http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/79 2026-04-08T12:25:42Z http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/1323 Title: Group Graph Measures of Brain Networks New Concepts and Definitions Authors: Subaramya, V; Perera, A.A.I; Ratnarajah, N Abstract: Brain networks consist of cortical and subcortical regions as nodes and connections between them as edges. These brain networks convey a huge amount of information that has been almost unexplored previously and the fundamentals of brain network analysis arrived from graph theory. Graph theoretical approaches have been used by many studies to investigate the human brain network related to health. Different aspects of global and local brain connectivity are characterized by graph measures of brain networks. However, the full potential of the graph theory on brain network has not been fully exploited by the research community as studies have used the same measures and techniques. All published local graph measures, such as centrality are intended to apply for a single node and the global graph measures such as efficiency have been applied for the whole-network. But a group of nodes such as a lobe would be benefited from a formulation that applies to group of nodes rather than a single node or the whole network. A question such as “which brain lobe is more structurally central in the normal brain?” could be answered to some extent by the application of a centrality measure that applied to a group of nodes. In brain asymmetric studies, global metrics have been applied to left and right hemispherical networks separately by considering only intra-hemispheric edges. However, for the valid comparison, global measures must include the inter-hemispheric edges as well. The concept of group graph measures has the potential to solve such problems. Here, we develop novel definitions for group graph measures based on the existing graph measures to the group context and these group measures make sure the proper generalization of the corresponding single node or whole network measure. These group measures are evaluated on structural brain networks of Alzheimer’s and healthy subjects and obtained number of significant results. The new outcomes of this study will push forward the understanding of the brain connectivity. 2018-11-10T00:00:00Z http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/1322 Title: Rubik’s Revenge Application Tool Authors: Subaramya, V Abstract: The Rubik's revenge is really captivating and fascinating puzzle. It is a three dimensional game. In the literary survey, there are several applications available in Rubik’s revenge. Some of them are in two dimensional games, some of them has not whole cube rotation using mouse. In this work, we introduces a new user friendly application tool which has three dimensional Rubik’s Revenge. To play the game using this application tool user can rotate whole cube using mouse and rotate each layer using buttons. If the user is not familiar with solving Rubik’s revenge, they can use the help menu. It has the algorithm for solve the Rubik’s Revenge. Learning to solve the Rubik's revenge helps teach many life lessons such as following direction, perseverance, memorization and focus 2016-12-21T00:00:00Z http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/1321 Title: Algorithm aspects of Rubik’s Cube 3D game Authors: Subaramya, V.; Krishnakumar, K. Abstract: The Rubik's Cube is really captivating and fascinating puzzle. It is a 3D game. In the literary survey, there are several applications available in Rubik’s cube. Some of them are games, some of them have solution. But there are no applications which guide how to solve Rubik’s cube. This application is very useful to study about solving the Rubik’s cube. User can play the Rubik’s Cube by turning 12 sides. No of Turns and the time of play will calculate. When User solved the cube there will be a Congratulation Message will be shown with the no of turns and Time. If the user is not familiar with solving cube, they can get the automatic solution which will give the steps to solve the cube. To solve the cube there are five steps. First take the edge pieces of the top layer then the corner pieces of the top layer. The third step is the edge pieces of the middle layer. Then the corner & edge pieces of the bottom layer. When user gets the solution it will give the total no of steps and user can do the next & previous or can auto solve. When going through the steps, it will give the step number, step description & turn Description. In the Right side of the Application, it will give the help file according to the step Number. So user can refer the help file & the steps. So this will be an easy guide for them to learn Rubik’s Cube. 2013-11-28T00:00:00Z http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/1320 Title: Environmentally friendly Methods to Control Eichhornia crassipes Authors: Vijitharan, S.; Sivarajag, S.; Loganathan, P. Abstract: The water hyacinth (Eichhornia crassipes) is a free-floats aquatic plant that has invaded the waterways and reservoirs in the Dry Zone of Sri Lanka. The study focused on preparing a compost using water hyacinth using the barrel-composting method, and to assess the nutritional value of prepared compost,and the heavy metal contamination of fresh water hyacinth, water and water hyacinth-compost. Primary data on the distribution and abundance of E. crassipesin the Vavuniya district in the Northern Province were collected by field visit. Water hyacinth samples were collected from the reservoirs in the district, which were heavily infested with this invasive alien plant. The compost was prepared using water hyacinth and paddy straw in different propositions such as 1:0 (C1), 1:0.5 (C2), 1:0.75 (C3), 1:1 (C4), 1:1.5 (C5) and 1:2 (C6) respectively. The pH, EC, Carbon:Nitrogen (C/N) ratio, primary macro nutrients (N, P and K) were analyzed. The analysis of heavy metals (Cr, As, and Cd) was carried out by the atomic absorption spectrophotometer. Data were analyzed using descriptive statistics in Minitab. The different composts C1-C6 showed a pH of 8.4 to 8.6, EC 3.2 to 3.5, Nitrogen 1.4 to 2.2%, Potassium 1.1 to 1.7% and Phosphorous 0.6 to0.75% were within the reference limits set by the Sri Lanka Standard 1246:2003. Arsenic (As) and Chromium (Cr) contamination has been detected in C1- C6 type compost such as 0.73 – 0.18 mg/kg and 9.3-4.3 mg/kg, respectively, however, these values varied within the compost heavy metal permissible limits (SLS 1246-2003). And in fresh water hyacinth, As and Cr contamination has been detected at 0.73 mg/kg and 11.8 mg/kg respectively but these values also varied within the permissible limits (WHO, 1999) and there was no As, Cr and Cd contamination in water sample which was collected from the study location. The C1 (1:0) compost, which only contains water hyacinth had a high C/N ratio (12.0) than other composts. This study recommend to control water hyacinth, by using it as only raw material to prepare composts to be used in crop production but heavy metal contamination test should be carried out before the application of this compost. 2017-01-20T00:00:00Z