Abstract This work explores the relationship between employee performance commitments and corporate earnings management, particularly in the context of mergers and acquisitions (M&A). It highlights that while performance commitments can align employee goals with corporate strategy, reduce information asymmetry, and signal market confidence, they may also incentivize earnings management when targets are unrealistically high. The study leverages a decision tree model based on information entropy... This work explores the relationship between employee performance commitments and corporate earnings management, particularly in the context of mergers and acquisitions (M&A). It highlights that while performance commitments can align employee goals with corporate strategy, reduce information asymmetry, and signal market confidence, they may also incentivize earnings management when targets are unrealistically high. The study leverages a decision tree model based on information entropy to analyze this dynamic using M&A data from 2009 to 2022. The model uses real earnings management as the dependent variable and includes commitment duration, commitment amount, and several financial controls. Results indicate a positive correlation between unrealistic performance commitments and heightened earnings management. A case study of "XX Smart Enterprise" illustrates how excessive commitments led to significant financial strain and potential earnings manipulation. The paper proposes an optimized commitment level of approximately 30% to mitigate such risks. By integrating a generalized entropy increase algorithm into the traditional decision tree framework, the model improves prediction accuracy and offers practical guidance for setting realistic performance targets. The findings caution against overly ambitious commitments in M&A agreements and emphasize the need for balanced incentive structures to prevent short term earnings manipulation that could harm long term corporate health and stakeholder trust. Read More Read Less

Abstract The paper proposes a hybrid risk assessment model combining Fuzzy Comprehensive Evaluation and Grey Relational Analysis to evaluate legal and other risks in cross border mergers and acquisitions (M&A) by Chinese energy enterprises. It highlights that while China's overseas M&A activity has grown significantly under the "going out" strategy, many ventures fail due to inadequate legal risk management particularly in environmental, labor, and tax... The paper proposes a hybrid risk assessment model combining Fuzzy Comprehensive Evaluation and Grey Relational Analysis to evaluate legal and other risks in cross border mergers and acquisitions (M&A) by Chinese energy enterprises. It highlights that while China's overseas M&A activity has grown significantly under the "going out" strategy, many ventures fail due to inadequate legal risk management particularly in environmental, labor, and tax regulations abroad. The study identifies six key risk categories: market, information, legal, industrial, integration, and financial risks. Using data from three hypothetical M&A plans, both methods consistently rank Plan 3 as the lowest risk option. The analysis underscores that market and integration risks are the most influential. The paper concludes by recommending thorough legal due diligence, professional legal strategies, and robust risk management frameworks to enhance M&A success. Despite its contributions, the author acknowledges limitations in scope and calls for more comprehensive future research. Read More Read Less

Abstract This work presents a study on extracting vocal melodies from polyphonic music using advanced signal processing and deep learning techniques. It emphasizes the importance of accurately identifying the main melody typically carried by the human voice for applications such as music information retrieval, cover song identification, and copyright protection. The proposed method involves several stages: signal preprocessing (including down sampling, normalization, and Short Time... This work presents a study on extracting vocal melodies from polyphonic music using advanced signal processing and deep learning techniques. It emphasizes the importance of accurately identifying the main melody typically carried by the human voice for applications such as music information retrieval, cover song identification, and copyright protection. The proposed method involves several stages: signal preprocessing (including down sampling, normalization, and Short Time Fourier Transform), note segmentation using the DIS algorithm, and multiple fundamental frequency (F0) estimation within the 70-1000 Hz range. A key innovation is the use of a sparse auto encoder neural network (SAENN) combined with a softmax classifier to distinguish vocal melody from instrumental accompaniment. Trained and tested on the MIR-1K dataset, the model achieves over 85.1% recognition accuracy. Experimental results show that the approach reduces melody localization errors and decreases average extraction time by 0.12 seconds compared to traditional methods. The study also introduces the Average Extraction Time (AET) as a new metric for evaluating computational efficiency. Overall, integrating improved pitch salience computation with deep learning significantly enhances both accuracy and processing speed in melody extraction, offering a robust solution for real-world audio analysis tasks. The findings suggest promising directions for future work in music signal processing and vocal separation technologies. Read More Read Less