Rational speculative bubbles are a factor that has often led to the collapse of the economy. In this study, the size of rational speculative bubble from the first cycle to the fifth cycle are identified by using a generalised Johansen-Ledoit-Sornette Model. This study also discussed the effects of the burst of the rational speculative bubble also known as the financial bubble. Then, the economic performance of the countries that pioneered the world economy will be analysed along with the stock prices, the performance of Gross Domestic Product (GDP) and the timeline of the global pandemic. Lastly, this paper discussed the effects of the existence of financial bubble and the presence of pandemics towards the sustainability of the living standards for the B40 and M40 household groups in Malaysia.

The variety of catastrophe bond issuances can be used for portfolio diversification. However, the structure of catastrophe bonds differs from traditional bonds in that the face value and coupons depend on triggering events. This study aims to build a diversification strategy model framework using probabilistic–possibilistic bijective transformation (PPBT) and credibility measures in fuzzy environments based on the payoff function. The stages of modeling include identifying the trigger distribution; determining the membership degrees for the face value and coupons using PPBT; calculating the average face value and coupons using the fuzzy quantification theory; formulating the fuzzy variables for the yield; defining the function of triangular fuzzy membership for the yield; defining the credibility distribution for the triangular fuzzy variables for the yield; determining the expectation and total variance for the yield; developing a model of the catastrophe bond diversification strategy; the numerical simulation of the catastrophe bond strategy model; and formulating a solution to the simulation model of the diversification strategy using the sequential method, quadratic programming, transformation, and linearization techniques. The simulation results show that the proposed model can overcome the self-duality characteristic not possessed by the possibilistic measures in the fuzzy variables. The results obtained are expected to contribute to describing the yield uncertainty of investing in catastrophe bond assets so that investors can make wise decisions.

Investor interest in single-regional earthquake catastrophe bonds has the potential to decline in the future. To pique investor interest, disaster bond prices can be determined by decomposed disaster zones using seismic parameters and Space Time Depth Magnitude (STDM) distance. Therefore, this study aims to develop a Decomposition of Disaster Region Using Earthquake Parameters and STDM Distance on the Earthquake Catastrophe Bond Pricing (DECBP) model for a single period. The basic idea of developing the model is to observe earthquake characteristics in an area by clustering the area based on the Earthquake Disaster Risk Index (EDRI), earthquake parameters (earthquake magnitude and depth), and STDM distance. The research and development (R&D) methodology used in this work is pursued through the creation of a mathematical model for calculating the price of earthquake catastrophe bonds over a single period. The development stages carried out are regional decomposition modelling, payment functions modelling, distribution of extreme earthquake magnitude values modelling, prediction of interest rates and coupons, numerical simulations, and analysis of the effect of interest rates, coupons, and the amount of regional decomposition on earthquake bond prices. Interest rates, coupons, and the number of regional decompositions that affect bond prices for earthquake events are the results of the analysis of the model that's been developed. The resulting model in this study is expected to assist the Super Purpose Vehicle (SPV) in determining the price of earthquake bonds and serve as a reference for future researchers developing models for the price of earthquake catastrophe bonds.

When the World Health Organization (WHO) declared COVID-19 as a pandemic that will be rapidly spreading around the world, it had significant implications on financial institutions, including Islamic banks, as they faced challenges in maintaining operations and managing risks during this unprecedented crisis. Thus, this study was conducted to analyse the relationship between the independent variables with return on assets and to determine the significant determinants of Islamic banks’ profitability performance during COVID-19. The dataset for this study were derived and collected from annual reports of the ten local Islamic banks in Malaysia for three years (2019-2021). Capital adequacy ratio (CAR), non-performing loan (NPL), operational efficiency (OEF) and bank size (SIZE) are chosen as the independent variables, while return of assets (ROA) as the dependent variable. Using Pearson correlation, it is found that CAR has a weak positive relationship, while NPL, OEF and SIZE have a weak negative relationship with ROA. This study also employs panel regression analysis, using a fixed effects model as the best estimator to determine the significant factors. The findings of the panel data regression analysis shows that only two variables were found to be positively significant, which are CAR and NPL, while OEF and SIZE are found to be insignificant with the profitability performance of Islamic banks.

Value at Risk (VaR) is one of the standard methods that can be used in measuring risk in stock investments. VaR is defined as the maximum possible loss for a particular position or portfolio in the known confidence level of a specific time horizon. The main topic discussed in this thesis is to estimate VaR using the TARCH (Threshold Autoregressive Conditional Heteroscedasticity) model in a time series by considering the effect of long memory. The TARCH model is applied to the daily log return data of a company's stock in Indonesia to estimate the amount of quantile that will be used in calculating VaR.Based on the analysis, it was found that with a significance level of 95% and assuming an investment of 200,000,000 IDR, the VaR using the TARCH model approach was 5,110,200 IDR per day.

Rational speculative bubble is a situation where the price of an asset exceeds its fundamental value. The burst of the rational speculative bubble may cause a negative impact towards the economy. If the bubble burst in the country which act as the world’s main economy, it will cause a big impact, and this will also affect the developing countries. Currently, there are no specific way to prevent this bubble from occurring but if the existence is known, several measures can be taken, so that, the impact can be minimized. This study aims to shows the size of the rational speculative bubble in the world’s main economy in four cycle. Also, the prediction on the size and the time of the bubbles burst of the fifth cycle will be discuss.

Value at Risk (VaR) has already becomes a standard measurement that must be carried out by financial institution for both internal interest and regulatory. VaR is defined as the value that portfolio will loss with a certain probability value and over a certain time horizon (usually one or ten days). In this paper we examine of VaR calculation when the volatility is not constant using generalized autoregressive conditional heteroscedastic (GARCH) model. We illustrate the method to real data from Indonesian financial market that is the stock of PT. Indosat Tbk.

The objective of this research was to identify and analyse the correlation between various factors that could impact the likelihood of medical and dental students at Hospital Universiti Sains Malaysia (Hospital USM), Kelantan, Malaysia to intend to exclusively breastfeed in the future. The questionnaire was designed and validated. The validated questionnaire was distributed among medical dan dental students. The study involved 162 participants, comprising 25 (15.4%) students from the dental program and 137 (84.6%) from the medical program. There are 56 (34.6%) male and 106 (65.4%) female involved in this study. At first, the collected data were analysed using MINITAB software through the contour plot and surface plot. Second, the data were analysed using the Spearman correlation. The result from correlation analysis shows that, most of the studied factors related to general knowledge rather than other studied factors. The discovery indicates that one approach to promoting the acceptance of exclusive breastfeeding among medical and dental students is by improving their general knowledge about the topic. The correlation analysis, 3-D plot, and contour plot demonstrate that factors such as gender, marital status, and specific and general knowledge are associated with the intention to exclusively breastfeed in the future. This finding is very important especially for awareness education, and establishing the future exclusive breastfeeding practiced among future parents.

In the face of investment risk, investors generally diversify and form an investment portfolio consisting of several assets. The problem is the fiery proportion of funds that must be allocated to each asset in the formation of investment portfolios. This paper aims to study the optimization of the Markowitz investment portfolio. In this study, the Markowitz model discussed is that which considers risk tolerance. Optimization is done by using the Lagrangean Multiplier method. From the study, an equation is obtained to determine the proportion (weight) of fund allocation for each asset in the formation of investment portfolios. So by using these equations, the determination of investment portfolio weights can be determined by capital.

Objective: This study aims to find the association of fractured orbital walls with other possible fractures reported in the maxillofacial trauma cases in the Oral Maxillofacial Clinic Oral Maxillofacial ward, Hospital USM Kelantan, Malaysia. Materials and methods: From 2013 to June 2018, records of patients who sustained maxillofacial fractures and presented them to the Accident and Emergency Department, Oral Maxillofacial Clinic, Hospital USM were reviewed, recorded, and analyzed. Data were obtained from 294 patients who met the study's eligibility requirements. The medical records of every patient with a comprehensive medical history were reviewed. The following factors were studied: age, gender, zygomatic arch, maxillary sinus, orbital wall, symphysis of the mandible, parasymphysis, and the condyle. The broken orbital walls in these patients were examined in detail. In the first stage, all of the variables that have been picked will be assessed for their significance from a clinical standpoint. All potential factors contributing to the orbital wall fracture were analyzed using the SPSS and R studio programs. As a result of meeting the inclusion criteria, 294 patients' data has been gathered. Each patient who had a complete medical record was subjected to an examination. In these patients, the cracked orbital walls were examined in greater depth. All chosen variables will be tested in the first stage to see if they are clinically significant. Results: The participants in this study were 228 men (77.6%) and 66 women (22.4%). It was found that the most common age ranges are 11-20 years (39.8%), 21-30 years, and 31-40 years (26.2%). According to Spearman correlation, all of the studied variables have a significant accosiation, with a p-value of less than 0.05. According to the findings of the multiple logistic regression, it was discovered that gender is significant, [0.2652 (0.1761); p < 0.25], Zygomatic Arch fracture, [ (SE)= -0.4511(0.2403); p < 0.25], Maxillary Sinus, [ (SE)= -0.5917 (0.2403) ; p < 0.25], Symphysis of the mandible, [ (SE)= 2.4826 (0.7298); p < 0.05], the condyle of the mandible, [ (SE) = 0.9479 (0.4315); exp (0.9479) = 2.58 3 times], the body of the mandible, [ (SE)= 0.4893 (0.4315) ; p < 0.25] and the angle of the mandible, [ (SE) = 0.6911 (0.4286); p < 0.25]. The validation of the factor through the Multilayer Neural Network (MLNN) and the accuracy obtained 97.71% with the predicted mean square error (PMSE) 0.159%. Conclusion: The matrix spearman correlation, multiple logistic regression, and neural network uncovered a clear association between orbital wall fracture and several other parameters. This discovery will help researchers understand the most common orbital wall fracture causes in maxillofacial trauma.

Background: Cholesterol levels in the blood, comprising both LDL and HDL cholesterol, can lead to artery plaque formation and potential blockages. Researchers are studying cholesterol levels in individuals with dyslipidemia and type 2 diabetes mellitus. Objective: The objective of this paper is to utilize the developed methodology to model the factor associated with the total cholesterol status in patients with dyslipidemia and type 2 diabetes mellitus. This undertaking has the potential to improve the prediction of total cholesterol levels among the analyzed patients by integrating comprehensive supplementary data from a statistical standpoint. Material and Methods: The data was collected from Hospital Universiti Sains Malaysia (Hospital USM), using statistical modelling techniques to evaluate data descriptions of numerous variables, including height, total cholesterol, triglyceride levels, low-density lipoprotein (LDL) levels, high-density lipoprotein (HDL) levels, and alkaline phosphatase (ALP) levels. The developed method was implemented and evaluated using the R-Studio, employing a neural network model with bootstrapping method and response surface methodology. Results: Our proposed method showed superior accuracy when dividing data into training and testing sets, offering a more precise prediction. The neural network's mean square error was approximately 0.021, demonstrating high precision. Conclusion: In this study, the proposed model demonstrates the method's capability for the improvement of research methodology. The outcome suggests that the methodology established for this investigation is capable of producing favourable results. The study's final analysis demonstrates that the model technique created for research is preferable.

Introduction: General anaesthesia (GA) dental care is one of the clinical strategies used to treat non-cooperative those, patients with chronic medical problems or with specialised and comprehensive treatment by some paediatric dentists. Objective: The purpose of this retrospective research was to analyse cases of general anaesthesia in paediatric dentistry at Hospital Universiti Sains Malaysia (USM), Kubang Kerian, Kelantan. Methods: A total of 298 patients reports were collected for data processing from 2015 to 2018. Results: About 54% of patients in the Malay ethnic community were male and the mean age was 5 years. The highest treatment is on the fissure sealant restoration, 100(33.6%) and follows by extraction of deciduous teeth 218(73.2%). The lowest treatment was found in Sandwich Technique Restoration 1(0.3%), excision of chronic mucocele, which is about 2(0.7%), and the treatment based on GIC Fuji IV 2(0.7%). The next analysis is focusing on the type of treatment. The result from multiple responses shows that patients with a combination of three treatment having 61%, this is the highest percentage. While patients with four types of treatment are the second highest, 59 cases or 25.4% and the third highest comes from the category of patients with two types of treatment. Conclusion: An annual rise in referred cases for dental care under GA has been observed. it is believed that the number of patients receiving dental treatment under GA is likely will continue to show an upward trend, and for the specific finding it was found that the extraction deciduous teeth are the highest case which is about 31.2%, fissure sealant restoration about 14.3% and stainless steel crown which is 13.3%.

The purpose of this study is to demonstrate the best strategy for the variable selection, using the developed Ordered Logistic Regression (OLR) and Multilayer Perceptron Neural Network (MLP). At the first stage, all the selected variables will be a screen for their important relationship point of view through ordered logistics regression and bootstrap methodology. After considering for 1500 of the bootstrapping methods, it was found that smoking factor, total cholesterol factor, and triglycerides come to a significant relationship to the level of hypertension. By considering the level of significance of 0.25 for ordered logistic regression, these three variables are being selected and used for the input of the MLP model. The performance of MLP was evaluated through the Predicted Mean Square Error (PMSE) of the neural network for the (MSE-forecasts the Network). PMSE is used as a measurement of how far away from our predictions are from the real data. The smallest MSE from MLP, indicate the best combination of variables selection in the model. In this research paper, we also provide the R syntax for OLR and MLP better illustration.

This research aims to investigate the influence of return on assets, return on equity and net profit margin on economic growth in Malaysia. The secondary data for this study were collected from annual reports of the five full-fledged Islamic banks in Malaysia for six years (2016-2021). This study employs panel data regression by indicating a random effect model as the best estimator. The findings of the panel regression analysis show return on assets (ROA) and net profit margin (NPM) of full-fledged Islamic banks in Malaysia have positive and significant effects on economic growth. Therefore, this proves solid evidence that Islamic banking institutions and their financial performances are one of Malaysia's economic growth channels. It will motivate many people to go for Islamic banking rather than conventional ones, given its contribution to the country's economic growth. From another perspective, Islamic banks too will inspire and be taken into consideration by the conventional banks towards opening Islamic windows to meet Malaysian's growing demand.

This paperdiscusses about the relationship between Gross Domestic Product with the size of speculative rational bubbles in stock market. The stock market chosen in this study are Hang Seng, Nikkei 225 and S&P 500. This is because China, Japan and United States are countries that pioneers the world economy. This paper presents analysis that have been done in order to view the relationship between GDP and thesize of speculative rational bubbles in stock market.

Traditional mean-variance (MV) models, considered effective in stable conditions, often prove inadequate in uncertain market scenarios. Therefore, there is a need for more robust and better portfolio optimization methods to handle the fluctuations and uncertainties in asset returns and covariances. This study aims to perform a Systematic Literature Review (SLR) on robust portfolio mean-variance (RPMV) in stock investment utilizing genetic algorithms (GAs). The SLR covered studies from 1995 to 2024, allowing a thorough analysis of the evolution and effectiveness of robust portfolio optimization methods over time. The method used to conduct the SLR followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. The result of the SLR presented a novel strategy to combine robust optimization methods and a GA in order to enhance RPMV. The uncertainty parameters, cardinality constraints, optimization constraints, risk-aversion parameters, robust covariance estimators, relative and absolute robustness, and parameters adopted were unable to develop portfolios capable of maintaining performance despite market uncertainties. This led to the inclusion of GAs to solve the complex optimization problems associated with RPMV efficiently, as well as fine-tuning parameters to improve solution accuracy. In three papers, the empirical validation of the results was conducted using historical data from different global capital markets such as Hang Seng (Hong Kong), Data Analysis Expressions (DAX) 100 (Germany), the Financial Times Stock Exchange (FTSE) 100 (U.K.), S&P 100 (USA), Nikkei 225 (Japan), and the Indonesia Stock Exchange (IDX), and the results showed that the RPMV model optimized with a GA was more stable and provided higher returns compared with traditional MV models. Furthermore, the proposed method effectively mitigated market uncertainties, making it a valuable tool for investors aiming to optimize portfolios under uncertain conditions. The implications of this study relate to handling uncertainty in asset returns, dynamic portfolio parameters, and the effectiveness of GAs in solving portfolio optimization problems under uncertainty, providing near-optimal solutions with relatively lower computational time.

: The investor interest in multi-regional earthquake bonds may drop because high-risk locations are less appealing to investors than low-risk ones. Furthermore, a single parameter (earthquake magnitude) cannot accurately express the severity due to an earthquake. Therefore, the aim of this research is to propose valuing a framework for single earthquake bonds (SEB) using a double parameter trigger type, namely magnitude and depth of earthquakes, based on zone division according to seismic information. The zone division stage is divided into two stages. The first stage is to divide the covered area based on regional administrative boundaries and clustering based on the earthquake disaster risk index (EDRI), and the second stage involves clustering based on magnitude and depth of earthquakes and distance between earthquake events using the K-Means and K-Medoids algorithms. The distribution of double parameter triggers is modeled using the Archimedean copula. The result obtained is that the price of SEB based on the clustering result of EDRI categories and K-Means is higher than the price obtained by clustering EDRI categories and K-Medoids with maturities of less than 5 years. The result of this research is expected to assist the Special Purpose Vehicle in determining the price of SEB.

Oral cancer is an important global health concern, representing the sixth most frequent malignant tumor. The oral squamous cell carcinoma (OSCC) is the most common malignant neoplasm of the oral cavity with up to 50% of mortality rate (highest prevalence being identified in Asia). In 2012, it has been reported that 14.1 million new cancer cases and 8.2 million cancer deaths. Numbers of studies have been performed to investigate the factors that have direct and indirect or both associated with the OSCC, including their survival time. In this paper, the potential clinicopathological prognostic factors will be determined in patients who attended Hospital Universiti Sains Malaysia from 2005 to 2015. For such prediction, the use of hazard regression is used previously, but here an attempt is made to propose a covariate-dependent prognostic model to identify the factors and the predictor importance according to the statistical significant point of view. The proposed model is very useful for the prediction and for the inferences of the patient’s management time with the high-risk clinicopathological factors.