This paper presents an effective method for performing audio steganography, which would help in improving the security of information transmission. Audio steganography is one of the techniques for hiding secret messages within an audio file to maintain communication secrecy from unwanted listeners. Most of these conventional methods have several drawbacks related to distortion, detectability, and inefficiency. To mitigate these issues, a new scheme is presented which combines the techniques of image interpolation with LSB encoding. It selects non-seed pixels to allow reversibility and diminish distortion in medical images. Our technique also embeds a fragile watermarking scheme to identify any breach during transmission to recover data securely and reliably. A magic rectangle has also been used for encryption to enhance data security. This paper proposes a robust, low-distortion audio steganography technique that provides high data integrity with undetectability and will have wide applications in sectors like e-healthcare, corporate data security, and forensic applications. In the future, this approach will be refined for broader audio formats and overall system robustness.

Catastrophes, like earthquakes, bring sudden and severe damage, causing fatalities, injuries, and property loss. This often triggers a rapid increase in insurance claims. These claims can encompass various types, such as life insurance claims for deaths, health insurance claims for injuries, and general insurance claims for property damage. For insurers offering multiple types of coverage, this surge in claims can pose a risk of financial losses or bankruptcy. One option for insurers is to transfer some of these risks to reinsurance companies. Reinsurance companies will assess the potential losses due to a catastrophe event, then issue catastrophe reinsurance contracts to insurance companies. This study aims to construct a valuation model for catastrophe reinsurance contracts that can cover claim losses arising from two types of insurance products. Valuation in this study is done using the Fundamental Theorem of Asset Pricing, which is the expected present value of the number of claims that occur during the reinsurance coverage period. The number of catastrophe events during the reinsurance coverage period is assumed to follow a Poisson process. Each impact of a catastrophe event, such as the number of fatalities and injuries that cause claims, is represented as random variables, and modeled using Peaks Over Threshold (POT). This study uses Clayton, Gumbel, and Frank copulas to describe various dependence characteristics between random variables. The parameters of the POT model and copula are estimated using Inference Functions for Margins method. After estimating the model parameters, Monte Carlo simulations are performed to obtain numerical solutions for the expected value of catastrophe reinsurance based on the Fundamental Theorem of Asset Pricing. The expected reinsurance value based on Monte Carlo simulations using Indonesian earthquake data from 1979–2021 is Rp 10,296,819,838.

Data literacy is an important skill for students in studying physics. With data literacy, students have the ability to collect, analyze and interpret data as well as construct data-based scientific explanations and reasoning. However, students’ ability to data literacy is still not satisfactory. On the other hand, various learning strategies still provide opportunities to design learning models that are more directed at data literacy skills. For this reason, in this research a physics learning model was developed that is oriented towards physics objects represented in various modes and is called the Object-Oriented Physics Learning (OOPL) Model. The learning model was developed through several stages and based on the results of the validity analysis; it shows that the OOPL model is included in the valid category. The OOPL model fulfils the elements of content validity and construct validity. The validity of the OOPL model and its implications are discussed in detail in the discussion.

In today’s digital education landscape, safeguarding the privacy and security of educational data, particularly the distribution of grades, is paramount. This research presents the “secure grade distribution scheme (SGDS)”, a comprehensive solution designed to address critical aspects of key management, encryption, secure communication, and data privacy. The scheme’s heart lies in its careful key management strategy, offering a structured approach to key generation, rotation, and secure storage. Hardware security modules (HSMs) are central to fortifying encryption keys and ensuring the highest security standards. The advanced encryption standard (AES) is employed to encrypt graded data, guaranteeing the confidentiality and integrity of information during transmission and storage. The scheme integrates the Diffie-Hellman key exchange protocol to establish secure communication, enabling users to securely exchange encryption keys without vulnerability to eavesdropping or interception. Secure communication channels further fortify graded data protection, ensuring data integrity in transit. The research findings underscore the SGDS’s efficacy in achieving the goals of secure grade distribution and data privacy. The scheme provides a holistic approach to safeguarding educational data, ensuring the confidentiality of sensitive information, and protecting against unauthorized access. Future research opportunities may centre on enhancing the scheme’s robustness and scalability in diverse educational settings.

This study aimed to measure the impact of implementing mechanisms of accounting data governance, represented by International Accounting Standards, internal auditing, external auditing, audit committees, disclosure and transparency, and performance evaluation, on the quality of financial reporting data for the commercial banks listed on the Amman Stock Exchange, totaling (15) banks. To achieve the objectives of this study, a descriptive-analytical approach was adopted by developing a questionnaire to collect the primary data measuring the study variables. The questionnaire was distributed to employees in the financial and control departments of these banks, with a total of (375) respondents from the total study population of (733) individuals. Appropriate statistical methods were used to analyze the data, test hypotheses, and the results of this study revealed a strong positive impact of five variables of accounting data governance mechanisms on achieving the quality of financial reporting data. These variables are ranked from highest to lowest in terms of the strength of impact and correlation with the quality of financial reports: disclosure and transparency, external auditing, International Accounting Standards, internal auditing, and audit committees. However, there was no impact of the performance evaluation governance variable on achieving the quality of financial reporting data. These results call on the management of commercial banks in the study to commit to the objective implementation of the requirements of accounting data governance mechanisms as stipulated by international professional assemblies.