The Effects of Governance Practices on the Performance of the Sri Lankan Public Sector Development Projects

The use of Project Governance Practices (PGPs) is increasingly taking a substantial stage in developing economies, particularly in a newly industrializing nation like Sri Lanka where public sector development projects have been implemented to expedite the nation's growth and prosperity. As it is essential to lay a transparent and tangible foundation for an effective public sector that can be sustained, the paper focuses on the significance of PGPs in enhancing the performance of the Sri Lankan public sector development initiatives. Structuring, normalizing, facilitating, and post-conflict-sensitive variables were used to measure the PGPs, whereas financial and non-financial performance measures were employed to evaluate the project performance. The researcher conducted direct observations and administered a comprehensive Likert-scaled questionnaire to 518 project administrators involved in various Sri Lankan public sector development projects, specifically focusing on projects related to irrigation, roads and highways, water and sanitation, and other infrastructure developmental projects. The data was analyzed using the structural equation modeling through the AMOS software. The results showed that PGPs created a considerable improvement in project performance, which increased support for expanding economic prosperity through balanced development strategies and sustainability-based policy formation. Keywords: Performance, Project governance practices, Public sector development projects, Sri Lank

Enhancing Skin Cancer Diagnosis with Deep Learning-Based Classification

The diagnosis of skin cancer has been identified as a significant medical challenge in the 21st century due to its complexity, cost, and subjective interpretation. Early diagnosis is critical, especially in fatal cases like melanoma, as it affects the likelihood of successful treatment. Therefore, there is a need for automated methods in early diagnosis, especially with a diverse range of image samples with varying diagnoses. An automated system for dermatological disease recognition through image analysis has been proposed and compared to conventional medical personnel-based detection. This project proposes an automated technique for skin cancer classification using images from the International Skin Imaging Collaboration (ISIC) dataset, incorporating deep learning (DL) techniques that have demonstrated significant advancements in artificial intelligence (AI) research. An automated system that recognizes and classifies skin cancer through deep learning techniques could prove useful in the medical field, as it can accurately detect the presence of skin cancer at an early stage. The ISIC dataset, which includes a vast collection of images of various skin conditions, provides an excellent opportunity to develop and validate deep learning algorithms for skin cancer classification. The proposed technique could have a significant impact on the medical industry by reducing the workload of medical personnel while providing accurate and timely diagnoses.

COLORIMETRIC METHODS FOR THE ESTIMATION OF TOPIRAMATE IN TABLETS

Two simple sensitive and precise colorimetric methods A and B were developed for the estimation of topiramate in bulk drug as well as in pharmaceutical dosage form. Methods A is based on the formation of yellow coloured chromogen by condensation reaction of topiramate with Ehrlich’s reagent ( p - dimethyl amino benzaldehyde ) which has absorption maximum at 547nm. Method B is based on the formation of an orange coloured complex by oxidation reaction of topiramate with 2,2’- bipyridyl in the presence of ferric chloride which has absorption maximum at 519nm. The proposed methods are statistically validated and found to be useful for the routine determination of topiramate in tablets. Keywords: Topiramate, Colorimetry, Tablets, Validatio

Stabilization of 2D NSHP Recursive Digital Filters with Guaranteed Stability Using PLSI Polynomials

Two-dimensional digital filters have gained wide acceptance in recent years. For recursive filters, nonsymmetric half-plane versions (also known as semicausal) are more general than quarter-plane versions (also known as causal) in approximating arbitrary magnitude characteristics. The major problem in designing two-dimensional recursive filters is to guarantee their stability with the expected magnitude response. In general, it is very difficult to take stability constraints into account during the stage of approximation. This is the reason why it is useful to develop techniques, by which stability problem can be separated from the approximation problem. In this way, at the end of approximation process, if the filter becomes unstable, there is a need for stabilization procedures that produce a stable filter with similar magnitude response as that of the unstable filter. This paper, demonstrates a stabilization procedure for a two-dimensional nonsymmetric half-plane recursive filters based on planar least squares inverse (PLSI) polynomials. The paper's findings prove that, a new way of form-preserving transformation can be used to obtain stable PLSI polynomials. Therefore obtaining PLSI polynomial is computationally less involved with the proposed form-preserving transformation as compared to existing methods, and the stability of the resulting filters is guaranteed