Visualising the Optimistic, Realistic, and Pessimistic Financial Distress Outlooks for Airport Operations in Malaysia

This paper aims to visualise three financial distress outlooks using computer simulations. The financial distress exposure for airport operations in Malaysia between 1991 and 2021 is given by Altman Z”-score and modelled by the multivariate generalized linear model (MGLM). Seven determinants contributing to the financial distress from literature are examined. The determinant series are fitted individually by using linear model with time series components and autoregressive integrated moving average models to forecast values for the next 10 financial years. Future short- to long-term memory effects following COVID-19 are apparent in time series plots. In the simulations, the MGLM procedure utilised Gaussian, gamma, and Cauchy probability distributions associated with expectations and challenges of doing business as well as uncertainties in the economy. The underlying trends of realistic, optimistic, and pessimistic financial distress outlooks insinuate that the increasing risk of financial distress of airport operations in Malaysia is expected to continue for the next decade.

FaST-Net: Face Style Self-Transfer Network for Masked Face Inpainting

During the COVID-19 coronavirus epidemic, people usually wear masks to prevent the spread of the virus, which has become a major obstacle when we use face-based computer vision techniques such as face recognition and face detection. So masked face inpainting technique is desired. Actually, the distribution of face features is strongly correlated with each other, but existing inpainting methods typically ignore the relationship between face feature distributions. To address this issue, in this paper, we first show that the face image inpainting task can be seen as a distribution alignment between face features in damaged and valid regions, and style transfer is a distribution alignment process. Based on this theory, we propose a novel face inpainting model considering the probability distribution between face features, namely Face Style Self-Transfer Network (FaST-Net). Through the proposed style self-transfer mechanism, FaST-Net can align the style distribution of features in the inpainting region with the style distribution of features in the valid region of a face. Ablation studies have validated the effectiveness of FaST-Net, and experimental results on two popular human face datasets (CelebA and VGGFace) exhibit its superior performance compared with existing state-of-the-art methods. © 2023 SPIE.

Accelerated Failure Time (Aft) Model: Determining the Factors Associated in the Recovery of Patients Diagnosed with Covid-19

In this survival study, the range on the days of observation is from January 2020 to December 2020 consisting of the patients diagnosed with COVID-19. An accelerated failure time (AFT) model is used to identify covariates associated with recovery time (days from result of test to death/recovery of patients). AFT models with five different distributions (exponential, log-normal, Weibull, generalized gamma, and log-logistic) are generated. Akaike's information criterion (AIC) is used to identify the most suitable model. The total number of patients used in this study is 66142 and is broken into 2116 events and 64026 censored patients. This study shows that generalized gamma having the lowest AIC value made the best fit of the model. The covariates used in determining the factors associated to the recovery of patient are age, sex, admitted and quarantined. The model shows that when patients are being quarantined, the recovery time of patients increases.

Can CO2 be used as an indicator of the probability of cross-infection?

The validity of using CO2 as an indicator of airborne infection probability was studied. Tracer gas measurements were conducted in a field lab with two breathing thermal manikins resembling "infected” and "susceptible” persons seated at desks. The room was ventilated with a mixing air distribution. Experiments were performed at three ventilation rates. CO2 gas was dosed into the air exhaled by the manikins to simulate the metabolic CO2 generation by people. Simultaneously, nitrous oxide (N2O) tracer gas was dosed into the air exhaled by one of the manikins ("infected person”) to simulate the emission of exhaled infectious particles. CO2 and N2O concentrations were measured at several points. The probability of infection was calculated based on the concentration of CO2 and N2O measured in the air inhaled by the exposed manikin ("susceptible person”). The results did not confirm that CO2 can be used as a proxy to assess the infection probability. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Predicting the infection probability distribution due to airborne and droplet transmission

In this study, a method was proposed to predict the infection probability distribution rather than the room-averaged value. The infection probability by airborne transmission was predicted based on the CO2 concentration. The infection probability by droplet transmission was predicted based on occupant position information. Applying the proposed method to an actual office confirmed that it could be used for quantitatively predicting the infection probability by integrating the ventilation efficiency and distance between occupants. The infection probability by airborne transmission was relatively high in a zone where the amount of outdoor air supply was relatively small. The infection probability by droplet transmission varied with the position of the occupants. The ability of the proposed method to analyze the relative effectiveness of countermeasures for airborne transmission and droplet transmission was verified in this study. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Evaluation of infection probability in a multiroom office space using computational fluid dynamics coupled with Wells-Riley model

The current COVID-19 pandemic has highlighted the importance of health safety assessment in various indoor scenarios. Computational fluid dynamics (CFD) combined with a modified Wells-Riley equation provides a powerful tool to analyse local infection probability in an indoor space. Compared to a single infection probability characterising the space in the traditional Wells-Riley model, the coupled approach provides a distribution of infection probability within the space. Furthermore, this approach avoids assuming a well-mixed state, usually related to Wells-Riley equation. This study compares displacement and mixing ventilation strategies with four different ventilation rates to assess the local quanta concentrations modelled using passive scalar transport approach. The simulation results are processed to also account for the effect of wearing masks and vaccinations. The result show that a well-designed displacement ventilation system can significantly reduce infection probability compared to mixing ventilation system at similar airflow rate. Additionally, the results emphasised the importance of wearing mask and getting vaccinated as a means of reducing infection probability. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

New Control Chart Based On Neutrosophic Maxwell Distribution with Decision Making Applications

The neutrosophic approach is a potential area to provide a novel framework for dealing with uncertain data. This study aims to introduce the neutrosophic Maxwell distribution (M̃D) for dealing with imprecise data. The proposed notions are presented in such a manner that the proposed model may be used in a variety of circumstances involving indeterminate, ambiguous, and fuzzy data. The suggested distribution is particularly useful in statistical process control (SPC) for processing uncertain values in data collection. The existing formation of VSQ-chart is incapable of addressing uncertainty on the quality variables being investigated. The notion of neutrosophic VSQchart (Ṽ SQ) is developed based on suggested neutrosophic distribution. The parameters of the suggested Ṽ SQ-chart and other performance indicators, such as neutrosophic power curve (P̃C), neutrosophic characteristic curve (C̃C) and neutrosophic run length (R̃L) are established. The performance of the Ṽ SQ-chart under uncertain environment is also compared to the performance of the conventional model. The comparative findings depict that the proposed Ṽ SQ-chart outperforms in consideration of neutrosophic indicators. Finally, the implementation procedure for real data on the COVID-19 incubation period is explored to support the theoretical part of the proposed model © 2023,Neutrosophic Sets and Systems. All Rights Reserved.

Modeling of dispersion of aerosolized airborne pathogens exhaled in indoor spaces

Since the beginning of the COVID19 pandemic, there has been a lack of data to quantify the role played by breathing-out of pathogens in the spread of SARS-Cov-2 despite sufficient indication of its culpability. This work aims to establish the role of aerosol dispersion of SARS-Cov-2 virus and similar airborne pathogens on the spread of the disease in enclosed spaces. A steady-state fluid solver is used to simulate the air flow field, which is then used to compute the dispersion of SARS-Cov-2 and spatial probability distribution of infection inside two representative classrooms. In particular, the dependence of the turbulent diffusivity of the passive scalar on the air changes per hour and the number of inlet ducts has been given due consideration. By mimicking the presence of several humans in an enclosed space with a time-periodic inhalation-exhalation cycle, this study firmly establishes breathing as a major contributor in the spread of the pathogen, especially by superspreaders. Second, a spatial gradient of pathogen concentration is established inside the domain, which strongly refutes the well-mixed theory. Furthermore, higher ventilation rates and proximity of the infected person to the inlet and exhaust vents play an important role in determining the spread of the pathogen. In the case of classrooms, a ventilation rate equivalent to 9 air changes or more is recommended. The simulations show that the "one-meter distance rule"between the occupants can significantly reduce the risk of spreading infection by a high-emitter. © 2023 Author(s).

Risk assessment using information entropy

Risk assessment, particularly when using simulations, requires that the analyst develops estimates of expected, low, and high values for inputs. Mean and standard deviation are often used to assess the variability of metrics, assuming that the underlying distribution is normal. However, it is increasingly realized that non-normal distributions are common and important. If data are available, it is simple and straightforward to check this assumption by computing higher order moments.Claude Shannon [1], [2] proposed that the information entropy for a set of N discrete events can be measured by (Formula Presented) E. T. Jaynes [3] proposed that, if data is available, information entropy can be maximized using Lagrangian multipliers and that the resulting probability distribution maximizes the uncertainty of that distribution given the data.In order to use entropy maximization, it is required to define constraints such that Σpi = 1, plus constraints on the mean, variance, skew, kurtosis, and other moments. This problem does not have a closed form solution but can be solved iteratively in a spreadsheet.The problem can be set up as follows for mean bar x and variance s2: (Formula Presented) This basic formulation models the normal distribution. The importance of non-normality can be estimated by adding higher order moments as desired. For n ≥ 3, constraints can be added using: (Formula Presented) where Mn is the computed nth moment of the data set.Differentiating ∂H/∂pi = 0 maximizes information entropy, and the resulting probability distribution has the most uncertainty given the observed data.This suggests that it is possible to develop an estimate of the distribution where some values are underrepresented in the sample. It further suggests that unusual or atypical results can be better estimated.This paper uses the method of maximizing entropy to model observed data and will study two time series applications. One problem of interest is sequential acquisition of data. For example, time to failure for a device may be a metric of concern. A maximum entropy model provides an empirical estimate of the distribution of this metric. A second problem of interest is forecasting the distribution of a metric at some point in the future. This applies to supply chain management. Project sponsors prepare cost and schedule estimates well in advance of placing the orders for the materials used in those projects. Management reserves for cost and schedule are typically set by subject matter experts, and recent experience (e.g., supply chain disruptions due to the COVID19 pandemic) may overemphasize current data when developing risk assessments. This approach offers a datadriven way to empirically develop risk assessments. © 2023 IEEE.

Leakage in air handling units, the effects on the transmission of airborne infections

The possibility of unfavorable leakages, especially with infectious diseases, in heat recovery systems in air handling units (AHU) is an essential issue. Typical configurations of AHU are analyzed in this aspect, based on their pressure distribution. It is shown that analyzing only for the design conditions is insufficient and that the changing pressure drops of the air filters due to their nonuniform soiling should be taken into account. The novelty of this paper is in proposed method of considering these leaks in the Wells-Riley model, widely used in the literature for airborne transmission of infectious diseases, including the leakage correction factor fhrleak (outdoor fresh air correction factor) based on EATR (exhaust air transfer ratio). Using the proposed method, for typical rooms, on the example of the SARS-CoV-2 virus and its Delta and Omicron variants, it is shown that considering leaks in heat recovery systems in AHU increases the probability of pathogen transmission. The highest increase in the absolute value of the probability of infection is observed in the single office scenario (4.1%) and in the auditorium with a sick speaker scenario (2.7%). The highest increase in reproduction number is observed in the auditorium with a sick speaker scenario (2.69). Such significant changes in reproduction number, including its change from R < 1.0 to R > 1.0 (auditorium with sick speaker for Delta variant of the virus), are crucial from the point of view of considering event scenarios;they slow down or accelerate the pandemic. © 2023 Elsevier Ltd

Visualization of the Risk and the Care Effects on the Viral Contact Infection in the Home

With the spread of COVID-19, the risk of droplet infection has been studied through interdisciplinary research. However, there is little information on the spread of the pathogen through human contact behavior. In this paper, we focus on the home, which is the private space of people, and propose a model to visualize the risk of contact infection to a family when people return home by combining calculation of contact behavior after returning home and study of virus transfer efficiency. First, from the contact behavior data for the first 30 minutes after returning home, we calculated the probability of flow line, the distribution of the number of contacts, the probability of initial action and the probability of contact behavior transmission. Next, we obtained the transfer efficiency between the substrate representing the household goods surface and the model skin, and the rate of change of the viral transfer efficiency when people continuously contact the household goods surface. According to these probabilities, we reproduced the state in which the virus attached to the hand or household goods surface by probabilistically performing the agent's movement and contact behavior after returning home. This result shows that when agents return home with viruses attached to their hands, the viruses are widely confirmed on household goods surfaces. Furthermore, by simulating the combination and timing of hygienic actions such as handwashing and disinfection, it was possible to visualize their effects on the risk of re-contact and care effects. © 2023, Japanese Society for Artificial Intelligence. All rights reserved.

Integrated supplier selection, scheduling, and routing problem for perishable product supply chain: A distributionally robust approach

Due to the global outbreak of COVID-19, the perishable product supply chains have been impacted in different ways, and consequently, the risks of food insecurity have been increased in many affected countries. The uncertainty in supply and demand of perishable products, are among the most influential factors impacting the supply chain networks. Accordingly, the provision and distribution of food and other perishable commodities have become much more important than in the past. In this study, a bi-objective optimization model is proposed for a three-echelon perishable food supply chain (PFSC) network with multiple products to formulate an integrated supplier selection, production scheduling, and vehicle routing problem. The proposed model aims to mitigate the risks of demand and supply uncertainties and reinforce the distribution-related decisions by simultaneously optimizing the total network costs and suppliers' reliability. Using the distributionally robust modeling paradigm, the probability distribution of uncertain demand is assumed to belong to an ambiguity set with given moment information. Accordingly, distributionally robust chance-constrained approach is applied to ensure that the demands of retailers and capacity of vehicles are satisfied with high probability. Leveraging duality and linearization techniques, the proposed model is reformulated as a mixed-integer linear program. Then, the weighted goal programming approach is adopted to address the multi-objectiveness of the proposed optimization model. To certify the performance and applicability of the model, a real-world case study in the poultry industry is investigated. Finally, the sensitivity analysis is conducted to evaluate the impacts of influential parameters on the objective functions and optimal decisions, and then some managerial insights are provided based on the obtained results. © 2022 Elsevier Ltd

THE INFLUENCE OF COVID RELATED VENTILATION RATE CHANGES ON THE ENERGY CONSUMPTION AND INFECTION PROBABILITY OF THE BUILDINGS: UNDERFLOOR AND OVERHEAD AIR DISTRIBUTION SYSTEMS

The COVID-19 pandemic has urged the need to reconsider how our built environments influence our health conditions. The new guidelines have highlighted the importance of environmental settings in the virus transmission process. Given that external air ventilation is a major element of a building's energy performance, it is necessary to investigate the influence of the new settings on the building's energy consumption. This study aims to determine the energy performance and infection risk of underfloor air distribution UFAD and overhead systems OH when exposed to varying levels of external air ventilation. The findings indicate that raising the rate of outside ventilation increases a building's energy usage in all climates. It is also shown that the UFAD system shows its energy-saving potential the most in cold climates and higher ventilation rates. These findings suggest that it is critical to consider distinct ventilation techniques to prevent rising energy consumption rates while lowering the risk of viral transmission. © 2022 Society for Modeling & Simulation International (SCS)

A Probabilistic Approach to Evaluate the Likelihood of Artificial Genetic Modification and Its Application to SARS-CoV-2 Omicron Variant

A method to find a probability that a given bias of mutations occur naturally is proposed to test whether a newly detected virus is a product of natural evolution or a product of non-natural process such as genetic manipulation. The probability is calculated based on the neutral theory of molecular evolution and binominal distribution of non-synonymous (N) and synonymous (S) mutations. Though most of the conventional analyses, including dN/dS analysis, assume that any kinds of point mutations from a nucleotide to another nucleotide occurs with the same probability, the proposed model takes into account the bias in mutations, where the equilibrium of mutations is considered to estimate the probability of each mutation. The proposed method is applied to evaluate whether the Omicron variant strain of SARS-CoV-2, whose spike protein includes 29 N mutations and only one S mutation, can emerge through natural evolution. The result of binomial test based on the proposed model shows that the bias of N/S mutations in the Omicron spike can occur with a probability of 2.0 × 10−3 or less. Even with the conventional model where the probabilities of any kinds of mutations are all equal, the strong N/S mutation bias in the Omicron spike can occur with a probability of 3.7 × 10−3, which means that the Omicron variant is highly likely a product of non-natural process including artifact. © 2022 Information Processing Society of Japan.

Joint Resource Scheduling for UAV-Enabled Mobile Edge Computing System in Internet of Vehicles

The sudden outbreak of COVID-19 brings many unpredictable situations to human travel, such as temporarily closed highways, parking lots, etc. The scenarios mentioned above will lead to a large backlog of vehicles, and the requirements of Internet of vehicle (IoV) applications increase sharply in a period of short time correspondingly. Mobile edge computing (MEC) is a key enabling technology that can guarantee the diverse requirements of IoV applications through the optimization of resource scheduling. However, the sharp increasing in requirements of IoV applications caused by the congestion of highways or parking lots still bring great challenges to the deployment of traditional MEC. Therefore, in this paper, we construct an unmanned aerial vehicle (UAV) enabled MEC system, in which the data generated from IoV applications is processed by offloading to UAVs with MEC servers to ensure the efficiency of data processing and the response time of IoV applications. In order to approximate real-world UAV enabled MEC system, we consider the stochastic offloading and downloading processing time. Moreover, the priority constraints of sensors from the same vehicle are taken into consideration since they have different importance degrees. Then, we propose an Markov network-based cooperative evolutionary algorithm (MNCEA) to search out the optimal UAV scheduling solution to guarantee the shortest response time, in which the solution space is divided into multiple sub-solution spaces with the help of MN structure and parameters. Finally, we construct multiple simulation experiments with different probability distributions to simulate uncertainty factors. The simulation results verify the validity of MNCEA compared with the state-of-the-art methods, which is reflected by the shortest response time of requirements of IoV applications IEEE

The Influence Of Covid Related Ventilation Rate Changes On The Energy Consumption And Infection Probability Of The Buildings: Underfloor And Overhead Air Distribution Systems

The COVID-19 pandemic has urged the need to reconsider how our built environments influence our health conditions. The new guidelines have highlighted the importance of environmental settings in the virus transmission process. Given that external air ventilation is a major element of a building's energy performance, it is necessary to investigate the influence of the new settings on the building's energy consumption. This study aims to determine the energy performance and infection risk of underfloor air distribution UFAD and overhead systems OH when exposed to varying levels of external air ventilation. The findings indicate that raising the rate of outside ventilation increases a building's energy usage in all climates. It is also shown that the UFAD system shows its energy-saving potential the most in cold climates and higher ventilation rates. These findings suggest that it is critical to consider distinct ventilation techniques to prevent rising energy consumption rates while lowering the risk of viral transmission. © 2022 SCS.

Investigating Hill's question for some probability distributions

Benford's law asserts that the lower first significant digit (FSD) occurs more frequently than the higher FSD in naturally produced datasets. The applications of the law vary from detecting election, tax, and Covid-19 data fraud to checking abnormalities in the stock market. Hence, it is vital to know which common probability distributions satisfy Benford's law, which is called Hill's question. Many research studies have been performed to answer this question by using various methods. The purpose of the work is to give a more simple and intuitive method to address the question for some common probability distributions. Moreover, statistical simulation is adopted to test their conformity to Benford's law. © 2022 Author(s).

Stochastic Modelling for Coronavirus (COVID’19) Pandemic in Bulgaria

The aim of this paper is to present the development and improvements done in the specific stochastic branching model during the progress of the COVID’19 pandemic caused by SARS-CoV-2 coronavirus up to spring of the year 2022. Our approach is data-driven and uses the parsimonious continuous time Crump-Mode-Jagers branching processes (CMJBP) model. The model provides a basis for decision makers to understand the likely trade-offs as an outbreak begins. © 2022 Copyright for this paper by its authors.

Probability Distributions for Modeling of COVID-19 cases and deaths in Thailand

The COVID-19 is a pandemic and continues to mutate and spread within Thailand and throughout the world. Recently, Omicron is a new COVID-19 variant of concern because it has several mutations that may have an impact on how it behaves. It is therefore important to understand COVID-19 dynamics in order to prevent or control infections appropriately. In this study, we analyzed a model of the daily number of COVID-19 cases and deaths in Thailand using five different probability distributions. Maximum likelihood estimation (MLE) is applied to estimate parameters of the five distributions. The results indicate that the Weibull distribution and the log-normal distribution are the most suitable distributions that fit the data on daily confirmed cases and on daily confirmed deaths, respectively, by using the Akaike information criterion (AIC) and the Bayes information criterion (BIC). © 2022. International Journal of Mathematics and Computer Science. All Rights Reserved.

A Relationship Between the SIR Model and the Generalized Logistic Distribution with Applications to SARS and COVID-19

This paper shows that the generalized logistic distribution model is derived from the well-known compartment model, consisting of susceptible, infected and recovered compartments, abbreviated as the SIR model, under certain conditions. In the SIR model, there are uncertainties in predicting the final values for the number of infected population and the infectious parameter. However, by utilizing the information obtained from the generalized logistic distribution model, we can perform the SIR numerical computation more stably and more accurately. Applications to severe acute respiratory syndrome (SARS) and Coronavirus disease 2019 (COVID-19) using this combined method are also introduced. © 2021 IEEE.