The determinants of internet financial disclosure: The perspective of Malaysian listed companies

This paper investigates whether Internet Financial Disclosure (IFD) can be explained by the elements of the company’s characteristics and dominant personalities in board committees.Ten variables have been tested using data collected from 194 Malaysian listed companies’ websites, namely, internationality, leverage, foreign shareholders, information technology (IT) experts, firm’s age, number of shareholders, listing status, dominant personalities in the audit committee, chairman of audit and nomination committees, and dominant personalities in the audit and nomination committees.It is found that IT experts, firm’s age, number of shareholders and listing status are significantly affected by the level of IFD. However dominant personalities in the audit and nomination committees are negatively related to the level of IFD in Malaysia.The study provides some evidence to support the signalling theory and the cost and benefit hypothesis in relation to Internet disclosure

Assessing inflation and greenhouse gas emissions interplay via neural network analysis : a comparative study of energy use in the USA, EU, and China

This study examines the relationship between inflation and greenhouse gas (GHG) emissions in three major economies: the United States of America (USA), the European Union (EU), and China. The analysis spans from 1960 to 2021 for the USA and EU, and from 1971 to 2021 for China. A feedforward neural network model, optimized using the Levenberg–Marquardt backpropagation algorithm, was employed to predict GHG emissions based on annual inflation rates and fossil fuel energy consumption. The study integrates historical data on inflation trends with GHG emissions, measured in CO2 equivalents, and fossil fuel energy consumption, expressed as a percentage of total energy use. This multidimensional approach allows for a nuanced understanding of the economic-environmental interplay in these regions. Key findings indicate a nonlinear response of GHG emissions to inflation rates. In the USA, GHG emissions begin to decrease when inflation rates exceed 4.7%. Similarly, in the EU, a steep reduction in emissions is observed beyond a 7.5% inflation rate. China presents a more complex pattern, with two critical inflection points: the first at a 4.5% inflation rate, where GHG emissions start to decline sharply, and the second at a 7% inflation rate, beyond which further increases in inflation do not significantly reduce emissions. A critical global insight is the identification of a uniform inflation rate, around 4.4%, across all regions, at which GHG emissions consistently increase by 1%, hinting at a shared global economic behavior impacting the environment. This discovery is vital for policymakers, emphasizing the need for tailored regional strategies that consider unique economic structures, energy policies, and environmental regulations, alongside a coordinated global approach

A Novel Spike-Wave Discharge Detection Framework Based on the Morphological Characteristics of Brain Electrical Activity Phase Space in an Animal Model

Background: Animal models of absence epilepsy are widely used in childhood absence epilepsy studies. Absence seizures appear in the brain’s electrical activity as a specific spike wave discharge (SWD) pattern. Reviewing long-term brain electrical activity is time-consuming and automatic methods are necessary. On the other hand, nonlinear techniques such as phase space are effective in brain electrical activity analysis. In this study, we present a novel SWD-detection framework based on the geometrical characteristics of the phase space.Methods: The method consists of the following steps: (1) Rat stereotaxic surgery and cortical electrode implantation, (2) Long-term brain electrical activity recording, (3) Phase space reconstruction, (4) Extracting geometrical features such as volume, occupied space, and curvature of brain signal trajectories, and (5) Detecting SDWs based on the thresholding method. We evaluated the approach with the accuracy of the SWDs detection method.Results: It has been demonstrated that the features change significantly in transition from a normal state to epileptic seizures. The proposed approach detected SWDs with 98% accuracy.Conclusion: The result supports that nonlinear approaches can identify the dynamics of brain electrical activity signals

New algorithms to Enhanced Fused Images from Auto-Focus Images

هذا البحث يقترح طريقة جديدة لدمج صورة ذات التركيز التلقائي بالاعتماد على خوارزميات جديدة. الخوارزمية الأولى تعتمد على حساب الانحراف المعياري لدمج صورتين. الخوارزمية الثانية تتركز على التباين عند نقاط الحافات وطريقة الترابط كعامل معيار لجودة الصورة الناتجة. هذه الخوارزمية تعتمد على ثلاثة مربعات بأحجام مختلفة عند المناطق المتجانسة وتتحرك 10 نقاط ضمن المنطقة المتجانسة.  الصورة الناتجة من الدمج تحتوي على نتائج جيدة في التباين بسبب إضافة نقاط حافات من الصورتين والتي تعتمد على الخوارزميات المقترحة. تم مقارنة النتائج مع طرق مختلفة.Enhancing quality image fusion was proposed using new algorithms in auto-focus image fusion. The first algorithm is based on determining the standard deviation to combine two images. The second algorithm concentrates on the contrast at edge points and correlation method as the criteria parameter for the resulted image quality. This algorithm considers three blocks with different sizes at the homogenous region and moves it 10 pixels within the same homogenous region. These blocks examine the statistical properties of the block and decide automatically the next step. The resulted combined image is better in the contrast value because of the added edge points from the two combined images that depend on the suggested algorithms. This enhancement in edge regions is measured and reaches to double in enhancing the contrast. Different methods are used to be compared with the suggested method

The Determinants of Internet Financial Disclosure: The Perspective of Malaysian Listed Companiesal

This paper investigates whether Internet Financial Disclosure (IFD) can be explained by the elements of the company’s characteristics and dominant personalities in board committees. Ten variables have been tested using data collected from 194 Malaysian listed companies’ websites, namely, internationality, leverage, foreign shareholders, information technology (IT) experts, firm’s age, number of shareholders, listing status, dominant personalities in the audit committee, chairman of audit and nomination committees, and dominant personalities in the audit and nomination committees. It is found that IT experts, firm’s age, number of shareholders and listing status are significantly affected by the level of IFD. However dominant personalities in the audit and nomination committees are negatively related to the level of IFD in Malaysia. The study provides some evidence to support the signaling theory and the cost and benefit hypothesis in relation to Internet disclosure.   Keywords: Determinants, Internet Financial Disclosure, listed companies

MicroRNA-29b/c-3p Indicate Advanced Liver Fibrosis/Cirrhosis in Univentricular Heart Patients With and Without Fontan Palliation

Aim: The present study aims to identify those microRNAs (miRNAs) in patients with univentricular heart (UVH) disease with and without Fontan palliation that may be associated with advanced liver fibrosis/cirrhosis. Materials and Methods: SurePrintTM 8 × 60K Human v21 miRNA arrays were used to determine the miRNA abundance profiles in the blood of 48 UVH patients with and without Fontan palliation and 32 matched healthy controls. The abundance levels of selected miRNAs have been validated by quantitative reverse transcription-polymerase chain reaction (RT-qPCR). Results: According to microarray analysis, 50 miRNAs were found to be significantly abundant in UVH patients of which miR-29b-3p and miR-29c-3p were significantly related to the model of end-stage liver disease (MELD)-Albumin and albumin-bilirubin (ALBI) score representing advanced liver fibrosis/cirrhosis. Relative expression levels of both miRNAs were significantly higher in patients with a higher collapsibility index representing venous hepatic congestion, a higher MELD-Albumin or ALBI score and incomplete or no Fontan palliation. In the logistic regression analysis, a MELD-Albumin score ≥ 11 or ALBI score > −2.6 were best predicted by total bilirubin (OR 6.630, P = 0.016), albumin (OR 0.424, P = 0.026), and miR-29c-3p (OR 33.060, P = 0.047). After adjustment to the status of Fontan palliation, however, no statistical significance of these parameters was found thus underlining the importance of palliation status on progression of liver fibrosis/ cirrhosis in UVH patients. Conclusions: In UVH patients with and without Fontan palliation, miR-29b-3p and miR-29c-3p seem to be markers of advanced liver fibrosis/cirrhosis and thus may be used in the risk assessment of these patients

Micro-RNA 150-5p predicts overt heart failure in patients with univentricular hearts

Background: In patients with left heart failure, micro-RNAs (miRNAs) have been shown to be of diagnostic and prognostic value. The present study aims to identify those miRNAs in patients with univentricular heart (UVH) disease that may be associated with overt heart failure. Methods: A large panel of human miRNA arrays were used to determine miRNA expression profiles in the blood of 48 UVH patients and 32 healthy controls. For further selection, the most abundantly expressed miRNA arrays were related to clinical measures of heart failure and selected miRNAs validated by polymerase chain reaction were used for the prediction of overt heart failure and all-cause mortality. Results: According to microarray analysis, 50 miRNAs were found to be significantly abundant in UVH patients of which miR-150-5p was best related to heart failure parameters. According to ROC analysis, NT-proBNP levels (AUC 0.940, 95% CI 0.873–1.000; p = 0.001), miR-150-5p (AUC 0.905, 95% CI 0.779–1.000; p = 0.001) and a higher NYHA class ≥ III (AUC 0.893, 95% CI 0.713–1.000; p = 0.002) were the 3 most significant predictors of overt heart failure. Using a combined biomarker model, AUC increased to 0.980 indicating an additive value of miR-150-5p. Moreover, in the multivariate analysis, a higher NYHA class ≥ III (p = 0.005) and miR-150-5p (p = 0.006) turned out to be independent predictors of overt heart failure. Conclusion: In patients with UVH, miR-150-5p is an independent predictor of overt heart failure and thus may be used in the risk assessment of these patients

A realization of classification success in multi sensor data fusion

The field of measurement technology in the sensors domain is rapidly changing due to the availability of statistical tools to handle many variables simultaneously.The phenomenon has led to a change in the approach of generating dataset from sensors. Nowadays, multiple sensors, or more specifically multi sensor data fusion (MSDF) are more favourable than a single sensor due to significant advantages over single source data and has better presentation of real cases.MSDF is an evolving technique related to the problem for combining data systematically from one or multiple (and possibly diverse) sensors in order to make inferences about a physical event, activity or situation. Mitchell (2007) defined MSDF as the theory, techniques, and tools which are used for combining sensor data, or data derived from sensory data into a common representational format. The definition also includes multiple measurements produced at different time instants by a single sensor as described by (Smith & Erickson, 1991)