This review comprehensively summarizes various preparatory methods of polymeric bone scaffolds using conventional and modern advanced methods. Compilations of the various fabrication techniques, specific composition, and the corresponding properties obtained under clearly identified conditions are presented in the commercial formulations of bone scaffolds in current orthopedic use. The gaps and unresolved questions in the existing database, efforts that should be made to address these issues, and research directions are also covered. Polymers are unique synthetic materials primarily used for bone and scaffold applications. Bone scaffolds based on acrylic polymers have been widely used in orthopedic surgery for years. Polymethyl methacrylate (PMMA) is especially known for its widespread applications in bone repair and dental fields. In addition, the PMMA polymers are suitable for carrying antibiotics and for their sustainable release at the site of infection.

The spread of the coronavirus disease in 2019 (COVID-19) in Thailand has led to a lack of liquidity and income for entrepreneurs, increasing the variety of distribution channels compared to store sales. This will be a solution for businesses struggling and creating value to raise the income levels of community enterprises in Thailand. This was an integrated and participatory action research using qualitative techniques through observation, interviews, recordings, analysis, and interpretation of the operational characteristics of community enterprises from field visits for consultation. This study aimed to examine the problems and obstacles of online selling by community enterprise entrepreneurs and to find guidelines for advising lead entrepreneurs in the Digital Market. These 25 community enterprise entrepreneurs produced community herbal products in Thailand. The research findings were analyzed using grounded theory according to the research objectives. From the research results, it is possible to summarize the problems and obstacles faced by entrepreneurs in selling products online among community enterprise entrepreneurs owing to the lack of knowledgeable administrators and the decline in demand for products affected by the COVID-19 pandemic. Furthermore, barriers to laws, regulations requirements related to cannabis products included legal controls only for cultivation and the production process until the product was sold, and production capacity could not be produced to meet the demand when there was a large volume of orders. Solutions were as follows: increasing skills and knowledge for entrepreneurs, especially in the potential; finding a way to pass on the business to the new generation to continue the business; using strategies to create cooperation with other enterprise networks and government agencies; creating online selling channels through various platforms; increasing funding to develop production processes; and using technology to create competitive advantages and marketing planning and delivery to make online sales an essential channel.

This study investigates the intricate relationship between awareness advertising and buying intention among Iraqi grocery shoppers, exploring the mediating role of consumer attitude. Employing a quantitative approach, the authors surveyed 300 shoppers. Using a random sampling technique. To ensure rigor and validity, the authors rigorously analyzed the relationships using partial least squares structural equation modelling (PLS-SEM) based on 288 valid responses. The findings reveal that awareness advertising significantly impacts buying intention, mediated by consumer attitude. These insights offer valuable management implications for product marketers. Sufficient brand awareness attracts consumer attention, shapes positive attitudes, and ultimately drives purchase decisions. This study further validates the theoretical model of consumer response by empirically establishing consumer attitude as a central intermediary between awareness advertising and buying intention within the Iraqi market context.

This research delves into the urgent requirement for innovative agricultural methodologies amid growing concerns over sustainable development and food security. By employing machine learning strategies, particularly focusing on non-parametric learning algorithms, we explore the assessment of soil suitability for agricultural use under conditions of drought stress. Through the detailed examination of varied datasets, which include parameters like soil toxicity, terrain characteristics, and quality scores, our study offers new insights into the complexities of predicting soil suitability for crops. Our findings underline the effectiveness of various machine learning models, with the decision tree approach standing out for its accuracy, despite the need for comprehensive data gathering. Moreover, the research emphasizes the promise of merging machine learning techniques with conventional practices in soil science, paving the way for novel contributions to agricultural studies and practical implementations.

The paper considers an important problem of the successful development of social qualities in an individual using machine learning methods. Social qualities play an important role in forming personal and professional lives, and their development is becoming relevant in modern society. The paper presents an overview of modern research in social psychology and machine learning; besides, it describes the data analysis method to identify factors influencing success in the development of social qualities. By analyzing large amounts of data collected from various sources, the authors of the paper use machine learning algorithms, such as Kohonen maps, decision tree and neural networks, to identify relationships between different variables, including education, environment, personal characteristics, and the development of social skills. Experiments were conducted to analyze the considered datasets, which included the introduction of methods to find dependencies between the input and output parameters. Machine learning introduction to find factors influencing the development of individual social qualities has varying dependence accuracy. The study results could be useful for both practical purposes and further scientific research in social psychology and machine learning. The paper represents an important contribution to understanding the factors that contribute to the successful development of individual social skills and could be useful in the development of programs and interventions in this area. The main objective of the research was to study the functionalities of the machine learning algorithms and various models to predict the students’s success in learning.

This study thoroughly examined the use of different machine learning models to predict financial distress in Indonesian companies by utilizing the Financial Ratio dataset collected from the Indonesia Stock Exchange (IDX), which includes financial indicators from various companies across multiple industries spanning a decade. By partitioning the data into training and test sets and utilizing SMOTE and RUS approaches, the issue of class imbalances was effectively managed, guaranteeing the dependability and impartiality of the model’s training and assessment. Creating first models was crucial in establishing a benchmark for performance measurements. Various models, including Decision Trees, XGBoost, Random Forest, LSTM, and Support Vector Machine (SVM) were assessed. The ensemble models, including XGBoost and Random Forest, showed better performance when combined with SMOTE. The findings of this research validate the efficacy of ensemble methods in forecasting financial distress. Specifically, the XGBClassifier and Random Forest Classifier demonstrate dependable and resilient performance. The feature importance analysis revealed the significance of financial indicators. Interest_coverage and operating_margin, for instance, were crucial for the predictive capabilities of the models. Both companies and regulators can utilize the findings of this investigation. To forecast financial distress, the XGB classifier and the Random Forest classifier could be employed. In addition, it is important for them to take into account the interest coverage ratio and operating margin ratio, as these finansial ratios play a critical role in assessing their performance. The findings of this research confirm the effectiveness of ensemble methods in financial distress prediction. The XGBClassifier and RandomForestClassifier demonstrate reliable and robust performance. Feature importance analysis highlights the significance of financial indicators, such as interest coverage ratio and operating margin ratio, which are crucial to the predictive ability of the models. These findings can be utilized by companies and regulators to predict financial distress.