Effects of short-duration and long-duration exercise on lipoprotein(a)

Purpose Most studies that use either a single exercise session, exercise training, or a cross-sectional design have failed to find a relationship between exercise and plasma lipoprotein(a) [Lp(a)] concentrations. However, a few studies investigating the effects of longer and/or more strenuous exercise have shown elevated Lp(a) concentrations, possibly as an acute-phase reactant to muscle damage. Based on the assumption that greater muscle damage would occur with exercise of longer duration, the purpose of the present study was to determine whether exercise of longer duration would increase Lp(a) concentration and creatine kinase. (CK) activity more than exercise of shorter duration. Methods Ten endurance-trained men (mean +/- SD: age, 27 +/- 6 yr; maximal oxygen consumption [(V)over dotO(2max)], 57 +/- 7 mL(.)kg(-1) min(-1)) completed two separate exercise sessions at 70% (V)over dotO(2max). One session required 900 kcal of energy expenditure (60 +/- 6 min), and the other required 1500 kcal (112 +/- 12 min). Fasted blood samples were taken immediately before (0-pre), immediately after (0-post), 1 d after (1-post), and 2 d after (2-post) each exercise session. Results CK activity increased after both exercise sessions (mean +/- SE; 800 kcal: 0-pre 55 +/- 11, 1-post 168 +/- 64 U(.)L(-1.)min(-1); 1500 kcal: 0-pre 51 +/- 5, 1-post 187 +/- 30, 2-post 123 +/- 19 U(.)L(-1.)min(-1); P < 0.05). However, median Lp(a) concentrations were not altered by either exercise session (800 kcal: 0-pre 5.0 mg(.)dL(-1), 0-post 3.2 mg(.)dL(-1), 1-post 4.0 mg(.)dL(-1), 2-post 3.4 mg(.)dL(-1); 1500 kcal: 0-pre 5.8 mg(.)dL(-1), 0-post 4.3 mg(.)dL(-1), 1-post 3.2 mg(.)dL(-1), 2-post 5.3 mg(.)dL(-1)). In addition, no relationship existed between exercise-induced changes in CK activity and Lp(a) concentration (800 kcal: r = -0.26; 1500 kcal: r = -0.02). Conclusion These results suggest that plasma Lp(a) concentration will not increase in response to minor exercise-induced muscle damage in endurance-trained runners.

Comparison of accelerometer cut points for predicting activity intensity in youth

The absence of comparative validity studies has prevented researchers from reaching consensus regarding the application of intensity-related accelerometer cut points for children and adolescents. PURPOSE This study aimed to evaluate the classification accuracy of five sets of independently developed ActiGraph cut points using energy expenditure, measured by indirect calorimetry, as a criterion reference standard. METHODS A total of 206 participants between the ages of 5 and 15 yr completed 12 standardized activity trials. Trials consisted of sedentary activities (lying down, writing, computer game), lifestyle activities (sweeping, laundry, throw and catch, aerobics, basketball), and ambulatory activities (comfortable walk, brisk walk, brisk treadmill walk, running). During each trial, participants wore an ActiGraph GT1M, and VO 2 was measured breath-by-breath using the Oxycon Mobile portable metabolic system. Physical activity intensity was estimated using five independently developed cut points: Freedson/Trost (FT), Puyau (PU), Treuth (TR), Mattocks (MT), and Evenson (EV). Classification accuracy was evaluated via weighted κ statistics and area under the receiver operating characteristic curve (ROC-AUC). RESULTS Across all four intensity levels, the EV (κ = 0.68) and FT (κ = 0.66) cut points exhibited significantly better agreement than TR (κ = 0.62), MT (κ = 0.54), and PU (κ = 0.36). The EV and FT cut points exhibited significantly better classification accuracy for moderate-to vigorous-intensity physical activity (ROC-AUC = 0.90) than TR, PU, or MT cut points (ROC-AUC = 0.77-0.85). Only the EV cut points provided acceptable classification accuracy for all four levels of physical activity intensity and performed well among children of all ages. The widely applied sedentary cut point of 100 counts per minute exhibited excellent classification accuracy (ROC-AUC = 0.90). CONCLUSIONS On the basis of these findings, we recommend that researchers use the EV ActiGraph cut points to estimate time spent in sedentary, light-, moderate-, and vigorous-intensity activity in children and adolescents. Copyright © 2011 by the American College of Sports Medicine.

Validity of the computer science and applications (CSA) activity monitor in children

Purpose The purpose of this study was to evaluate the validity of the CSA activity monitor as a measure of children's physical activity using energy expenditure (EE) as a criterion measure. Methods Thirty subjects aged 10 to 14 performed three 5-min treadmill bouts at 3, 4, and 6 mph, respectively. While on the treadmill, subjects wore CSA (WAM 7164) activity monitors on the right and left hips. (V) over dot O-2 was monitored continuously by an automated system. EE was determined by multiplying the average (V) over dot O-2 by the caloric equivalent of the mean respiratory exchange ratio. Results Repeated measures ANOVA indicated that both CSA monitors were sensitive to changes in treadmill speed. Mean activity counts from each CSA unit were not significantly different and the intraclass reliability coefficient for the two CSA units across all speeds was 0.87. Activity counts from both CSA units were strongly correlated with EE (r = 0.86 and 0.87, P < 0.001). An EE prediction equation was developed from 20 randomly selected subjects and cross-validated on the remaining 10. The equation predicted mean EE within 0.01 kcal.min(-1). The correlation between actual and predicted values was 0.93 (P < 0.01) and the SEE was 0.93 kcal.min(-1). Conclusion These data indicate that the CSA monitor is a valid and reliable tool for quantifying treadmill walking and running in children.

Predictive validity of three ActiGraph energy expenditure equations for children

Purpose This Study evaluated the predictive validity of three previously published ActiGraph energy expenditure (EE) prediction equations developed for children and adolescents. Methods A total of 45 healthy children and adolescents (mean age: 13.7 +/- 2.6 yr) completed four 5-min activity trials (normal walking. brisk walking, easy running, and fast running) in ail indoor exercise facility. During each trial, participants were all ActiGraph accelerometer oil the right hip. EE was monitored breath by breath using the Cosmed K4b(2) portable indirect calorimetry system. Differences and associations between measured and predicted EE were assessed using dependent t-tests and Pearson correlations, respectively. Classification accuracy was assessed using percent agreement, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve. Results None of the equations accurately predicted mean energy expenditure during each of the four activity trials. Each equation, however, accurately predicted mean EE in at least one activity trial. The Puyau equation accurately predicted EE during slow walking. The Trost equation accurately predicted EE during slow running. The Freedson equation accurately predicted EE during fast running. None of the three equations accurately predicted EE during brisk walking. The equations exhibited fair to excellent classification accuracy with respect to activity intensity. with the Trost equation exhibiting the highest classification accuracy and the Puyau equation exhibiting the lowest. Conclusions These data suggest that the three accelerometer prediction equations do not accurately predict EE on a minute-by-minute basis in children and adolescents during overground walking and running. The equations maybe, however, for estimating participation in moderate and vigorous activity.

Artificial neural networks to predict activity type and energy expenditure in youth

Previous studies have demonstrated that pattern recognition approaches to accelerometer data reduction are feasible and moderately accurate in classifying activity type in children. Whether pattern recognition techniques can be used to provide valid estimates of physical activity (PA) energy expenditure in youth remains unexplored in the research literature. Purpose: The objective of this study is to develop and test artificial neural networks (ANNs) to predict PA type and energy expenditure (PAEE) from processed accelerometer data collected in children and adolescents. Methods: One hundred participants between the ages of 5 and 15 yr completed 12 activity trials that were categorized into five PA types: sedentary, walking, running, light-intensity household activities or games, and moderate-to-vigorous intensity games or sports. During each trial, participants wore an ActiGraph GTIM on the right hip, and (V) Over dotO(2) was measured using the Oxycon Mobile (Viasys Healthcare, Yorba Linda, CA) portable metabolic system. ANNs to predict PA type and PAEE (METs) were developed using the following features: 10th, 25th, 50th, 75th, and 90th percentiles and the lag one autocorrelation. To determine the highest time resolution achievable, we extracted features from 10-, 15-, 20-, 30-, and 60-s windows. Accuracy was assessed by calculating the percentage of windows correctly classified and root mean square en-or (RMSE). Results: As window size increased from 10 to 60 s, accuracy for the PA-type ANN increased from 81.3% to 88.4%. RMSE for the MET prediction ANN decreased from 1.1 METs to 0.9 METs. At any given window size, RMSE values for the MET prediction ANN were 30-40% lower than the conventional regression-based approaches. Conclusions: ANNs can be used to predict both PA type and PAEE in children and adolescents using count data from a single waist mounted accelerometer.

Physical activities and sedentary pursuits in African American and Caucasian girls

The purposes of this study were to describe and compare the specific physical activity choices and sedentary pursuits of African American and Caucasian American girls. Participants were 1,124 African American and 1,068 Caucasian American eighth grade students from 31 middle schools. The 3-Day Physical Activity Recall (3DPAR) was used to measure participation in physical activities and sedentary pursuits. The most frequently reported physical activities were walking, basketball, jogging or running, bicycling, and social dancing. Differences between groups were found in 11 physical activities and 3 sedentary pursuits. Participation rates were higher in African American girls (p<.001)for social dancing, basketball, watching television, and church attendance but lower in calisthenics, ballet and other dance, jogging or running, rollerblading, soccer, softball or baseball, using an exercise machine, swimming, and homework. Cultural differences of groups should be considered when planning interventions to promote physical activity.

Experimental and finite element analysis to identify the source of vibration of a coach

This paper presents an investigation on the cause of severe vibration problem of a coach with four-cylinder engine running at an idle state using vibration and impact hammer modal experiments to obtain the main vibration frequency components and the natural characteristics of the coach. The vibration results indicate that the main vibration component comes from the vibration transmitted from the engine to the chassis frame, which is closely related with the engine idle speed. Based on structural simulation analysis of the coach’s chassis frame and comparison with modal testing, the coach severe vibration problem was due to coupling resonance between the engine idle frequency and the fourth natural frequency of the chassis frame. The solution to eliminate the vibration problem is provided by changing the local structure stiffness of the chassis frame. The contribution of this paper lies in providing a solution to solve similar problems.

A preliminary decision model for shared services : insights from an Australian university context

Shared Services involves the convergence and streamlining of an organisation’s functions to ensure timely service delivery as effectively and efficiently as possible. This would result in lower cost, improved service delivery and economies of scale. The conventional wisdom of today is that the potential for Shared Services is increasing due to the increasing costs of changing systems and business requirements and also in implementing and running information systems (IS). However many organizations opt instead for an outsourcing arrangement as the alternative towards cost savings, due in essence to a lack of realization of this potential for Shared Services. This paper rationales turning from outsourcing (to looking within organisations) to leverage on Shared Services for similar cost savings and reaping other potential benefits. The paper’s objectives and contributions are three-fold: (1) distinguish between Shared Services and Outsourcing, (2) report on insights from a single Australian university case study through a transaction cost lens, and to demonstrate the potential for Shared Services and (3) develop a decision model to gauge the potential of implementing Shared Services across similar organisations.

Authenticating Multicast Streams in Lossy Channels Using Threshold Techniques

We first classify the state-of-the-art stream authentication problem in the multicast environment and group them into Signing and MAC approaches. A new approach for authenticating digital streams using Threshold Techniques is introduced. The new approach main advantages are in tolerating packet loss, up to a threshold number, and having a minimum space overhead. It is most suitable for multicast applications running over lossy, unreliable communication channels while, in same time, are pertain the security requirements. We use linear equations based on Lagrange polynomial interpolation and Combinatorial Design methods.

Cryptanalysis of FORK-256

In this paper we present a cryptanalysis of a new 256-bit hash function, FORK-256, proposed by Hong et al. at FSE 2006. This cryptanalysis is based on some unexpected differentials existing for the step transformation. We show their possible uses in different attack scenarios by giving a 1-bit (resp. 2-bit) near collision attack against the full compression function of FORK-256 running with complexity of 2^125 (resp. 2^120) and with negligible memory, and by exhibiting a 22-bit near pseudo-collision. We also show that we can find collisions for the full compression function with a small amount of memory with complexity not exceeding 2^126.6 hash evaluations. We further show how to reduce this complexity to 2^109.6 hash computations by using 273 memory words. Finally, we show that this attack can be extended with no additional cost to find collisions for the full hash function, i.e. with the predefined IV.

Reinforced insulation properties of epoxy resin/SiO2 nanocomposites by atmospheric pressure plasma modification

Nanocomposite dielectrics hold a promising future for the next generation of insulation materials because of their excellent physical, chemical, and dielectric properties. In the presented study, we investigate the use of plasma processing technology to further enhance the dielectric performance of epoxy resin/SiO2 nanocomposite materials. The SiO2 nanoparticles are treated with atmospheric-pressure non-equilibrium plasma prior to being added into the epoxy resin host. Fourier transform infrared spectroscopy (FTIR) results reveal the effects of the plasma process on the surface functional groups of the treated nanoparticles. Scanning electron microscopy (SEM) results show that the plasma treatment appreciably improves the dispersion uniformity of nanoparticles in the host polymer. With respect to insulation performance, the epoxy/plasma-treated SiO2 specimen shows a 29% longer endurance time than the epoxy/untreated SiO2 nanocomposite under electrical aging. The Weibull plots of the dielectric breakdown field intensity suggest that the breakdown strength of the nanocomposite with the plasma pre-treatment on the nanoparticles is improved by 23.3%.

Plasma functionalization of SiO2 nanoparticles for the synthesis of polymer nano-dielectrics

In this study, we improve the insulation performance of polymeric nano-dielectrics by using plasma pre-treatment on the filled nanoparticles. Non-equilibrium atmospheric-pressure plasma is employed to modify a commercial type of silane-coated SiO2 nanoparticles. The treated nanoparticles and the synthesized epoxy-based nanocomposites are characterized using scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and X-ray photoelectron spectroscopy (XPS). The plasma-treated SiO2 nanoparticles can disperse uniformly and form strong covalent bonds with the molecules of the polymer matrix. Moreover, the electrical insulation properties of the synthesized nanocomposites are investigated. Results show that the nanocomposites with plasma-treated SiO2 nanoparticles obtain improved dielectric breakdown strength and extended endurance under intense electrical ageing process.

Dielectric performance of polymer nanocomposites synthesized by atmospheric-pressure plasma-treated silica nanoparticles

In this study, atmospheric-pressure plasmas were applied to modify the surface of silane-coated silica nanoparticles. Subsequently nanocomposites were synthesized by incorporating plasma-treated nanoparticles into an epoxy resin matrix. Electrical testing showed that such novel dielectric materials obtained high partial discharge resistance, high dielectric breakdown strength, and enhanced endurance under highly stressed electric field. Through spectroscopic and microscopic analysis, we found surface groups of nanoparticles were activated and radicals were created after the plasma treatment. Moreover, a uniform dispersion of nanoparticles in nanocomposites was observed. It was expected that the improved dielectric performance of the nanocomposites can attribute to stronger chemical bonds formed between surface groups of plasma-treated nanoparticles and molecules in the matrix. This simple yet effective and environmentally friendly approach aims to synthesize the next generation of high-performance nanocomposite dielectric insulation materials for applications in high-voltage power systems.

Silica nanoparticles treated by cold atmospheric-pressure plasmas improve the dielectric performance of organic–inorganic nanocomposites

We report on the application of cold atmospheric-pressure plasmas to modify silica nanoparticles to enhance their compatibility with polymer matrices. Thermally nonequilibrium atmospheric-pressure plasma is generated by a high-voltage radio frequency power source operated in the capacitively coupled mode with helium as the working gas. Compared to the pure polymer and the polymer nanocomposites with untreated SiO2, the plasma-treated SiO2–polymer nanocomposites show higher dielectric breakdown strength and extended endurance under a constant electrical stress. These improvements are attributed to the stronger interactions between the SiO2 nanoparticles and the surrounding polymer matrix after the plasma treatment. Our method is generic and can be used in the production of high-performance organic–inorganic functional nanocomposites.

Degradation modeling and monitoring of machines using operation-specific hidden Markov models

In this paper, a novel data-driven approach to monitoring of systems operating under variable operating conditions is described. The method is based on characterizing the degradation process via a set of operation-specific hidden Markov models (HMMs), whose hidden states represent the unobservable degradation states of the monitored system while its observable symbols represent the sensor readings. Using the HMM framework, modeling, identification and monitoring methods are detailed that allow one to identify a HMM of degradation for each operation from mixed-operation data and perform operation-specific monitoring of the system. Using a large data set provided by a major manufacturer, the new methods are applied to a semiconductor manufacturing process running multiple operations in a production environment.