A New Approach for Forecasting Crude Oil Prices Based on Stochastic and Deterministic Influences of LMD Using ARIMA and LSTM Models

Crude oil is one of the non-renewable power sources and is the lifeblood of the contemporary industry. Every significant change in the price of crude oil (CO) will have an effect on how the global economy, including COVID-19, develops. This study developed a novel hybrid prediction technique that depends on local mean decomposition, Autoregressive Integrated Moving Average (ARIMA), and Long Short-term Memory (LSTM) models to increase crude oil price prediction accuracy. The original data is decomposed by local mean decomposition (LMD), and the decomposed components are reconstructed into stochastic and deterministic (SD) components by average mutual information to reduce the computation cost and enhance forecasting accuracy, predict each individual reconstructed component by ARIMA, and integrate the residuals with LSTM to capture the nonlinearity in residuals and help to find the final prediction result. The new hybrid model LMD-SD-ARIMA-LSTM has reduced the volatility and solved the issue of the overfitting problem of neural networks. The proposed hybrid technique is validated using publicly accessible data from the West Texas Intermediate (WTI), and forecast accuracy are compared using accuracy measures. The value of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) for ARIMA, LSTM, LMD-ARIMA, LMD-SD-ARIMA, LMD-ARIMA-LSTM, LMD-SD-ARIMA-LSTM, and Naïve are 1.00, 1.539, 5.289, 0.873, 0.359, 0.106, 4.014 and 2.165, 1.832, 9.165, 1.359, 1.139, 1.124 and 3.821 respectively. From these results, it is concluded that the proposed model LMD-SD-ARIMA-LSTM has minimum values for MAE and MAPE which assured the superiority of the proposed model in One-step ahead forecasting. Moreover, forecasting performance is also compared up to five steps ahead. The findings demonstrate that the suggested approach is a helpful tool for predicting CO prices both in the short and long term. Furthermore, the current study reduces labor costs by combing the stationary and non-stationary Product Functions (PFs) into stochastic and deterministic components with improved accuracy. Meanwhile, the traditional econometric model can strengthen the prediction behavior of CO prices after decomposition and reconstruction, and the new hybrid forecasting method has better performance in medium and long-term forecasting of the CO price. Moreover, accurate predictions can provide reasonable advice for relevant departments to make correct decisions. © 2013 IEEE.

Mathematical Model and Analysis on the Impacts of Vaccination and Treatment in the Control of the COVID-19 Pandemic with Optimal Control

In this study, a nonlinear deterministic mathematical model that evaluates two important therapeutic measures of the COVID-19 pandemic: vaccination of susceptible and treatment for infected people who are in quarantine, is formulated and rigorously analyzed. Some of the fundamental properties of the model system including existence and uniqueness, positivity, and invariant region of solutions are proved under a certain meaningful set. The model exhibits two equilibrium points: disease-free and endemic equilibrium points under certain conditions. The basic reproduction number, R0, is derived via the next-generation matrix approach, and the dynamical behavior of the model is explored in detail. The analytical analysis reveals that the disease-free equilibrium solution is locally as well as globally asymptotically stable when the associated basic reproduction number is less than unity which indicates that COVID-19 dies out in the population. Also, the endemic equilibrium point is globally asymptotically stable whenever the associated basic reproduction number exceeds a unity which implies that COVID-19 establishes itself in the population. The sensitivity analysis of the basic reproduction number is computed to identify the most dominant parameters for the spreading out as well as control of infection and should be targeted by intervention strategies. Furthermore, we extended the considered model to optimal control problem system by introducing two time-dependent variables that represent the educational campaign to susceptibles and continuous treatment for quarantined individuals. Finally, some numerical results are illustrated to supplement the analytical results of the model using MATLAB ode45. © 2023 Alemzewde Ayalew et al.

Predicting the Performance of Highway Project Using Gray Numbers

Earned Value Analysis is a methodology used to monitor project performance in terms of time, scope and cost and also to deal with uncertain situations that come within. Uncertainty is a part of construction project and sometimes these situations can cause a great loss in the project's success. Recently, to deal with uncertain situations a different approach has been developed to predict the project performance in a non-deterministic way, i.e., using gray interval numbers. A framework using gray interval numbers has been developed to predict the project performance and hence this study aims at using the framework to predict the performance of a real-life highway project of total duration of approximately 2 years. The analysis involves the verbal directed data from the site by the experts which were denoted as gray interval numbers. The results indicate that the project is under budget as the CPI is 1.06 and ahead of schedule as the SPI is 1.2. The results also show the worst case scenario that the project may exceed the budget as CPI is 0.83 and may run behind the schedule as SPI is 0.69. The outcomes of the study are in the form of range which provides the overall profile of the project and also helps the project team members to not always be accurate or deterministic with the outcomes. Since the construction sector was majorly hit by an uncertain event, i.e., COVID-19, this study can be very helpful in determining the performance after facing such a huge gap. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Calibrating deterministic compartmental models of infection dynamics using neural network and data sampling approaches

Pandemics caused by the new coronavirus has spread globally with a strong contagion rate and death rate. In this paper the deterministic SEIR model is calibrated with Metropolis Hasting algorithm, physics-informed neural network (PINN) and latin hypercube sampling (LHS) method to identify the optimal hyper parameters of SEIR model and to forecast the dynamics of COVID-19 incidence in Saint-Petersburg, Russia, its retrospective analysis and evaluation of the effectiveness of control measures. © 2022 IEEE.

Caps-3WD: A three-way diagnostic CapsNet for COVID-19

Pulmonary medical image processing is an effective diagnostic method for COVID-19, and CapsNet-based methods have achieved good performance. However, as cost-blind methods, these diagnostic methods only consider immediate and deterministic decisions, which easily lead to misdiagnosis and high costs. Therefore, based on a revised CapsNet, we propose a cost-sensitive three-way decision (3WD) method for COVID-19 diagnosis, named as Caps-3WD. To enhance the feature extraction ability for pneumonia areas, we introduce a Restage module to improve convolution layer of the original CapsNet. Further, to lighten the model, we introduce depth wise separable convolution to reconstruct decoder. Additionally, three options are considered in the decision set: infected, normal, and suspected, which are given different costs, respectively. The lowest-cost decision is chosen for each input. In the experimental analysis, we compare Caps-3WD with CNN-based and CapsNet-based methods on COVID-CXR dataset, which proves the effectiveness of 3WD and the superiority of Caps-3WD in COVID-19 diagnosis. © 2022 SPIE. Downloading of the is permitted for personal use only.

Why Lockdowns Are Futile and Face Masks Effective in Slowing down COVID-19-Like Pandemic — Quantitative Analysis with Compartmental Model

There exist a number of complex and often nonlinear phenomena in physical and biophysical systems that can be efficiently approached by systems of differential equations. Even though the deterministic character of the solutions is typically not adequate to describe the actual behaviour of given systems, this approach is very well suited to describe the influence of the well-defined factors on the evolution of the systems described by properly chosen dynamical models. For example, a compartmental model with three groups of people (susceptible, infected, and recovered) is able to capture some of the general principles related to the dynamics of a pandemic in a biophysical system such as the human population. Here, motivated by the ongoing COVID-19 pandemic, with the help of a proper generalisation of the simple model, we analyse influence and efficacy of commonly invoked counter-pandemic actions — lockdowns and mandatory face masks — in reducing the number of fatalities. To reach this goal, our model takes into account the number of hospitalised persons and the fraction of those hospitalised who need special treatment in intensive care units. We show that even if there is an optimal time for introduced lockdowns to be effective, it is impossible to reach in practice due to the limited capacity of the health system. The calculations indicate that wearing face masks decreases the number of hospitalised people and the total death toll. Half of the population appropriately wearing masks, even the home-made ones (with an efficacy of only about 60%), would halve the peak value of those needing intensive medical treatment. Our study indicates a slightly greater effectiveness of masks worn by healthy people, which is related to the fact that ill people do not protect themselves. © 2022 Polish Academy of Sciences. All rights reserved.

An Adaptive algorithm to predict bad debts during COVID-19 Pandemic

Loan recovery during the COVID-19 pandemic is anxious. Automated decision-making would boost the identification of bad debts while issuing loans. The objective of the proposed work is thus to design and implement an adaptive algorithm, which will be used to predict bad debts. Machine learning is an artificial intelligence technology, which gives systems the ability to automatically learn and improve from experience without explicit programming. The adaptive algorithm proposed is deterministic, uses two parameters known as neighborhood distance and minimum support threshold value for the risk profile, and can be very useful in predicting bad debts. It produces overlapped as well as non-overlapped clusters. This algorithm can detect the outliers with the help of an adaptive threshold value for the object's risk profile attribute. Objects with a moderately high or high value of risk profile attribute may emerge as outliers, and these outliers can be known as bad debts. The clusters generated are labeled as paid fully, not paid fully, and not paid. It can also generate clusters of different sizes. The proposed adaptive deterministic algorithm clusters the dataset without knowing the number of clusters. Many clusters are generated using this algorithm, but the parameter risk profile minimum threshold value prunes the clusters being formed. This proposed adaptive algorithm is testedusing real and artificial data sets and shows 83% accuracy in bad debt prediction. © 2022 IEEE

Montecarlo Simulation in COVID-19 Infection Scenarios to Forecast Industrial Labor Suspensions in Mexico

In the present investigation, the Monte Carlo simulation was applied to the COVID-19 contagion scenario in Mexico. This to evaluate a possible suspension from work through the use of the deterministic SIR model. The research was developed using a descriptive model, since it is only intended to expose the operation of the system. The random variable considered was the infection rate, also known as the basic reproduction number R0, which fluctuates between 0.5 and 2.5. A dynamic transmission rate and a constant recovery rate were applied in the investigation. An Excel spreadsheet was used, and the data obtained was plotted. The data taken for the simulation was from February 20, 2020, to January 31, 2021. The result reflected a significant difference between the historical data and the data obtained in the simulation, this due to the behavior of the dynamic variables that indicated an approximate error of 6,600,000. It can also be observed that the infected cases obtained from the simulation maintain a positive slope, therefore, there is the possibility that this variable will continue to grow. It is worth mentioning that for there to be a work suspension, it was considered that the average R0 was greater than 1.79, considering this as an intermediate value when industrial work was suspended in Mexico. The result obtained from the average R0 was 1.43, which promises a considerable decrease in infections and in view of the restriction, it was concluded that there is no new work suspension in Mexico due to COVID-19. But considering that R0 is greater than 1, there is latency of infections, therefore, preventive measures must be maintained. © 2021 IEEE.

Deep Reinforcement Learning Algorithms for Multi-Agent Systems-A Solution for Modeling Epidemics

Multi-agent reinforcement learning (MARL) consists of large number of artificial intelligence-based agents interacting with each other in the same environment, often collaborating towards a common end goal. In single-agent reinforcement learning system the change in the environment is only due to the actions of a particular agent. In contrast, a multi-agent environment is subject to the actions of all the agents involved. Multiagent systems can be deployed in various applications like stock trading to maximize profits in stock market, control and coordination of a swarm of robots, modeling of epidemics, autonomous vehicle and traffic control, smart grids and self-healing networks. It is not possible to solve these complex tasks with a pre-programmed single agent. Instead, the many agents should be trained to automatically search for a solution through reinforcement learning (RL) based algorithms. In general, arriving at a decision in a multi-agent system is almost close to impossible due to exponential increase of problem size with an increase in the number of agents. In this paper, multi-agent systems using Deep Reinforcement Learning (DRL) is explored with a possible application in modeling of epidemics. Different stochastic environments are considered, and various multi-agent policies are implemented using DRL. The performance of various MARL algorithms was evaluated against single agent RL algorithms under different environments. MARL agents were able to learn much faster compared to single RL agents with a more stable training phase. Mean Field Q-Learning was able to scale and perform much better even in the situation of hundreds of agents in the environment and is a sure candidate to model and predict the epidemics, in the existing frightening dangerous situation of corona pandemic. © 2021 IEEE.

The value of probabilistic forecasting in emergency medical resource planning under uncertainty

Purpose: Most epidemic transmission forecasting methods can only provide deterministic outputs. This study aims to show that probabilistic forecasting, in contrast, is suitable for stochastic demand modeling and emergency medical resource planning under uncertainty. Design/methodology/approach: Two probabilistic forecasting methods, i.e. quantile regression convolutional neural network and kernel density estimation, are combined to provide the conditional quantiles and conditional densities of infected populations. The value of probabilistic forecasting in improving decision performances and controlling decision risks is investigated by an empirical study on the emergency medical resource planning for the COVID-19 pandemic. Findings: The managerial implications obtained from the empirical results include (1) the optimization models using the conditional quantile or the point forecasting result obtain better results than those using the conditional density;(2) for sufficient resources, decision-makers' risk preferences can be incorporated to make tradeoffs between the possible surpluses and shortages of resources in the emergency medical resource planning at different quantile levels;and (3) for scarce resources, the differences in emergency medical resource planning at different quantile levels greatly decrease or disappear because of the existing of forecasting errors and supply quantity constraints. Originality/value: Very few studies concern probabilistic epidemic transmission forecasting methods, and this is the first attempt to incorporate deep learning methods into a two-phase framework for data-driven emergency medical resource planning under uncertainty. Moreover, the findings from the empirical results are valuable to select a suitable forecasting method and design an efficient emergency medical resource plan. © 2022, Emerald Publishing Limited.

2AI&7D Model of Resistomics to Counter the Accelerating Antibiotic Resistance and the Medical Climate Crisis

The antimicrobial resistance (AMR) crisis is referred to as ‘Medical Climate Crisis’. Inappropriate use of antimicrobial drugs is driving the resistance evolution in pathogenic microorganisms. In 2014 it was estimated that by 2050 more people will die due to antimicrobial resistance compared to cancer. It will cause a reduction of 2% to 3.5% in Gross Domestic Product (GDP) and cost the world up to 100 trillion USD. The indiscriminate use of antibiotics for COVID-19 patients has accelerated the resistance rate. COVID-19 reduced the window of opportunity for the fight against AMR. This man-made crisis can only be averted through accurate actionable antibiotic knowledge, usage, and a knowledge driven Resistomics. In this paper, we present the 2AI (Artificial Intelligence and Augmented Intelligence) and 7D (right Diagnosis, right Disease-causing-agent, right Drug, right Dose, right Duration, right Documentation, and De-escalation) model of antibiotic stewardship. The resistance related integrated knowledge of resistomics is stored as a knowledge graph in a Neo4j properties graph database for 24 × 7 access. This actionable knowledge is made available through smartphones and the Web as a Progressive Web Applications (PWA). The 2AI&7D Model delivers the right knowledge at the right time to the specialists and non-specialist alike at the point-of-action (Stewardship committee, Smart Clinic, and Smart Hospital) and then delivers the actionable accurate knowledge to the healthcare provider at the point-of-care in realtime. © 2021, Springer Nature Switzerland AG.

The Conjunction of Deterministic and Probabilistic Events in Realistic Scenarios of Outdoor Infections

The aim of this paper is the derivation of an robust formalism that calculates the so-called social distancing as already determined in the ongoing Corona Virus Disease 2019 (Covid-19 in short) being established in various places in the world between 1.5 m and 2.5 m. This would constitutes a critic space of separation among people in the which aerosols might not be effective to infect healthy people. In addition to wearing masks and face protection, the social distancing appears to be critic to keep people far of infections and consequences produced from it. In this way, the paper has opted by the incorporation of a full deterministic model inside the equation of Weiss, by the which it fits well to the action of outdoor infection when wind manages the direction and displacement of aerosols in space. Thus, while a deterministic approach targets to propose a risk’s probability, a probabilistic scenario established by Weiss in conjunction to the deterministic events would yield an approximated model of outdoor infection when there is a continuous source of infected aerosols that are moving through air in according to a wind velocity. The simulations have shown that the present approach is valid to some extent in the sense that only the 1D case is considered. The model can be extended with the implementation of physical variables that can attenuate the presence of disturbs and random noise that minimizes the effectiveness of present proposal. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Structure preserving algorithm for fractional order mathematical model of COVID-19

In this article, a brief biological structure and some basic properties of COVID-19 are described. A classical integer order model is modified and converted into a fractional order model with ξ as order of the fractional derivative. Moreover, a valued structure preserving the numerical design, coined as Grunwald–Letnikov non-standard finite difference scheme, is developed for the fractional COVID-19 model. Taking into account the importance of the positivity and boundedness of the state variables, some productive results have been proved to ensure these essential features. Stability of the model at a corona free and a corona existing equilibrium points is investigated on the basis of Eigen values. The Routh–Hurwitz criterion is applied for the local stability analysis. An appropriate example with fitted and estimated set of parametric values is presented for the simulations. Graphical solutions are displayed for the chosen values of ξ (fractional order of the derivatives). The role of quarantined policy is also determined gradually to highlight its significance and relevancy in controlling infectious diseases. In the end, outcomes of the study are presented. © 2022 Tech Science Press. All rights reserved.

Surface temperature distribution aluminum block with cone tube profile calculated using ANSYS fluent

Thermal Cycler is the main part of the Polymerase Chain Reaction (PCR), which becoming a gold standard for Covid-19 diagnosis. The virus multiplication in an order to a detectable concentration is done by placing the virus solution at a deterministic temperature cycle. The solution is placed in a small tube inserted in a temperature block. Temperature distribution of the thermal block is important to make all the tube with sample treated at the same at desired target temperature. Study on the thermal block made of aluminium 7075 was simulated using fluid dynamic finite element method. Heating and colling to the target temperature was done by providing heat source and heat absorber. The temperature distribution on the surface was mapped. The temperature gradient perpendicular to the heat source was calculated. Assuming the environment of the thermal block was still air, the heating and cooling speed at given heat source and heat removal were calculated using the model. The temperature gradient from the top surface to the bottom surface is less than 2.5?. The temperature difference among point at the surface is less than 0.1?. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence.