Financial crisis and dynamic the dependency between six international currencies volatility with sectors volatility: evidence from six Australian sectors

This paper investigates the influence of volatility of foreign exchange rate of the U.S., the U.K., Netherlands, Japan, China and Singapore to the volatility of the six Australian sectors within the investigated period controlling for the time periods global financial crisis 2007-2008.The volatility in this study was estimated using GARCH(1,1) models. Daily data is collected for a period of 2002 to 2014. The dataset is divided into three sub periods: before GFC (July 2002 to July 2007), during GFC (July 2007 to July 2009) and after GFC (July 2009 to July 2014). The estimated results find strong relationship between exchange rates for the six countries with six Australian sectors volatility, except health care sectors during GFC. The same relationship is evident before GFC, except banks sector. The statistically significant impact of these foreign exchange on the six Australian sectors continues after GFC, except materials sector is weakly significantly. This result is important for the investors and other market participants to understand the risk factors related to the sectors of the Australian stock market

Contagion risk for Australian authorised deposit-taking institutions

This paper investigates the contagion risk for Australian-owned authorized deposit taking institutions (ADIs) spilling from the US and UK banks. We hypothesized that Australian ADIs are prone to extreme shocks experienced by its US and UK counterparts. We define four discrete events for the Australian banking sector in terms of the number of banks exceeding at a time an extreme value. The extreme value is defined as the 90th percentile on the negative tail of the distribution of changes in the distance to default obtained through Black and Scholes (1973) and Merton (1974) formula. Then we fit a multinomial logistic model (MLM) to relate these events to the number of exceedances (extreme events) occurring in the US and the UK in the previous day for the time period September 2006 to September 2011. The MLM estimates reveal strong contagion effects for Australian ADIs from the US and UK banks

Oil and coal price shocks and coal industry returns: international evidence

This paper examines the effect of energy price shocks on coal sector stock returns and supplements studies evaluating the effect of oil prices on the stock price of oil and gas companies. A 1% increase in coal price return raises coal sector returns by between 0.22% and 0.30%. This result is robust across developed, emerging and differing groups of Asia-Pacific and Pacific countries, and is analogous with findings that a 1% increase in oil price raises the return of oil and gas companies by between 0.14% and 0.38% depending on country and time period studied. Oil price return also significantly influences coal sector return even controlling for coal price return. Relatively large increases in coal and oil price returns have statistically significant and disproportionate effects on raising coal sector returns. Market return, interest rate premium, and foreign exchange rate risk are also significant risk factors for excess coal sector stock returns. The sensitivity of coal sector returns to oil price shocks suggest a role for investment in stocks that rise when energy prices increase in a well balanced portfolio and in pursuing profitable investment strategies. Natural gas price returns do not influence coal sector returns in the presence of coal price returns

Analyzing Code Tracking Algorithms for Galileo Open Service Signal

The ever-increasing public interest on location and positioning services has originated a demand for higher performance Global Navigation Satellite Systems (GNSSs). Galileo Open Service (OS) signal, part of the European contribution to future GNSS, was designed to respond to the above demand. In all GNSSs, the estimation with high accuracy of the Line-Of-Sight (LOS) delay is a prerequisite. The Delay Lock Loops (DLLs) and their enhanced variants (i.e., feed-back code tracking loops) are the structures of choice for the commercial GNSS receivers, but their performance in severe multipath scenarios is still rather limited. In addition, the new satellite positioning system proposals specify the use of a new modulation, the Binary Offset Carrier (BOC) modulation, which triggers a new challenge in the code tracking stage. Therefore, in order to meet this emerging challenge and to improve the accuracy of the delay estimation in severe multipath scenarios, this thesis analyzes feed-back as well as feed-forward code tracking algorithms and proposes a novel algorithm, namely Peak Tracking (PT), which is a combination of both feed-back and feed-forward structures and utilizes the advantages inherent in these structures. In this thesis, the code tracking algorithms are studied and analyzed for Sine BOC (SinBOC) modulated Galileo OS signal for various multipath profiles in Rayleigh fading channel model. The performance of the analyzed algorithms are measured in terms of various well-known criteria such as Root-Mean-Square-Error (RMSE), Mean-Time-to-Lose Lock (MTLL), delay error variance and Multipath Error Envelopes (MEEs). The simulation results show that the proposed PT algorithm outperforms all other analyzed algorithms in various multipath profiles in good Carrier-to-Noise-Ratios (CNRs). The simulation results are compared with the theoretical Cramer-Rao Bound (CRB) and the comparison shows that the delay error variance for PT algorithm approaches the theoretical limit with the increase in CNR. Therefore, the proposed algorithm can be considered as an excellent candidate for implementation in future Galileo receivers, especially when tracking accuracy is a concern. /Kir1

An Enhanced FGI-GSRx Software-Defined Receiver for the Execution of Long Datasets

The Global Navigation Satellite System (GNSS) software-defined receivers offer greater flexibility, cost-effectiveness, customization, and integration capabilities compared to traditional hardware-based receivers, making them essential for a wide range of applications. The continuous evolution of GNSS research and the availability of new features require these software-defined receivers to upgrade continuously to facilitate the latest requirements. The Finnish Geospatial Research Institute (FGI) has been supporting the GNSS research community with its open-source implementations, such as a MATLAB-based GNSS software-defined receiver `FGI-GSRx’ and a Python-based implementation `FGI-OSNMA’ for utilizing Galileo’s Open Service Navigation Message Authentication (OSNMA). In this context, longer datasets are crucial for GNSS software-defined receivers to support adaptation, optimization, and facilitate testing to investigate and develop future-proof receiver capabilities. In this paper, we present an updated version of FGI-GSRx, namely, FGI-GSRx-v2.0.0, which is also available as an open-source resource for the research community. FGI-GSRx-v2.0.0 offers improved performance as compared to its previous version, especially for the execution of long datasets. This is carried out by optimizing the receiver’s functionality and offering a newly added parallel processing feature to ensure faster capabilities to process the raw GNSS data. This paper also presents an analysis of some key design aspects of previous and current versions of FGI-GSRx for a better insight into the receiver’s functionalities. The results show that FGI-GSRx-v2.0.0 offers about a 40% run time execution improvement over FGI-GSRx-v1.0.0 in the case of the sequential processing mode and about a 59% improvement in the case of the parallel processing mode, with 17 GNSS satellites from GPS and Galileo. In addition, an attempt is made to execute v2.0.0 with MATLAB’s own parallel computing toolbox. A detailed performance comparison reveals an improvement of about 43% in execution time over the v2.0.0 parallel processing mode for the same GNSS scenario.Peer reviewe

Integrated bioinformatics and statistical approach to identify the common molecular mechanisms of obesity that are linked to the development of two psychiatric disorders: Schizophrenia and major depressive disorder

Obesity is a chronic multifactorial disease characterized by the accumulation of body fat and serves as a gateway to a number of metabolic-related diseases. Epidemiologic data indicate that Obesity is acting as a risk factor for neuro-psychiatric disorders such as schizophrenia, major depression disorder and vice versa. However, how obesity may biologically interact with neurodevelopmental or neurological psychiatric conditions influenced by hereditary, environmental, and other factors is entirely unknown. To address this issue, we have developed a pipeline that integrates bioinformatics and statistical approaches such as transcriptomic analysis to identify differentially expressed genes (DEGs) and molecular mechanisms in patients with psychiatric disorders that are also common in obese patients. Biomarker genes expressed in schizophrenia, major depression, and obesity have been used to demonstrate such relationships depending on the previous research studies. The highly expressed genes identify commonly altered signalling pathways, gene ontology pathways, and gene-disease associations across disorders. The proposed method identified 163 significant genes and 134 significant pathways shared between obesity and schizophrenia. Similarly, there are 247 significant genes and 65 significant pathways that are shared by obesity and major depressive disorder. These genes and pathways increase the likelihood that psychiatric disorders and obesity are pathogenic. Thus, this study may help in the development of a restorative approach that will ameliorate the bidirectional relation between obesity and psychiatric disorder. Finally, we also validated our findings using genome-wide association study (GWAS) and whole-genome sequence (WGS) data from SCZ, MDD, and OBE. We confirmed the likely involvement of four significant genes both in transcriptomic and GWAS/WGS data. Moreover, we have performed co-expression cluster analysis of the transcriptomic data and compared it with the results of transcriptomic differential expression analysis and GWAS/WGS

Analyzing Code Tracking Algorithms for Galileo Open Service Signal

The ever-increasing public interest on location and positioning services has originated a demand for higher performance Global Navigation Satellite Systems (GNSSs). Galileo Open Service (OS) signal, part of the European contribution to future GNSS, was designed to respond to the above demand. In all GNSSs, the estimation with high accuracy of the Line-Of-Sight (LOS) delay is a prerequisite. The Delay Lock Loops (DLLs) and their enhanced variants (i.e., feed-back code tracking loops) are the structures of choice for the commercial GNSS receivers, but their performance in severe multipath scenarios is still rather limited. In addition, the new satellite positioning system proposals specify the use of a new modulation, the Binary Offset Carrier (BOC) modulation, which triggers a new challenge in the code tracking stage. Therefore, in order to meet this emerging challenge and to improve the accuracy of the delay estimation in severe multipath scenarios, this thesis analyzes feed-back as well as feed-forward code tracking algorithms and proposes a novel algorithm, namely Peak Tracking (PT), which is a combination of both feed-back and feed-forward structures and utilizes the advantages inherent in these structures. In this thesis, the code tracking algorithms are studied and analyzed for Sine BOC (SinBOC) modulated Galileo OS signal for various multipath profiles in Rayleigh fading channel model. The performance of the analyzed algorithms are measured in terms of various well-known criteria such as Root-Mean-Square-Error (RMSE), Mean-Time-to-Lose Lock (MTLL), delay error variance and Multipath Error Envelopes (MEEs). The simulation results show that the proposed PT algorithm outperforms all other analyzed algorithms in various multipath profiles in good Carrier-to-Noise-Ratios (CNRs). The simulation results are compared with the theoretical Cramer-Rao Bound (CRB) and the comparison shows that the delay error variance for PT algorithm approaches the theoretical limit with the increase in CNR. Therefore, the proposed algorithm can be considered as an excellent candidate for implementation in future Galileo receivers, especially when tracking accuracy is a concern. /Kir1

Optimizing Seaweed (<i>Ascophyllum nodosum</i>) Thermal Pyrolysis for Environmental Sustainability: A Response Surface Methodology Approach and Analysis of Bio-Oil Properties

This study focuses on optimizing the thermal pyrolysis process to maximize pyrolysis oil yield using marine biomass or seaweed. The process, conducted in a batch reactor, was optimized using response surface methodology and Box–Behnken design. Variables like temperature, residence time, and stirring speed were adjusted to maximize bio-oil yield. The optimal conditions yielded 42.94% bio-oil at 463.13 °C, with a residence time of 65.75 min and stirring speed of 9.74 rpm. The analysis showed that temperature is the most critical factor for maximizing yield. The bio-oil produced contains 11 functional groups, primarily phenol, aromatics, and alcohol. Its high viscosity and water content make it unsuitable for engines but suitable for other applications like boilers and chemical additives. It is recommended to explore the potential of refining the bio-oil to reduce its viscosity and water content, making it more suitable for broader applications, including in engine fuels. Further research could also investigate the environmental impact and economic feasibility of scaling up this process

Aroma based localization in GNSS-denied environments

This paper studies infrastructure less localization solutions using aroma fingerprints. These fingerprints are collected under varying conditions from different indoor locations using Ion Mobility Spectrometry based Electronic Noses. A supervised machine learning algorithm for data processing location estimation is proposed. The non-parametric system is trained with data from all locations, and its performance evaluated using data from the same locations collected under different environmental conditions. Five different classifiers are studied and tested for location estimation. The Stochastic Gradient Descent classifier achieved the highest accuracy, with the 푘NN with Euclidian distance also performing reliably under different conditions

Prevalence and determinants of fetal macrosomia in Bangladesh

Background: Fetal macrosomia, marked by excessive birth weight, is a significant public health issue in developing countries, yetit has received less attention compared to low birth weight. This study aims to determine the prevalence of fetal macrosomia inBangladesh and its associated factors.The study utilized data from 4,754 women with complete birth weight information of theirchildren from the Bangladesh Multiple Indicator Cluster Survey (MICS) -2019, defining fetal macrosomia as newborns with a birthweight ≥4000 grams regardless of gestational age. Bivariate logistic regression assessed associations between independentvariables and fetal macrosomia, presenting adjusted odds ratios (AOR) and a 95% confidence interval (CI), while controlling forpotential confounders such as women's age, wealth index, education, healthcare utilization, comorbidities, newborn sex, and placeof residence.Results: The prevalence of fetal macrosomia was 11.6%. Significant associations with fetal macrosomia included highermaternal age group (30-34 years) (AOR=1.36, 95% CI=1.07-1.74), secondary level of mother's education (AOR=1.95, 95% CI=1.43-2.66),experienced physical attacks (AOR=1.41, 95% CI=1.06-1.88), hypertension during pregnancy (AOR=1.54, 95% CI=1.15-2.07), and ruralresidence (AOR=1.25, 95% CI=1.15-1.49). Female infants had 18% lower odds of being macrosomic compared to male infants(AOR=0.82, 95% CI=0.72-0.93).One in ten infants in Bangladesh are born with macrosomia, necessitating a multifaceted approachinvolving improving maternal nutrition, promoting healthy lifestyles, enhancing access to quality prenatal care, and addressing socioeconomic, residential, and healthcare system challenges, underlining the importance of further community-based research toexpand the study's scope