This review focuses on ferrites, which are gaining popularity with their unique properties like high electrical resistivity, thermal stability, and chemical stability, making them suitable for versatile applications both in industry and in biomedicine. This review is highly indicative of the importance of synthesis technique in order to control ferrite properties and, consequently, their specific applications. While synthesizing the materials with consideration of certain properties that help in certain methods of preparation using polyol route, green synthesis, sol-gel combustion, or other wise to tailor make certain properties shown by ferrites, this study also covers biomedical applications of ferrites, including magnetic resonance imaging (MRI), drug delivery systems, cancer hyperthermia therapy, and antimicrobial agents. This was able to inhibit the growth of all tested Gram-negative and positive bacteria as compared with pure ferrite nanoparticles without Co, Mn or Zn doping. In addition, ferrites possess the ability to be used in environmental remediation; such as treatment of wastewater which makes them useful for high-surface-area and adsorption capacity due heavy metals and organic pollutants. A critical analysis of functionalization strategies and possible applications are presented in this work to emphasize the capability of nanoferrites as an aid for the advancement both biomedical technology and environmental sustainability due to their versatile properties combined with a simple, cost effective synthetic methodology.

The integration of medical images is the process of registering and fusing them to obtain a greater amount of diagnostic information. In this work an analysis is performed for the integration of images obtained through computed axial tomography and magnetic resonance imaging, for which a tool was developed in the Matlab program, where the registration is implemented through equivalent features; in addition, the pairs of images are compared by several fusion rules, with a view to identify the best algorithm in which the resulting fused image contains the most information from the original representations.

Entrepreneurial resilience in regions is essential for enabling the entrepreneurial ecosystem to overcome natural disasters, catastrophes, wars, and various crisis situations it may face. However, this phenomenon has been underexplored in the literature despite its critical importance for business development, and consequently, for social progress. Therefore, the objective of this article is to conduct a systematic literature review to identify the antecedents of regional entrepreneurial resilience in situations of adversity. To achieve this goal, a qualitative, descriptive research approach was employed. Specifically, a systematic literature review was carried out following the PRISMA method, which included a total of 231 scientific articles retrieved from high impact journals. Of these, only 12% (27 documents) focused on regional entrepreneurial resilience. Five key antecedents of regional entrepreneurial resilience were identified: action orientation, the region’s historical precedents, opportunity exploitation, collaboration, resources, and preparedness. Additionally, it is suggested that future research should focus on understanding the impact of crises, identifying agile response models to crises, defining roles for each member of the entrepreneurial ecosystem to achieve economic recovery in regions, and analyzing the design of public policies that contribute to overcoming adversity. The study concludes that when a region is resilient, it is more likely to overcome crises and adversity.

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.

Objectives: This research aimed to empirically examine the transformative impacts of Artificial Intelligence (AI) adoption on financial reporting quality in Jordanian banking, with internal controls as a hypothesized mediation mechanism. Methodology: Quantitative survey data was collected from 130 bank personnel. Multi-item reflective measures assessed AI adoption, internal controls, and financial reporting quality—structural equation modelling analysis relationships between constructs. Findings: The research tested four hypotheses grounded in agency and contingency theories. Confirmatory factor analysis demonstrated sound measurement models. Structural equation modelling revealed that AI adoption significantly transformed financial reporting quality. The mediating effect of internal controls on the AI-quality relationship was supported. Specifically, the path from AI adoption to quality was significant, indicating a positive impact. Despite internal controls strongly predicting quality, its mediating effect significantly shaped the degree of transformation driven by AI adoption. The indirect effect of AI on quality through internal controls was also significant. Findings imply a growing diffusion of AI applications in core financial reporting systems. Practical implications: Increasing AI applications focus on holistically transforming systems, reflecting committing adoption. Jordanian banks selectively leverage controls to moderate AI-induced transformations. Originality/value: This study provides essential real-world insights into how AI is adopted and impacts the Jordanian banking sector, a key player in a fast-evolving developing economy. By examining the role of internal controls, it deepens our understanding of how AI works in practice and offers practical advice for integrating technology effectively and improving information quality. Its mixed methods, unique context, and focus on AI’s impact on organizations significantly enrich academic literature. Recommendations: Banks should invest in integrated AI architectures, strategically strengthen critical controls to steer transformations, and incrementally translate AI innovations into core processes.