Improved Data Analysis Research Articles

Research on data fusion scheme of power internet of things based on cloud and NFV

Abstract With the development of the cloud platform and power Internet of Things, how to effectively connect the physical world and the digital world, realize the interconnection of all things in power terminals, deeply integrate data, and tap the economic value of data is an important issue. Based on the company’s business and technology development status, this paper proposes the power Internet of Things data fusion based on cloud and NFV, discusses data storage, circulation and management methods in the power Internet of Things cloud platform environment, and completes accurate and efficient data analysis. Responding to the access of a large number of distributed energy sources, multiple business concurrency and joint data analysis needs, and improve data analysis and sharing capabilities.

Improvement of substation Monitoring aimed to improve its efficiency with the help of Big Data Analysis**

Abstract Data analysis has become most widespread field of research and it has extended in almost every field of study. Considering the recent trends and developments in the field of communication and information technology, there is a scope of combining the monitoring of substation equipment with big data analysis technology. That will result in an improved data analysis ability, information sharing and utilization rate of monitoring data. In the proposed work, the authors have introduced the big data analysis and its corresponding application in the monitoring of substations. Basic concepts and the procedures of the typical data analysis for general problems are also discussed. As a main part of the paper, different types of distributed data analysis techniques have been proposed, in which two relational online analysis, namely Hive and Impala and one H Base multidimensional online analysis are important. These data analysis techniques are proposed considering the analysis efficiency, storage performance from the business development requirements point of view of the substation. The result obtained depicts that the proposed model has an advantage in storage overhead and roll-up performance, when compared with the traditional method, although the data loading speed is approximately 1.7–1.9 times of the traditional model. Some experiments are carried out in order to verify the validity of the model.

Chimpanzee brain morphometry utilizing standardized MRI preprocessing and macroanatomical annotations.

Chimpanzees are among the closest living relatives to humans and, as such, provide a crucial comparative model for investigating primate brain evolution. In recent years, human brain mapping has strongly benefited from enhanced computational models and image processing pipelines that could also improve data analyses in animals by using species-specific templates. In this study, we use structural MRI data from the National Chimpanzee Brain Resource (NCBR) to develop the chimpanzee brain reference template Juna.Chimp for spatial registration and the macro-anatomical brain parcellation Davi130 for standardized whole-brain analysis. Additionally, we introduce a ready-to-use image processing pipeline built upon the CAT12 toolbox in SPM12, implementing a standard human image preprocessing framework in chimpanzees. Applying this approach to data from 194 subjects, we find strong evidence for human-like age-related gray matter atrophy in multiple regions of the chimpanzee brain, as well as, a general rightward asymmetry in brain regions.

Long ECGs reveal rich and robust dynamical regimes in patients with frequent ectopy.

We have analyzed the electrocardiographic data collected during continuous 7-day ambulatory recordings in patients with frequent premature ventricular complexes (PVCs). We analyze the dependence of the frequency and patterns of PVCs on the heart rate and the time of the day. Patients display rhythms of a complex yet consistent structure. In a given patient, the pattern remains robust over different days and particular repetitive patterns appear at specific heart rates, suggesting the appearance of bifurcations in the dynamics. Over the course of 24 h, we find that in some patients, patterns appear to depend only on the heart rate, whereas in others, both the time of the day and the heart rate play a role in controlling the dynamics. Identifying parameter values at which bifurcations occur facilitates the development of dynamical models for arrhythmia. The use of powerful recording and analysis techniques will enable improved analysis of data and better understanding of mechanisms of arrhythmia in individual patients.

Improving Data Analysis in Chemistry and Biology Through Versatile Baseline Correction

AbstractAccurate data analysis is a cornerstone for the meaningful interpretation of measurements in chemistry and biology. To enable accurate analysis, it is often necessary to remove the background from a measurement via baseline correction, as is commonly done for spectroscopy or chromatography. However, no equivalent methods for baseline correction exist for an entire group of measurements, which includes chemical reaction measurements, quantitative polymerase chain reaction and X‐ray absorption spectroscopy. This is because these measurements give rise to a different class of features in their signals, which prevent the application of classical baseline correction methods. In this work, a general method for baseline correction of these features is developed, which is shown to simplify and improve data analysis for these measurements. Through publicly accessible and easy to use software we expect this method to be broadly useful to improve and simplify data analysis for chemists and biologists.

Fine-Scale Microclimate Pattern in Forest-Steppe Habitat

Microclimate and vegetation architecture are interdependent. Little information is available, however, about the fine-scale spatio-temporal relationship between the microclimate and herb layer of forest-steppe mosaics. In 2018 a three-season-long vegetation sampling and measurements of air temperature and air humidity were performed along 4 transects (44 m long each) in the herb layer with 89 dataloggers in the sandy region of Central Hungary, in a poplar grove and the surrounding open grassland. In order to improve data analysis, we introduced the use of a duration curve widely used in hydrology and proved to be useful in the processing of intensive climatic data. We analysed the effect of the direction and altitude of the solar irradiation and the edge effect on the microclimatic pattern. We also surveyed, seasonally, the spatial pattern of the exceedance rate for the vapour pressure deficit (VPD) in relation to the transect direction and to the edge of the grove. The exceedance rate for the VPD indicated considerable seasonal differences. The VPD exceedance rate indicates the stress effect for the vegetation. The moderating effect of the grove was small at 1.2 kPa VPD, but at 3.0 kPa—stronger stress—it was considerable. On the warmer side of the transects, mostly exposed at the south-eastern edge, the exceedance rate rose abruptly with distance from the edge compared to the gradual increase on the colder side. The cardinal and intercardinal directions as well as the altitude of the Sun all had influences on the moderating and shading effects of the grove. The southern edge was not always consistently the warmest. The distribution of the VPD values above the 3.0 kPa threshold varied within a seemingly homogeneous grassland, which highlights the importance of fine-scale sampling and analysis. This knowledge is valuable for assessing the dynamics and spatio-temporal patterns of abiotic factors and physiognomy in this type of ecosystem.

Implementation of a learning healthcare system for sickle cell disease.

ABSTRCTObjectiveUsing sickle cell disease (SCD) as a model, the objective of this study was to create a comprehensive learning healthcare system to support disease management and research. A multidisciplinary team developed a SCD clinical data dictionary to standardize bedside data entry and inform a scalable environment capable of converting complex electronic healthcare records (EHRs) into knowledge accessible in real time.Materials and MethodsClinicians expert in SCD care developed a data dictionary to describe important SCD-associated health maintenance and adverse events. The SCD data dictionary was deployed in the EHR using EPIC SmartForms, an efficient bedside data entry tool. Additional data elements were extracted from the EHR database (Clarity) using Pentaho Data Integration and stored in a data analytics database (SQL). A custom application, the Sickle Cell Knowledgebase, was developed to improve data analysis and visualization. Utilization, accuracy, and completeness of data entry were assessed.ResultsThe SCD Knowledgebase facilitates generation of patient-level and aggregate data visualization, driving the translation of data into knowledge that can impact care. A single patient can be selected to monitor health maintenance, comorbidities, adverse event frequency and severity, and medication dosing/adherence.ConclusionsDisease-specific data dictionaries used at the bedside will ultimately increase the meaningful use of EHR datasets to drive consistent clinical data entry, improve data accuracy, and support analytics that will facilitate quality improvement and research.

Scientific Research Basics of Fire Resistance Testing for Reinforced Concrete Structures and Buildings

Breakthroughs in the development of science and technology of fire testing and fire resistance tests, their present state are discussed, and ideas about the future development of fire resistance tests are given, furthermore the directions in which deeper studies are necessary, are marked. The first revolution in fire resistance testing began not so long ago, in the nineteenth century, when basic tools were developed to measure temperature and heat fluxes. The second revolution in the measurement and understanding of fire occurred in the early twentieth century with the worldwide recognition that scientifically well-reasoned fire-fighting standards are needed to protect people's lives and their property. At the same time, engineering innovations were developed for electromechanical equipment that could automatically record data obtained during a fire experimentally. This progress in data recording has made it possible to study fire behaviour in details. The third revolution in fire resistance testing took place in the second half of the twentieth century with the development of accessible digital data and computer recording equipment that improved data analysis and the development of evidence-based forecast models. The widespread application of technology and computational methods have opened up a theoretical world that provides nuanced insight of fire dynamics and gives the means for development of more effective fire test methods.

Development of an Enhanced Total Ion Current Chromatogram Algorithm to Improve Untargeted Peak Detection.

Accurate analyte peak detection from the background noise is a fundamental step in data analysis. Often, this is initially performed on the total ion current chromatogram (TIC), which is the summed signal from all mass spectral channels. Despite the detection of many of the most abundant peaks within a chromatogram, a large fraction of peaks remains undetected in the standard TIC due to their signal being below the limit of detection. To find peaks obscured by background noise, an untargeted peak detection method termed the "enhanced TIC algorithm" was developed for comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC × GC-TOFMS). The reported algorithm utilizes the entire mass spectral dimension to find regions of analytical signal above a threshold while zeroing the background noise. The resulting chromatographic data is summed together to create the enhanced TIC. The utility of the enhanced TIC algorithm is demonstrated using serial dilutions from a 10 parts-per-thousand (ppth) test mixture. For the chromatograms collected at 1 and 10 parts-per-million (ppm), the enhanced TIC algorithm recovered 62% and 93%, respectively, of the original peaks observed in the 10 ppth mixture, while the standard TIC recovered only 0% and 45%, respectively. The improvement in signal enhancement was also shown on a separation of a yeast cell metabolite extract, where the enhanced TIC found 33-64% more peaks than the standard TIC. Chromatographic simulations with increasing levels of background noise were also conducted to compare the enhanced and standard TICs in the context of statistical overlap theory (SOT). Simulated chromatograms with lower signal-to-noise were more accurately modeled by the SOT after enhanced TIC processing compared to those processed by the standard TIC. The enhanced TIC method demonstrates an immense benefit in peak discovery to improve data analysis efforts.

Adequação de protocolos de exportação de um sistema de gestão de experimentos neurocientíficos às especificações de dados sem atrito

O Neuroscience Experiments System (NES) é um sistema desenvolvido para gerenciar informações de experimentos de neurociência. O módulo de exportação do NES permite que o pesquisador obtenha os dados e metadados de seus experimentos em formatos interoperáveis, porém a compreensão desses dados nem sempre é simples. Seguindo os conceitos da metodologia ágil, adotamos a filosofia e as especificações técnicas do Frictionless Data com o intuito de reduzir o atrito frequentemente encontrado ao trabalhar com dados. A adoção do Frictionless Data proporcionou uma melhor compreensão dos dados pelos pesquisadores, além de permitir a criação de scripts e programas para realizar a análise dos dados coletados.

Integrated time-frequency wavelet analysis and impulse response filtering on SASW test for rigid pavement stiffness prediction

This paper employs an improved continuous wavelet transform (CWT) technique of Gaussian derivative function and impulse response filtering (IRF) in seismic surface wave analysis to improve data analysis in the Spectral Analysis of Surface Wave (SASW) method. Two procedures were used: first, the response spectrum of interest was chosen using a time–frequency wavelet spectrogram. Then, noisy distortions were eliminated by utilizing a time–frequency wavelet and impulse response filtering. The outcomes show that the SASW method is able to identify the surface wave velocity which coincides with the dynamic stiffness parameters of Portland Cement Concrete (PCC) slabs. The values of surface wave velocity increased during the curing stage, and in situ measurement of wave velocity could be helpful in determining the degree of curing. A good congruence has been demonstrated between the stiffness of PCC obtained from SASW measurements vis a vis that obtained using the American Concrete Institute (ACI) formulation.

Pyphe, a python toolbox for assessing microbial growth and cell viability in high-throughput colony screens.

Microbial fitness screens are a key technique in functional genomics. We present an all-in-one solution, pyphe, for automating and improving data analysis pipelines associated with large-scale fitness screens, including image acquisition and quantification, data normalisation, and statistical analysis. Pyphe is versatile and processes fitness data from colony sizes, viability scores from phloxine B staining or colony growth curves, all obtained with inexpensive transilluminating flatbed scanners. We apply pyphe to show that the fitness information contained in late endpoint measurements of colony sizes is similar to maximum growth slopes from time series. We phenotype gene-deletion strains of fission yeast in 59,350 individual fitness assays in 70 conditions, revealing that colony size and viability provide complementary, independent information. Viability scores obtained from quantifying the redness of phloxine-stained colonies accurately reflect the fraction of live cells within colonies. Pyphe is user-friendly, open-source and fully documented, illustrated by applications to diverse fitness analysis scenarios.

PolyA-miner: accurate assessment of differential alternative poly-adenylation from 3'Seq data using vector projections and non-negative matrix factorization.

Almost 70% of human genes undergo alternative polyadenylation (APA) and generate mRNA transcripts with varying lengths, typically of the 3′ untranslated regions (UTR). APA plays an important role in development and cellular differentiation, and its dysregulation can cause neuropsychiatric diseases and increase cancer severity. Increasing awareness of APA’s role in human health and disease has propelled the development of several 3′ sequencing (3′Seq) techniques that allow for precise identification of APA sites. However, despite the recent data explosion, there are no robust computational tools that are precisely designed to analyze 3′Seq data. Analytical approaches that have been used to analyze these data predominantly use proximal to distal usage. With about 50% of human genes having more than two APA isoforms, current methods fail to capture the entirety of APA changes and do not account for non-proximal to non-distal changes. Addressing these key challenges, this study demonstrates PolyA-miner, an algorithm to accurately detect and assess differential alternative polyadenylation specifically from 3′Seq data. Genes are abstracted as APA matrices, and differential APA usage is inferred using iterative consensus non-negative matrix factorization (NMF) based clustering. PolyA-miner accounts for all non-proximal to non-distal APA switches using vector projections and reflects precise gene-level 3′UTR changes. It can also effectively identify novel APA sites that are otherwise undetected when using reference-based approaches. Evaluation on multiple datasets—first-generation MicroArray Quality Control (MAQC) brain and Universal Human Reference (UHR) PolyA-seq data, recent glioblastoma cell line NUDT21 knockdown Poly(A)-ClickSeq (PAC-seq) data, and our own mouse hippocampal and human stem cell-derived neuron PAC-seq data—strongly supports the value and protocol-independent applicability of PolyA-miner. Strikingly, in the glioblastoma cell line data, PolyA-miner identified more than twice the number of genes with APA changes than initially reported. With the emerging importance of APA in human development and disease, PolyA-miner can significantly improve data analysis and help decode the underlying APA dynamics.

Una Aplicación de una Historia Clínica Electrónica (HCE) Basado en Modelos Conceptuales para Integrar Datos Clínicos y Genómicos y Orientar la Estrategia Terapéutica

Currently, data management in oncology department is complex and requires advanced Information Systems (ISs) to process data where “omic” information should be integrated together with patient’s clinical data to improve data analysis and decision-making process. This research paper reports a practical experience in this context. A Conceptual Model (CM) has been designed to develop an Information System (IS) in order to manage clinical, pathological, and molecular data in a holistic way at the oncology department of two main Hospitals in Paraguay. Additionally, model-based archetypes have been proposed to specify the selected user interaction strategy. The CM and its associated archetypes are the basis to develop a clinical IS in order to load -firstly- and manage -secondly- all the clinical data that the domain requires, showing how feasible the approach is in practice, and how much the corresponding clinical data management is improved. In this work, we want to reinforce with this real experience how using a CM along with archetypes correctly helps to design, develop and manage better information systems, emphasizing the relevance of the selected clinical domain.

Use of Machine Learning with Temporal Photoluminescence Signals from CdTe Quantum Dots for Temperature Measurement in Microfluidic Devices

Because of the vital role of temperature in many biological processes studied in microfluidic devices, there is a need to develop improved temperature sensors and data analysis algorithms. The photoluminescence (PL) of nanocrystals (quantum dots) has been successfully used in microfluidic temperature devices, but the accuracy of the reconstructed temperature has been limited to about 1 K over a temperature range of tens of degrees. A machine learning algorithm consisting of a fully-connected network of seven layers with decreasing numbers of nodes was developed and applied to a combination of normalized spectral and time-resolved PL data of CdTe quantum dot emission in a microfluidic device. The data used by the algorithm was collected over two temperature ranges: 10 K to 300 K, and 298 K to 319 K. The accuracy of each neural network was assessed via mean absolute error of a holdout set of data. For the low temperature regime, the accuracy was 7.7 K, or 0.4 K when the holdout set is restricted to temperatures above 100 K. For the high temperature regime, the accuracy was 0.1 K. This method provides demonstrates a potential machine learning approach to accurately sense temperature in microfluidic (and potentially nanofluidic) devices when the data analysis is based on normalized PL data when it is stable over time.

Reionization optical depth determination from Planck HFI data with ten percent accuracy

We present an estimation of the reionization optical depth τ from an improved analysis of data from the High Frequency Instrument (HFI) on board the Planck satellite. By using an improved version of the HFI map-making code, we greatly reduce the residual large-scale contamination affecting the data, characterised in, but not fully removed from, the Planck 2018 legacy release. This brings the dipole distortion systematic effect, contaminating the very low multipoles, below the noise level. On large-scale polarization-only data, we measure τ = 0.0566−0.0062+0.0053 at 68% C.L., reducing the Planck 2018 legacy release uncertainty by ∼40%. Within the ΛCDM model, in combination with the Planck large-scale temperature likelihood, and the high-ℓ temperature and polarization likelihood, we measure τ = 0.059 ± 0.006 at 68% C.L., which corresponds to a mid-point reionization redshift of zre = 8.14 ± 0.61 at 68% C.L. This estimation of the reionization optical depth with 10% accuracy is the strongest constraint to date.

A Universal Score for Deconvolution of Intact Protein and Native Electrospray Mass Spectra.

The growing use of intact protein mass analysis, top-down proteomics, and native mass spectrometry have created a need for improved data analysis pipelines for deconvolution of electrospray (ESI) mass spectra containing multiple charge states and potentially without isotopic resolution. Although there are multiple deconvolution algorithms, there is no consensus for how to judge the quality of the deconvolution, and many scoring schemes are not published. Here, an intuitive universal score (UniScore) for ESI deconvolution is presented. The UniScore is the weighted average of deconvolution scores (DScores) for each peak multiplied by the R2 of the fit to the data. Each DScore is composed of separate components to score (1) the uniqueness and fit of the deconvolution to the data, (2) the consistency of the peak shape across different charge states, (3) the smoothness of the charge state distribution, and (4) symmetry and separation of the peak. Example scores are provided for a range of experimental and simulated data. By providing a means of judging the quality of the overall deconvolution as well as individual mass peaks, the UniScore scheme provides a foundation for standardizing ESI data analysis of larger molecules and enabling the use of ESI deconvolution in automated data analysis pipelines.

Time series segmentation for state-model generation of autonomous aquatic drones: A systematic framework

Autonomous surface vessels are becoming increasingly important for water monitoring. Their aim is to navigate rivers and lakes with limited intervention of human operators, to collect real-time data about water parameters. To reach this goal, these intelligent systems must interact with the environment and act according to the situations they face. In this work we propose a framework based on the integration of recent time-series clustering/segmentation methods and cluster validity indices, for detecting, modeling and evaluating aquatic drone states. The approach is completely data-driven and unsupervised. It takes unlabeled multivariate time series of sensor traces and returns both a set of statistically significant state-models (generated by different mathematical approaches) and a related segmentation of the dataset. We test the approach on a real dataset containing data of six campaigns, two in rivers and four in lakes, in different countries for about 5.6 h of navigation. Results show that the methodology is able to recognize known states and to discover unknown states, enabling novelty detection. The approach is therefore an easy-to-use tool for discovering and interpreting significant states in sensor data, that enables improved data analysis and drone autonomy.

Functional near-infrared spectroscopy for speech protocols: characterization of motion artifacts and guidelines for improving data analysis.

.Monitoring speech tasks with functional near-infrared spectroscopy (fNIRS) enables investigation of speech production mechanisms and informs treatment strategies for speech-related disorders such as stuttering. Unfortunately, due to movement of the temporalis muscle, speech production can induce relative movement between probe optodes and skin. These movements generate motion artifacts during speech tasks. In practice, spurious hemodynamic responses in functional activation signals arise from lack of information about the consequences of speech-related motion artifacts, as well as from lack of standardized processing procedures for fNIRS signals during speech tasks. To this end, we characterize the effects of speech production on fNIRS signals, and we introduce a systematic analysis to ameliorate motion artifacts. The study measured 50 healthy subjects performing jaw movement (JM) tasks and found that JM produces two different patterns of motion artifacts in fNIRS. To remove these unwanted contributions, we validate a hybrid motion-correction algorithm based sequentially on spline interpolation and then wavelet filtering. We compared performance of the hybrid algorithm with standard algorithms based on spline interpolation only and wavelet decomposition only. The hybrid algorithm corrected 94% of the artifacts produced by JM, and it did not lead to spurious responses in the data. We also validated the hybrid algorithm during a reading task performed under two different conditions: reading aloud and reading silently. For both conditions, we observed significant cortical activation in brain regions related to reading. Moreover, when comparing the two conditions, good agreement of spatial and temporal activation patterns was found only when data were analyzed using the hybrid approach. Overall, the study demonstrates a standardized processing scheme for fNIRS data during speech protocols. The scheme decreases spurious responses and intersubject variability due to motion artifacts.

Analyses on Programme Outcomes Measurements for Continuous Quality Improvement of an Undergraduate Engineering Programme

This article discusses new methods for the analyses of program outcome measurements of an engineering undergraduate program, enabling a robust effort to continually improve the quality of the program. The analyses were conducted on an undergraduate engineering program at University Malaysia Sarawak (UNIMAS), based on program outcome achievements of 80 students undertaking selected courses from 2015–2019. Three techniques were utilized: i.e., graphical visualization of data using boxplots, association analysis using Spearman’s rank correlation coefficient, and consistency analysis using Cronbach’s alpha. Using these techniques, the authors identified trends among the courses measuring the same program outcome. Boxplots are very effective in gaining an overview of the achievements of courses measuring the same program outcome, and in highlighting outliers and anomalies in the data. It was also found that the Cronbach’s alpha result is coherent with that of the rank correlation coefficient. The techniques reported in this study can be applied to enhance data analysis for quality improvement of any academic program focusing on outcome-based education. Therefore, the study presented in this paper is both relevant and valuable to engineering programs working towards obtaining international accreditation.