Modeling Scientific Software Architecture from Regression Tests using Data Mining Techniques
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Mosby, Matthew David↗
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How do the research and public communities view biofuel development?
Understanding how public and research view biofuel development, especially the unsynchronized views, can explain the gaps between the actual biofuel production and consumption and the government mandates in the US. Applying a comparative text mining technique, these views are explored using 9924 news articles and 57,849 research abstracts. It is found both the public and research communities respond actively to major policy and incentive programs and market conditions and the public has been closely following important research advances. However, research and practice should be coordinated to achieve economies of scale for regular and alternative biofuels. Priority is particularly needed for policy research to support legislation and enforcement and remove technical barriers to commercialization; meanwhile, greater attention is needed to prompt the commercialization of mature technologies. In addition, sentiment analysis shows positive public perceptions in general and negative perceptions primarily stemming from fraud in biofuel tax-credit programs with minor concerns on unintended negative impacts of biofuel development and policy implementation issues. This study contributes to methodology by using cutting-edge text-mining techniques to the largest and most up-to-date datasets of public news articles and research publication abstracts so as to compare public and academic views on biofuel development. As a result, this study also contributes to knowledge discovery by providing new perspectives on biofuel development, which will support policy design and identify future research directions.
09 BIOMASS FUELS↗
A data mining approach to investigate food groups related to incidence of bladder cancer in the BLadder cancer Epidemiology and Nutritional Determinants International Study
Abstract At present, analysis of diet and bladder cancer (BC) is mostly based on the intake of individual foods. The examination of food combinations provides a scope to deal with the complexity and unpredictability of the diet and aims to overcome the limitations of the study of nutrients and foods in isolation. This article aims to demonstrate the usability of supervised data mining methods to extract the food groups related to BC. In order to derive key food groups associated with BC risk, we applied the data mining technique C5.0 with 10-fold cross-validation in the BLadder cancer Epidemiology and Nutritional Determinants study, including data from eighteen case–control and one nested case–cohort study, compromising 8320 BC cases out of 31 551 participants. Dietary data, on the eleven main food groups of the Eurocode 2 Core classification codebook, and relevant non-diet data (i.e. sex, age and smoking status) were available. Primarily, five key food groups were extracted; in order of importance, beverages (non-milk); grains and grain products; vegetables and vegetable products; fats, oils and their products; meats and meat products were associated with BC risk. Since these food groups are corresponded with previously proposed BC-related dietary factors, data mining seems to be a promising technique in the field of nutritional epidemiology and deserves further examination.
Nutrition & Dietetics↗
Process mining for healthcare: Characteristics and challenges
Process mining techniques can be used to analyse business processes using the data logged during their execution. These techniques are leveraged in a wide range of domains, including healthcare, where it focuses mainly on the analysis of diagnostic, treatment, and organisational processes. Despite the huge amount of data generated in hospitals by staff and machinery involved in healthcare processes, there is no evidence of a systematic uptake of process mining beyond targeted case studies in a research context. When developing and using process mining in healthcare, distinguishing characteristics of healthcare processes such as their variability and patient-centred focus require targeted attention. Against this background, the Process-Oriented Data Science in Healthcare Alliance has been established to propagate the research and application of techniques targeting the data-driven improvement of healthcare processes. This paper, an initiative of the alliance, presents the distinguishing characteristics of the healthcare domain that need to be considered to successfully use process mining, as well as open challenges that need to be addressed by the community in the future.
59 BASIC BIOLOGICAL SCIENCES↗
Pressing needs of biomedical text mining in biocuration and beyond: opportunities and challenges
Text mining in the biomedical sciences is rapidly transitioning from small-scale evaluation to large-scale application. In this article, we argue that text-mining technologies have become essential tools in real-world biomedical research. We describe four large scale applications of text mining, as showcased during a recent panel discussion at the BioCreative V Challenge Workshop. We draw on these applications as case studies to characterize common requirements for successfully applying text-mining techniques to practical biocuration needs. We note that system ‘accuracy’ remains a challenge and identify several additional common difficulties and potential research directions including (i) the ‘scalability’ issue due to the increasing need of mining information from millions of full-text articles, (ii) the ‘interoperability’ issue of integrating various text-mining systems into existing curation workflows and (iii) the ‘reusability’ issue on the difficulty of applying trained systems to text genres that are not seen previously during development. We then describe related efforts within the text-mining community, with a special focus on the BioCreative series of challenge workshops. We believe that focusing on the near-term challenges identified in this work will amplify the opportunities afforded by the continued adoption of text-mining tools. In conclusion, in order to sustain the curation ecosystem and have text-mining systems adopted for practical benefits, we call for increased collaboration between text-mining researchers and various stakeholders, including researchers, publishers and biocurators.
59 BASIC BIOLOGICAL SCIENCES↗
Evolving landscape of low-energy nuclear physics publications
Evolution of low-energy nuclear physics publications over the last 120 years has been analyzed using nuclear physics databases. An extensive study of Nuclear Science References, Experimental Nuclear Reaction Data (EXFOR), and Evaluated Nuclear Structure Data File (ENSDF) contents provides a unique picture of refereed and non-refereed nuclear physics references. Significant fractional contributions of non-refereed reports, private communications and conference proceedings in EXFOR and ENSDF databases in the 1970’s reflect extensive experimental campaigns and an insufficient number of research journals. This trend has been reversed in recent years because the number of measurements is much lower, while number of journals is higher. In addition, nuclear physics results are mainly published in a limited number of journals, such as Physical Review C and Nuclear Physics A. In the present work, historic publication trends and averages have been extracted and analyzed using nuclear data mining techniques. Lastly, the results of this study and implications are discussed and conclusions presented.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
A Data Mining Approach to Predict In Situ Detoxification Potential of Chlorinated Ethenes
Despite advances in physicochemical remediation technologies, in situ bioremediation treatment based on Dehalococcoides mccartyi (Dhc) reductive dechlorination activity remains a cornerstone approach to remedy sites impacted with chlorinated ethenes. Selecting the best remedial strategy is challenging due to uncertainties and complexity associated with biological and geochemical factors influencing Dhc activity. Guidelines based on measurable biogeochemical parameters have been proposed, but contemporary efforts fall short of meaningfully integrating the available information. Extensive groundwater monitoring data sets have been collected for decades, but have not been systematically analyzed and used for developing tools to guide decision-making. In the present study, geochemical and microbial data sets collected from 35 wells at five contaminated sites were used to demonstrate that a data mining prediction model using the classification and regression tree (CART) algorithm can provide improved predictive understanding of a site’s reductive dechlorination potential. The CART model successfully predicted the 3-month-ahead reductive dechlorination potential with 75.8% and 69.5% true positive rate (i.e., sensitivity) for the training set and the test set, respectively. The machine learning algorithm ranked parameters by relative importance for assessing in situ reductive dechlorination potential. The abundance of Dhc 16S rRNA genes, CH 4 , Fe 2+ , NO 3 – , NO 2 – , and SO 4 2– concentrations, total organic carbon (TOC) amounts, and oxidation–reduction potential (ORP) displayed significant correlations (p < 0.01) with dechlorination potential, with NO 3 – , NO 2 – , and Fe 2+ concentrations exhibiting precedence over other parameters. Contrary to prior efforts, the power of data mining approaches lies in the ability to discern synergetic effects between multiple parameters that affect reductive dechlorination activity. Overall, these findings demonstrate that data mining techniques (e.g., machine learning algorithms) effectively utilize groundwater monitoring data to derive predictive understanding of contaminant degradation, and thus have great potential for improving decision-making tools. Finally, a major need for realizing the predictive capabilities of data mining approaches is a curated, open-access, up-to-date and comprehensive collection of biogeochemical groundwater monitoring data.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Application of materials informatics to vapor-grown carbon nanofiber/vinyl ester nanocomposites through self-organizing maps and clustering techniques
Data mining and knowledge discovery techniques were employed herein to acquire new information on the viscoelastic, flexural, compressive, and tensile properties of vapor-grown carbon nanofiber (VGCNF)/vinyl ester (VE) nanocomposites. Formulation and processing factors (curing environment, presence or absence of dispersing agent, mixing method, VGCNF weight fraction, VGCNF type, high-shear mixing time, and sonication time) and testing temperature were utilized as inputs and the true ultimate strength, true yield strength, engineering elastic modulus, engineering ultimate strength, flexural modulus, flexural strength, storage modulus, loss modulus, and tan delta were selected as outputs. The data mining and knowledge discovery algorithms used in this study include self-organizing maps (SOMs) and clustering techniques. SOMs demonstrated that temperature and tan delta had the most significant effects on the output responses followed by the VGCNF high-shear mixing time, and sonication time. SOMs were also used to produce optimal responses using certain combination(s) of inputs. Fuzzy C-means algorithm (FCM) was also applied to discover patterns in the nanocomposite behavior subsequent to a principal component analysis (PCA), which is a dimensionality reduction technique. Utilizing these techniques, the nanocomposite specimens were separated into different clusters based on the testing temperature (30 °C and 120 °C being the most dominant responses), tan delta, high-shear mixing time, and sonication time. Furthermore, the VGCNF/VE specimens were separated into a cluster based on their viscoelastic responses (storage and loss moduli) at the same temperature. The FCM results indicate that, while all nanocomposite properties in the new framework are essential, the viscoelastic responses of the VGCNF/VE specimens are the most significant. Here this work highlights the utility of data mining and knowledge discovery techniques in the context of materials informatics for the discovery of patterns and trends in the material behavior that are not immediately known.
36 MATERIALS SCIENCE↗
The synergy between stakeholders for cellulosic biofuel development: Perspectives, opportunities, and barriers
While understanding individual stakeholders' perspectives on the adoption and conversion to a biofuel-based landscape has been a subject of many previous studies on biofuels, there has been relatively little attention given to understanding how the interaction between multiple stakeholders involved in biofuel development could influence the widespread adoption of biofuel production. Here, this paper analyzes the key stakeholder interactions utilizing various data sources including survey results, social media posts, and empirical and theoretical analyses. An intensive review is conducted for a number of surveys and research papers on different aspects of biofuel development such as land use choices, biorefinery and transportation, infrastructure development, consumer priorities, environmental impacts, etc. Following that, a stakeholder synergy approach is applied to synthesizing typical responses of stakeholders, such as producers, consumers, biorefineries, rural communities, and the government, and discussing how their responses influence each other's decisions and the overall system performance. Based on the findings of inadequate stakeholder synergy, it is recommended that new surveys and further research should be conducted to understand why synergy between stakeholders in biofuel development is absent. Additionally, this paper provides research perspectives, including (1) applying cutting-edge text-mining techniques to conduct sentiment analysis, and research and public attention analysis; (2) using an agent-based model to simulate stakeholder interactions and understand the factors that influence stakeholder synergy and the emergence of a bioeconomy.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Advanced Retrieval Demonstrations of the Hanford Waste End Effector - 19383
This paper describes advanced retrieval demonstrations of the Hanford Waste End Effector (HWEE) at the Washington River Protection Solutions (WRPS) Cold Test Facility conducted by Pacific Northwest National Laboratory (PNNL) in co-operation with the U.S. Department of Energy - Office of River Protection and WRPS. The HWEE is a water-based scarifier originally developed by PNNL for the U.S. Department of Energy tank retrieval systems in the 1990's as the confined sluicing end effector and later used with additional development in the waste retrieval of tanks at Oak Ridge National Laboratory. The HWEE is being assessed for potential use in Hanford tank retrievals because it is expected to use less water during waste retrieval than other water-based methods (e.g., sluicing) previously used in Hanford tanks. This is primarily because the HWEE uses three rotating high-pressure jets for dislodging the waste and a high-velocity conveyance system to retrieve the water and the waste locally at the point of waste dislodging. The retrieval demonstrations build on previous development testing (Phase I) completed in 2017 by addressing key tank waste challenges, including retrieval of a mixed layered simulant and retrieval of uneven simulant profiles like that found near tank walls. The tests involved retrieval of water and kaolin (sludge) simulant to determine pumping rate, dilution factors, and a demonstration of vertical pumping capability. Upgrades to the HWEE for Phase II testing included a more robust inlet screen, internal flush jet modification for lower water usage, and removal of the external flush mole that was not used during Phase I retrieval testing. In addition, a rotational positioning capability was added to the three-dimensional gantry to gain additional movement capability and flexibility needed to implement more complex retrieval mining techniques. Two simulant configuration options using existing simulant materials were defined to represent key Hanford tank waste properties: 1) layered waste (e.g., strong crust over weaker sediment) and 2) topography that is representative of tank waste (uneven surface). Historical information from Hanford waste, including tank history, sampling reports, and images, was investigated to define the simulant characteristics and physical configuration. Using the layered simulant configuration, retrieval tests focused on demonstrating the same operational parameters irrespective of simulant strength. The focus of the topographical retrieval test was to demonstrate the ability to clean next to the tank wall using a tilted HWEE and to demonstrate water confinement using a specific mining strategy. This paper will describe the Phase II test equipment, instrumentation, simulant preparation procedures, and testing methodology. Testing results that will be presented include HWEE water usage versus operating pressure, simulant characterization data, dilution ratios, and retrieval rates. Reaction forces and other factors that would affect the remote operation of the HWEE will also be presented. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA
Background Adverse Drug Reactions (ADRs) are of great public health concern. FDA-approved drug labeling summarizes ADRs of a drug product mainly in three sections, i.e., Boxed Warning (BW), Warnings and Precautions (WP), and Adverse Reactions (AR), where the severity of ADRs are intended to decrease in the order of BW > WP > AR. Several reported studies have extracted ADRs from labeling documents, but most, if not all, did not discriminate the severity of the ADRs by the different labeling sections. Such a practice could overstate or underestimate the impact of certain ADRs to the public health. In this study, we applied the Medical Dictionary for Regulatory Activities (MedDRA) to drug labeling and systematically analyzed and compared the ADRs from the three labeling sections with a specific emphasis on analyzing serious ADRs presented in BW, which is of most drug safety concern. Results This study investigated New Drug Application (NDA) labeling documents for 1164 single-ingredient drugs using Oracle Text search to extract MedDRA terms. We found that only a small portion of MedDRA Preferred Terms (PTs), 3819 out of 21,920 or 17.42%, were observed in a whole set of documents. In detail, 466/3819 (12.0%) PTs were in BW, 2023/3819 (53.0%) were in WP, and 2961/3819 (77.5%) were in AR sections. We also found a higher overlap of top 20 occurring BW PTs with WP sections compared to AR sections. Within the MedDRA System Organ Class levels, serious ADRs (sADRs) from BW were prevalent in Nervous System disorders and Vascular disorders. A Hierarchical Cluster Analysis (HCA) revealed that drugs within the same therapeutic category shared the same ADR patterns in BW (e.g., nervous system drug class is highly associated with drug abuse terms such as dependence, substance abuse, and respiratory depression). Conclusions This study demonstrated that combining MedDRA standard terminologies with data mining techniques facilitated computer-aided ADR analysis of drug labeling. We also highlighted the importance of labeling sections that differ in seriousness and application in drug safety. Using sADRs primarily related to BW sections, we illustrated a prototype approach for computer-aided ADR monitoring and studies which can be applied to other public health documents.
59 BASIC BIOLOGICAL SCIENCES↗
Redefining the Practice of Peer Review Through Intelligent Automation—Part 3: Automated Report Analysis and Data Reconciliation
One method for addressing existing peer review limitations is the assignment of peer review cases on a completely blinded basis, in which the peer reviewer would create an independent report which can then be cross-referenced with the primary reader report of record. By leveraging existing computerized data mining techniques, one could in theory automate and objectify the process of report data extraction, classification, and analysis, while reducing time and resource requirements intrinsic to manual peer review report analysis. Once inter-report analysis has been performed, resulting inter-report discrepancies can be presented to the radiologist of record for review, along with the option to directly communicate with the peer reviewer through an electronic data reconciliation tool aimed at collaboratively resolving inter-report discrepancies and improving report accuracy. All associated report and reconciled data could in turn be recorded in a referenceable peer review database, which provides opportunity for context and user-specific education and decision support.
42 ENGINEERING↗
Glass-like energy and property landscape of Pt nanoclusters
Pt nanoclusters play an important role in catalysis-related applications. Essential to their activities are their geometries and energy landscapes. In this work, we studied the energy landscapes of Pt clusters using a parallel differential evolution optimization algorithm and an accelerated ab initio atomic relaxation method, which allowed us to explore unprecedentedly large numbers of geometry local minima at ab initio level. We found many lower-energy isomers with low symmetry in their geometry. The energy landscapes were demonstrated to be glass-like with a large number of local minimum structures close to the global minimum. The electronic and magnetic properties of most glass-like local minima were dramatically different from the global minimum, and they should be observed in the experimental measurements. The connections between these local minima were further analyzed using data mining techniques.
Chen, Zhanghui↗
Landscape composition and configuration have scale-dependent effects on agricultural pest suppression
Increasing landscape heterogeneity (composition and configuration) can enhance natural enemy populations and support pest suppression in agricultural landscapes. Using a network-based data mining approach, we examined independent gradients of landscape composition and configuration at six spatial scales that were associated with pest suppression services measured at 32 sites in Michigan and Wisconsin, USA. We compared the relative effects of landscape composition and configuration across scales with those of local crop type (corn or grassland). We found that multiple gradients of configurational heterogeneity were independent of composition and strongly associated with pest suppression, with different configuration metrics being predictive of pest suppression depending on the spatial scales and regions considered. Landscapes that were more configurationally heterogeneous at smaller spatial scales consistently supported higher pest suppression. In Michigan, pest suppression increased in landscapes with high edge contrast between annual crops and surrounding habitats and high edge density of grassland within 250-500 m radii. In Wisconsin, pest suppression increased with large core area of grassland and high field density within a 250 m radius. The main compositional effect we found was a positive relationship between grassland cover and pest suppression occurring at larger spatial scales (1000-1500 m) and occurring in Wisconsin but not in Michigan. Our findings demonstrate that effects of landscape composition and configuration on pest suppression differ across spatial scales and vary regionally. The network-based data mining techniques used here could be useful for disentangling intercorrelated landscape metrics in a variety of other contexts in landscape ecology.
54 ENVIRONMENTAL SCIENCES↗
Advanced data mining in field ion microscopy
Field ion microscopy (FIM) allows to image individual surface atoms by exploiting the effect of an intense electric field. Widespread use of atomic resolution imaging by FIM has been hampered by a lack of efficient image processing/data extraction tools. Recent advances in imaging and data mining techniques have renewed the interest in using FIM in conjunction with automated detection of atoms and lattice defects for materials characterization. After a brief overview of existing routines, we review the use of machine learning (ML) approaches for data extraction with the aim to catalyze new data-driven insights into high electrical field physics. Apart from exploring various supervised and unsupervised ML algorithms in this context, we also employ advanced image processing routines for data extraction from large sets of FIM images. The outcomes and limitations of such routines are discussed, and we conclude with the possible application of energy minimization schemes to the extracted point clouds as a way of improving the spatial resolution of FIM.
36 MATERIALS SCIENCE↗
Nuclear data uncertainty propagation and modeling uncertainty impact evaluation in neutronics core simulation
Uncertainty analysis is a critical requirement in reactor simulation as it is used to quantify the reliability of best-estimate calculation. A comprehensive uncertainty analysis should characterize all sources of uncertainties in a computationally-feasible and scientifically-defendable manner. Here we employ a well-established reduced order modeling (ROM) based uncertainty quantification methodology to propagate uncertainties throughout neutronic calculations. ROM relies on recent advances in randomized data mining techniques applied to large data streams. In our proposed implementation, the nuclear data uncertainties are first propagated from multi-group level through lattice physics calculation to generate few-group parameter uncertainties, described using a vector of mean values and a covariance matrix. Employing an ROM-based compression of the covariance matrix, the few-group uncertainties are then propagated through downstream core simulation in a computationally efficient manner. This straightforward approach, albeit efficient as compared to brute force forward and/or adjoint-based methods, often employs a number of assumptions that have been unquestioned in the literature of neutronic uncertainty analysis. This manuscript argues that these assumptions could introduce another source of uncertainty referred to as modeling uncertainties, whose magnitude needs to be quantified in tandem with nuclear data uncertainties. Thus, our primary goal is to explore the interactions between these two uncertainty sources in order to assess whether modeling uncertainties have an impact on parameter uncertainties. To explore this endeavor, the impact of a number of modeling assumptions on core attributes uncertainties is quantified. The study employs a CANDU reactor model, with Serpent and NEWT as lattice physics solvers and NESTLE-C as core simulator. The modeling assumptions investigated include those related with the uncertainty propagation method employed, e.g., deterministic vs. stochastic, the few-group energy structure employed to represent the cross-sections, the resonance treatment in lattice physics calculation, the reference values for the cross-section, and the number of samples employed to render ROM compression. Results indicate that some of the modeling assumptions could have a non-negligible impact on the core responses propagated uncertainties, highlighting the need for a more comprehensive approach to combine parameter and modeling uncertainties.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Comparison of ANN (MLP), ANFIS, SVM, and RF models for the online classification of heating value of burning municipal solid waste in circulating fluidized bed incinerators
Highlights: • A rapid, cost-effective, and comparative methodology was proposed to evaluate the HVs of burning MSW online. • Models ranked in descending order of performance are ANFIS, RF, SVM, MLP. • A well trained model is capable of characterizing the variation trend of HVs accurately. - Abstract: The heating values, particularly lower heating values of burning municipal solid waste are critically important parameters in operating circulating fluidized bed incineration systems. However, the heating values change widely and frequently, while there is no reliable real-time instrument to measure heating values in the process of incinerating municipal solid waste. A rapid, cost-effective, and comparative methodology was proposed to evaluate the heating values of burning MSW online based on prior knowledge, expert experience, and data-mining techniques. First, selecting the input variables of the model by analyzing the operational mechanism of circulating fluidized bed incinerators, and the corresponding heating value was classified into one of nine fuzzy expressions according to expert advice. Development of prediction models by employing four different nonlinear models was undertaken, including a multilayer perceptron neural network, a support vector machine, an adaptive neuro-fuzzy inference system, and a random forest; a series of optimization schemes were implemented simultaneously in order to improve the performance of each model. Finally, a comprehensive comparison study was carried out to evaluate the performance of the models. Results indicate that the adaptive neuro-fuzzy inference system model outperforms the other three models, with the random forest model performing second-best, and the multilayer perceptron model performing at the worst level. A model with sufficient accuracy would contribute adequately to the control of circulating fluidized bed incinerator operation and provide reliable heating value signals for an automatic combustion control system.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Machine learning the metastable phase diagram of covalently bonded carbon
Abstract Conventional phase diagram generation involves experimentation to provide an initial estimate of the set of thermodynamically accessible phases and their boundaries, followed by use of phenomenological models to interpolate between the available experimental data points and extrapolate to experimentally inaccessible regions. Such an approach, combined with high throughput first-principles calculations and data-mining techniques, has led to exhaustive thermodynamic databases (e.g. compatible with the CALPHAD method), albeit focused on the reduced set of phases observed at distinct thermodynamic equilibria. In contrast, materials during their synthesis, operation, or processing, may not reach their thermodynamic equilibrium state but, instead, remain trapped in a local (metastable) free energy minimum, which may exhibit desirable properties. Here, we introduce an automated workflow that integrates first-principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the metastable phases to construct “metastable” phase diagrams for materials far-from-equilibrium. Using carbon as a prototypical system, we demonstrate automated metastable phase diagram construction to map hundreds of metastable states ranging from near equilibrium to far-from-equilibrium (400 meV/atom). We incorporate the free energy calculations into a neural-network-based learning of the equations of state that allows for efficient construction of metastable phase diagrams. We use the metastable phase diagram and identify domains of relative stability and synthesizability of metastable materials. High temperature high pressure experiments using a diamond anvil cell on graphite sample coupled with high-resolution transmission electron microscopy (HRTEM) confirm our metastable phase predictions. In particular, we identify the previously ambiguous structure of n -diamond as a cubic-analog of diaphite-like lonsdaelite phase.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗