Aims/IntroductionA meta‐analysis was carried out to evaluate the efficacy of yoga in adults with type 2 diabetes mellitus.Materials and MethodsThe PubMed, EMBASE and Cochrane databases were searched to obtain eligible randomized controlled trials. The primary outcome was fasting blood glucose, and the secondary outcomes included glycosylated hemoglobin A1c, total cholesterol, high‐density lipoprotein cholesterol, low‐density lipoprotein cholesterol, triglyceride and postprandial blood glucose. Weighted mean differences and 95% confidence intervals (CIs) were calculated. The I 2 statistic represented heterogeneity.ResultsA total of 12 randomized controlled trials with a total of 864 patients met the inclusion criteria. The pooled weighted mean differences were −23.72 mg/dL (95% CI −37.78 to −9.65; P = 0.001; I 2 = 82%) for fasting blood glucose and −0.47% (95% CI −0.87 to −0.07; P = 0.02; I 2 = 82%) for hemoglobin A1c. The weighted mean differences were −17.38 mg/dL (95% CI −27.88 to −6.89; P = 0.001; I 2 = 0%) for postprandial blood glucose, −18.50 mg/dL (95% CI −29.88 to −7.11; P = 0.001; I 2 = 75%) for total cholesterol, 4.30 mg/dL (95% CI 3.25 to 5.36; P < 0.00001; I 2 = 10%) for high‐density lipoprotein cholesterol, −12.95 mg/dL (95% CI −18.84 to −7.06; P < 0.0001; I 2 = 37%) for low‐density lipoprotein cholesterol and −12.57 mg/dL (95% CI −29.91 to 4.76; P = 0.16; I 2 = 48%) for triglycerides.ConclusionsThe available evidence suggests that yoga benefits adult patients with type 2 diabetes mellitus. However, considering the limited methodology and the potential heterogeneity, further studies are necessary to support our findings and investigate the long‐term effects of yoga in type 2 diabetes mellitus patients.

SimRank has become an important similarity measure to rank web documents based on a graph model on hyperlinks. The existing approaches for conducting SimRank computation adopt an iteration paradigm. The most efficient deterministic technique yieldsworst-case time per iteration with the space requirement O (, where n is the number of nodes (web documents). In this paper, we propose novel optimization techniques such that each iteration takes O (min {n · m, n r }) time and O (n + m) space, where m is the number of edges in a web-graph model and r ≤ log 2 7. In addition, we extend the similarity transition matrix to prevent random surfers getting stuck, and devise a pruning technique to eliminate impractical similarities for each iteration. Moreover, we also develop a reordering technique combined with an over-relaxation method, not only speeding up the convergence rate of the existing techniques, but achieving I/O efficiency as well. We conduct extensive experiments on both synthetic and real data sets to demonstrate the efficiency and effectiveness of our iteration techniques.

SimRank has become an important similarity measure to rank web documents based on a graph model on hyperlinks. The existing approaches for conducting SimRank computation adopt an iteration paradigm. The most efficient deterministic technique yieldsworst-case time per iteration with the space requirement O (, where n is the number of nodes (web documents). In this paper, we propose novel optimization techniques such that each iteration takes O (min {n · m, n r }) time and O (n + m) space, where m is the number of edges in a web-graph model and r ≤ log 2 7. In addition, we extend the similarity transition matrix to prevent random surfers getting stuck, and devise a pruning technique to eliminate impractical similarities for each iteration. Moreover, we also develop a reordering technique combined with an over-relaxation method, not only speeding up the convergence rate of the existing techniques, but achieving I/O efficiency as well. We conduct extensive experiments on both synthetic and real data sets to demonstrate the efficiency and effectiveness of our iteration techniques.

No abstract

Abstract. SimRank has been considered as one of the promising link-based ranking algorithms to evaluate similarities of web documents in many modern search engines. In this paper, we investigate the optimization problem of SimRank similarity computation on undirected web graphs. We first present a novel algorithm to estimate the SimRank between vertices in O n 3 + K · n 2 time, where n is the number of vertices, and K is the number of iterations. In comparison, the most efficient implementation of SimRank algorithm in [1] takes O K · n 3 time in the worst case. To efficiently handle large-scale computations, we also propose a parallel implementation of the SimRank algorithm on multiple processors. The experimental evaluations on both synthetic and real-life data sets demonstrate the better computational time and parallel efficiency of our proposed techniques.