Influence of rest interval duration on muscular power production in the lower-body power profile

There is a paucity of evidence-based support for the allocation of rest interval duration between incremental loads in the assessment of the load-power profile. We examined the effect of rest interval duration on muscular power production in the load-power profile and sought to determine if greater rest is required with increasing load (i.e., variable rest interval). Ten physically trained men completed 4 experimental conditions in a crossover balanced design. Participants performed jump squats across incremental loads (0-60 kg) on 4 occasions, with an allocated recovery interval of 1, 2, 3, or 4 minutes. The mean log-transformed power output at each load was used for comparison between conditions (rest intervals). Unloaded jump squats (0 kg) maximized power output at each condition. The maximal mechanical power output was 66.6 ± 6.5 W·kg (1 minute), 66.2 ± 5.2 W·kg (2 minutes), 67.1 ± 5.9 W·kg (3 minutes), and 66.2 ± 6.5 W·kg (4 minutes). Trivial or unclear differences in power output were observed between rest intervals at each incremental load. As expected, power declined per 10 kg increment in load, the magnitude of decrease was 13.9-14.5% (confidence limits [CL]: ±1.3-2.0%) and 13.4-14.6% (CL: ±2.4-3.9%) for relative peak and mean power, respectively, yet differences in power output between conditions were likely insubstantial. The prescription of rest intervals between loads that are longer than 1 minute have a likely negligible effect on muscular power production in the jump squat incremental load-power profile. Practitioners should select either a 1- to 4-minute rest interval to best accommodate the logistical constraints of their monitoring sessions.

Can the direct medical cost of chronic disease be transferred across different countries? Using cost-of-illness studies on type 2 diabetes, epilepsy and schizophrenia as examples

OBJECTIVES: To systematically review cost of illness studies for schizophrenia (SC), epilepsy (EP) and type 2 diabetes mellitus (T2DM) and explore the transferability of direct medical cost across countries.

METHODS: A comprehensive literature search was performed to yield studies that estimated direct medical costs. A generalized linear model (GLM) with gamma distribution and log link was utilized to explore the variation in costs that accounted by the included factors. Both parametric (Random-effects model) and non-parametric (Boot-strapping) meta-analyses were performed to pool the converted raw cost data (expressed as percentage of GDP/capita of the country where the study was conducted).

RESULTS: In total, 93 articles were included (40 studies were for T2DM, 34 studies for EP and 19 studies for SC). Significant variances were detected inter- and intra-disease classes for the direct medical costs. Multivariate analysis identified that GDP/capita (p<0.05) was a significant factor contributing to the large variance in the cost results. Bootstrapping meta-analysis generated more conservative estimations with slightly wider 95% confidence intervals (CI) than the parametric meta-analysis, yielding a mean (95%CI) of 16.43% (11.32, 21.54) for T2DM, 36.17% (22.34, 50.00) for SC and 10.49% (7.86, 13.41) for EP.

CONCLUSIONS: Converting the raw cost data into percentage of GDP/capita of individual country was demonstrated to be a feasible approach to transfer the direct medical cost across countries. The approach from our study to obtain an estimated direct cost value along with the size of specific disease population from each jurisdiction could be used for a quick check on the economic burden of particular disease for countries without such data.

Experience report: anomaly detection of cloud application operations using log and cloud metric correlation analysis

Failure of application operations is one of the maincauses of system-wide outages in cloud environments. Thisparticularly applies to DevOps operations, such as backup,redeployment, upgrade, customized scaling, and migration that areexposed to frequent interference from other concurrent operations,configuration changes, and resources failure. However, currentpractices fail to provide a reliable assurance of correct execution ofthese kinds of operations. In this paper, we present an approach toaddress this problem that adopts a regression-based analysistechnique to find the correlation between an operation’s activity logsand the operation activity’s effect on cloud resources. Thecorrelation model is then used to derive assertion specifications,which can be used for runtime verification of running operations andtheir impact on resources. We evaluated our proposed approach onAmazon EC2 with 22 rounds of rolling upgrade operations whileother types of operations were running and random faults wereinjected. Our experiment shows that our approach successfullymanaged to raise alarms for 115 random injected faults, with aprecision of 92.3%.

A survey on quantum computing with main focus on the methods of implementation and commercialization gaps

This research focuses on surveying in an attractive field of quantum computing. The paper begins by highlighting a brief history of quantum mechanics. Major elements of quantum computing such as quantum superposition, quantum tunnelling and qubits are addressed at next from a physics perspective. In addition, various methods and applications of quantum physics are also examined. This paper discusses the power and efficiencies that a quantum computer provides and the basis for these claims. Furthermore, the level of research in quantum computing and it’s commercial markets assays to find out the major contributions and developments in the field of quantum computing. The top two leading organisations in quantum computing are picked and reviewed with their up to date contributions. This paper expresses the methods and techniques which are being used by these two organisations to implement a quantum processor and the level of success that has been achieved. This research attempts to log the challenges and limitations that these organisations face in the development of quantum computing. Finally, the research compares quantum model with classical computing model.