scholarly journals Feature Extraction, Ageing Modelling and Information Analysis of a Large-Scale Battery Ageing Experiment

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5295
Author(s):  
Jose Genario de Oliveira ◽  
Vipul Dhingra ◽  
Christoph Hametner
Keyword(s):  
Data Reduction ◽  
Large Scale ◽  
Real Data ◽  
Accelerated Ageing ◽  
Testing Time ◽  
Similar Model ◽  
Information Analysis ◽  
Large Scale Testing ◽  
Unseen Data ◽  
Ageing Model

Large scale testing of newly developed Li-ion cells is associated with high costs for the interested parties, and ideally, testing time should be kept to a minimum. In this work, an ageing model was developed and trained with real data from a large-scale testing experiment in order to answer how much testing time and data would have been really needed to achieve similar model generalisation performance on previously unseen data. A linear regression model was used, and the feature engineering, extraction and selection steps are shown herein, alongside accurate prediction results for the majority of the accelerated ageing experiments. Information analysis was performed to achieve the desired data reduction, obtaining similar model properties with a fifth of the number of cells and half of the testing time. The proposed ageing model uses features commonly found in the literature, and the structure is simple enough for the training to be performed online in an EV. It has good generalisation capabilities. Lastly, the data reduction approach used here is model-independent, allowing a similar methodology to be used with different modelling assumptions.

scholarly journals Constructing Large-Scale Genetic Maps Using an Evolutionary Strategy Algorithm

Genetics ◽  
2003 ◽  
Vol 165 (4) ◽  
pp. 2269-2282
Author(s):  
D Mester ◽  
Y Ronin ◽  
D Minkov ◽  
E Nevo ◽  
A Korol
Keyword(s):  
Discrete Optimization ◽  
High Performance ◽  
Large Scale ◽  
Simulated Data ◽  
Real Data ◽  
Genetic Maps ◽  
Chromosome 1 ◽  
Evolutionary Strategy ◽  
Group A ◽  
The One

Abstract This article is devoted to the problem of ordering in linkage groups with many dozens or even hundreds of markers. The ordering problem belongs to the field of discrete optimization on a set of all possible orders, amounting to n!/2 for n loci; hence it is considered an NP-hard problem. Several authors attempted to employ the methods developed in the well-known traveling salesman problem (TSP) for multilocus ordering, using the assumption that for a set of linked loci the true order will be the one that minimizes the total length of the linkage group. A novel, fast, and reliable algorithm developed for the TSP and based on evolution-strategy discrete optimization was applied in this study for multilocus ordering on the basis of pairwise recombination frequencies. The quality of derived maps under various complications (dominant vs. codominant markers, marker misclassification, negative and positive interference, and missing data) was analyzed using simulated data with ∼50-400 markers. High performance of the employed algorithm allows systematic treatment of the problem of verification of the obtained multilocus orders on the basis of computing-intensive bootstrap and/or jackknife approaches for detecting and removing questionable marker scores, thereby stabilizing the resulting maps. Parallel calculation technology can easily be adopted for further acceleration of the proposed algorithm. Real data analysis (on maize chromosome 1 with 230 markers) is provided to illustrate the proposed methodology.

scholarly journals Meta-Descent for Online, Continual Prediction

2019 ◽  
Vol 33 ◽  
pp. 3943-3950
Author(s):  
Andrew Jacobsen ◽  
Matthew Schlegel ◽  
Cameron Linke ◽  
Thomas Degris ◽  
Adam White ◽  
...  
Keyword(s):  
Gradient Descent ◽  
Large Scale ◽  
Time Series Prediction ◽  
Real Data ◽  
Second Order ◽  
Stochastic Gradient Descent ◽  
Step Size ◽  
Vector Approximation ◽  
Prediction Problems ◽  
Stationary Problems

This paper investigates different vector step-size adaptation approaches for non-stationary online, continual prediction problems. Vanilla stochastic gradient descent can be considerably improved by scaling the update with a vector of appropriately chosen step-sizes. Many methods, including AdaGrad, RMSProp, and AMSGrad, keep statistics about the learning process to approximate a second order update—a vector approximation of the inverse Hessian. Another family of approaches use meta-gradient descent to adapt the stepsize parameters to minimize prediction error. These metadescent strategies are promising for non-stationary problems, but have not been as extensively explored as quasi-second order methods. We first derive a general, incremental metadescent algorithm, called AdaGain, designed to be applicable to a much broader range of algorithms, including those with semi-gradient updates or even those with accelerations, such as RMSProp. We provide an empirical comparison of methods from both families. We conclude that methods from both families can perform well, but in non-stationary prediction problems the meta-descent methods exhibit advantages. Our method is particularly robust across several prediction problems, and is competitive with the state-of-the-art method on a large-scale, time-series prediction problem on real data from a mobile robot.

Methodology for Evaluating Inlet Protection Practices Using Large-Scale Testing Techniques

2015 ◽  
Vol 20 (4) ◽  
pp. 04014049 ◽  
Author(s):  
M. A. Perez ◽  
W. C. Zech ◽  
W. N. Donald ◽  
X. Fang
Keyword(s):  
Large Scale ◽  
Large Scale Testing ◽  
Testing Techniques

Adaptive data reduction for large-scale transaction data

2008 ◽  
Vol 188 (3) ◽  
pp. 910-924 ◽  
Author(s):  
Xiao-Bai Li ◽  
Varghese S. Jacob
Keyword(s):  
Data Reduction ◽  
Large Scale ◽  
Transaction Data

scholarly journals Direct detection of SARS-CoV-2 using non-commercial RT-LAMP reagents on heat-inactivated samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alisa Alekseenko ◽  
Donal Barrett ◽  
Yerma Pareja-Sanchez ◽  
Rebecca J. Howard ◽  
Emilia Strandback ◽  
...  
Keyword(s):  
Large Scale ◽  
Direct Detection ◽  
Dna Polymerases ◽  
Scale Up ◽  
Lamp Assay ◽  
Cost Effective ◽  
Reaction Conditions ◽  
Clinical Patient ◽  
Reverse Transcriptases ◽  
Large Scale Testing

AbstractRT-LAMP detection of SARS-CoV-2 has been shown to be a valuable approach to scale up COVID-19 diagnostics and thus contribute to limiting the spread of the disease. Here we present the optimization of highly cost-effective in-house produced enzymes, and we benchmark their performance against commercial alternatives. We explore the compatibility between multiple DNA polymerases with high strand-displacement activity and thermostable reverse transcriptases required for RT-LAMP. We optimize reaction conditions and demonstrate their applicability using both synthetic RNA and clinical patient samples. Finally, we validate the optimized RT-LAMP assay for the detection of SARS-CoV-2 in unextracted heat-inactivated nasopharyngeal samples from 184 patients. We anticipate that optimized and affordable reagents for RT-LAMP will facilitate the expansion of SARS-CoV-2 testing globally, especially in sites and settings where the need for large scale testing cannot be met by commercial alternatives.

Selection and Substantiation of Methods for Evaluating Cosmetic Products for Deodorant and Antiperspirant Action

2021 ◽  
Vol 37 (2) ◽  
pp. 54-64
Author(s):  
D.V. Barabash ◽  
I.A. Butorova
Keyword(s):  
Large Scale ◽  
Gravimetric Method ◽  
Population Data ◽  
Diffusion Method ◽  
Disc Diffusion ◽  
Disc Diffusion Method ◽  
Large Scale Testing ◽  
Sweat Secretion ◽  
Microbiological Methods ◽  
Number Of Microorganisms

The possibility of using simple and available methods for analyzing deodorants/antiperspirants has been studied. The gravimetric method was shown to have acceptable metrological characteristics under repeatability conditions when evaluating antiperspirant activity. A decrease in the number of microorganisms (CFU) on the axilla skin was observed in a rinse test experiment 4 h and 8 h after the application of deodorants/antiperspirants. The microbial population data were inversely proportional to the antiperspirant activity values of the tested compositions. The sweat secretion reducing decreases the amount of nutrients required for microbial development, which makes it possible to use the rinse test to indirectly evaluate deodorant activity in research and development of personal care products. However, due to its laboriousness and the need for volunteers, the method cannot be recommended for large-scale testing. It was shown that the disc diffusion method (DDM) used to detect Staphylococcus aureus, Pseudomonas aeruginosa and Bacillus subtilis cannot be applied to the assessment of the intrinsic antimicrobial activity of the tested cosmetic compositions. This indicates the necessity of additional studies to select test microorganisms typical for the armpit area. In addition, DDM is useful if the deodorant effect of the composition is created by the addition of low-volatile antibacterial compounds. Therefore, microbiological methods have limited applications and are not suitable for widespread use. deodorant action; antiperspirant action, gravimetry, disc diffusion method, rinse test; deodorant; antiperspirant; cosmetic; efficiency; consumer properties, functional properties This work was supported by MUCTR (project no. K-2020-007).

Trust, efficacy and ethicacy when testing prisoners for COVID-19

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Steve Lambert ◽  
Dean Wilkinson
Keyword(s):  
Large Scale ◽  
Current System ◽  
Epidemiological Studies ◽  
Careful Consideration ◽  
Power Relationships ◽  
England And Wales ◽  
Content Type ◽  
Large Scale Testing ◽  
The Uk ◽  
The Impact

Purpose The outbreak of the severe acute respiratory syndrome coronavirus 2 virus and subsequent COVID-19 illness has had a major impact on all levels of society internationally. The extent of the impact of COVID-19 on prison staff and prisoners in England and Wales is unknown. Testing for COVID-19 both asymptomatic and symptomatic, as well as for antibodies, to date, has been minimal. The purpose of this paper is to explore the widespread testing of COVID-19 in prisons poses philosophical and ethical questions around trust, efficacy and ethicacy. Design/methodology/approach This paper is both descriptive, providing an overview of the widespread testing of COVID-19 in prisoners in England and Wales, and conceptual in that it discusses and argues the issues associated with large-scale testing. This paper provides a discussion, using comparative studies, of the issues associated with large-scale testing of prisoners across the prison estate in England and Wales (120 prisons). The issues identified in this paper are contextualised through the lens of COVID-19, but they are equally transferrable to epidemiological studies of any pandemic. Given the prevalence of COVID-19 globally and the lack of information about its spread in prisons, at the time of writing this paper, there is a programme of asymptomatic testing of prisoners. However, there remains a paucity of data on the spread of COVID-19 in prisons because of the progress with the ongoing testing programme. Findings The authors argue that the widespread testing of prisoners requires careful consideration of the details regarding who is included in testing, how consent is gained and how tests are administered. This paper outlines and argues the importance of considering the complex nuance of power relationships within the prison system, among prisoner officers, medical staff and prisoners and the detrimental consequences. Practical implications The widespread testing of COVID-19 presents ethical and practical challenges. Careful planning is required when considering the ethics of who should be included in COVID-19 testing, how consent will be gained, who and how tests will be administered and very practical challenges around the recording and assigning of COVID-19 test kits inside the prison. The current system for the general population requires scanning of barcodes and registration using a mobile number; these facilities are not permitted inside a prison. Originality/value This paper looks at the issues associated with mass testing of prisoners for COVID-19. According to the authors’ knowledge, there has not been any research that looks at the issues of testing either in the UK or internationally. The literature available details countries’ responses to the pandemic rather and scientific papers on the development of vaccines. Therefore, this paper is an original review of some of the practicalities that need to be addressed to ensure that testing can be as successful as possible.

scholarly journals Self-Adaptive K-Means Based on a Covering Algorithm

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Yiwen Zhang ◽  
Yuanyuan Zhou ◽  
Xing Guo ◽  
Jintao Wu ◽  
Qiang He ◽  
...  
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Real Data ◽  
Second Phase ◽  
Data Sets ◽  
Number Of Clusters ◽  
Large Scale Data ◽  
Long Time ◽  
Two Phases ◽  
Selection Of

The K-means algorithm is one of the ten classic algorithms in the area of data mining and has been studied by researchers in numerous fields for a long time. However, the value of the clustering number k in the K-means algorithm is not always easy to be determined, and the selection of the initial centers is vulnerable to outliers. This paper proposes an improved K-means clustering algorithm called the covering K-means algorithm (C-K-means). The C-K-means algorithm can not only acquire efficient and accurate clustering results but also self-adaptively provide a reasonable numbers of clusters based on the data features. It includes two phases: the initialization of the covering algorithm (CA) and the Lloyd iteration of the K-means. The first phase executes the CA. CA self-organizes and recognizes the number of clusters k based on the similarities in the data, and it requires neither the number of clusters to be prespecified nor the initial centers to be manually selected. Therefore, it has a “blind” feature, that is, k is not preselected. The second phase performs the Lloyd iteration based on the results of the first phase. The C-K-means algorithm combines the advantages of CA and K-means. Experiments are carried out on the Spark platform, and the results verify the good scalability of the C-K-means algorithm. This algorithm can effectively solve the problem of large-scale data clustering. Extensive experiments on real data sets show that the accuracy and efficiency of the C-K-means algorithm outperforms the existing algorithms under both sequential and parallel conditions.

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