GLA University

<P> In the herbal medicine system, N. cadamba is one of the most significant plants in the Rubiaceae family. N. cadamba contains numerous phytochemicals and secondary metabolites, in-cluding β-sitosterol, quinovic acid, cadambagenic acid, cadamine, and cadambine, which exhibit pharmacological and biological effects, making it a highly valuable medicinal plant. N. cadamba has demonstrated potential in managing conditions such as diabetes, epilepsy, hypertension, and other disorders, offering a natural alternative to synthetic chemical treatments. </P> <P> Over a century of research has been conducted to identify its phytochemicals and their effects. How-ever, due to a lack of appropriate model systems, only a small percentage (≤2%) of these compounds have been successfully marketed. Beyond its medicinal properties, N. cadamba is also valued as an ornamental plant with considerable cultural and religious importance. </P> <P> To stimulate interest and support the commercialization of N. cadamba phytochemicals, this review consolidates current knowledge on these compounds and highlights their pharmacological signifi-cance. By addressing the challenges of commercialization, including contentious issues, model sys-tem inadequacies, and solubility problems, the potential of N. cadamba as a cornerstone in herbal medicine can be fully realized. This comprehensive understanding and subsequent application could transform treatment options, providing natural and effective alternatives for managing and potentially curing various diseases.

To understand how software quality improves during testing, SRGMs play a significant role. SRGMs are even more critical, especially when human learning, debugging practices, and uncertainties in the testing environment influence fault detection processes. Traditional SRGMs often assume ideal or simplified conditions, which may not fully reflect real testing scenarios. To address this gap, this study proposes two SRGMs that consider imperfect debugging, S-shaped learning pattern, and uncertainty modeled through a Weibull distribution. These additions allow the models to capture fault detection behaviors more realistically. The performance of the proposed models is evaluated using three datasets and compared with some well-known SRGMs. Results show that the proposed models perform competitively across all datasets. Although no single model dominates across all cases, the results indicate that combining learning and uncertainty provides flexibility and enhanced predictive ability in various testing environments.