human excreta, wastewater, soils, crops, and other environmental matrices, that may be reached through human excreta and sewage products spreading. Little is known about the persistence and survival of SARS-CoV-2 in the environment. Up to now sewage sludge, soil and crops are seldom investigated implying the convenience of considering future researches focusing on SARS-CoV-2 in soils receiving wastewater and sewage sludge, as well as on grown crops. Information regarding SARS-CoV-2 persistence in environmental media is crucial to establish and implement effective policies and measures for mitigating the transmission of COVID-19 and tackling eventual future outbreaks.

Using Discarded Facial Tissues to Monitor and Diagnose Viral Respiratory Infections.

Molecular biology amplification enables sensitive detection of most respiratory viruses through nasopharyngeal swabbing. We developed an innovative approach to detect viral genomes on used facial tissues. In 2 communities of children, used tissues were collected once weekly for 1 year. Pooled analysis of tissues enabled detection of successive virus circulation in 4 age groups over time and forecasted by several weeks the circulation of influenza in the general population. At the individual level, in a proof-of-concept study of 30 volunteers with influenza-like signs/symptoms, we identified common respiratory viruses. The signals for SARS-CoV-2 obtained in parallel from 15 facial tissues and swab samples were similar and often higher for the tissues (11/15). Individual analysis of tissues offers a noninvasive, sensitive, and affordable alternative to self-sampling without a medical care requirement. Pooled analyses may be used to detect virus spread in specific communities, predict seasonal epidemics, and alert the population to viral infections.

A Bayesian approach for the estimation of weight matrices in spatial autoregressive models

We develop a Bayesian approach to estimate weight matrices in spatial autoregressive (or spatial lag) models. Datasets in regional economic literature are typically characterized by a limited number of time periods relative to spatial units . When the spatial weight matrix is subject to estimation severe problems of over-parametrization are likely. To make estimation feasible, our approach focusses on spatial weight matrices which are binary prior to row-standardization. We discuss the use of hierarchical priors which impose sparsity in the spatial weight matrix. Monte Carlo simulations show that these priors perform very well where the number of unknown parameters is large relative to the observations. The virtues of our approach are demonstrated using global data from the early phase of the COVID-19 pandemic.

UNIPOLARITY, MULTIPOLARITY AND BIPOLAR COALITIONS. XXI CENTURY

The paper considers the consequences of one of the topical aspects of the complex and, according to some estimates, catastrophic state of the modern world – the collision of conflicting trends in globalization and sovereignty. The downward trend of globalization (not to be confused with internationalization) is observed after the global financial and economic crisis of 2008–2009. It has intensified under the influence of the covid pandemic and sanctions restrictions. Sovereignty trends, including those influenced by these factors have, on the contrary, an upward trend. In the 2020s, the contradictory tendencies of globalization and sovereignty increased the threat of world chaos. The resolution of contradictory tendencies and the formation of a new world order is possible on the basis of one of the models of a (new) unipolar, multipolar or bipolar world. The paper compares the perspectives of each of these actively discussed models of world development. On the basis of the analysis carried out, based on the theory of institutional X-Y matrices and empirical data, the greatest probability is the formation of a bipolar world. Its peculiarity, however, lies not in the crystallization of two "poles of power” in the form of rival states, but in the institutionalization of similar powerful international bipolar coalitions. Their composition was predicted by the author in the book "Institutional Matrices and the Development of Russia…” (Kirdina, 2014) and is confirmed by rесent practice. © 2022, Russian Academy of Sciences. All rights reserved.

THE COVID-19 MOBILITY IMPACTS ON THE MIGRATION FLOW IN SOUTH-EAST EUROPE: THE SITUATION IN 2021 AND BEFORE 1989

In this paper, we analyze the current travel restrictions due to the COVID-19 pandemic imposed by the countries from South-East Europe and briefly compare them with those imposed by the Central European countries. By using official data collection of displacement tracking matrices and analyzing the porosity of the borders in this part of Europe, we research the impact of COVID-19 on human mobility and the related economic and social aspects. Discussions are presented regarding this impact on the travelers, the seasonal workers from some of the selected countries, and the immigrants from the Middle-East. A formal analysis is performed relating the current travel restrictions and the travel ban during the iron curtain.

Effect of coronavirus pandemic in changing the performance barriers for textile and apparel industry in an emerging market.

In the past two years, coronavirus pandemic has severely impacted global industries and altered market dynamics. The present study compares the challenges facing Indian textile and apparel industry before and after the coronavirus pandemic. The context of our study focuses on handloom industry, as the primary financial risk for handloom micro entrepreneurs lies in capital requirements for raw materials, equipment and their lack of formal management structures to tackle the pressure of uncertainty. Thus, studying and mitigating internal and external barriers of the traditional manufacturing micro entrepreneurs during and post pandemic remains crucial to frame policy decisions for sustainability of this vulnerable sector. We have employed a two-phase (before and after the onset of pandemic) successive exploratory mixed method, starting with the Delphi technique (qualitative phase) and concluding with multi-criteria decision-making. In Phase 2 analysis, seventeen key critical barriers identified in Phase 1reduced to twelve. Phase 1 modelling suggests that lack of effective government policies, demonetization, and tax policy implementation are the most significant barriers. Further, Phase 2 identifies the absence of effective government policies as the most significant obstacle to the performance of Indian handloom industry, especially after the pandemic. Additionally, lack of branding was found to be most critically linked between independent and dependent barriers.

Modeling of Factors Impacting Remote Workforce During COVID-19

The way an organization operates has a pattern to it. A knowledge-based way of understanding these patterns and implementing according to them retains the competitive advantage of the organizations. Thus, identifying factors is important because, if successful, it results in shared intellectual capital. Changing the core of the pattern upon which the organization works creates several problems in retaining an organization's competitiveness. This research focuses on identifying the elements which have a significant influence on an organization's operations due to the remote working of employees during situations like the COVID-19 pandemic. Further, the relationships of factors among each other have been explored from the available research. Based on the study of various organizations it has been found that not much work has been done to identify such factors even though several organizations have suddenly opted for their workforce to work remotely due to the COVID-19 pandemic. This has resulted in lost productivity and opportunities, organizational dis-balances, and a slower rate of development. The generated model may help organizations to understand the weak notes of remote working and implement structural changes accordingly to improve the productivity in remote working and tackle the productivity and opportunity loss due to remote workforce. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Comparison of different estimators of SARS-CoV-2 pandemic activity on geographical and temporal levels.

Background: Studies began investigating occupational exposures as a source of contamination to SARS-CoV-2, yet few considered the variation in SARS-Cov2 pandemic activity for these exposures. Several indicators were built to assess SARS-Cov2 activity though they usually serve a specific purpose and have limitations. The aim was to compare qualitatively different estimators of the SARS-CoV-2 pandemic activity and to create an estimator of pandemic activity level based on daily hospital admissions for job-exposure matrices (JEM) usage. Methods: From publicly available French databases, we retrieved all data from March 19, 2020 (first day available) to March 25, 2021 (day of data collection) on four different estimators: percentage of intensive care bed occupied, reproductive number, tests' positive rate and number positive tests. An indicator based on new daily hospital admissions was created for a COVID JEM. Due to the heterogeneity of the estimators, a qualitative comparison was carried out. Results: During the study period, three major outbreaks took place. Though the number of positive tests was the first indicator to worsen during the 2nd outbreak, it failed to identify variation during the outbreak. Though each indicators behaved differently during the study period, the indicator based on new daily hospital admissions and the positive rate seemed to be the closest to one another. Conclusion: This study highlights the heterogeneity of the indicators used during the first and second SARS-Cov2 outbreaks in France. An indicator based on new daily hospital admissions seems to be a good candidate for estimating SARS-CoV-2 epidemic activity for COVID JEMs and is easily available in countries where usual indicators are not commonly accessible.