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The results show that following access to microcredit, 25.6% and 51.3% of business owners reported significant and moderate increases in their business workload respectively.
Table 2 and Table 3 present the results of the experiments when using the LIGO workload and the Genome workload, respectively.
{xi}^t=1-left(frac{T_Q-{C}_Q}{T_Q}right), kern2.25em mathrm{s}.mathrm{t}., 0<{xi}^t<1 (5)where T Q and C Q are the total cloud capacity and the current cloud workload, respectively.
Data (pre) were collected for both groups using physiological signals i.e., EEG and ECG, and MATB task was given for 5 min for baseline (BL) recording and 8 min for each level, LWL and HWL (C1 BL, C1 LWL, C1 HWL, E1 BL, E1 LWL, E1 HWL for pre-control baseline, low workload, high workload, pre-experimental baseline, low workload and high workload, respectively).
In order to assess the relationship between the hand hygiene events (HHE) and the workload, we correlated HHEs per patient-day (PD) with the staff time/PD (h), the nursing effort/PD (h) and the C-value indexing the workload, respectively.
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Figure 7 compares homogeneous and heterogeneous scaling for the scenario where the 4 applications are subjected to bursty workloads, respectively.
The parameters λCS, λLG, and λGN specify the job arrival rates used for the CyberShake, LIGO, and Genome workloads, respectively.
Hadoop and NoSQL have clearly emerged as preferred approaches for analytical and operational workloads respectively, and companies like Couchbase, Cloudera, HortonWorks, MapR and MongoDB are taking market share from incumbents.
Tables 4, 5 and 6 present the training and the testing accuracy of the MLWPD, MLP, and SVM for predicting the periodic, the growing and the unpredictable workloads, respectively.
Figures 12,13 and 14 show the average MLPWD, MLP, and SVM prediction results in the test phase (window size = 3 min) for the periodic, growing, and unpredicted workload patterns, respectively.
In broadcast communication, the scalability of the single-hop WiNoC relative to packet losses is excellent, given the absence of packet losses for the EP workload and, respectively, the low and decreasing loss rates for the IS and MG workloads as the number of network nodes increases.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com