Amidst an upsurge in the quantity of delinquent loans, the financial industry is experiencing a fundamental transformation in the approaches utilised for debt recovery. The debt collection process is presently undergoing automation and improvement through the utilisation of Artificial Intelligence (AI), an emergent technology that holds the potential to revolutionise this sector. By leveraging machine learning, natural language processing, and predictive analytics, automated debt recovery systems analyse vast quantities of data, generate forecasts regarding the likelihood of recovery, and streamline operational processes. Debt collection systems powered by AI are anticipated to be compliant, precise, and effective. On the other hand, conventional approaches are linked to increasing expenditures and inefficiencies in operations. These solutions facilitate efficient resource allocation, customised communication, and rapid data analysis, all while minimising the need for human intervention. Significant progress has been made in data analytics, predictive modelling, and decision-making through the application of artificial intelligence (AI) in debt recovery; this has the potential to revolutionize the financial sector’s approach to debt management. The findings of the research underscore the criticality of artificial intelligence (AI) in attaining efficacy and precision, in addition to the imperative of a data-centric framework to fundamentally reshape approaches to debt collection. In conclusion, artificial intelligence possesses the capacity to profoundly transform the existing approaches utilized in debt management, thereby guaranteeing financial institutions’ sustained profitability and efficacy. The application of machine learning methodologies, including predictive modelling and logistic regression, signifies the potential of the system.

The study aims to explain the relationship between the effectiveness of a business and its management through the analysis of working capital. The findings prove the complementary relationship. The analysis of working capital will always have a significant impact on the effectiveness of business management. The main objective of any corporation is to be effective in business, which can be achieved by analyzing the working capital. The result shows that analysis of working capital based on factors like operational efficiency, the company’s earnings and profitability, cash management, corporate receivable management, and corporate inventory management creates room for improvement and effectiveness in business management. Firms might enhance finances for business expansion by lowering their working capital requirements. It has also been revealed that there is a considerable difference in industries across time. It was observed that there is a high association between working capital efficiency and firm profitability. A highly efficient corporation is less vulnerable to liquidity risk and is also self-sufficient in terms of external finance. Numerous studies have been done to regulate the true rapport between working capital investments and their impact on financial presentation. It demonstrates that effective investment in working capital management may boost profitability and business value. The relationship between accounting and finance was explained by measuring working capital management in demand to illustrate the status of profitability. It was suggested that accountants take a more professional approach to updating their accounting and finance skills in their organization through effective working capital management.

Education is one of the basic needs that every child should have. Information communication technology has a significant influence on special needs children’s schooling. Instead of considering learning a difficult chore, the adoption of measures such as ICT can simplify it and make inclusive education a reality. Aim: This current systematic literature review aims to determine the extent of ICT adoptions in special education scenarios. Method: This paper examined pertinent literature on ICT in special education in the period 2000 to 2023. The key articles extracted through keyword search were gathered from databases indexed in Web of Science and Scopus. The collected data were then screened using a VOS viewer for the most relevant information. From the web of Science, 31 articles were found to have connections with one another while the same process when applied to the Scopus database, helped obtain 8 articles. Results: A total of 39 articles fulfilled the search inclusion criteria of minimum two keyword occurrences. These articles were all written in English and published between 2000 and 2023. The in-depth analysis of all these articles was performed along three broad themes, viz., availability of SEN based ICTs and their impact on children with disabilities, quality of available ICT integrated curriculum for SEN and the challenges in promoting ICTs for inclusive education. Conclusions: The paper concludes that ICT integration in special education would make learning easier for children with disabilities when compared to learning using traditional methods. Implications: The paper pinpoints significant limitations in ICT use found in existing literature and the lack of it to support inclusive education. The authors make recommendations for improved ICT integrated curriculum to improve inclusivity.

Disaster Risk Management benefits from innovative techniques including AI and Multi Sensor Fusion. The Firefguard Approach uses such technologies to improve the Wildfire Management works in Saxony, Eastern Germany by supporting standing efforts in Early Warning, Disaster Response and Monitoring. Unmanned Aerial Systems (UAS) play a vital role in providing real-time information via a 5G network to a central information management system that delivers geospatial information to response teams. This study highlights the potential of combining UAS, AI, geospatial solutions and existing data for real-time wildfire monitoring and risk assessment systems.

The use of porous media to simplify the thermohydraulic of a nuclear reactor is the topic of recent research. As a case study, the rector of 200 kW installed at Missouri University of Science and Technology is modeled in this paper. To help this objective, a fundamental CFD examination was completed to supplement the neutronics investigation on the present reactor. Characteristics of thermal energy removal from a typical research reactor are modeled by numerical thermal transport in porous media. The neutron flux is modeled by the nodal expansion method. For the thermo-hydraulic part, a three-dimensional governing equation is solved by an iterative method to find the steady-state solution of fluid flow and temperature in loss of coolant condition where the heat produced in the reactor core is removed by free convection. The profiles of heat flux for various power levels are benchmarked. Pressure, temperature, and velocity contours in the power passage were assessed at 300 kW and 500 kW power levels. To reduce the computational cost, a porous media approach for the whole geometry was utilized. The numerical results agree with the experimental results. The developed model can be used for safety and reliability analysis for various loss of coolant accidents.