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.

With the rapid development of artificial intelligence (AI) technology, its application in the field of auditing has gained increasing attention. This paper explores the application of AI technology in audit risk assessment and control (ARAC), aiming to improve audit efficiency and effectiveness. First, the paper introduces the basic concepts of AI technology and its application background in the auditing field. Then, it provides a detailed analysis of the specific applications of AI technology in audit risk assessment and control, including data analysis, risk prediction, automated auditing, continuous monitoring, intelligent decision support, and compliance checks. Finally, the paper discusses the challenges and opportunities of AI technology in audit risk assessment and control, as well as future research directions.

The policy to accelerate the design of the Detailed Spatial Plan regulation document (RDTR) is a strategic step to enhance ease of doing business and promote sustainable development in Indonesia. Targeting 2036 RDTR sites nationwide, the initiative relies on various policy interventions and technical approaches. However, as of 8 January 2024, only 399 RDTRs (19.59%) were enacted after four years of implementation. This underperformance suggests the need to examine factors influencing the process, including issues at each stage of the RDTR design business process. While often overlooked due to its perceived irrelevance to the core substance of planning, analyzing the process is crucial to addressing operational and procedural challenges. This research identifies critical issues arising from the preparation to the enactment stage of RDTR regulations and proposes necessary policy changes. Using an explanatory approach, the study employs methods such as Analytic Hierarchy Process (AHP), post-review analysis, stakeholder analysis, business process evaluation, and scenario planning. Results show several impediments, including challenges related to commitment, technical and substantive issues, managerial coordination, policy frameworks, ICT support, and data availability. These findings serve as inputs for the development of business process improvement scenarios and reengineering schemes based on Business Process Management principles.

Background: Digital transformation in the sports industry has become increasingly crucial for sustainable development, yet comprehensive empirical evidence on policy effectiveness and risk management remains limited. Purpose: This study investigates the impact of policy support and risk factors on digital transformation in sports companies, examining heterogeneous effects across different firm characteristics and regional contexts. Methods: Using panel data from 168 sports companies listed on China’s A-shares markets and the New Third Board from 2019 to 2023, this study employs multiple regression analyses, including baseline models, instrumental variables estimation, and robustness tests. The digital transformation level is measured through a composite index incorporating digital infrastructure, capability, and innovation dimensions. Results: The findings reveal that policy support significantly enhances digital transformation levels (coefficient = 0.238, p < 0.01), while financial risks demonstrate the strongest negative impact (−0.162, p < 0.01). Large firms and state-owned enterprises show stronger responses to policy support (0.312 and 0.278, respectively, p < 0.01). Regional development levels significantly moderate the effectiveness of policy implementation. Conclusions: The study provides empirical evidence for the differential effects of policy support and risk factors on digital transformation across various firm characteristics. The findings suggest the need for differentiated policy approaches considering firm size, ownership structure, and regional development levels. Implications: Policy makers should develop targeted support mechanisms addressing specific challenges faced by different types of firms, while considering regional disparities in digital transformation capabilities.