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.

Spatial variation of covid-19 morbidity status and identification of risk zone in Tamil Nadu (India) during first wave.

COVID-19 is a pandemic disease caused by severe acute respiratory syndrome corona virus 2 (SARS-CoV-2). The study area, Tamilnadu, is a southern state in India. The present study demonstrates the significance of mapping in identifying the risk zones of COVID-19 disease by taking a study of COVID-19 cases in Tamilnadu state. The main objective of the study was to identify the risk zone of COVID-19 disease in the first wave of Tamilnadu through geospatial mapping. Data on COVID-19 cases collected during March 2020 to March 2021 month wise in all 37 districts (unit area) of Tamilnadu in two-month interval wise was analysed. The study is based on secondary sources from respective office in Tamilnadu. Z-score techniques were used for standardizing data. Correlation analysis provided a measure of correlation between COVID-19 and working population. Location Quotient analysis aided the identification of COVID-19 risk zones in Tamilnadu. Present study indicates tertiary workers are more vulnerable to COVID-19 disease.

A study on the impact of COVID-19 on the financial health of Indian companies

Unexpected lockdown of the country for more than four months has led to a huge reduction of economic activity and adversely affected the profitability and liquidity of companies. The studyaims to assess the potential damage caused by COVID-19 to the financial health of Indian companies. NSE NIFTY 50 companies were selected as a sample for the study and Altman Z-Score model was used to measure their financial health at the end of December 2019, March 2020 and June 2020. Paired sample t-test was used to test significant changes in financial healthduring the period of study. The study witnessed adverse impact of COVID-19 on the financial health of selected companies. Oil, Gas, Metals, Power, Construction, Fertilisers, Pesticides, Services and Telecom are the sectors which are adversely affected by the pandemic. Copyright © 2022 Inderscience Enterprises Ltd.

Measuring Evidence Affecting the Financial Stability of Airport Operations in Malaysia

The indicator of bankruptcy exposure for airport operations in Malaysia is calculated by using Altman's Z'-score. Financial and non-financial attributes related to the bankruptcy exposure show multicollinearity, and the redundant information was identified and removed. The common period for the variables is from 1999-2021, which includes the period of COVID-19 pandemic. Models with a combination of financial and non-financial attributes further reduce the deviation between the estimated standard deviation of the residuals and the marginal standard deviation of the bankruptcy risk in comparison to models without the combination. The best model provides improvements in terms of the mean of the absolute errors (MAE), mean of absolute percentage errors (MAPE), and mean absolute scaled errors (MASE). Furthermore, all determinants in the best model are statistically significant. We suggest that the opportunity for optimisation, including total movements of passenger, cargo and mail, could reduce the company's bankruptcy exposure. Findings indicate that reducing the financial leverage could improve the financial distress risk while liquidity, net operating margin, and asset turnover are positively contributed to the financial stability of the largest airport operator in Malaysia. If the marginal average of annual exposures to bankruptcy of 4.04% continues linearly into the future, the company is expected to transition from being financially stable to experiencing financial distress in 2030. © 2022 IEEE.

Assessment of the Bankruptcy Risk in the Hotel Industry as a Condition of the COVID-19 Crisis Using Time-Delay Neural Networks

In this paper we demonstrate a new conceptual framework in the application of multilayer perceptron (MLP) artificial neural networks (ANNs) to bankruptcy risk prediction using different time-delay neural network (TDNN) models to assess Altman's EM Z″-score risk zones of firms for a sample of 100 companies operating in the hotel industry in the Republic of Serbia. Hence, the accuracies of 9580 forecasting ANNs trained for the period 2016 to 2021 are analyzed, and the impact of various input parameters of different ANN models on their forecasting accuracy is investigated, including Altman's bankruptcy risk indicators, market and internal nonfinancial indicators, the lengths of the learning periods of the ANNs and of their input parameters, and the K-means clusters of risk zones. Based on this research, 11 stability indicators (SIs) for the years under analysis are formulated, which represent the generalization capabilities of ANN models, i.e., differences in the generalization errors between the preceding period and the year for which zone assessment is given;these are seen as a consequence of structural changes at the industry level that occurred during the relevant year. SIs are validated through comparison with the relative strength index (RSI) for descriptive indicators of Altman's model, and high correlation is found. Special focus is placed on the identification of the stability in 2020 in order to assess the impact of the COVID-19 crisis during that year. It is established that despite the fact that the development of bankruptcy risk in the hotel industry in the Republic of Serbia is a highly volatile process, the largest changes in the analyzed period occurred in 2020, i.e., the potential applications of ANNs for forecasting zones in 2020 are limited. © 2022 by the authors.

Computational Analysis Predicts Correlations among Amino Acids in SARS-CoV-2 Proteomes.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a serious global challenge requiring urgent and permanent therapeutic solutions. These solutions can only be engineered if the patterns and rate of mutations of the virus can be elucidated. Predicting mutations and the structure of proteins based on these mutations have become necessary for early drug and vaccine design purposes in anticipation of future viral mutations. The amino acid composition (AAC) of proteomes and individual viral proteins provide avenues for exploitation since AACs have been previously used to predict structure, shape and evolutionary rates. Herein, the frequency of amino acid residues found in 1637 complete proteomes belonging to 11 SARS-CoV-2 variants/lineages were analyzed. Leucine is the most abundant amino acid residue in the SARS-CoV-2 with an average AAC of 9.658% while tryptophan had the least abundance of 1.11%. The AAC and ranking of lysine and glycine varied in the proteome. For some variants, glycine had higher frequency and AAC than lysine and vice versa in other variants. Tryptophan was also observed to be the most intolerant to mutation in the various proteomes for the variants used. A correlogram revealed a very strong correlation of 0.999992 between B.1.525 (Eta) and B.1.526 (Iota) variants. Furthermore, isoleucine and threonine were observed to have a very strong negative correlation of -0.912, while cysteine and isoleucine had a very strong positive correlation of 0.835 at p < 0.001. Shapiro-Wilk normality test revealed that AAC values for all the amino acid residues except methionine showed no evidence of non-normality at p < 0.05. Thus, AACs of SARS-CoV-2 variants can be predicted using probability and z-scores. AACs may be beneficial in classifying viral strains, predicting viral disease types, members of protein families, protein interactions and for diagnostic purposes. They may also be used as a feature along with other crucial factors in machine-learning based algorithms to predict viral mutations. These mutation-predicting algorithms may help in developing effective therapeutics and vaccines for SARS-CoV-2.

In silico analysis reveals hypoxia-induced miR-210-3p specifically targets SARS-CoV-2 RNA.

Human coronaviruses (HCoVs) until the emergence of SARS in 2003 were associated with mild cold and upper respiratory tract infections. The ongoing pandemic caused by SARS-CoV-2 has enhanced the potential for infection and transmission as compared to other known members of this family. MicroRNAs (miRNA) are 21-25 nucleotides long non-coding RNA that bind to 3' UTR of genes and regulate almost every aspect of cellular function. Several human miRNAs have been known to target viral genomes, mostly to downregulate their expression and sometimes to upregulate also. In some cases, host miRNAs could be sequestered by the viral genome to create a condition for favourable virus existence. The ongoing SARS CoV-2 pandemic is unique based on its transmissibility and severity and we hypothesised that there could be a unique mechanism for its pathogenesis. In this study, we exploited in silico approach to identify human respiratory system-specific miRNAs targeting the viral genome of three highly pathogenic HCoVs (SARS-CoV-2 Wuhan strain, SARS-CoV, and MERS-CoV) and three low pathogenic HCoVs (OC43, NL63, and HKU1). We identified ten common microRNAs that target all HCoVs studied here. In addition, we identified unique miRNAs which targeted specifically one particular HCoV. miR-210-3p was the single unique lung-specific miRNA, which was found to target the NSP3, NSP4, and NSP13 genes of SARS-CoV-2. Further miR-210-NSP3, miR-210-NSP4, and miR-210-NSP13 SARS-CoV-2 duplexes were docked with the hAGO2 protein (PDB ID 4F3T) which showed Z-score values of -1.9, -1.7, and -1.6, respectively. The role of miR-210-3p as master hypoxia regulator and inflammation regulation may be important for SARS-CoV-2 pathogenesis. Overall, this analysis advocates that miR-210-3p be investigated experimentally in SARS-CoV-2 infection.Communicated by Ramaswamy H. Sarma.

Is excess of everything bad? Ramifications of excess liquidity on bank stability: Evidence from the dual banking system

After the 2008 financial crisis, the primary focus of global banking regulators has been to make banks more liquid by maintaining excess liquidity buffers. This paper investigates a paradox where high liquidity reduces bank stability instead of improving it by examining the relationship between excess liquidity and stability between Islamic and conventional banks. This paper uses data on 42 Islamic and 106 conventional banks from 6 emerging countries between 2009 and 2018, providing empirical evidence that excess banking liquidity impedes bank stability. The results show that conventional banks are more vulnerable to the adversities of excess liquidity, while Islamic banks exhibit more resilience. This study raises ‘red flags’ for policymakers and regulators advocating high liquidity as a source of financial stability. It suggests that regulators should be cautious when using the so-called ‘liquidity bazooka’ to guard banks against any economic downturn, such as during the COVID-19 era.

Financial inclusion and bank risk taking: The effect of information sharing

We explore how information-sharing and financial inclusion influence bank risk in 84 countries from 1996-2020. Results show that greater information-sharing and financial inclusion lessen bank risk levels. The average bank's Z-score increases by 3.51 – 9.09%, attributable to enhanced information-sharing and higher financial inclusion. Inclusion-based deposit mobilization reduces bank probability of insolvency, suggesting that inclusion provides banks with cheap funding sources, reducing moral hazard problems and risky behaviors. Results remain robust across risk indicators and controlling for FinTech-based inclusion and COVID-19-induced risk. They support the hypothesis of increased social returns to transparency, information-sharing, and reaching out to financially excluded segments.

Modified body mass index z-scores in children in New York City during the COVID-19 pandemic.

OBJECTIVES: Determine whether the negative impact of the COVID-19 pandemic on weight gain trajectories among children attending well-child visits in New York City persisted after the public health restrictions were reduced. STUDY DESIGN: Multicenter retrospective chart review study of 7150 children aged 3-19 years seen for well-child care between 1 January 2018 and 4 December 2021 in the NYC Health and Hospitals system. Primary outcome was the difference in annual change of modified body mass index z-score (mBMIz) between the pre-pandemic and early- and late-pandemic periods. The mBMIz allows for tracking of a greater range of BMI values than the traditional BMI z-score. The secondary outcome was odds of overweight, obesity, or severe obesity. Multivariable analyses were conducted with each outcome as the dependent variable, and year, age category, sex, race/ethnicity, insurance status, NYC borough, and baseline weight category as independent variables. RESULTS: The difference in annual mBMIz change for pre-pandemic to early-pandemic = 0.18 (95% confidence interval [CI]: 0.15, 0.20) and for pre-pandemic to late-pandemic = 0.04 (95% CI: 0.01, 0.06). There was a statistically significant interaction between period and baseline weight category. Those with severe obesity at baseline had the greatest mBMIz increase during both pandemic periods and those with underweight at baseline had the lowest mBMIz increase during both pandemic periods. CONCLUSION: In NYC, the worsening mBMIz trajectories for children associated with COVID-19 restrictions did not reverse by 2021. Decisions about continuing restrictions, such as school closures, should carefully weigh the negative health impact of these policies.

Financial distress prediction using integrated Z-score and multilayer perceptron neural networks.

The COVID-19 pandemic led to a great deal of financial uncertainty in the stock market. An initial drop in March 2020 was followed by unexpected rapid growth over 2021. Therefore, financial risk forecasting continues to be a central issue in financial planning, dealing with new types of uncertainty. This paper presents a stock market forecasting model combining a multi-layer perceptron artificial neural network (MLP-ANN) with the traditional Altman Z-Score model. The contribution of the paper is presentation of a new hybrid enterprise crisis warning model combining Z-score and MLP-ANN models. The new hybrid default prediction model is demonstrated using Chinese data. The results of empirical analysis show that the average correct classification rate of thew hybrid neural network model (99.40%) is higher than that of the Altman Z-score model (86.54%) and of the pure neural network method (98.26%). Our model can provide early warning signals of a company's deteriorating financial situation to managers and other related personnel, investors and creditors, government regulators, financial institutions and analysts and others so that they can take timely measures to avoid losses.

Harmonized-Multinational qEEG norms (HarMNqEEG).

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

The Impact of Onset Age on Eosinophils in Kawasaki Disease.

(1) Background: Kawasaki disease (KD) mainly affects children under the age of 5 years and eosinophilia in KD patients might be associated with the development of allergic diseases. We compared the age-adjusted Z-score (Z) of eosinophils and aimed to evaluate the impact of onset age on eosinophils in KD patients. (2) Methods: We divided 398 KD patients into seven age subgroups. Laboratory data and the age-adjusted Z-score of eosinophils during the phases of Kawasaki disease were analyzed. (3) Results: The absolute eosinophil count among all age groups showed significant differences in the post-intravenous immunoglobulin (IVIG) phase and throughout the course of KD with Z-score adjusted for age. Further analysis showed persistent elevation of the age-adjusted Z-score of eosinophils (Z-eosinophil) especially in the under six-month-old age subgroup. In addition, we divided the Z-eosinophil into two groups to find the relationship with coronary artery lesions (CALs). Patients with a higher eosinophil count than average age values had a higher risk of developing CALs, while those with a lower eosinophil count than average age values had a lower risk of having CALs. (4) Conclusions: These findings may provide information to clinicians to pay attention to allergic diseases during the follow-up of KD, especially for children who are younger than 6 months old at the onset of KD, and eosinophil count could be a crucial focus in KD.

Evaluation of the Financial Distress Level of Construction Companies in Malaysia Using Z-score Model

The companies’ financial distress is a popular issue nowadays due to the impact of the Covid-19 pandemic recently. The Covid-19 pandemic has been greatly deteriorated and jeopardized the financial health of the companies from all sectors, including the construction sector. The analysis on the financial performance of the companies is a good indicator to determine the financial distress level of the companies. This study aims to measure the financial health of listed construction companies in Malaysia with Altman Z-score model. Altman Z-score model comprises five important and significant financial ratios that are utilized to analyze the financial distress level of the companies. The power of the Z-score model is able to categorize the financial performance of the companies into three zones, namely safe zone, grey zone or distress zone. This study is significant because it helps to identify the financial distress level of the company. Hence, the companies can take remedial actions in order to improve themselves in terms of financial health. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Sedentary Time and Fast-Food Consumption Associated With Weight Gain During COVID-19 Lockdown in Children and Adolescents With Overweight or Obesity.

BACKGROUND: The coronavirus disease pandemic is predicted to have adverse health effects on children and adolescents who are overweight or obese due to restricted school activity and stay-at-home orders. The purpose of this observational study was to determine the factors associated with weight gain in children and adolescents with overweight and obesity during coronavirus disease 2019 (COVID-19) lockdown. METHODS: Ninety-seven participants (sex- and age-specific body mass index (BMI) ≥ 85th percentile) were included. A baseline examination was conducted pre-COVID-19 (August 2019 to January 2020), and re-examination was performed post-lockdown (June to September 2020) and the results were compared. Correlation and regression analyses were conducted to investigate the association among changes in cardiometabolic markers and lifestyle behaviors with changes in BMI z-score. RESULTS: During the COVID-19 pandemic, an increase in BMI z-score (2.56 [2.01-2.94] to 2.62 [2.03-3.18]) was noticed in children and adolescents with obesity. Changes in cardiometabolic markers including liver enzymes, triglycerides (r = 0.398), leptin (r = 0.578), and adiponectin (r = -0.326), as well as muscular strength (r = -0.212), were correlated with the increase in BMI z-score. According to a multivariate regression analysis, changes in sedentary time (B = 0.016; 95% confidence interval [CI], 0.001-0.032) and fast-food consumption (B = 0.067; 95% CI, 0.013-0.122) were the lifestyle variables associated with BMI z-score increase. CONCLUSION: Changes in lifestyle behaviors including fast-food consumption and sedentary time during the COVID-19 pandemic may be associated with weight gain. In order to prevent health-related risks in children and adolescents with obesity during the pandemic, it is important to maintain the level of physical activity and healthy dietary habits.

Thomas Cook(ed): using Altman's z-score analysis to examine predictors of financial bankruptcy in tourism and hospitality businesses

PurposeFinancial bankruptcy is inevitable in the tourism and hospitality ecosystem. Despite the pertinence of tourism and hospitality businesses going into bankruptcy, limited studies have investigated the early warning signs and likelihood of a financial bankruptcy occurring in tourism and hospitality firms. This study examined the predictive value of financial ratios as potential indicators in predicting bankruptcy among tourism and hospitality firms.Design/methodology/approachAltman's z-score bankruptcy prediction model was applied through five key financial ratios to predict bankruptcy of the Thomas Cook Travel Group over a ten year period (2008–2018).FindingsThe key findings of this study strongly suggest that besides the size and location of the firm, financial ratios are reliable predictors and play a pivotal role in predicting the bankruptcy of a tourism and hospitality business.Practical implicationsThe paper provides key stakeholders to adopt checks and balances to identify financial distressed tourism firms through financial ratios.Originality/valueThis is the first academic paper to inspect the financial history of Thomas Cook Travel Group in a financial ratio context, particularly following the bankruptcy of the firm in 2019.

Implications of updated protocol for classification of childhood malnutrition and service delivery in world's largest refugee camp amid this COVID-19 pandemic.

OBJECTIVES: During the COVID-19 pandemic, the authorities made a change in the classification of malnutrition and concomitant service delivery protocol among the Rohingya children, residing in world's largest refugee camp, located in Cox's Bazar, Bangladesh. In this paper, we discussed the potential implications of this updated protocol on the malnutrition status among children residing in the Rohingya camps. DESIGN: This paper reviewed relevant literature and authors' own experience to provide a perspective of the updated protocol for the classification of malnutrition among the children in the Rohingya camps and its implication from a broader perspective. SETTING: Rohingya refugee camps, Bangladesh. PARTICIPANTS: Children aged less than five years residing in the Rohingya camps. RESULTS: Major adaptation during this COVID-19 was the discontinuation of using weight-for-height z-score (WHZ) and the use of only mid upper arm circumference (MUAC) and presence of oedema for admission, follow-up and discharge of malnourished children in the camps. However, evidence suggests that use of MUAC only can underestimate the prevalence of malnutrition among the children in Rohingya camps. These apparently non-malnourished children are devoid of the rations that they would otherwise receive if classified as malnourished, making them susceptible to more severe malnutrition. CONCLUSIONS: Our analysis suggests that policymakers should consider using the original protocol of using both MUAC and WHZ to classify malnutrition and retain the guided ration size. We also believe that it would not take an extra effort to adopt the original guideline as even with MUAC only guideline, certain health measures needed to adopt during this pandemic.

COVID-19, liquidity and financial health: empirical evidence from South Asian economy

PurposeThe pandemic COVID-19 has affected every sector of an economy in every possible way. Banking sector of Bangladesh has been affected by it badly. The purpose of this paper is to find out the impact of COVID-19 on the liquidity and financial health of the listed banks in Bangladesh.Design/methodology/approachLiquidity ratios are calculated to measure the liquidity condition of the banks and revised Altman's Z-Score Model for non-manufacturing companies is used to measure the financial health. The ratios are compared before and during the COVID-19 periods to assess the impact.FindingsThe findings of this study indicate a deterioration of liquidity position and financial health of the listed banks after the emergence of this pandemic. Though the banks have poor liquidity ratios and financial health prior to the emergence of this pandemic, they have decreased more in the second quarter of 2020. Most of the banks have poor liquidity ratios and cash position. The listed Islamic Banks have poor financial health than the listed Commercial Banks and all the banks belong to the red zone in all the quarters.Practical implicationsThe results of this study will have policy implications for companies and regulators of money market.Originality/valueThis paper is a pioneer initiative in assessing the impact of COVID-19 pandemic on liquidity and financial health based on empirical data.

Assessing the financial soundness of indian aviation sector companies by using altman's z-score model & pilarski's p- score model

The COVID 19 has brought down aviation industry to its knees. Till March 2020, before getting sense of distraction by this pandemic, Indian economy was eager to take off to reach further heights including the Aviation Sector. The growing middle class was the base reason for the prosperity in the Aviation Sector, but the pandemic has changed the whole scenario at least temporarily for next couple of years. However, even before the pandemic the history is showing that almost all Indian airlines companies are in losses. Jet Airways is almost grounded permanently whereas Kingfisher Airlines is now a history. Being one of the important sectors for the economy and even for the investors, an attempt is made to find out the reasons behind financial failure of selected Indian Aviation Companies by using Altaman's Z Score Model and Pilarski's P -Score Model and various problems faced by them. The secondary data is collected mainly by using Annual Reports of 4 leading Airlines Companies in India. Analysis is showing that various internal and external factors which are responsible for such pathetic financial position of these companies and a serious overhauling is required not only by those companies but also from the government side. © 2021by authors.

Quantifying and communicating the burden of COVID-19.

BACKGROUND: An essential aspect of preventing further COVID-19 outbreaks and to learn for future pandemics is the evaluation of different political strategies, which aim at reducing transmission of and mortality due to COVID-19. One important aspect in this context is the comparison of attributable mortality. METHODS: We give a comprehensive overview of six epidemiological measures that are used to quantify COVID-19 attributable mortality (p-score, standardized mortality ratio, absolute number of excess deaths, per capita rate, z-score and the population attributable fraction). RESULTS: By defining the six measures based on observed and expected deaths, we explain their relationship. Moreover, three publicly available data examples serve to illustrate the interpretational strengths and weaknesses of the various measures. Finally, we give recommendation which measures are suitable for an evaluation of public health strategies against COVID-19. The R code to reproduce the results is available as online supplementary material. CONCLUSION: The number of excess deaths should be always reported together with the population attributable fraction, the p-score or the standardized mortality ratio instead of a per capita rate. For a complete picture of COVID-19 attributable mortality, quantifying and communicating its relative burden also to a lay audience is of major importance.