Shortest path routing algorithm for hierarchical interconnection network-on-chip

Interconnection networks play a significant role in efficient on-chip communication for multicore systems. This paper introduces a new interconnection topology called the Hierarchical Cross Connected Recursive network (HCCR) and a shortest path routing algorithm for the HCCR. Proposed topology offers a high degree of regularity, scalability, and symmetry with a reduced number of links and node degree. A unique address encoding scheme is proposed for hierarchical graphical representation of HCCR networks, and based on this scheme a shortest path routing algorithm is devised. The algorithm requires 5(k-1) time where k=logn4-2 and k>0, in worst case to determine the next node along the shortest path

Areca nut chewing and the risk of re-hospitalization and mortality among patients with acute coronary syndrome in Pakistan

Objectives: Areca nut is widely consumed in many parts of the world, especially in South and Southeast Asia, where cardiovascular disease (CVD) is also a huge burden. Among the forms of CVD, acute coronary syndrome (ACS) is a major cause of mortality and morbidity. Research has shown areca nut chewing to be associated with diabetes, hypertension, oropharyngeal and esophageal cancers, and CVD, but little is known about mortality and re-hospitalization secondary to ACS among areca nut users and non-users. Methods: A prospective cohort was studied to quantify the effect of areca nut chewing on patients with newly diagnosed ACS by categorizing the study population into exposed and non-exposed groups according to baseline chewing status. Cox proportional hazards models were used to examine the associations of areca nut chewing with the risk of re-hospitalization and 30-day mortality secondary to ACS. Results: Of the 384 ACS patients, 49.5% (n=190) were areca users. During 1-month of follow-up, 20.3% (n=78) deaths and 25.1% (n=96) re-hospitalizations occurred. A higher risk of re-hospitalization was found (adjusted hazard ratio [aHR], 2.05; 95% confidence interval [CI], 1.29 to 3.27; p=0.002) in areca users than in non-users. Moreover, patients with severe disease were at a significantly higher risk of 30-day mortality (aHR, 2.77; 95% CI, 1.67 to 4.59; p<0.001) and re-hospitalization (aHR, 2.72; 95% CI, 1.73 to 4.26; p<0.001). Conclusions: The 30-day re-hospitalization rate among ACS patients was found to be significantly higher in areca users and individuals with severe disease. These findings suggest that screening for a history of areca nut chewing may help to identify patients at a high risk for re-hospitalization due to secondary events

The role of computed tomography for identifying mechanical bowel obstruction in a Pakistani population

Objective: To retrospectively review our experience of CT scan in cases with a final diagnosis of surgically confirmed mechanical bowel obstruction. Methods: It is a retrospective analytical study, done from 2003 to 2008. All adult patients having undergone laparotomy in addition to a preoperative abdominal CT scan over a 5 year period were identified through the medical records and their case notes reviewed. Taking surgery to be the gold standard for diagnosing mechanical bowel obstruction, we compared results of the CT with operative findings to determine the sensitivity, specificity, positive and negative predictive values of CT scans. The data was analyzed using SPSS version 16.0. Results: A total of 271 patient records were reviewed. The mean age was 46 +/- 19 years and (64%) were men. Mechanical intestinal obstruction was found in 104 patients on laparotomy and CT scan had diagnosed 97 of these. The sensitivity and specificity was 93% respectively. CT scanning correctly identified the cause of the obstruction in 72 (74%) cases. The common reasons for bowel obstruction identified by surgery were adhesions 29 (40%), neoplasm 12 (17 %) and hernias 7 (10%). Conclusion: CT scans are reliable at diagnosing intestinal obstruction with a high sensitivity and specificity but they are not as accurate at defining the etiology of the obstruction

Healthy lifestyle as a preventive measure against victimization among school-going adolescents

Background: Bullying and violence are problems of aggression in adolescents. Healthy lifestyle practices are common health promotion approaches in school settings; however, their association with aggressive behaviours in school-going adolescents is less explored. Aims: This study examined the associations of healthy lifestyle behaviours including good hygiene, physical activity, recommended diet and refrainment from tobacco use with bully victimization and violence among adolescents. Methods: Data were obtained from the Global School Health Survey conducted in Pakistan (2009). The study population consisted of school-going adolescents aged 13 to 15 years. We constructed our final dataset using information from 4102 participants. Association of healthy lifestyle behaviours with bully victimization and violence experience were assessed using multivariate logistic regression. Results: Results indicate lower odds of being bullied (good hygiene: OR = 0.62, 95% CI 0.50–0.76, P Conclusions: Our study supports the significance of healthy lifestyle as a preventive measure against victimization. Anti-bullying programmes focusing on social–emotional skill development may also consider promotion of healthy lifestyle behaviours among adolescents, aiming at reducing victimization and its related consequences

Group based shortest path routing algorithm for hierarchical cross connected recursive networks (HCCR)

Interconnection networks play a significant role in efficient on-chip communication for multicore systems. This paper introduces a new interconnection topology called the Hierarchical Cross Connected Recursive network (HCCR) and a Group-based Shortest Path Routing algorithm (GSR) for the HCCR. Network properties of HCCR are compared with Spidergon, THIN, 2-D Mesh and Hypercube. It is shown that the proposed topology offers a high degree of regularity, scalability, and symmetry with a reduced number of links, small diameter and low node degree. A unique address encoding scheme is proposed for hierarchical graphical representation of HCCR networks, based on which the GSR was developed. . The proposed addressing scheme divides the HCCR network into logical groups of same as well as different sizes. Packets move towards receiver using local or global routing. Simulations are performed to find all the possible shortest paths with GSR in HCCR networks (up to 1024 nodes). All the shortest paths produced by GSR are verified against Dijkstra’s algorithm. The GSR for k-level HCCR (L_k) with N= 〖4 〗^((2+k) )nodes, requires 5(k-1) time in the worst case to determine the next node along the shortest path. Average distance and frequency of hop counts of HCCR networks are investigated using GSR. The results are compared with average distance of 2-D Mesh. Experimental results show that with a network size of 1024 nodes, there is only a 7.7% increase in the average distance of L_3 HCCR in comparison to 2-D Mesh. However L_k have fewer paths with high hop count in comparison to 2-D Mesh

CS848

For the requirements of the CS848 project, we have decided to implement a system that automatically learns surface text patterns from a set of training data. The techniques employed in our system are similar to those described by Hovy [1]. In addition, we implemented the extension proposed by Greenwood [2] to generalize surface text pattern

Iterative Schemes to Solve Low-Dimensional Calibration Equations in Parallel MR Image Reconstruction with GRAPPA

GRAPPA (Generalized Autocalibrating Partially Parallel Acquisition) is a widely used parallel MRI reconstruction technique. The processing of data from multichannel receiver coils may increase the storage and computational requirements of GRAPPA reconstruction. Random projection on GRAPPA (RP-GRAPPA) uses random projection (RP) method to overcome the computational overheads of solving large linear equations in the calibration phase of GRAPPA, saving reconstruction time. However, RP-GRAPPA compromises the reconstruction accuracy in case of large reductions in the dimensions of calibration equations. In this paper, we present the implementation of GRAPPA reconstruction method using potential iterative solvers to estimate the reconstruction coefficients from the randomly projected calibration equations. Experimental results show that the proposed methods withstand the reconstruction accuracy (visually and quantitatively) against large reductions in the dimension of linear equations, when compared with RP-GRAPPA reconstruction. Particularly, the proposed method using conjugate gradient for least squares (CGLS) demonstrates more savings in the computational time of GRAPPA, without significant loss in the reconstruction accuracy, when compared with RP-GRAPPA. It is also demonstrated that the proposed method using CGLS complements the channel compression method for reducing the computational complexities associated with higher channel count, thereby resulting in additional memory savings and speedup