The highly sensitive, nonlinear, and unpredictable stock marketbehaviours are always challenging for researchers. Stock markets ofPakistan and China, i.e., KSE-100 and SSE-100, respectively, are thetwo most attractive stock markets after the official announcementof CPEC. Thus, the daily closing price of KSE-100 and SSE-100 Stockreturns are used to evaluate the volatility forecast performance ofthe machine learning technique, GARCH family and the nonlinearregime-switching models. The findings of this study revealed that thestandard GARCH model is the best-fitted model based on Akaike’sInformation Criteria (AIC) and Bayesian Information Criteria (BIC).Furthermore, the forecast performance of the machine learning LSTMmodel outperforms other models based on RMSE for SSE-100. Incontrast, the forecast performance of CGARCH for SSE-100 and theMarkov-regime-switchingmodelforKSE-100outperformsothermodelsbased on MAE, MAPE, and SMAPE evaluation criteria. It is also revealedthat the predictive power of the machine learning model is very closeto CGARCH and MRS model; therefore, the LSTM model can be used asan alternative to GARCH and regime-switching models for stock marketvolatility. These findings will help national and international investors,policy-makers, geographical economists, and industrialists to use thebestforecastmodeltomakebetterpoliciesandgaintremendousprofit.

IntroductionV olatility plays a key role in derivative pricing and hedging, risk management and optimal portfolio selection.Modeling and forecasting stock market data are always challenging for market practitioners and researchers.Past literatures show that financial return series contains different characteristics such as: Volatility clustering, leverage effect, and long persistence etc.

Acute poisoning is a global public health challenge. Several factors played role in high mortality among acute organophosphorus poisoning (OP) poisoning patients including clinical, vitals, and biochemical properties. The traditional analysis techniques use baseline measurements whereas latent profile analysis is a person-centered approach using repeated measurements. To determine varying biochemical parameters and their relationship with intensive care unit (ICU) mortality among acute organophosphorus poisoning patients through a latent class trajectory analysis. The study design was a retrospective cohort and we enrolled data of 299 patients admitted between Aug’10 to Sep’16 to ICU of Dr. Ruth K. M. Pfau, Civil Hospital, Karachi. The dependent variable was ICU-mortality among OP poisoning patients accounting for ICU stay, elapsed time since poison ingestion, age, gender, and biochemical parameters (including electrolytes (potassium, chloride, sodium), creatinine, urea, and random blood sugar). Longitudinal latent profile analysis is used to form the trajectories of parameters. In determining and comparing the risk of ICU-mortality we used Cox-Proportional-Hazards models, repeated measures and trajectories were used as independent variables. The patients’ mean age was 25.4 ± 9.7 years and ICU-mortality was (13.7%, n = 41). In trajectory analysis, patients with trajectories (normal-increasing and high-declining creatinine, high-remitting sodium, normal-increasing, and high-remitting urea) observed the highest ICU-mortality i.e. 75% (6/8), 67% (2/3), 80% (4/5), 75% (6/8), and 67% (2/3) respectively compared with other trajectories. On multivariable analysis, compared with patients who had normal consistent creatinine levels, patients in normal-increasing creatinine class were 15 times [HR:15.2, 95% CI 4.2–54.6], and the high-declining class was 16-times [HR 15.7, 95% CI 3.4–71.6], more likely to die. Patients in with high-remitting sodium, the trajectory was six-times [HR 5.6, 95% CI 2.0–15.8], normal-increasing urea trajectory was four times [HR 3.9, 95% CI 1.4–11.5], and in extremely high-remitting urea trajectory was 15-times [HR 15.4, 95% CI 3.4–69.7], more likely to die compared with those who were in normal-consistent trajectories of sodium and urea respectively. Among OP poisoning patients an increased risk of ICU-mortality were significantly associated with biochemical parameters (sodium, urea, creatinine levels) using latent profile technique.

Volatility plays a crucial role in financial markets and accurate prediction of the stock price indices is of high interest. In multivariate time series, Dynamic Conditional Correlation (DCC)-Generalized Autoregressive Conditional Heteroscedastic (GARCH) is used to model and forecast the volatility (risk) and co-movement between stock prices data. We propose multivariate artificial neural networks (MANNs) hybridized with the DCC-GARCH model to forecast the volatility of stock prices and to examine the time-varying correlation. The daily share price data of five stock markets: S&P 500 (USA), FTSE-100 (UK), KSE-100 (Pakistan), Malaysia (KLSE) and BSESN (India) covering the period from 1st, January 2013 to 17th, March 2020 are considered for empirical analysis. Moreover, the hybrid models of MANNs and DCC-GARCH are developed in two ways: (i) MANNs is provided as an input to DCC-GARCH (1,1) producing a hybrid model of DCC-GARCH(1,1)-MANNs and (ii) DCC-GARCH(1,1) model is set as an input to MANNs resulting hybrid model of MANNs-DCC-GARCH(1,1). Furthermore, the performances of the proposed models are compared with single models via the root mean square (RMSE), mean absolute error (MAE) and relative mean absolute error (RMAE). The empirical results show that DCC-GARCH (1,1)-MANNs, a parametric model, outperforms both in-sample and out-sample forecasts and helps to examine the time-varying correlation and also provides volatility forecast as well, whereas the hybrid model MANNs-DCC-GARCH (1,1) provides forecast only. Therefore, the hybrid model of DCC-GARCH (1,1)-MANNs is found suitable as compared to MANNs-DCC-GARCH(1,1) to model and forecast the stock price indices under consideration.

Background Attainment of healthcare in respectful and dignified manner is a fundamental right for every woman regardless of the individual status. However, social exclusion, poor psychosocial support, and demeaning care during childbirth at health facilities are common worldwide, particularly in low- and middle-income countries. We concurrently examined how women with varying socio-demographic characteristics are treated during childbirth, the effect of women’s empowerment on mistreatment, and health services factors that contribute to mistreatment in secondary-level public health facilities in Pakistan. Methods A cross-sectional survey was conducted during August–November 2016 among 783 women who gave birth in six secondary-care public health facilities across four contiguous districts of southern Sindh. Women were recruited in health facilities and later interviewed at home within 42 days of postpartum using a WHO’s framework-guided 43-item structured questionnaire. Means, standard deviation, and average were used to describe characteristics of the participants. Multivariable linear regression was applied using Stata 15.1. Results Women experiencing at least one violation of their right to care by hospital staff during intrapartum care included: ineffective communication (100%); lack of supportive care (99.7%); loss of autonomy (97.5%); failure of meeting professional clinical standards (84.4%); lack of resources (76.3%); verbal abuse (15.2%); physical abuse (14.8%); and discrimination (3.2%). Risk factors of all three dimensions showed significant association with mistreatment: socio-demographic: primigravida and poorer were more mistreated; health services: lesser-education on birth preparedness and postnatal care leads to higher mistreatment; and in terms of women’s empowerment: women who were emotionally and physically abused by family, and those with lack of social support and lesser involvement in joint household decision making with husbands are more likely to be mistreated as compared to their counterparts. The magnitude of relationship between all significant risk factors and mistreatment, in the form of β coefficients, ranged from 0.2 to 5.5 with p-values less than 0.05. Conclusion There are glaring inequalities in terms of the way women are treated during childbirth in public health facilities. Measures of socio-demographic, health services, and women’s empowerment showed a significant independent association with mistreatment during childbirth. At the health system level, there is a need for urgent solutions for more inclusive care to ensure that all women are treated with compassion and dignity, complemented by psychosocial support for those who are emotionally disturbed and lack social support.

This paper intended to analyze key Macroeconomic factor’s effect on Pakistan’s economic development. The annual time-series data has been taken from 1980 to 2018 on External Debts, Foreign Direct investment. Consumer Price Index and Term of Trade. Variables stationarity is analyzed by ADF and Ng-Perron tests; afterwards, JJ test and Granger Causality test are used for Long-run (LR) & Short-run(SR) associations between variables, respectively. Also, Residuals Diagnostic Test used for checking residuals assumptions and CUSUM and CUSUMSQ are used for checking parameter constancy. The result shows significantly negative and positive long-run effects of External Debts and Foreign Direct Investment (FDI) respectively on the economic growth of Pakistan. Albeit, Consumer Price Index (CPI), Term of Trade (TOT) and, FDI significantly Granger cause economic growth in the short-run. Research suggests that economic policies devised in such a way that deteriorates External Debts and attract foreign investments and strengthen the economic growth of Pakistan in the long-term.

Background Acute organophosphorus (OP) poisoning is one of the major causes of mortality among patients presenting to emergency departments in developing countries. Although various predictors of mortality among OP poisoning patients have been identified, the role of repeated measurements of vital signs in determining the risk of mortality is not yet clear. Therefore, the present study examined the relationship between trajectories of vital signs and mortality among OP poisoning patients using latent class growth analysis (LCGA). Methods This was a retrospective cohort study using data for 449 OP poisoning patients admitted to Civil-Hospital Karachi from Aug’10 to Sep’16. Demographic data and vital signs, including body temperature, blood pressure, heart rate, respiratory rate, and partial-oxygen pressure, were retrieved from medical records. The trajectories of vital signs were formed using LCGA, and these trajectories were applied as independent variables to determine the risk of mortality using Cox-proportional hazards models. P -values of < 0.05 were considered statistically significant. Results Data for 449 patients, with a mean age of 25.4 years (range 13–85 years), were included. Overall mortality was 13.4%( n = 60). In trajectory analysis, a low-declining systolic blood pressure, high-declining heart rate trajectory, high-remitting respiratory rate trajectory and normal-remitting partial-oxygen pressure trajectory resulted in the greatest mortality, i.e. 38.9,40.0,50.0, and 60.0%, respectively, compared with other trajectories of the same parameters. Based on multivariable analysis, patients with low-declining systolic blood pressure were three times [HR:3.0,95%CI:1.2–7.1] more likely to die compared with those who had a normal-stable systolic blood pressure. Moreover, patients with a high-declining heart rate were three times [HR:3.0,95%CI:1.5–6.2] more likely to die compared with those who had a high-stable heart rate. Patients with a high-remitting respiratory rate were six times [HR:5.7,95%CI:1.3–23.8] more likely to die than those with a high-stable respiratory rate. Patients with normal-remitting partial oxygen pressure were five times [HR:4.7,95%CI:1.4–15.1] more likely to die compared with those who had a normal-stable partial-oxygen pressure. Conclusion The trajectories of systolic blood pressure, heart rate, respiratory rate and partial-oxygen pressure were significantly associated with an increased risk of mortality among OP poisoning patients.

This paper evaluates the forecasting performance of linear and non-linear time series models of some macroeconomic variables viz a viz the forecasts outlook of these variables generated by professionals in international economic organizations i.e. the International Monetary Fund (IMF) and the Organization of Economic Cooperation and Development (OECD). Many time series and econometrics models are used to forecast financial and macroeconomic variables. The accuracy of such forecasts depends crucially on careful handling of nonlinearity present in the time series. The debate of forecasting ability of linear vs nonlinear models is far from settled. These models use the past patterns of the economic time series to infer the parameters of the underlying stochastic process and use them to make forecasts. In doing so these models use only the information contained in the past data. However the economists working in professional international economic organizations not only look at the past trends but use the condition of local and global economy prevailing at the time and expected future path of economies as well as their professional expertise and judgment to arrive at forecasts of macroeconomic variables. However the specific underlying models and methodology used by the economists generating these forecast is usually not communicated to the public. In comparison to the forecasts of these organizations the time series models are well developed and accessible to researchers working anywhere around the globe. Thus it is an interesting task to compare the foresting ability of linear and nonlinear time series models. This paper aims at comparing the forecasts from these models to assess how well they compete with forecasts generated from the professional economists employed by international economic organizations. The nonlinear models employed in this study are quite well known namely the Self Exciting Threshold Autoregressive (SETAR) model and the Markov Switching Autoregressive (MSAR) model. The linear models employed are the AR and ARMA models. The paper have used annual data of three macroeconomic time series variables GDP growth, consumer price inflation and exchange rate of G7 countries i.e. Canada, France, Germany, Italy, Japan, United Kingdom (UK) and United States of America (USA) as well as an emerging south Asian economy namely Pakistan. Three forecast accuracy criteria i.e. Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are employed and the statistical significance of difference in forecasts is assessed using the Diebold-Mariono test. The results show that the forecasting ability of nonlinear Regime Switching models SETAR and MSAR is superior to the linear models. Further, although the point forecasts of linear and nonlinear models are not superior to that of economic organizations but in more than 60 percent of the cases considered the forecasting accuracy of two sets of forecast is not statistically significantly different.

Introduction. The term dental caries is used to describe the results the sign and symptoms of a localized chemical dissolution of the tooth surface caused by metabolic events taking place in the biofilm (dental plaque) covering the affected area. Dental caries is caused by dietary carbohydrates fermentation and bacteria deteriorating dental hard tissues. It is a biofilm-mediated, sugar-driven, multifactorial, dynamic disease that results in the phasic demineralization and re-mineralization of dental hard tissues. Dental caries is a chronic condition; therefore progression is slow and can be observed in root caries and coronal parts of permanent and primary teeth. Several studies indicate that children and adults are found at risk with this disease in both developing and developed countries. The prevalence of dental caries in Pakistan is 51%. A study in Saudi Arabia demonstrated that 68.9% of school children are suffered from dental caries. Literature shows many factors associated with dental caries, including body mass index, which is one of the important parameters to estimate the dental caries. Objectives. This study was aimed to study the distribution of dental caries of Pakistani children, and to estimate the dental caries using Poisson regression model. Methodology. STATA 12.0 MP was used for statistical computation. Poisson regression was used to estimate the dental caries and then multiple linear regression analysis was done based on estimated dental caries of Poisson regression model. Results. In the present study, a total of 3,358 children were examined for dental caries. Fifty two percent of the participants were females and 64.1% were from KPK province of Pakistan. This study showed mean count of dental caries of Pakistani children as 2.18 +/- 2.49 units and body mass index was 16.92 +/- 3.49kg/m(2). Distribution of dental caries was found positively skewed. Poisson regression showed that one unit change in body mass index gives 0.002 unit increase in dental caries count on average. Conclusion. This study concludes that obesity gives positive association with dental caries.

Modeling and forecasting the volatility of daliy closing price series is a signifi cant area of fi nancial econometrics since last few decades. Due to regional integration of the fi nancial markets, investors not only interested in investing in their own countries stock markets but also investing in another countries stock markets. The aim of this study is to investigate the more volatile market and modelling the volatility.We use daily closing index of KSE-100 (Pakistan), BSESN (India) and CSE (Sri Lanka) as they are the member of SAARC countries covering the period 1st January, 2011 to 30th November, 2016. Empirical analysis shows that GARCH-inmean model is found insignifi cant for BSESN and CSE. It reveals that there is no relationship between risk and expected return. Furthermore, CSE is more persistent stock market than the other two, but KSE-100 is highly volatile during the study period. GARCH-in-mean model with log variance in mean return equation is suggested for out-sample forecast of KSE-100. On the other hand, in CSE IGARCH and for BSESN any one from IGARCH and GARCH are suggested suitable model.

Time series modelling and the forecasting of economic, financial time series is an active and fascinating area of research due to the presence of structural changes i.e. political regimes, business cycle variations and financial crises etc. In these cases, a careful handling is required to model time series when nonlinearity present in the data. Due to the nonlinear behavior of economic and financial time series, it is not possible to rely only on predictions from the simple estimated linear time series models. This study aims to explore and compare the forecasting performance time series models i.e. linear Autoregressive (AR) model with two nonlinear regime switching models namely Markov Regime Switching Autoregressive (MSAR) and Self-Exciting Threshold Autoregressive (SETAR). Macroeconomic variables i.e. interest rate, inflation (CPI), industrial production, GDP growth, and exchange rate from some developed and developing countries included G7 countries are chosen for this study. Quarterly based time series data from 1970 to 2016 is used. Empirically, the forecast performance of nonlinear time series model namely SETAR is found to be superior to the linear Auto Regressive model as well as nonlinear MSAR model. The results are evaluated on the basis of forecast accuracy criteria namely RMSE, MAE and MAPE.

Dental caries or breakdown of teeth is one of the frequent health problems from chronic disease among children and adults. Several studies showed a higher burden of this disease in their population, statistics showed 51% Pakistani population getting effected with this disease, increased in body mass index, poor oral hygiene and sugar consuming at higher level are the common risk factors of this problem, our aim of study was to provide an estimated of primary and permanent decayed, filled and missing teeth among school going children of sind and KPK province of Pakistan and comparison of these two estimates between boys and girls, for this purpose secondary data of 3358 children was used and stata software gives the results of studied samples, this study concludes that in sind and KPK primary caries were significantly higher as compare to permanent caries and gender gives significant on dental caries.

Dental caries or tooth decayed is one of the major public health problem among children and adults, an increased in body mass index, poor oral hygiene practices and bad diet intake are common causes of increasing trend in dental caries all around the world. In the present study we aimed to see the association of primary and permanent dental caries of Larkana and Peshwar city children of Pakistan with body mass index, oral hygiene and diet habits, a secondary baseline data were used to find the results of this study, total 3358 children were examined for dental caries, information on age, body mass index, oral hygiene practice and diet habits was obtained, 64.1% data were obtained from Peshawar children, 52.2% were females, 7% samples found with none meat intake per week, 5.6% with none vegetable intake, 4.3% with none rice intake and 38% found with none intake of milk in a week, a significant association of primary and permanent dental caries were obtained with body mass index, oral hygiene and diet habits using Pearson chi square test with p-value less than 0.05.

Dental caries is a biofilm-mediated, sugar-driven, multifactorial, dynamic disease that results in the phasic demineralization and re-mineralization of dental hard tissues, population of several countries is suffering from this problem, children and adults both are found at risk with this disease. Literature gives the evidence that dental caries are associated with age, primary caries found more prevalent at early age group and permanent caries found more prevalent at older age group, whereas some of the studies did not give any significant association between dental caries and body mass index. This study was aimed to see the relationship of primary and permanent caries with age and body mass index, and developing a regression model for caries prediction, In the current study a part of a secondary baseline data for dental caries project of Pakistani children was used, STATA 12.0 MP was used to estimate multiple regression models for primary and permanent dental caries prediction using and age in years and body mass index as predictor variables. results showed, primary DMFT gives 27.2% negative correlation with age, permanent DMFT gives 23.8% positive correlation with age and 22.5% positive correlation with body mass index, This study concludes that increase in age and body mass index could less the chances of primary caries but increase the chances of permanent caries in the population.

Much efforts have been done for modeling of financial data theoretically and empirically for the international stock markets, for example: Asia, Europe and Australia etc. But no frequent research has been done for the SAARC countries stock markets. Therefore, bench mark Index of Pakistan; Karachi Stock Exchange (KSE-100) and Bombay Stock Exchange (BSNSE) of India are selected as case study. They are not only the member of SAARC but also sharing the common border, due to this they are also involving in bilateral trading. We used closing indices of daily share price for the period of 1st January, 2010 to 15th January 2016. This study compares the forecasting performance and also investigates more volatile stock markets using Asymmetric GARCH (A-GARCH) models and non-parametric method (Artificial Neural Networks). In the A-GARCH; EGARCH and PGARCH models are used. Firstly, suitable Asymmetric GARCH (A-GARCH) model was developed for forecasting and investigating leverage effect. Secondly, an Artificial Neural Networks model was developed for the said stock markets. Lastly, forecasting performance of the FA-GARCH and ANN models both in and out sample were evaluated using root mean square error. In the A-GARCH; EGARCH (1,1) performed better than PGARCH(1,1) in both stock market data. However, when comparing A-GARCH with ANN, it was found that ANN gave minimum out sample forecasting error as compared to A-GARCH models. Therefore, ANN out played other studied models.

Dental caries or tooth decay is a breakdown of teeth due to bacterial activities, in bacterial breakdown hard tissues is one of the damage of dental caries; it becomes a public health concern, Studies showed that body mass index gives association with many diseases including dental caries. Our objective of this study was to drive the relationship of body mass index and dietary habits on dental caries experience of Pakistani children. The data for this study is the part of a multicenter country wide study being conducted to determine the eruption of permanent teeth and dental caries of Pakistani children. Data from Larkana and Peshawar the cities of two major Provinces of Pakistan were examined. 3358 children of just erupted teeth of aged between 5 to 19 years from these two cities were examined. 64.1% data were received from Peshawar city and 35.9% data were obtained from Larkana city. Results showed 52.2% were female samples and 47.8% were male sample. Outcome of Mann-Whitney U test gives the evidence that there was significant mean difference observed for carries of two cities, poisson regression model evident that the association of body mass index with carries experience. In our study mean permanent caries among Peshawar city children was almost double than Larkana city, even though height, weight and body mass index of Peshawar children higher ,one of the reason of Larkana child protection from caries was more used of milk and rise as compared to Peshawar Children. This study concludes that permanent DMFT risk for Peshawar city children was higher, and primary dmft had negative association with body mass index.

INTRODUCTION:Coronary artery disease (CAD) is a persistent public health problem worldwide. Chest pain is one of the perceptible symptoms of the same disease. Literature has found acute chest pain as plausible risk factors for CAD. Nevertheless, none of the study has estimated duration from chronic chest pain to the diagnosis of CAD. The objective of the study was to estimate duration from chronic chest pain to CAD and to assess impact of risk factors on same duration.METHODS:Data were obtained from community based study on 17,232 Saudi adults. History of patients about onset of chest pain and other risk factors were inquired. Descriptive measures were obtained by Kaplan-Meier curve. Effect of demographic and clinical factors was assessed by Cox regression models.RESULTS:Out of 24% patients with chest pain, 21% diagnosed with CAD. The average duration was 5 years. About 12% of patients with chest pain diagnosed with CAD after one year. Advancing age, female gender, no exercise and reduced high density lipoprotein (HDL) were significantly hazardous predictors throughout duration from chest pain to diagnosis of CAD.CONCLUSION:The duration from chest pain to CAD was 5 years. Age, gender, exercise and HDL can be variables of concern to deteriorate hazards of CAD for patients with chest pain.

Cox proportional hazard model is the widely used technique for studying duration data. However, studies showed parametric modeling yields better estimation; though these models are less used due to their complicated application and interpretation. Alongside, the duration from chronic chest pain to the diagnosis of coronary artery disease has not evaluated in the literature. Therefore, this research investigated application of parametric modeling on the current duration while studying outcome from cross-sectional study. Akaike Information Criteria was used to adjudicate different duration models.

Coronary artery disease is a persistent public health problem worldwide. Chest pain is one of the perceptible symptoms of the same disease. This study reviewed global insight on epidemiology of coronary artery disease and cardiac chest pain. We first highlighted worldwide prevalence and mortality ratio of coronary artery disease. Then, we discussed epidemiology of risk factors reported for different regions. Statistics on hazards pertaining to chest pain are reported in the aftermost section. This narrative review revealed that despite reduction on the prevalence of coronary artery disease in developed countries, developing regions are at highrisk to endure the disease. Urbanization is considered to be the superseded risk factor for the same. Strategies should be made worldwide to ameliorate treatment of CAD from first onset of its symptom.

Conjoint Analysis (CA) is one of the most popular techniques, which is being used in predication of customer’s behavior. Also used with it is the evaluation of part worth utilities of a product, service, or both with the estimation of factor importance in percentage. CA is applied in every field of division. Credit card service of Iranian bank was considered (Baheri et.al 2011) and used the fuzzy approach to calculate the result in the form of ranking. We have used the same data for developing our conjoint model named Faisal Conjoint Model (FCM). The algorithm of our developed model is written in R statistical language, package “faisalconjoint” is available. Both our and Baheri et al. have high correlation, this indicates that both methods have the same results.