PLANT TESTING MAGNETIC CONDITIONING ON KENNECOTT'S Cu-Mo PORPHYRY ORE

Over it's more than 100-year history, the Kennecott operation has often been at the forefront of innovation;driven by the demands of the lower grade ore-body and the higher costs of operating in the US where wages are generally higher and regulation more restrictive. One way of reducing operating costs in c/lb is to increase the lbs produced at minimal cost. Despite the relatively coarse grind at Kennecott - about 30% >150μm, approximately 20% of the Cu lost to tail is liberated chalcopyrite in the <20μm fraction, and about 30%-40% in the <37μm fraction. In 2020 Kennecott undertook a detailed plant scale test of the magnetic aggregation technology to increase copper recovery by reducing fine copper losses. A paired statistical plant test of magnetic conditioning on one rougher line showed a 1.12% increase in Cu recovery to 97% statistical confidence. The next challenge, unforeseen at the start of the project, was the fabrication and transportation to site of the equipment for the three remaining rougher rows, during the severe supply-chain constraints of the Covid pandemic in 2021. This resulted in delays and unforeseen costs as world-wide transportation became chaotic, particularly transportation via west coast USA. Nevertheless, the project was completed and commissioned, with only minor delays and cost increases, due to a flexible approach to overcoming the hurdles encountered. Copyright © 2023 by SME.

A Time Series Forecast of COVID-19 Infections, Recoveries and Fatalities in Nigeria

The study investigated COVID-19 pandemic infections, recoveries, and fatalities in Nigeria to forecast future values of infections, recoveries, and fatalities and thus ascertain the extent to which the pandemic appeared to be converging with time. The prediction of COVID-19 infections, recoveries, and fatalities was necessitated by the impact that the pandemic had exerted in world economies since its outbreak in late 2019. The quantitative method was employed, and a longitudinal research design was applied. Data were obtained from the Nigeria Centre for Disease Control (NCDC). The least-squares test and autoregressive distributed lag (ARDL) tests were performed to forecast infections, recoveries, and fatalities. The results of the predicted infections for the last five months of the year (August–December 2020) shows that the cases of infections will narrow down within the period. The need for policymakers to implement complete unlocking of the economy for speedy economic recovery was suggested, among others.

Risk Modeling Framework for Strategic and Operational Intervention to Enhance the Effectiveness of a Closed-Loop Supply Chain

In a closed-loop supply chain (CLSC), acquiring end-of-life vehicles (ELVs) and their components from both primary and secondary markets has posed a huge uncertainty and risk. Moreover, the constant supply of ELV components with minimization of cost and exploitation of natural resources is another pressing challenge. To address the issues, the present study has developed a risk simulation framework to study market uncertainty/risk in a CLSC. In the first phase of the framework, a total of 12 important variables are identified from the existing studies. The total interpretive structural model (TISM) is used to develop a causal relationship network among the variables. Then, Matriced Impacts Cruoses Multiplication Applique a un Classement is used for determining the nature of relationships (i.e., driving or dependence power). In the second phase, the relationship of TISM is used to derive a Bayesian belief network model for determining the level of risks (i.e., high, medium, and low) associated with the CLSC through the generation of conditional probabilities across 1) multi-, 2) single-, and 3) without-parent nodes. The study findings will help decision-makers in adopting strategic and operational interventions to increase the effectiveness and resiliency of the network. Furthermore, it will help practitioners to make decisions on change management implementation for stakeholders'performance audits on the attributes of the ELV recovery program and developing resilience in the CLSC network. Overall, the present study holistically contributes to a broader investigation of the implications of strategic decisions in automobile manufacturers and resellers. IEEE

The Approach of Machine Learning to Optimize the Bank-Customer Interaction at Pandemic Epochs

Along the pandemic created by the Corona virus 2019 (Covid-19 in shorthand), the global economy was observed to experience various turbulent months that were reflected by the increasing of unemployment and the apparition of a procrastinator behavior in all those customers that received a loan at the months before the beginning of pandemic. Because the apparition of pandemic was totally random, it had effects on the micro-economy that in most cases have turned out on the cuts of salaries. From a basic modeling of loan and Gaussian approach, the criteria of Mitchell are employed. The resulting simulations have yielded that up to a 50% of loaned volume of cash would be recovery. It was found that entropic situations would be in part a cause for the deficient management of loans in epochs of pandemic and crisis. © 2022 IEEE.

A Tabu-Based Multi-objective Particle Swarm Optimization for Irregular Flight Recovery Problem

Air transportation is eminent for its fast speed and low cargo damage rate among other ways. However, it is greatly limited by emergent factors like bad weather and current COVID-19 epidemic, where irregular flights may occur. Confronted with the negative impact caused by irregular flight, it is vital to rearrange the preceding schedule to reduce the cost. To solve this problem, first, we established a multi-objective model considering cost and crew satisfaction simultaneously. Secondly, due to the complexity of irregular flight recovery problem, we proposed a tabu-based multi-objective particle swarm optimization introducing the idea of tabu search. Thirdly, we devised an encoding scheme focusing on the characteristic of the problem. Finally, we verified the superiority of the tabu-based multi-objective particle swarm optimization through the comparison against MOPSO by the experiment based on real-world data. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Human capital heterogeneity of the unemployed and jobless recoveries

The jobless recovery enigma remains largely unsolved. As a special case of broader unemployment, the term "jobless recovery” describes an economic recovery where output recovers—and even expands—yet employment growth remains anemic. While the effects of these prolonged recoveries are significant—from increased crime to a lifetime reduction in wages—they are not well understood. Building on the insights of labor market matching models that incorporate heterogeneity among workers, this paper sheds light on jobless recoveries, developing a first-of-its-kind index of human capital heterogeneity for the unemployed, and testing that index using of a Structural Vector Autoregression. I demonstrate that the extent to which unemployed human capital is heterogeneous and specific, rather than homogeneous and general, plays a key and under-appreciated role in the labor market;increases in human capital heterogeneity can account for between one-quarter to three-quarters of the joblessness of the past three recoveries in the pre-COVID era.

Soft computing techniques for forecasting of COVID-19 in Pakistan

Novel Pandemic COVID-19 led globally to severe health barriers and financial issues in different parts of the world. The forecast on COVID-19 infections is significant. Demeanor vital data will help in executing policies to reduce the number of cases efficiently. Filtering techniques are appropriate for dynamic model structures as it provide reasonable estimates over the recursive Bayesian updates. Kalman Filters, used for controlling epidemics, are valuable in knowing contagious infections. Artificial Neural Networks (ANN) have generally been used for classification and forecasting problems. ANN models show an essential role in several successful applications of neural networks and are commonly used in economic and business studies. Long short-term memory (LSTM) model is one of the most popular technique used in time series analysis. This paper aims to forecast COVID-19 on the basis of ANN, KF, LSTM and SVM methods. We applied ANN, KF, LSTM and SVM for the COVID-19 data in Pakistan to find the number of deaths, confirm cases, and cases of recovery. The three methods were used for prediction, and the results showed the performance of LSTM to be better than that of ANN and KF method. ANN, KF, LSTM and SVM endorsed the COVID-19 data in closely all three scenarios. LSTM, ANN and KF followed the fluctuations of the original data and made close COVID-19 predictions. The results of the three methods helped significantly in the decision-making direction for short term strategies and in the control of the COVID-19 outbreak. © 2022 Faculty of Engineering, Alexandria University

Recovery Algorithms for Pooled RT-qPCR based Covid-19 Screening

We consider the problem of sparse signal recovery in a non-adaptive pool-test setting using quantitative measurements from a non-linear model. The quantitative measurements are obtained using the reverse transcription (quantitative) polymerase chain reaction (RT-qPCR) test, which is the standard test used to detect Covid-19. Each quantitative measurement refers to the cycle threshold, a proxy for the viral load in the test sample. We propose two novel, robust recovery algorithms based on alternating direction method of multipliers and block coordinate descent to recover the individual sample cycle thresholds and hence determine the sick individuals, given the pooled sample cycle thresholds and the pooling matrix. We numerically evaluate the normalized mean squared error, false positive rate, false negative rate, and the maximum sparsity levels up to which error-free recovery is possible. We also demonstrate the advantage of using quantitative measurements (as opposed to binary outcomes) in non-adaptive pool testing methods in terms of the testing rate using publicly available data on Covid-19 testing. The simulation results show the effectiveness of the proposed algorithms. IEEE

An Improved Particle Swarm Optimization Algorithm for Irregular Flight Recovery Problem

As with the rapid development of air transportation and potential uncertainties caused by abnormal weather and other emergencies, such as Covid-19, irregular flights may occur. Under this situation, how to reduce the negative impact on airlines, especially how to rearrange the crew for each aircraft, becomes an important problem. To solve this problem, firstly, we established the model by minimizing the cost of crew recovery with time-space constraints. Secondly, in view of the fact that crew recovery belongs to an NP-hard problem, we proposed an improved particle swarm optimization (PSO) with mutation and crossover mechanisms to avoid prematurity and local optima. Thirdly, we designed an encoding scheme based on the characteristics of the problem. Finally, to verify the effectiveness of the improved PSO, the variant and the original PSO are used for comparison. And the experimental results show that the performance of the improved PSO algorithm is significantly better than the comparison algorithms in the irregular flight recovery problem covered in this paper. © 2022, Springer Nature Switzerland AG.

Soft Computing Techniques for Forecasting of COVID-19 in Pakistan

Novel Pandemic COVID-19 led globally to severe health barriers and financial issues in different parts of the world. The forecast on COVID-19 infections is significant. Demeanor vital data will help in executing policies to reduce the number of cases efficiently. Filtering techniques are appropriate for dynamic model structures as it provide reasonable estimates over the recursive Bayesian updates. Kalman Filters, used for controlling epidemics, are valuable in knowing contagious infections. Artificial Neural Networks (ANN) has generally been used for classification and forecasting problems. ANN models show an essential role in several successful applications of neural networks and are commonly used in economic and business studies. Long short-term memory (LSTM) model is one of the most popular techniques used in time series analysis. This paper aims to forecast COVID-19 on the basis of ANN, KF, LSTM and SVM methods. We applied ANN, KF, LSTM and SVM for the COVID-19 data in Pakistan to find the number of deaths, confirm cases, and cases of recovery. The three methods were used for prediction, and the results showed the performance of LSTM to be better than that of ANN and KF method. ANN, KF, LSTM and SVM endorsed the COVID-19 data in closely all three scenarios. LSTM, ANN and KF followed the fluctuations of the original data and made close COVID-19 predictions. The results of the three methods helped significantly in the decision-making direction for short term strategies and in the control of the COVID-19 outbreak.

Coronavirus disease 2019 in proportion to population: a historical analysis of Saudi Arabia.

Background: Saudi Arabia is one of the countries seriously affected by coronavirus disease 2019 (COVID-19) worldwide. With a few cases in early March, the daily spread of this disease increased to nearly 5000 at one point in time during the first wave to mid-June 2020. With committed efforts and public health interventions, it has been controlled to nearly 1000 by the end of August 2020 and less than 217 by November 28, 2020; thereafter, reporting declines and small increases. However, by December 2021, a third wave started, lasting for 2 months, during which the infection rate increased rapidly. By April 1, 2022, the number of infected persons in the country was 750,998, with 9047 deaths, 7131 active, and approximately 400 critical cases. This analysis of COVID-19 statistics of the Ministry of Health of Saudi Arabia (March 2020-April 2022) is carried out along with population data to extract patient proportions per 100,000 persons to illustrate the hypothesized social and community impact, which influences families and households. Results: The results showed a high rate of infection and mortality, but with recovery. These rates varied across localities and cities. A few cities with higher population densities are less affected by the spread of the epidemic. However, few localities and upcoming cities/townships were severely affected. These effects are explained as the percentage of the population affected, which exposes the impact on societies, families, and individual members. With concerted efforts, they are brought under control through recovery and adopting mitigation methods. Conclusions: Localities could be classified into four categories based on the proportion of the infected population: rapidly increasing, moderately increasing, declining, and stabilizing. Moreover, differential proportions of the affected population have implications at social and familial levels. Analysis and understanding of these trends, considering the base population, are important for policy building and intervention strategies accounting for grassroots-level demographics, which might serve as a tool to enhance interventions at population and family levels. Strategies for awareness creation and compassionate care are essential to address the psychosocial impact of health emergencies, as proved by the Ministry of Health, Saudi Arabia.

The Use of Digital Technology for Collection Efficiency and Investment Recovery in Manila Urban Settlements

This research is entitled "The use of digital technology for collection efficiency and investment recovery in Manila urban settlements."As of June 2021, about 3,322 are active lot awardees of the City of Manila's land-for-the-landless program (LLP) that administers 101 city-landed estates. However, its collection efficiency has continued to plummet in the last three years. There was a 19% decrease from 2018 to 2019, 47% from 2019 to 2020, and 20% by the end of the second quarter of 2021. When the pandemic happened, there was a sudden drop of 54% in the LLP collection rate. The researchers formulated a workable solution to improve the collection efficiency and investment recovery of the LLP. Oyelami et al. (2020) [1] study revealed a significant positive relationship between the electronic payment systems determinants and e-payment adoption. Around 60% of the respondents agreed that they are willing to adapt to e-payment schemes because of convenience, security and safety, and social influences. However, the results from their estimations also show that factors such as educational attainment, financial inclusion, income level, internet service availability, and other financial infrastructures are critical determinants of e-payment adoption. This paper on digital technology for collection efficiency and investment recovery highly recommends the shift to digital payments. The study revealed that 68% of the lot awardees prefer sending SMS billing reminders to receive a printed account statement. Furthermore, 77% of the respondents in this study have shown a strong interest in paying electronically through the "E-Cash"online payment platform, while 12% prefer other online modes. Therefore, the shift to online payments is desired. © 2022 ACM.

Short-term forecasting of daily infections, fatalities and recoveries about COVID-19 in Algeria using statistical models.

BACKGROUND: A viral disease due to a virus called SARS-Cov-2 spreads globally with a total of 34,627,141 infected people and 1,029,815 deaths. Algeria is an African country where 51,690, 1,741 and 36,282 are currently reported as infected, dead and recovered. A multivariate time series model has been used to model these variables and forecast their future scenarios for the next 20 days. RESULTS: The results show that there will be a minimum of 63 and a maximum of 147 new infections in the next 20 days with their corresponding 95% confidence intervals of - 89 to 214 and 108-186, respectively. Deaths' forecast shows that there will be 8 and 12 minimum and maximum numbers of deaths in the upcoming 20 days with their 95% confidence intervals of 1-17 and 4-20, respectively. Minimum and maximum numbers of recovered cases will be 40 and 142 with their corresponding 95% confidence intervals of - 106 to 185 and 44-239, respectively. The total number of infections, fatalities and recoveries in the next 20 days will be 1850, 186 and 1680, respectively. CONCLUSION: The results of this study suggest that the new infections are higher in number than recover cases, and therefore, the number of infected people may increase in future. This study can provide valuable information for policy makers including health and education departments.

Forecasting COVID-19 situation in Bangladesh.

Forecasting the COVID-19 confirmed cases, deaths, and recoveries demands time to know the severity of the novel coronavirus. This research aims to predict all types of COVID-19 cases (verified people, deaths, and recoveries) from the deadliest 3rd wave data of the COVID-19 pandemic in Bangladesh. We used the official website of the Directorate General of Health Services as our data source. To identify and predict the upcoming trends of the COVID-19 situation of Bangladesh, we fit the Auto-Regressive Integrated Moving Average (ARIMA) model on the data from Mar. 01, 2021 to Jul. 31, 2021. The finding of the ARIMA model (forecast model) reveals that infected, deaths, and recoveries number will have experienced exponential growth in Bangladesh to October 2021. Our model reports that confirmed cases and deaths will escalate by four times, and the recoveries will improve by five times at a later point in October 2021 if the trend of the three scenarios of COVID-19 from March to July lasts. The prediction of the COVID-19 scenario for the next three months is very frightening in Bangladesh, so the strategic planner and field-level personnel need to search for suitable policies and strategies and adopt these for controlling the mass transmission of the virus.

The nCOVID-19 and financial stress in the USA: health is wealth.

Since its first report in the USA on 13 January 2020, the novel coronavirus (nCOVID-19) pandemic like in other previous epicentres in India, Brazil, China, Italy, Spain, UK, and France has until now hampered economic activities and financial markets. To offer one of the first empirical insights into the economic/financial effect of the COVID-19 pandemic, especially in the USA, this study utilized the daily frequency data for the period 25 February 2020-30 March 2020. By employing the empirical Markov switching regression approach and the compliments of cointegration techniques, the study establishes a two-state (stable and distressing) financial stress situation resulting from the effects of COVID-19 daily deaths, COVID-19 daily recovery, and the USA' economic policy uncertainty. From the result, it is assertive that daily recovery from COVID-19 eases financial stress, while the reported daily deaths from COVID-19 further hamper financial stress in the country. Moreover, the uncertainty of the USA' economic policy has also cost the Americans more financial stress and other socio-economic challenges. While the cure for COVID-19 remains elusive, as a policy instrument, the USA and similar countries with high severity of COVID-19 causalities may intensify and sustain the concerted efforts targeted at attaining a landmark recovery rate.

The shape of recovery: Implications of past experience for the duration of the COVID-19 recession.

In this paper we seek to make headway on the question of what recovery from Covid-19 recession may look like, focusing on the duration of the recovery - that is, how long it will take to re-attain the levels of output and employment reached at the prior business cycle peak. We start by categorizing all post-1960 recessions in advanced countries and emerging markets into supply-shock, demand-shock and both-shock induced recessions. We measure recovery duration as the number of years required to re-attain pre-recession levels of output or employment. We then rely on the earlier literature on business cycle dynamics to identify candidate variables that can help to account for variations in recovery duration following different kinds of shocks. By asking which of these variables are operative in the Covid-19 recession, we can then draw inferences about the duration of the recovery under different scenarios. A number of our statistical results point in the direction of lengthy recoveries.

Global Healthcare Resource Efficiency in the Management of COVID-19 Death and Infection Prevalence Rates.

The scale of impact of the COVID-19 pandemic on society and the economy globally provides a strong incentive to thoroughly analyze the efficiency of healthcare systems in dealing with the current pandemic and to obtain lessons to prepare healthcare systems to be better prepared for future pandemics. In the absence of a proven vaccine or cure, non-pharmaceutical interventions including social distancing, testing and contact tracing, isolation, and wearing of masks are essential in the fight against the worldwide COVID-19 pandemic. We use data envelopment analysis and data compiled from Worldometers and The World Bank to analyze how efficient the use of resources were to stabilize the rate of infections and minimize death rates in the top 36 countries that represented 90% of global infections and deaths out of 220 countries as of November 11, 2020. This is the first paper to model the technical efficiency of countries in managing the COVID-19 pandemic by modeling death rates and infection rates as undesirable outputs using the approach developed by You and Yan. We find that the average efficiency of global healthcare systems in managing the pandemic is very low, with only six efficient systems out of a total of 36 under the variable returns to scale assumption. This finding suggests that, holding constant the size of their healthcare systems (because countries cannot alter the size of a healthcare system in the short run), most of the sample countries showed low levels of efficiency during this time of managing the pandemic; instead it is suspected that most countries literally "threw" resources at fighting the pandemic, thereby probably raising inefficiency through wasted resource use.

Analysing COVID-19 pandemic through cases, deaths, and recoveries.

BACKGROUND AND AIMS: The novel Coronavirus disease (COVID-19) in Wuhan, China, became a pandemic after its outbreak in January 2020. Countries one after the other are witnessing peak effects of the disease, and they need to learn from the experience of others already affected or peaked countries. Thus, this paper aims to analyse the effect of the COVID-19 pandemic on different countries through COVID-19 cases, resulting in deaths and recoveries. METHODS: This study analyses quantitatively the lethal effects of the pandemic through the study of infections, deaths, and recoveries on the 13 most-affected COVID-19 countries as of 1 s t June. The daily change in cases, deaths, and recoveries for all the 13 countries were considered. Combined analysis for comparison and separate analysis for the detailed study were both taken for every country. All the graphs were made in RStudio using the R programming language, as it is best for statistical analysis. RESULTS: The casual and ignorant behaviour of people is a major reason for such a large scale spread of the coronavirus. The government of every country should be strict as well as considerate to all sections of people while making policies. There is no room for mistakes, as one wrong decision or one delayed decision can worsen the situation. However, some countries which were once the epicentre of this pandemic are now corona-free, proving that this global threat can be tackled and we should all keep our morale high. CONCLUSIONS: The coronavirus disease is not any ordinary viral infection; it has become a pandemic as it has an impact on health, mortality, economy and social well being of the entire world. Qualitative and Quantitative analysis of the statistics related to COVID-19 in different countries is done based on their officials' data. The primary objective of this analysis is to learn about the relationships of various countries in containing the spread of COVID-19 and the various factors such as government policies, the cooperation of people, economy, and tourism.

Modelling and forecasting of new cases, deaths and recover cases of COVID-19 by using Vector Autoregressive model in Pakistan.

COVID-19 emerged in Wuhan, China in December 2019 has now spread around the world causes damage to human life and economy. Pakistan is also severely effected by COVID-19 with 202,955 confirmed cases and total deaths of 4,118. Vector Autoregressive time series models was used to forecast new daily confirmed cases, deaths and recover cases for ten days. Our forecasted model results show maximum of 5,363/day new cases with 95% confidence interval of 3,013-8,385 on 3rd of July, 167/day deaths with 95% confidence interval of 112-233 and maximum recoveries 4,016/day with 95% confidence interval of 2,182-6,405 in the next 10 days. The findings of this research may help government and other agencies to reshape their strategies according to the forecasted situation. As the data generating process is identified in terms of time series models, then it can be updated with the arrival of new data and provide forecasted scenario in future.

Analysis on novel coronavirus (COVID-19) using machine learning methods.

In this paper, we are working on a pandemic of novel coronavirus (COVID-19). COVID-19 is an infectious disease, it creates severe damage in the lungs. COVID-19 causes illness in humans and has killed many people in the entire world. However, this virus is reported as a pandemic by the World Health Organization (WHO) and all countries are trying to control and lockdown all places. The main objective of this work is to solve the five different tasks such as I) Predicting the spread of coronavirus across regions. II) Analyzing the growth rates and the types of mitigation across countries. III) Predicting how the epidemic will end. IV) Analyzing the transmission rate of the virus. V) Correlating the coronavirus and weather conditions. The advantage of doing these tasks to minimize the virus spread by various mitigation, how well the mitigations are working, how many cases have been prevented by this mitigations, an idea about the number of patients that will recover from the infection with old medication, understand how much time will it take to for this pandemic to end, we will be able to understand and analyze how fast or slow the virus is spreading among regions and the infected patient to reduce the spread based clear understanding of the correlation between the spread and weather conditions. In this paper, we propose a novel Support Vector Regression method to analysis five different tasks related to novel coronavirus. In this work, instead of simple regression line we use the supported vectors also to get better classification accuracy. Our approach is evaluated and compared with other well-known regression models on standard available datasets. The promising results demonstrate its superiority in both efficiency and accuracy.