University of Southern Queensland

About: University of Southern Queensland is a education organization based out in Toowoomba, Queensland, Australia. It is known for research contribution in the topics: Population & Higher education. The organization has 3037 authors who have published 11241 publications receiving 234781 citations. The organization is also known as: USQ.

Internet traffic modeling and future technology implications

Abstract: This paper presents the Poisson Pareto burst process (PPBP) as a simple but accurate model for Internet traffic. It presents formulae relating the parameters of the PPBP to measurable traffic statistics, and describes a technique for fitting the PPBP to a given traffic stream. The PPBP is shown to accurately predict the queueing performance of a sample trace of aggregated Internet traffic. We predict that in few years, natural growth and statistical multiplexing will lead to an efficient optical Internet.

Meta-analysis and systematic review of laparoscopic versus open mesh repair for elective incisional hernia.

Abstract: Context The utility of laparoscopic repair in the treatment of incisional hernia repair is still contentious.

An extreme learning machine model for the simulation of monthly mean streamflow water level in eastern Queensland

Abstract: A predictive model for streamflow has practical implications for understanding the drought hydrology, environmental monitoring and agriculture, ecosystems and resource management. In this study, the state-or-art extreme learning machine (ELM) model was utilized to simulate the mean streamflow water level (Q WL) for three hydrological sites in eastern Queensland (Gowrie Creek, Albert, and Mary River). The performance of the ELM model was benchmarked with the artificial neural network (ANN) model. The ELM model was a fast computational method using single-layer feedforward neural networks and randomly determined hidden neurons that learns the historical patterns embedded in the input variables. A set of nine predictors with the month (to consider the seasonality of Q WL); rainfall; Southern Oscillation Index; Pacific Decadal Oscillation Index; ENSO Modoki Index; Indian Ocean Dipole Index; and Nino 3.0, Nino 3.4, and Nino 4.0 sea surface temperatures (SSTs) were utilized. A selection of variables was performed using cross correlation with Q WL, yielding the best inputs defined by (month; P; Nino 3.0 SST; Nino 4.0 SST; Southern Oscillation Index (SOI); ENSO Modoki Index (EMI)) for Gowrie Creek, (month; P; SOI; Pacific Decadal Oscillation (PDO); Indian Ocean Dipole (IOD); EMI) for Albert River, and by (month; P; Nino 3.4 SST; Nino 4.0 SST; SOI; EMI) for Mary River site. A three-layer neuronal structure trialed with activation equations defined by sigmoid, logarithmic, tangent sigmoid, sine, hardlim, triangular, and radial basis was utilized, resulting in optimum ELM model with hard-limit function and architecture 6-106-1 (Gowrie Creek), 6-74-1 (Albert River), and 6-146-1 (Mary River). The alternative ELM and ANN models with two inputs (month and rainfall) and the ELM model with all nine inputs were also developed. The performance was evaluated using the mean absolute error (MAE), coefficient of determination (r (2)), Willmott's Index (d), peak deviation (P dv), and Nash-Sutcliffe coefficient (E NS). The results verified that the ELM model as more accurate than the ANN model. Inputting the best input variables improved the performance of both models where optimum ELM yielded R(2) ≈ (0.964, 0.957, and 0.997), d ≈ (0.968, 0.982, and 0.986), and MAE ≈ (0.053, 0.023, and 0.079) for Gowrie Creek, Albert River, and Mary River, respectively, and optimum ANN model yielded smaller R(2) and d and larger simulation errors. When all inputs were utilized, simulations were consistently worse with R (2) (0.732, 0.859, and 0.932 (Gowrie Creek), d (0.802, 0.876, and 0.903 (Albert River), and MAE (0.144, 0.049, and 0.222 (Mary River) although they were relatively better than using the month and rainfall as inputs. Also, with the best input combinations, the frequency of simulation errors fell in the smallest error bracket. Therefore, it can be ascertained that the ELM model offered an efficient approach for the streamflow simulation and, therefore, can be explored for its practicality in hydrological modeling.

Effects of music in exercise and sport: A meta-analytic review.

Abstract: Regular physical activity has multifarious benefits for physical and mental health, and music has been found to exert positive effects on physical activity. Summative literature reviews and conceptual models have hypothesized potential benefits and salient mechanisms associated with music listening in exercise and sport contexts, although no large-scale objective summary of the literature has been conducted. A multilevel meta-analysis of 139 studies was used to quantify the effects of music listening in exercise and sport domains. In total, 598 effect sizes from four categories of potential benefits (i.e., psychological responses, physiological responses, psychophysical responses, and performance outcomes) were calculated based on 3,599 participants. Music was associated with significant beneficial effects on affective valence (g = 0.48, CI [0.39, 0.56]), physical performance (g = 0.31, CI [0.25, 0.36]), perceived exertion (g = 0.22, CI [0.14, 0.30]), and oxygen consumption (g = 0.15, CI [0.02, 0.27]). No significant benefit of music was found for heart rate (g = 0.07, CI [-0.03, 0.16]). Performance effects were moderated by study domain (exercise > sport) and music tempo (fast > slow-to-medium). Overall, results supported the use of music listening across a range of physical activities to promote more positive affective valence, enhance physical performance (i.e., ergogenic effect), reduce perceived exertion, and improve physiological efficiency. (PsycINFO Database Record (c) 2020 APA, all rights reserved).