Polyurethane is a multipurpose polymer with valuable mechanical, thermal, and chemical stability, and countless other physical features. Polyurethanes can be processed as foam, elastomer, or fibers. This innovative overview is designed to uncover the present state and opportunities in the field of polyurethanes and their nanocomposite sponges. Special emphasis has been given to fundamentals of polyurethanes and foam materials, related nanocomposite categories, and associated properties and applications. According to literature so far, adding carbon nanoparticles such as graphene and carbon nanotube influenced cell structure, overall microstructure, electrical/thermal conductivity, mechanical/heat stability, of the resulting polyurethane nanocomposite foams. Such progressions enabled high tech applications in the fields such as electromagnetic interference shielding, shape memory, and biomedical materials, underscoring the need of integrating these macromolecular sponges on industrial level environmentally friendly designs. Future research must be intended to resolve key challenges related to manufacturing and applicability of polyurethane nanocomposite foams. In particular, material design optimization, invention of low price processing methods, appropriate choice of nanofiller type/contents, understanding and control of interfacial and structure-property interplay must be determined.

Credit risk assessment is one of the most important aspects of financial decision-making processes. This study presents a systematic review of the literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in credit risk assessment, offering insights into methodologies, outcomes, and prevalent analysis techniques. Covering studies from diverse regions and countries, the review focuses on AI/ML-based credit risk assessment from consumer and corporate perspectives. Employing the PRISMA framework, Antecedents, Decisions, and Outcomes (ADO) framework and stringent inclusion criteria, the review analyses geographic focus, methodologies, results, and analytical techniques. It examines a wide array of datasets and approaches, from traditional statistical methods to advanced AI/ML and deep learning techniques, emphasizing their impact on improving lending practices and ensuring fairness for borrowers. The discussion section critically evaluates the contributions and limitations of existing research papers, providing novel insights and comprehensive coverage. This review highlights the international scope of research in this field, with contributions from various countries providing diverse perspectives. This systematic review enhances understanding of the evolving landscape of credit risk assessment and offers valuable insights into the application, challenges, and opportunities of AI and ML in this critical financial domain. By comparing findings with existing survey papers, this review identifies novel insights and contributions, making it a valuable resource for researchers, practitioners, and policymakers in the financial industry.

This study aims to investigate the enhancement in electrical efficiency of a polycrystalline photovoltaic (PV) module. The performance of a PV module primarily depends upon environmental factors like temperature, irradiance, etc. Mainly, the PV module performance depends upon the panel temperature. The performance of the PV module has an inverse relationship with temperature. The open circuit voltage of a module decreases with the increase in temperature, which consequently leads to the reduction in maximum power, efficiency, and fill factor. This study investigates the increase in the efficiency of the PV module by lowering the panel temperature with the help of water channel cooling and water-channel accompanied with forced convection. The two arrangements, namely, multi-inlet outlet and serpentine, are used to decrease the temperature of the polycrystalline PV module. Copper tubes in the form of the above arrangements are employed at the back surface of the panel. The results demonstrate that the combined technique is more efficient than the simple water-channel cooling technique owing to multi-heat dissipation and effective heat transfer, and it is concluded that the multi-inlet outlet cooling technique is more efficient than the serpentine cooling technique, which is attributed to uniform cooling over the surface and lesser pressure losses.

The reform of the training mode for English majors in the context of the new liberal arts is a dynamic process that conforms to social needs, continuously improves and optimizes. After a series of special investigations, this article proposes development ideas for the cultivation of English majors in local applied undergraduate universities based on the analysis of the demand for English majors' abilities in the job market. The aim is to solve the problems of unclear characteristics of English major talent cultivation in local applied undergraduate universities and weak competitiveness of graduates in employment.

Under the background of engineering education certification, the traditional personnel training model can’t meet the requirements of high-quality personnel training under the new engineering background. Taking Surveying and mapping engineering major of Liaoning Institute of Science and Technology as an example, this paper explores the continuous improvement of the output-oriented talent training model through collaborative education of talents training objectives, curriculum system, practical teaching system, teacher team construction, enterprises and graduates. Over the years, the surveying and mapping engineering major of our school has achieved good results in personnel training. The major actively ADAPTS to the regional development of the local economy, closely connects with the needs of regional talents, and highlights its characteristics in serving the local economy.