Data Reduction Tools Research Articles

A fast open data reduction workflow for the electron microprobe flank method to determine Fe3+/ΣFe contents in minerals

Abstract Knowing the Fe3+/ΣFe ratio in minerals is important to understand the formation and evolution of minerals and their host rocks. Variable Fe3+/ΣFe in, e.g., garnet is accompanied by a change of their characteristic FeLα and FeLβ X-ray emission lines, which can be quantified with electron microprobe measurements using the flank method. Previously, the required data reduction process to determine the Fe3+/ΣFe was complex. Here, we present a new data reduction tool taking garnet data as an example. This new Flank Reduction app is a freely available, graphical user interface (GUI)-driven, web-based application to reduce flank method data quickly and easily. The entire data reduction process is achieved in minutes compared to hours or days, as it was before. Flank Reduction provides comprehensive insights into, e.g., the flank method standards employed, data obtained, and errors through a wide array of controls and visualization tools. Documentation with comprehensive information on the flank method, data reduction, as well as training material such as video tutorials or sample data sets, are available on a dedicated webpage. Flank Reduction emphasizes the high value of Findable, Accessible, Interoperable, Reproducible (FAIR) and open research software and demonstrates how current developments in coding and app implementation can facilitate the development of powerful and expandable research software.

10Boron-film-based gas detectors at ESS

The development of detectors for the European Spallation Source is an important parallel element to the effort put into the design and construction of the neutron source and instruments. The 10Boron-film-based detector developments that started over a decade ago as basic detector concepts have now reached technical maturity after intense prototyping work and numerous testing campaigns. Several of the ESS beamlines that will soon enter the commissioning phase started welcoming the detector systems built with the 10B-film converter technology. The real-size demonstrators evolved in the last 2–3 years into a diverse suite of gas proportional counters for use in diffraction, reflectometry and small-angle scattering studies in spite of the operational complexity posed by the requirements for large-area coverage, low material budget and robustness for operation in the high-flux and high-radiation environment of ESS. The detectors are now being shipped to ESS or undergoing the last performance and calibration tests before being handed over to the instrument teams for installation in the host beamlines. A common feature of these detector systems is the very large number of readout channels that they are instrumented with in order to fulfill the demanding requirements for sensitivity, spatial resolution and count-rate capability. Across all of the detector types that will operate at ESS, the integration, testing and commissioning of the read-out technologies and software tools for data reduction, calibration and analysis are the focus of the detector and integration teams. These will yield a wealth of knowledge about their operation as well as initial results on the in-situ performance, a very important asset to ensure rapid commissioning of the detectors when neutrons from the ESS source become available. In this paper we will give an overview of the 10B-film-based detector technologies included in the ESS detector suite that are currently facing the transition from the production phase to installation and integration with the other beamline components, which comes with its own specific challenges of both organizational and technical nature.

Soil Quality under Polyhouse and Open Field Conditions as Affected by Continuous Fertilizer and Manure Applications

ABSTRACT Intensive cultivation coupled with scrupulous use of fertilizers could be detrimental to soil health and quality of the produce in the long run. Therefore, this study aimed to assess the predominant protected/polyhouse and open field vegetable production on soil quality. The changes in soil quality in comparison to control (fallow) indicate, whether the management practices being adopted under both the systems are sustainable or not. Thus, a study was conducted to identify the most appropriate soil quality indicators and finally to know about the status of soil quality both under polyhouse and open field system of cultivation in mid hills of North western Himalayas. Principal component analysis (PCA), a data reduction tool was employed to get the minimum data set for the assessment of soil quality. Geo-referenced soil samples were collected from 25 progressive farmers’ cultivating vegetables under polyhouse, as well as open field conditions and analyzed for various physical, chemical and biological indicators of soil quality. The PCA was performed which screened out the three principal components (PCs) with eigen value > 1. Average soil quality index (SQI) for different sites was observed to be on higher under polyhouse conditions (0.815) as compared to open field cultivation for vegetables (0.760) and control (0.703). Where, soil organic carbon contributed maximum to soil quality (75.6%) followed by pH (13.9%), water holding capacity (6.8%) and EC (3.6%). Conclusively, polyhouse cultivation system revealed high category of soil quality. Whereas, open field cultivation system revealed medium category of soil quality.

Power-knowledge: How the corporate strategy will be competitive in the future?

Environmental changes require resources that are able to adapt. This condition requires a special strategy to remain competitive. This can be realized from real resources and employee skills that match market needs (Dubey et al., 2019). Therefore, continuous training is the “way” to face business scenarios (Santana-Domínguez et al., 2022). So, employee training is the best solution to maintain company sustainability. This study aims to explore the importance of training to maintain the stability of the company’s future. This research is qualitative research with in-depth interviews as the main data collection method. The analytical method uses Foucault’s (1980) power-knowledge with three main focuses as a tool for data reduction, namely sharing, generating and using knowledge. The results of the study show that training is important to be carried out by companies due to three main reasons, namely training as a place to transfer knowledge, training to produce employee/company performance, and training as an inseparable part of the company’s sustainability process. In addition, the results show that continuous learning is a necessity that must be carried out to deal with environmental change. It is important to know that everything will change except the change itself. The next research approach is to examine the training section with a quantitative approach.

Modal Decomposition of Wake Flow Behind a Circular Cylinder

This study demonstrates the effectiveness of Proper Orthogonal Decomposition (POD) as a method for analyzing flow structures and as a data reduction tool for both numerical simulations and experimental techniques. The reduced data can be integrated to data driven inference of partial differential equations which can lead to reduced order modeling. By focusing on the flow around a cylinder, we show that POD efficiently identifies the most energetic modes, allowing for the flow's reconstruction with a limited dataset. Our findings indicate that the first 10 POD modes appear in pairs with similar spatial patterns, frequency spectra, and energy contributions. These modes encapsulate nearly 92% of the total energy, highlighting the method's precision in capturing essential dynamics. Specifically, modes 1 and 2 correspond to streamwise vortex shedding, while modes 3 and 4 relate to the lateral flow motion. The reconstructed flow, based on these dominant modes, closely matches numerical analysis results, validating POD's utility in developing reduced order models and enhancing our understanding of fluid-structure interactions.

US National Gemini Office in the NOIRLab era

This article presents an overview of the US National Gemini Office (US NGO) and its role within the International Gemini Observatory user community. Throughout the years, the US NGO charter changed considerably to accommodate the evolving needs of astronomers and the observatory. The current landscape of observational astronomy requires effective communication between stakeholders and reliable/accessible data reduction tools and products, which minimize the time between data gathering and publication of scientific results. Because of that, the US NGO heavily invests in producing data reduction tutorials and cookbooks. Recently, the US NGO started engaging with the Gemini user community through social media, and the results have been encouraging, increasing the observatory's visibility. The US NGO staff developed tools to assess whether the support provided to the user community is sufficient and effective, through website analytics and social media engagement numbers. These quantitative metrics serve as the baseline for internal reporting and directing efforts to new or current products. In the era of the NSF's National Optical-Infrared Astronomy Research Laboratory (NOIRLab), the US NGO is well-positioned to be the liaison between the US user base and the Gemini Observatory. Furthermore, collaborations within NOIRLab programs, such as the Astro Data Lab and the Time Allocation Committee, enhance the US NGO outreach to attract users and develop new products. The future landscape laid out by the Astro 2020 report confirms the need to establish such synergies and provide more integrated user support services to the astronomical community at large.

Exploring Prognostic and Diagnostic Techniques for Jet Engine Health Monitoring: A Review of Degradation Mechanisms and Advanced Prediction Strategies

Maintenance is crucial for aircraft engines because of the demanding conditions to which they are exposed during operation. A proper maintenance plan is essential for ensuring safe flights and prolonging the life of the engines. It also plays a major role in managing costs for aeronautical companies. Various forms of degradation can affect different engine components. To optimize cost management, modern maintenance plans utilize diagnostic and prognostic techniques, such as Engine Health Monitoring (EHM), which assesses the health of the engine based on monitored parameters. In recent years, various EHM systems have been developed utilizing computational techniques. These algorithms are often enhanced by utilizing data reduction and noise filtering tools, which help to minimize computational time and efforts, and to improve performance by reducing noise from sensor data. This paper discusses the various mechanisms that lead to the degradation of aircraft engine components and the impact on engine performance. Additionally, it provides an overview of the most commonly used data reduction and diagnostic and prognostic techniques.

Parmanu‐Gunak: Data Reduction Software for Isotope Dilution Analysis

Isotope dilution (ID) is a widely used analytical technique to determine elemental abundance to a high degree of accuracy and precision, using a spiked isotope tracer. This technique also enables efficient correction of the inevitable phenomenon of analytical mass fractionation during mass spectrometric analysis. It is also used to determine stable isotope variations of an element in a sample relative to a reference material, using tracers enriched in two isotopes (popularly known as the double spike method). Isotope dilution data reduction can be performed in Microsoft® Excel™ using different algorithmic approaches. Additional software such as iolite and MATLAB® offer algorithm implementations to perform these calculations. These are however limited to use within particular laboratories/research groups, or either require additional cost‐bound software or some degree of knowledge in computer programming languages for use, or all of these. To ease this situation, a graphical user interface‐based software is proposed (here named Parmanu‐Gunak, meaning Atom‐Calculator in Sanskrit text) to invert both single and double spiked isotope data, with the aim of making it a standard tool for ID data reduction. Examples of Nd and Mo isotopes are used to demonstrate the robustness of the program.

Rediscovering reliable components analysis: An application to executive function skills in early childhood.

Executive function (EF) assessments often involve the administration of multiple tasks. Although factor analytic methods are routinely used to summarize performance across multiple tasks, they may not be optimal for this purpose. We introduce reliable component analysis (RCA) as a strategy for summarizing EF task performance and demonstrate how it compares to traditional methods. Participants included 259 children (M = 4.5, SD = 0.6 years old; 55% female; 41% White, 35% Black, 14% Hispanic, 1% Asian, 1% American Indian, and 8% of more than one race) from the Kids Activity and Learning Study. Data collection occurred in center-based preschools and involved direct child assessments of EF, motor, and math skills. Principal components analysis (PCA), principal axis factor analysis (FA), and RCA methods were used to summarize children's performance across a battery of six EF tasks. Whereas PCA and FA indicated that a single composite or factor provided the best representation of EF task data, RCA indicated that three composites were justifiable. RCA composites were moderately to strongly correlated with PCA and FA scores (rs = .39-.79). Regression models indicated that all three approaches for combining EF task scores explained the same proportion of variance in motor and math skills outcomes, though the contributions of individual composite and factor scores varied. Results are discussed with respect to how RCA differs from more commonly used tools for data reduction. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Principal component analysis as tool for data reduction with an application

The recent trends in collecting huge datasets have posed a great challenge that is brought by the high dimensionality and aggravated by the presence of irrelevant dimensions. Machine learning models for regression is recognized as a convenient way of improving the estimation for empirical models. Popular machine learning models is support vector regression (SVR). However, the usage of principal component analysis (PCA) as a variable reduction method along with SVR is suggested. The principal component analysis helps in building a predictive model that is simple as it contains the smallest number of variables and efficient. In this paper, we investigate the competence of SVR with PCA to explore its performance for a more accurate estimation. Simulation study and Renal Failure (RF) data of SVR optimized by four different kernel functions; linear, polynomial, radial basis, and sigmoid functions using R software, version (R x64 3.2.5) to compare the behavior of ε SVR and v-SVR models for different sample sizes ranges from small, moderate to large such as; 50, 100, and 150. The performance criteria are root mean squared error (RMSE) and coefficient of determination R2 showed the superiority of ε-SVR over v- SVR. Furthermore, the implementation of SVR after employing PCA improves the results. Also, the simulation results showed that the best performing kernel function is the linear kernel. For real data the results showed that the best kernels are linear and radial basis function. It is also clear that, with ε-SVR and v-SVR, the RMSE values for almost kernel functions decreased with increasing sample size. Therefore, the performance of ε-SVR improved after applying PCA. In addition sample size n=50 gave good results for linear and radial kernel

Empirical Model and PSO-Based Algorithm for Efficient Measurement of Gas Permeation Through High Barrier

Efficient measurement of gas permeation through high-barrier materials requires effective theoretical and numerical tools for data reduction and parameter extraction, to reduce the measurement period within error tolerance. An empirical permeation model was developed in this regard, by comprehensively considering gas–surface interactions as well as time and concentration-dependent diffusivity of the permeant particle. An algorithm was developed based on particle swarm optimization (PSO), to numerically fit the experimental data to the permeation model and to extract the characteristic permeation parameters. Compared with the traditional models, the comprehensive model is more accurate to predict the actual permeation phenomena, allowing 75% reduction of the measurement period within 10% tolerance on the extracted steady-state permeability. In addition, the PSO algorithm demonstrates extended applications in other non-steady-state transport problems such as convective–diffusive heat transfer.

Mapping the evolution of topics published by Education for Information: Interdisciplinary Journal of Information Studies

The need to map the evolution of trends in any field of activity arises when a large amount of data is available on that activity, thus making impossible a manual exploration of the data in order to understand how the field or the activity is evolving. Topic and trend mapping is a mature field with hundreds of publications on approaches, methods and tools for data collection, analysis, feature extraction and reduction, clustering and visualisation tools and algorithms. Our study aims to map the evolution of topics published by the journal Education for Information. Interdisciplinary Journal on Information Studies (EFI henceforth) which has been in existence since 1983, in order to understand how this journal has evolved and how it is positioned with regard to the field of Library and Information Science to which it belongs. Our study is part of the body of work on topic detection and text mining. Our results showed that the journal displayed a remarkable stability in its editorial policy over more than three decades. With the arrival of its third Editor in Chief in 2018, a shift towards more technologically oriented topics and to specialties from other fields are perceptible such as health information, data science and digital humanities.

Predicting the dynamic response of a structure using an artificial neural network

Machine learning and artificial intelligence has been applied to other facets of structural mechanics and structural dynamics, mainly for structural optimization and structural reliability analysis, but have seen little use in surrogate modeling for structural time series prediction. The current research will focus on how data reduction tools (such as mathematical morphology) can be applied to dynamic structural data and can be used in conjunction with clustering methodologies as well as artificial neural networks (ANNs) to create a useful and highly computationally efficient dynamic model that can be used to make predictions on the acceleration response of a structural system. The current study utilizes training data developed from finite element modeling of a simple system that demonstrates a nonlinear behavior of interest (a change in the dynamic response with a change in loading magnitude), but the methodologies developed could be applied to other modeling or analysis schemes, test data, or in-situ measurements of a structure of interest. The results of the study show how mathematical morphology tools can reduce the dimensionality of time series data while still preserving important characteristics like natural frequency, and the use of ANNs shows promise as a surrogate model for dynamic response prediction.

Unique ion filter-A data reduction tool for chemometric analysis of raw comprehensive two-dimensional gas chromatography-mass spectrometry data.

Comprehensive gas chromatography with time of flight mass spectrometry is a powerful tool in the analysis of complex samples. Chemometric analysis of raw chromatographic data is more useful in one- and two-dimensional separations relative to peak tables. The data volume from such experiments generally necessitates the use of data reduction tools. Such tools often sacrifice some of the multivariate information in the mass to charge ratio dimension. The unique ion filter reduces the over-redundancy in two-dimensional gas chromatography-mass spectrometry data by limiting the data to a few unique/pseudo-unique ions, sub-peaks/slices in the first dimension, and spectra in the second dimension. We explore the performance of this algorithm through careful inspection of two-dimensional gas chromatography-mass spectrometry data before and after application of the filter. A reduction (99%) in the number of variables in a two-dimensional gas chromatography-mass spectrometry chromatogram passed on to subsequent analysis was observed. Feature selection times for model optimization reduced from 229 (±13) to 6.8 (±0.5) min when the filter was applied. An estimate of two unique/pseudo-unique ions, one sub-peak in the first dimension and five spectra in the second dimension were considered to provide a true representation of each chromatogram and provided enough information to achieve 100% model prediction accuracy.

Abstract 17285: Metabolite-Derived Network Reveals Cluster of Acylcholine Metabolites Associated With Better Diet Quality and Lower Prevalence of Type 2 Diabetes: Findings From the Boston Puerto Rican Health Study

Introduction: Puerto Rican (PR) adults living on the US mainland are at high risk for developing type 2 diabetes (T2D), and dietary factors may contribute to this increased risk. Network analysis is a data-reduction tool that can identify correlated clusters of co-regulated metabolites that reflect mechanisms underlying diet-T2D associations. Hypothesis: Diet quality will associate with T2D-associated metabolite clusters among PR adults. Methods: We used LC/MS to measure fasting plasma metabolites (>700) among Boston PR Health Study participants, aged 45-75 years, with (n=258) and without (n=421) T2D. We applied an unsupervised correlation network-based method to identify metabolite clusters within a global metabolite network and calculated a score for each cluster using a weighted sum of metabolite concentrations. To estimate diet quality, we calculated a modified version of a previously validated American Heart Association diet score (AHA-DS). Logistic regression was used to assess cross-sectional associations between metabolite clusters and prevalent T2D, and linear regression was used to assess associations between the continuous AHA-DS and T2D-associated metabolite clusters among controls, adjusting for potential confounders and correcting for multiple testing. Results: We identified 7 metabolite clusters that were associated with prevalent T2D ( p <0.05). For every 1-standard deviation (SD) increase in cluster score, the odds ratios for prevalent T2D and 95% confidence intervals were as the follows: acylcholines [0.40 (0.31, 0.50)], aromatic hydrocarbon derivatives [0.33 (0.22, 0.47)], sphingolipids [0.46 (0.33, 0.64)], tricarboxylic acid (TCA) cycle amino acids/peptides [0.39 (0.25, 0.62)], branched-chain amino acid metabolites [4.1 (2.9, 6.0)], acylcarnitines [1.8 (1.3, 2.5)], and TCA cycle/energy metabolites [2.0 (1.4, 3.0)]. The AHA-DS was only significantly associated with the acylcholine metabolites cluster [β (standard error) = 0.01 (0.004) SD increase in cluster score, p=0.02]. Conclusions: In individuals of PR descent, we identified a cluster of acylcholine metabolites where concentrations are higher among those with better diet quality and lower among those with prevalent T2D.

Old age is associated with decreased wealth in rural villages in Mtwara, Tanzania: findings from a cross-sectional survey.

ObjectiveIn many countries, housing is used for wealth accumulation and provides financial security in old age. We tested the hypothesis that household wealth, measured by housing quality and ownership of durable assets, would increase with age of the household head.MethodsWe conducted a survey of household heads in 68 villages surrounding Mtwara town, Tanzania and recorded relevant demographic, housing and social characteristics for each household. The primary analysis assessed the relationship between age of the household head, quality of the house structure and socio‐economic score (SES) using multivariate analysis. Principal Components Analysis (PCA) was used as a data reduction tool to estimate the social‐economic status of subjects based on relevant variables that are considered as proxy for SES.ResultsOf 13 250 household heads were surveyed of whom 49% were male. Those at least 50 years old were more likely to live in homes with an earth floor (86%) compared to younger household heads (80%; P < 0.0001), wattle and daub walls (94% vs. 90%; P < 0.0001) and corrugated iron roofs (56% vs. 52%; P < 0.0001). Wealth accumulation in the villages included in the study tends to be an inverted V‐relationship with age. Housing quality and SES rose to a peak by 50 years and then rapidly decreased. Households with a large number of members were more likely to have better housing than smaller households.ConclusionsHousing plays a critical role in wealth accumulation and socio‐economic status of a household in rural villages in Tanzania. Households with a head under 50 years were more likely to live in improved housing and enjoyed a higher SES, than households with older heads. Larger families may provide protection against old age poverty in rural areas. Assuring financial security in old age, specifically robust and appropriate housing would have wide‐ranging benefits.

Cost factors in Zero-Carbon Technologies Applied in Buildings: Nigeria s Perspective

The integration of renewable energy technologies in buildings across Africa is low. Enormous improvements to intrinsic cost drivers affecting their affordability are therefore prerequisite to achieve diffused adoption. In a search for the relevant strategies to improve the adoption of these technologies, this paper appraised critical cost factors inhibiting the affordability of solar photovoltaic (PV) in Nigeria. The research adopted a structured questionnaire survey administered to 480 stakeholders in the building sub-sector and PV value chain. Data reduction tool (factor analysis) determined the components of principal factors critical to the affordability of PV in the research environment. The result showed that the dearth of local competencies, lack of skilled labour, lack of research, lack of locally accessible technologies, high cost of maintenance, foreign exchange fluctuation, and inflation are important drivers of PV costs that must improve to facilitate PV integration in buildings. The strategies to improving PV adoption must stimulate affordability by mitigating technical and economic cost factors. The study recommends strong government incentives using waivers on tariff, education and awareness, training in requisite skills, and local manufacturing of components as prerequisite drivers needed to improve affordability.

Trend filtering – II. Denoising astronomical signals with varying degrees of smoothness

ABSTRACTTrend filtering – first introduced into the astronomical literature in Paper I of this series – is a state-of-the-art statistical tool for denoising 1D signals that possess varying degrees of smoothness. In this work, we demonstrate the broad utility of trend filtering to observational astronomy by discussing how it can contribute to a variety of spectroscopic and time-domain studies. The observations we discuss are (1) the Lyman-α (Lyα) forest of quasar spectra; (2) more general spectroscopy of quasars, galaxies, and stars; (3) stellar light curves with planetary transits; (4) eclipsing binary light curves; and (5) supernova light curves. We study the Lyα forest in the greatest detail – using trend filtering to map the large-scale structure of the intergalactic medium along quasar-observer lines of sight. The remaining studies share broad themes of: (1) estimating observable parameters of light curves and spectra; and (2) constructing observational spectral/light-curve templates. We also briefly discuss the utility of trend filtering as a tool for 1D data reduction and compression.

Scaling the Construction of Wavelet Synopses for Maximum Error Metrics

Modern analytics involve computations over enormous numbers of data records. The volume of data and the stringent response-time requirements place increasing emphasis on the efficiency of approximate query processing. A major challenge over the past years has been the construction of synopses that provide a deterministic quality guarantee, often expressed in terms of a maximum error metric. By approximating sharp discontinuities, wavelet decomposition has proved to be a very effective tool for data reduction. However, existing wavelet thresholding schemes that minimize maximum error metrics are constrained with impractical complexities for large datasets. Furthermore, they cannot efficiently handle the multi-dimensional version of the problem. In order to provide a practical solution, we develop parallel algorithms that take advantage of key-properties of the wavelet decomposition and allocate tasks to multiple workers. To that end, we present (i) a general framework for the parallelization of existing dynamic programming algorithms, (ii) a parallel version of one such DP algorithm, and (iii) two highly efficient distributed greedy algorithms that can deal with data of arbitrary dimensionality. Our extensive experiments on both real and synthetic datasets over Hadoop show that the proposed algorithms achieve linear scalability and superior running-time performance compared to their centralized counterparts.

Systematic Measurement of Hydrocarbon Fuel Droplet Burning Rate Constants and Ignition Delays

In this experimental study, a new data reduction approach was developed for measuring droplet burning rate constants that automatically finds the most linear part of the -squared curve for the longest period of time. The approach, here called , was developed in response to the observation that burning rate constants can vary by up to 10%, depending on the reduction approach used, especially for fuel types such as nonvolatile simple hydrocarbons and kerosenes. To evaluate this new method, burning rate constants were measured for 10 fuels, including two alcohols, four simple hydrocarbons and four kerosene blends. The measured rates ranged from 0.74 to , typically grouped by similarities in thermophysical properties of the fuel types. Notably, and its simpler equivalent yielded burning rates with small statistical uncertainty ( average) across all fuels and were associated with the highest coefficient of determination for all measurement approaches, indicating that these two methods are viable options for improved consistency in measuring burning rate constants. In addition, a systematic method is presented for measuring ignition delays using a short-duration plasma source for ignition. The ignition delays ranged from 65 to 110 ms for most fuels, correlating well with the fuel boiling temperature. Combined, this work provides baseline data and data reduction tools suitable for future droplet combustion studies.