Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology

This study aims to explore the multifractal characteristics and resilience of Sukuk and green bond indices during three major global crises: the COVID-19 outbreak (December 1, 2019, to February 23, 2022), the Russo-Ukrainian War (February 24, 2022, to September 22, 2024). In this paper, we use daily return data from Sukuk indices, including Sukuk AAA, Sukuk AA, Sukuk A, and Sukuk BBB, as well as green bond indices, including S P Green Bond Index, Green Bond Select Index, and clean equity Kensho Clean Energy Index, and Cleantech Index to use the multifractal detrended fluctuation analysis (MFDFA) method. It is to evaluate their sustainability, consistency, and ability to cope with market shocks resulting from these crises. The results show that all the indices are multifractal. However, Sukuk with higher ratings (AAA and AA) demonstrate better stability and more remarkable persistence by having higher values of hq and a narrow scaling exponent spectrum. A and BBB Sukuk’s lower credit ranks were characterized by higher multifractality and increased responses to market dynamics. Among all the green bonds, the Green Bond Select Index exhibited high multifractal properties and a widespread and symmetrical multifractal spectrum, apart from the high value of persistence and adaptability, which was also reflected in the Kensho Clean Energy Index. Conversely, the Cleantech Index showed clearer market trends, along with a small set of quantifiable characteristics. This study, then, emphasizes the value of analyzing high-rated Sukuk, as well as strong, green bond indices, while appreciating the role of these within the geopolitical and pandemic trading context. This comparative approach during several crises recognizes the value of these instruments and how they mitigate losses and stabilize the turbulent phases of the financial markets. It illustrates the risks associated when turbulent phases of financial markets ensue and how these instruments advocates for a financial market.

The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system.

This research explores the importance of assisting students who may learn at a slower pace than their peers in school through lens of available research literature. These students have just as much potential as others but might need extra time and support to understand their school lessons. The study emphasizes how crucial it is to find and help these students early in their education. It's like offering them a helping hand precisely when they need it the most and also discusses what makes these students unique and how teachers, parents, and others can collaborate to provide the support they need. Discovering and assisting these students early can greatly improve their school performance and boost their self-confidence. It's like creating a personalized plan that suits each student's learning style, helping them excel in school and feel proud of their achievements. Furthermore, this study suggests that future research could develop deeper into the most effective strategies for identifying and helping slow learners. It also encourages exploring innovative ways to create inclusive and supportive educational environments that cater to the individual strengths and needs of these students. In essence, this research advocates for ensuring that every student, regardless of their learning pace, receives the necessary support for a promising future in school and beyond. It highlights the importance of early identification and intervention, and it paves the way for potential future research to continue improving educational practices for slow learners.

The excessive use of electricity, particularly driven by the surge in Fintech and Bigtech credit lines as part of the digital revolution, has become a focal point for environmentalists. This concern has resonated within the broader community. Consequently, the stakeholders are reassessing the overall costs and benefits of technological advancements, specifically in Fintech. The focus is on directing efforts toward restoring environmental quality. Considering that technology is seen as a double-edged sword for environmental performance, we examine the non-linear impact of Fintech and Bigtech credit on CO2 emissions across 85 countries over a period from 2013-2019. The results indicate a non-linear relationship between Fintech credit and CO2 emissions. Our study brings forth new insights, revealing a more pronounced inverted U-shaped relationship in developed countries Compared to developing. Crucially, this research underscores the importance for policymakers to encourage the adoption of Fintech to achieve environmental sustainability.

Research shows that the retention of entirely motivated teachers has had a powerful impact on students' performance. Public schools still need to maintain and sustain the motivation of teachers. Variables in research can help the school management retain teachers' lost motivation. The variables compensation, working condition, and working experience are not supported through research; one variable, professional development, is significant. This study aimed to determine factors based on motivation in public sectors in Karachi district South secondary schools. A total of the teachers of the public sectors in District South, Karachi were taken as population, and 140 respondents constitute the study sample by simple random sampling. The questionnaire was designed based on choices and used for data collection. The data was analyzed through SPSS in terms of frequency distribution and percentage. Finding suggested that all teachers were motivated and satisfied with the school's working conditions, which ultimately enhanced students' academic performance in class and at school.