Coefficient of Variation Can Identify the Most Important Effects of Experimental Treatments

Lorenzo, José Carlos; Yabor, Lourdes; Medina, Norma; Quintana, Nicolás; Wells, Vanessa

doi:10.15835/nbha4319881

Cited by 31 publications

(9 citation statements)

References 16 publications

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“…In this formula, we considered the average values of the three treatments compared (seed sizes) to calculate the standard deviation and average. Therefore, the higher the difference between the three materials compared, the higher the OCV (Lorenzo et al 2015). The OCVs were classi ed as Low from 1.73 to 24.42%, Medium from 24.42 to 47.11% and High from 47.11 to 69.79%.…”

Section: Methodsmentioning

confidence: 99%

Chickpea seed mass influences agronomical performance: a case for seed heteromorphism?

Villalobos-Olivera

Campbell

Martínez-Montero

et al. 2023

Preprint

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Cicer arietinum L. is a major food legume across the globe. However, the yield of legume crops appears to have reached a plateau in developing countries where yield is often impacted by poor crop establishment. Therefore, seed physiological characteristics (specific to the cultivars/landraces adapted to various regions of the world) and their impacts on plantlet establishment and performance should be investigated. This study determined the effect of seed size on germination, plant development and agronomic performance in Cuba. Biochemical parameters were also evaluated for up to 21 d of growth. The results showed that seeds of the largest mass (89 ± 3 mg, group 3) showed more rapid germination, emergence and plant growth than the other tested mass categories. This trend was sustained until plant maturity where group 3 seeds also generated the highest yields. Differences were also noted in the antioxidant profiles in developing plants with the highest levels of SOD and PER found in plants generated from seeds with the smallest mass (55 ± 3 mg per seed, group 1). The above findings raise the questions as to whether seeds of chickpea display heteromorphic behavior, however, further studies are required.

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Section: Methodsmentioning

confidence: 99%

Chickpea seed mass influences agronomical performance: a case for seed heteromorphism?

Villalobos-Olivera

Campbell

Martínez-Montero

et al. 2023

Preprint

Self Cite

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show abstract

“…In this formula, we considered the average values of the four treatments to calculate the standard deviation and average. For this comparison, the larger the difference between the four treatments compared, the higher the OCV (Lorenzo et al 2015). The OCVs were classi ed as Low = 8.77 to 19.58%, Medium = 19.58 to 30.38% and High = 30.38 to 41.19%.…”

Section: Characterization Of Argovit™ Agnps Have Been Previously Repo...mentioning

confidence: 99%

Are silver nanoparticles the “silver bullet” to promote diterpene production in Stevia rebaudiana?

Andújar

González

García‐Ramos

et al. 2023

Plant Cell Tiss Organ Cult

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S. rebaudiana is a sought after sweetener because of its low-calorie properties. However, the supply of suitable quantities of high quality propagation material is limited by ine cient propagation methods using conventional strategies. In vitro techniques combined with nanotechnology tools offer an attractive alternative not only for improved propagation but also for the stimulation of secondary metabolites which represent the targeted sweetener product for this crop. This short report provides an evaluation of silver nanoparticles applied in temporary immersion bioreactors for the abovementioned purpose.Different levels of AgNPs were supplied (0.0-37.5 mg/L) and after 21 d of growth, morphological and biochemical indicators were evaluated. Silver nanoparticles at 25 and 37.5 mg/L decreased shoot multiplication rate, shoot length, and the number of nodes and leaves per shoot compared with the control while no adverse effect was found at the lower tested concentration (12.5 mg/L). Shoot fresh and dry weights also showed statistically signi cant differences. Regarding the biochemical phenotypes, chlorophyll a, carotenoids and soluble phenolics were increased in plants supplied with 25 mg/L AgNPs, with the latter two indicators suggesting oxidative stress. Interestingly, endogenous levels of diterpenes were signi cantly increased with the application of 12.5 mg/L AgNPs. It is suggested that AgNPs show potential to act as elicitors to promote the production of diterpenes in stevia but that further work is required to understand the balance between oxidative damage and secondary metabolite production and that optimization of the protocol is required to improve the propagation potential of this strategy.

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“…We therefore need a relative way to determine the threshold for 𝐶𝑉 . It's been proved that classifying 𝐶𝑉 into high, medium and low is good enough [38,61] to leverage 𝐶𝑉 . We therefore equally divide the value range of a 𝐶𝑉 into three non-overlapped partitions, as shown in equation (4).…”

Section: Query Configuration Sensitivity Analysismentioning

confidence: 99%

LOCAT: Low-Overhead Online Configuration Auto-Tuning of Spark SQL Applications

Xin¹,

Hwang²,

Yu³

2022

Preprint

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Spark SQL has been widely deployed in industry but it is challenging to tune its performance. Recent studies try to employ machine learning (ML) to solve this problem. They however suffer from two drawbacks. First, it takes a long time (high overhead) to collect training samples. Second, the optimal configuration for one input data size of the same application might not be optimal for others.To address these issues, we propose a novel Bayesian Optimization (BO) based approach named LOCAT to automatically tune the configurations of Spark SQL applications online. LOCAT innovates three techniques. The first technique, named QCSA, eliminates the configuration-insensitive queries by Query Configuration Sensitivity Analysis (QCSA) when collecting training samples. The second technique, dubbed DAGP, is a Datasize-Aware Gaussian Process (DAGP) which models the performance of an application as a distribution of functions of configuration parameters as well as input data size. The third technique, called IICP, Identifies Important Configuration Parameters (IICP) with respect to performance and only tunes the important parameters. As such, LOCAT can tune the configurations of a Spark SQL application with low overhead and adapt to different input data sizes.We employ Spark SQL applications from benchmark suites𝑇 𝑃𝐶− 𝐷𝑆, 𝑇 𝑃𝐶 − 𝐻 , and 𝐻𝑖𝐵𝑒𝑛𝑐ℎ running on two significantly different clusters, a four-node ARM cluster and an eight-node x86 cluster, to evaluate LOCAT. The experimental results on the ARM cluster show that LOCAT accelerates the optimization procedures of Tuneful [22], DAC [66], GBO-RL [36], and QTune [37] by factors of 6.4×, 7.0×, 4.1×, and 9.7× on average, respectively. On the x86 cluster, LOCAT reduces the optimization time of Tuneful, DAC, GBO-RL, and QTune by factors of 6.4×, 6.3×, 4.0×, and 9.2× on average, respectively. Moreover, LOCAT improves the performance of the applications on

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Coefficient of Variation Can Identify the Most Important Effects of Experimental Treatments

Cited by 31 publications

References 16 publications

Chickpea seed mass influences agronomical performance: a case for seed heteromorphism?

Chickpea seed mass influences agronomical performance: a case for seed heteromorphism?

Are silver nanoparticles the “silver bullet” to promote diterpene production in Stevia rebaudiana?

LOCAT: Low-Overhead Online Configuration Auto-Tuning of Spark SQL Applications

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