The distress of commercial companies is considered one of the most critical stages leading to the liquidation and termination of the business. This danger increases in the context of poor management, stagnation, and the occurrence of crises and external circumstances that affect the company’s ability to cope. Rules regarding financial restructuring of distressed commercial companies may be regarded as the most prominent legal framework adopted by Emirati, Kuwaiti and French legislators to address the instability and distress of commercial enterprises and to provide solutions to mitigate the risk of bankruptcy and liquidation. It is a preventive measure aimed at reaching an agreement between the debtor and creditors to resolve the disturbances or difficulties faced by the company, which may affect its obligations to others. Therefore, financial restructuring is considered a mean of prevention and rescue for commercial companies, and the success of this rescue is linked to the debtor’s cooperation and seriousness in overcoming such issue.

The study documents the model of the knowledge transfer process between the University, the Vocational Training Center and the industrial actors. The research seeks to answer to the following questions. Where is new knowledge generated? Where does knowledge originate from? Is there a central actor? If so, which organization? Hypotheses tested by the research: H1: Knowledge starts from the higher education institution. H2: Most “new knowledge” is generated in universities and large multinational companies. H3: The university is a central actor in the knowledge flow, transmitting both hard and soft skills, as well as subject (‘know-what’), organizational (‘know-why’), use (‘know-how’), relational (‘know-who’), and creative (‘care-why’) knowledge. The aim of the research is to model the way of knowledge flow between the collaborating institutions. The novelty of this research is that it extends the analysis of the knowledge flow process not only to the actors of previous researches (higher education institutions, business organizations, and government) but also to secondary vocational education and training institutions. The methodology used in the research is the analysis of the documents of the actors investigated and the questionnaire survey among the participants. Knowledge transfer is the responsibility of the university and its partner training and business organizations. In vocational education and training, knowledge flows based on the knowledge economy, innovation and technological development are planned, managed and operational. The research has shown that knowledge is a specific good that it is indivisible in its production and consumption, that it is easy and cheap to transfer and learn.

The objective of this research paper is to investigate potential avenues for value creation in the refined sugar market for domestic use, a market currently facing a critical juncture. The growing concerns about the health impacts of sugar have resulted in a notable decline in demand. Furthermore, changes in European Union regulations have introduced additional operators into the Spanish market, increasing competition and amplifying the need for innovation. This study examines how brands can respond to these challenges by enhancing their value proposition through market segmentation, targeted marketing strategies, and adaptive packaging solutions. To achieve this objective, we have conducted market research, which involved an in-depth interview, and a questionnaire distributed to 402 individuals responsible for household purchases. The findings suggest potential approaches for addressing the needs of consumers with a focus on health and well-being, while simultaneously enhancing the durability of products, thus facilitating greater brand differentiation. Furthermore, the study underscores the pivotal role of public policies and regulatory frameworks in influencing consumer behavior and market dynamics. Policies promoting sugar alternatives, labelling requirements, and packaging innovations have been demonstrated to impact brand strategies and consumer preferences. By aligning with these policy-driven shifts, companies can enhance their positioning in a mature and competitive market. This research contributes to the existing literature on brand value in commodity markets by integrating insights into the impact of regulation and consumer segmentation. Our recommendations emphasize the importance of marketing strategies that are informed by an understanding of the policy context, which not only enhances brand equity but also promotes sustainable growth in the retail sugar industry.

This article investigates the income and expenditure patterns of individuals, with a specific focus on investments in luxury items, real estate, and expensive modes of transportation. Using global databases such as “Luxury Goods—Worldwide/Statista Market Forecast” and “Data—WID—World Inequality Database”, the authors explore the correlation between high demand for luxury items and economic inequality. The study emphasizes the role of luxury tax as essential for implementing a progressive personal income tax system in Russia. By examining country-specific factors, particularly in China and Russia, and conducting a comparative analysis of progressive tax systems globally, the research highlights the potential of luxury tax to enhance the efficacy of income tax in reducing inequality.

This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.