Case Study Dataset Research Articles

Choosing pasture maps: An assessment of pasture land classification definitions and a case study of Brazil

Pasture land occupies extensive areas and is increasingly of interest for sustainable intensification, land use diversification, greenhouse gas emission mitigation, and bioenergy expansion. Accurate maps of pasture and other managed land covers are needed for monitoring, intercomparison, assessing potential uses, and planning. Yet, land maps can be generated from different types of classification datasets – i.e. as a land use or land cover type – as well as different sources. In this study our aim was to assess and compare land use and land cover definitions for pasture, and examine variability in the resulting pasture land classification maps. First, we conducted a review of pasture definitions in commonly used mapping databases. We then performed a case study involving Brazil, a dominant global producer of pasture-based livestock. Six geospatial databases were harmonized and compared to each other and to MODIS land cover for Brazil including the Cerrado and Amazon biomes, which are internationally recognized for their ecological value. Total pasture area estimates for Brazil ranged by a factor greater than four, from about 430,000 km2 to over 1.7 million km2. Our analysis showed high variability in pasture land maps depending on the definitions, methods and underlying datasets used to generate them. The results are illustrative of a symptomatic problem for all manage land datasets, demonstrating the need for land categories studies and geospatial data resources that fully define land terms and describe measurable management attributes. Additionally, the suitability of individual geospatial datasets for different types of land mapping must be better described and reported. These recommendations would help bring more consistency in the consideration of managed lands in research, reporting, and policy development, as demonstrated here for pasture land using six case study datasets from multiple sources.

Simulated annealing algorithm for solving the capacitated vehicle routing problem: a case study of pharmaceutical distribution

This study aims to find a set of vehicles routes with the minimum total transportation time for pharmaceutical distribution at PT. XYZ in West Jakarta. The problem is modeled as the capacitated vehicle routing problem (CVRP). The CVRP is known as an NP-Hard problem. Therefore, a simulated annealing (SA) heuristic is proposed. First, the proposed SA performance is compared with the performance of the algorithm form previous studies to solve CVRP. It is shown that the proposed SA is useful in solving CVRP benchmark instances. Then, the SA algorithm is compared to a commonly used heuristic known as the nearest neighborhood heuristics for the case study dataset. The results show that the simulated Annealing and the nearest neighbor algorithm is performing well based on the percentage differences between each algorithm with the optimal solution are 0.03% and 5.50%, respectively. Thus, the simulated annealing algorithm provides a better result compared to the nearest neighbour algorithm. Furthermore, the proposed simulated annealing algorithm can find the solution as same as the exact method quite consistently. This study has shown that the simulated annealing algorithm provides an excellent solution quality for the problem.

CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data

An important challenge in pre-processing data from droplet-based single-cell RNA sequencing protocols is distinguishing barcodes associated with real cells from those binding background reads. Existing methods test barcodes individually and consequently do not leverage the strong cell-to-cell correlation present in most datasets. To improve cell detection, we introduce CB2, a cluster-based approach for distinguishing real cells from background barcodes. As demonstrated in simulated and case study datasets, CB2 has increased power for identifying real cells which allows for the identification of novel subpopulations and improves the precision of downstream analyses.

Tri-Clustering Based Exploration of Temporal Resolution Impacts on Spatio-Temporal Clusters in Geo-Referenced Time Series

Unprecedented amounts of spatio-temporal data instigates an urgent need for patterns exploration in it. Clustering analysis is useful in extracting patterns from big data by grouping similar data elements into clusters. Compared with one-way clustering and co-clustering methods, tri-clustering methods are more capable of exploring complex patterns. However, the explored patterns or clusters could be different due to varying temporal resolutions of input data. This study presents a tri-clustering based method to explore the impacts of different temporal resolutions on spatio-temporal clusters identified in geo-referenced time series (GTS), one type of spatio-temporal data. Dutch daily temperature data at 28 stations over 20 years was used to illustrate this study. The temperature data at daily, monthly, and yearly resolutions were subjected to the Bregman cube average tri-clustering algorithm with I-divergence (BCAT_I) to detect spatio-temporal clusters, which were then compared in terms of patterns exhibited, compositions, and changed elements. Results confirm the temporal resolution impacts on the spatio-temporal clusters identified in the Dutch temperature data: most compositions of clusters are varying when changing the temporal resolutions of input data in the GTS. Nevertheless, there is almost no change of elements in certain clusters (12 stations in the northeast of the country; years 1996, 2010) at all temporal resolutions, suggesting them as the “true” clusters in the case study dataset.

Forecasting model selection using intermediate classification: Application to MonarchFx corporation

Organizations rely on accurate demand forecasts to make production and ordering decisions in a variety of supply chain positions. Significant research in time series forecasting techniques and a variety of forecasting methods are available in the market. However, selecting the most accurate forecasting model for a given time series has become a complicated decision. Prior studies of forecasting methods have used either in-sample or out-of-sample performance as the basis for model selection procedures, but typically fail to incorporate both in their decision-making framework. In this research, we develop an expert system for time series forecasting model selection, using both relative in-sample performance and out-of-sample performance simultaneously to train classifiers. These classifiers are employed to automatically select the best performing forecasting model without the need for decision-maker intervention. The new model selection scheme bridges the gap between using in-sample and out-of-sample performance separately. The best performing model on the validation set is not necessarily selected by the expert system, since both in-sample and out-of-sample information are essential in the selection process. The performance of the proposed expert system is tested using the monthly dataset from the M3-Competition, and the results demonstrate an overall minimum of 20% improvement in the optimality gap comparing to the train/validation method. The new forecasting expert system is also applied to a real case study dataset obtained from MonarchFx (a distributed logistics solutions provider). This result demonstrates a robust predictive capability with lower mean squared errors, which allows organizations to achieve a higher level of accuracy in demand forecasts.

Predictive modelling to support sensitivity analysis for robust design in aerospace engineering

The design of aircraft engines involves computationally expensive engineering simulations. One way to solve this problem is the use of response surface models to approximate the high-fidelity time-consuming simulations while reducing computational time. For a robust design, sensitivity analysis based on these models allows for the efficient study of uncertain variables’ effect on system performance. The aim of this study is to support sensitivity analysis for a robust design in aerospace engineering. For this, an approach is presented in which random forests (RF) and multivariate adaptive regression splines (MARS) are explored to handle linear and non-linear response types for response surface modelling. Quantitative experiments are conducted to evaluate the predictive performance of these methods with Turbine Rear Structure (a component of aircraft) case study datasets for response surface modelling. Furthermore, to test these models’ applicability to perform sensitivity analysis, experiments are conducted using mathematical test problems (linear and non-linear functions) and their results are presented. From the experimental investigations, it appears that RF fits better on non-linear functions compared with MARS, whereas MARS fits well on linear functions.

Advanced Cyberinfrastructure to Enable Search of Big Climate Datasets in THREDDS

Understanding the past, present, and changing behavior of the climate requires close collaboration of a large number of researchers from many scientific domains. At present, the necessary interdisciplinary collaboration is greatly limited by the difficulties in discovering, sharing, and integrating climatic data due to the tremendously increasing data size. This paper discusses the methods and techniques for solving the inter-related problems encountered when transmitting, processing, and serving metadata for heterogeneous Earth System Observation and Modeling (ESOM) data. A cyberinfrastructure-based solution is proposed to enable effective cataloging and two-step search on big climatic datasets by leveraging state-of-the-art web service technologies and crawling the existing data centers. To validate its feasibility, the big dataset served by UCAR THREDDS Data Server (TDS), which provides Petabyte-level ESOM data and updates hundreds of terabytes of data every day, is used as the case study dataset. A complete workflow is designed to analyze the metadata structure in TDS and create an index for data parameters. A simplified registration model which defines constant information, delimits secondary information, and exploits spatial and temporal coherence in metadata is constructed. The model derives a sampling strategy for a high-performance concurrent web crawler bot which is used to mirror the essential metadata of the big data archive without overwhelming network and computing resources. The metadata model, crawler, and standard-compliant catalog service form an incremental search cyberinfrastructure, allowing scientists to search the big climatic datasets in near real-time. The proposed approach has been tested on UCAR TDS and the results prove that it achieves its design goal by at least boosting the crawling speed by 10 times and reducing the redundant metadata from 1.85 gigabytes to 2.2 megabytes, which is a significant breakthrough for making the current most non-searchable climate data servers searchable.

Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning.

Industry 4.0 leaders solve problems all of the time. Successful problem-solving behavioral pattern choice determines organizational and personal success, therefore a proper understanding of the problem-solving-related neurological dynamics is sure to help increase business performance. The purpose of this paper is two-fold: first, to discover relevant neurological characteristics of problem-solving behavioral patterns, and second, to conduct a characterization of two problem-solving behavioral patterns with the aid of deep-learning architectures. This is done by combining electroencephalographic non-invasive sensors that capture process owners’ brain activity signals and a deep-learning soft sensor that performs an accurate characterization of such signals with an accuracy rate of over 99% in the presented case-study dataset. As a result, the deep-learning characterization of lean management (LM) problem-solving behavioral patterns is expected to help Industry 4.0 leaders in their choice of adequate manufacturing systems and their related problem-solving methods in their future pursuit of strategic organizational goals.

Bernard, an energy intelligent system for raising residential users awareness

Energy efficiency is still a hot topic today. Coming roughly the 25% of the energy consumption in EU from the residential sector, very few cheap and simple tools to promote energy efficiency in home users have been developed. The purpose of this paper is to present Bernard, a concept proof designed for filling this gap. This aims that householders become aware of their energy habits and have useful information that help them to redirect their consumption pattern. To achieve these goals, Bernard offers, through a mobile application, the home energy consumption monitoring in real time, the energy price forecast for the next hour and the appliances which are switched on, among others. Furthermore, it is important to highlight that the system has been designed with the premises of being cheap, non-intrusive, reliable and easily scalable, in order that utilities can gradually deploy and provide it to their customers, gaining at the same time valuable information for decision making and improving its corporate social image. Therefore, the adopted solution is based on a real time streaming data architecture suitable for handling huge volumes of data and applying predictive techniques on a cloud-computing environment. The paper provides a detailed description of the system and experimental results evaluating the performance of the predictive modules built. As case study, REFIT and REDD datasets were used.

Current and emerging statistical techniques for aquatic telemetry data: A guide to analysing spatially discrete animal detections

Abstract Telemetry, or the remote monitoring of animals with electronic transmitters and receivers, has vastly enhanced our ability to study aquatic animals. Radio telemetry, acoustic telemetry and passive integrated transponders are three common technologies that generate detection data — time‐stamped, tag‐specific records that are logged by receivers. We review current statistical methods and comment on potential future directions for analysing detection data derived from fixed telemetry receiver arrays. To illustrate how different methods may be used to achieve diverse study objectives, we provide a case study dataset collected by an array of 42 acoustic telemetry receivers on 187 bull trout in the Kinbasket Reservoir of British Columbia. To close, we present a decision tree for guiding the selection of a method based on study objectives and sampling design. This paper provides both experienced and novice telemetry researchers with the knowledge and tools to facilitate more comprehensive analysis of detection data and, in so doing, ask a wide variety of ecological questions that will enhance our understanding of aquatic organisms.

Integration of single-cell RNA-seq data into population models to characterize cancer metabolism.

Metabolic reprogramming is a general feature of cancer cells. Regrettably, the comprehensive quantification of metabolites in biological specimens does not promptly translate into knowledge on the utilization of metabolic pathways. By estimating fluxes across metabolic pathways, computational models hold the promise to bridge this gap between data and biological functionality. These models currently portray the average behavior of cell populations however, masking the inherent heterogeneity that is part and parcel of tumorigenesis as much as drug resistance. To remove this limitation, we propose single-cell Flux Balance Analysis (scFBA) as a computational framework to translate single-cell transcriptomes into single-cell fluxomes. We show that the integration of single-cell RNA-seq profiles of cells derived from lung adenocarcinoma and breast cancer patients into a multi-scale stoichiometric model of a cancer cell population: significantly 1) reduces the space of feasible single-cell fluxomes; 2) allows to identify clusters of cells with different growth rates within the population; 3) points out the possible metabolic interactions among cells via exchange of metabolites. The scFBA suite of MATLAB functions is available at https://github.com/BIMIB-DISCo/scFBA, as well as the case study datasets.

UMSG: An Extended Model to Investigate the Use of Mobile Social Games

Mobile Social Games (MSGs) are becoming increasingly popular worldwide, and the factors leading to the diffusion and use of such games need to be further investigated. This study proposes an extended model that aims to explore the Use of Mobile Social Games (UMSG). The UMSG model was inspired by two of the most recognized theories: the Theory of Reasoned Action (TRA) and the Diffusion of Innovations (DOI) theory, in addition to newly introduced factors drawn from the unique characteristics of MSGs. This paper describes the model construction and then validates it using two case studies. The investigation adopts a sequential mixed-method design that gathers qualitative and quantitative data. The first phase of the investigation includes a focus group of 11 MSG players. The results of the focus group inspired improvements to the proposed model. The second phase included two large-scale quantitative studies that gathered data from 890 participants through an online questionnaire. The results demonstrated that the UMSG model was a good fit with the case study datasets and was able to explain the discrepancy between the different MSGs. Most of the diffusion attributes were found to be significant, in addition to a newly introduced factor that represents communication.

Key Factors Affecting Temporal Variability in Stream Water Quality

AbstractUnderstanding the factors that influence temporal variability in water quality is critical for designing water quality management strategies. In this study, we explore the key factors that affect temporal variability in stream water quality across multiple catchments using a Bayesian hierarchical model. We apply this model to a case study data set consisting of monthly water quality measurements obtained over a 20‐year period from 102 water quality monitoring sites in the state of Victoria (Southeast Australia). We investigate six water quality constituents: total suspended solids, total phosphorus, filterable reactive phosphorus, total Kjeldahl nitrogen, nitrate‐nitrite (NOx), and electrical conductivity. We find that same‐day streamflow has the greatest effect on water quality variability for all constituents. Additional important predictors include soil moisture, antecedent streamflow, vegetation cover, and water temperature. Overall, the models do not explain a large proportion of temporal variation in water quality, with Nash‐Sutcliffe coefficients lower than 0.49. However, when considering performance on a site‐by‐site basis, we see high model performance in some locations, with Nash‐Sutcliffe coefficients of up to 0.8 for NOx and electrical conductivity. The effect of the temporal predictors on water quality varies between sites, which should be explored further for potential spatial patterns in future studies. There is also potential for further extension of these temporal variability models into a predictive spatiotemporal model of riverine constituent concentrations, which will be a useful tool to inform decision making for catchment water quality management.

Computationally efficient methods for fitting mixed models to electronic health records data.

Motivated by two case studies using primary care records from the Clinical Practice Research Datalink, we describe statistical methods that facilitate the analysis of tall data, with very large numbers of observations. Our focus is on investigating the association between patient characteristics and an outcome of interest, while allowing for variation among general practices. We explore ways to fit mixed-effects models to tall data, including predictors of interest and confounding factors as covariates, and including random intercepts to allow for heterogeneity in outcome among practices. We introduce (1) weighted regression and (2) meta-analysis of estimated regression coefficients from each practice. Both methods reduce the size of the dataset, thus decreasing the time required for statistical analysis. We compare the methods to an existing subsampling approach. All methods give similar point estimates, and weighted regression and meta-analysis give similar standard errors for point estimates to analysis of the entire dataset, but the subsampling method gives larger standard errors. Where all data are discrete, weighted regression is equivalent to fitting the mixed model to the entire dataset. In the presence of a continuous covariate, meta-analysis is useful. Both methods are easy to implement in standard statistical software.

Exploratory data analysis of a clinical study group: Development of a procedure for exploring multidimensional data.

Thorough knowledge of the structure of analyzed data allows to form detailed scientific hypotheses and research questions. The structure of data can be revealed with methods for exploratory data analysis. Due to multitude of available methods, selecting those which will work together well and facilitate data interpretation is not an easy task. In this work we present a well fitted set of tools for a complete exploratory analysis of a clinical dataset and perform a case study analysis on a set of 515 patients. The proposed procedure comprises several steps: 1) robust data normalization, 2) outlier detection with Mahalanobis (MD) and robust Mahalanobis distances (rMD), 3) hierarchical clustering with Ward’s algorithm, 4) Principal Component Analysis with biplot vectors. The analyzed set comprised elderly patients that participated in the PolSenior project. Each patient was characterized by over 40 biochemical and socio-geographical attributes. Introductory analysis showed that the case-study dataset comprises two clusters separated along the axis of sex hormone attributes. Further analysis was carried out separately for male and female patients. The most optimal partitioning in the male set resulted in five subgroups. Two of them were related to diseased patients: 1) diabetes and 2) hypogonadism patients. Analysis of the female set suggested that it was more homogeneous than the male dataset. No evidence of pathological patient subgroups was found. In the study we showed that outlier detection with MD and rMD allows not only to identify outliers, but can also assess the heterogeneity of a dataset. The case study proved that our procedure is well suited for identification and visualization of biologically meaningful patient subgroups.

A combined approach for the analysis of large occupational accident databases to support accident-prevention decision making

Occupational accidents are commonly collected in large databases by National Workers Compensation Authorities and companies’ safety and prevention teams. The analysis of the data can be difficult because the database elements are characterized by many parameters, which are not of a numerical nature. Data mining techniques could represent an efficient tool for the identification of useful information in large databases. In 2011, a two-level clustering method, made of SOM and numerical clustering, obtained positive results in identifying critical accident dynamics. The present research proceeds from that initial methodology.A sensitivity analysis of the coupled clustering method was carried out.Some improvements have been designed, and an enhanced methodology has been applied to the original case study data set, for validation purposes.This method represents an efficient tool for the analyst that has to deal with the occupational accidents data, thanks to its capability of grouping and visualizing data in a readable and exportable outcome.The information acquired by this method can help analysts to better address the measures to be adopted in a work environment, in order to prevent occupational accidents.

Sustainable coastal zone planning based on historical coastline changes: A model from case study in Tainan, Taiwan

Coastal regions are threatened by natural processes, such as erosion driven by storm surge and the effect of jetties, as well as by human behavior. This study outlines a management framework based on an analysis of a case study dataset of long-term coastal change in southwestern Taiwan. Specifically, the coastal structure of Taiwan’s largest lagoon, Cigu, was analyzed over a 300-year-long period. A series of satellite-derived images between 1974 and 2015 were assessed. The dataset was integrated with a geographic information system (GIS). The results revealed that human activities, including the construction of hard structures along the coastline and along an upstream reservoir, altered the balance of sediment transmission and resulted in the retreat and erosion of barrier sandbars along the coast. A lagoon evolution model revealed that the Cigu Lagoon may disappear in the future. Furthermore, the coastline will continue to retreat because harbor dykes and offshore breakwaters stop sediment transportation to the downdrift. Soft erosion mitigation is suggested as an immediate approach. However, recovering the sediment budget from reservoirs and dykes could be a long-term solution. In principle, the best coastal environmental protection plan from the national involves delaying urbanization and preserving unexploited coastal wetlands.

Similarity Score Estimation and Gaps Trimming of Multiple Sequence Alignment for Phylogenetic Tree Analysis

Phylogenetic tree analysis is a process for finding the highest possible revolution tree history of an interested organism. The important step of the process is multiple sequences alignment (MSA) which is operated using any MSA tool that produces a result in blocks of the Phylip format. Bioinformaticians have to manually determine and trim gaps of the MSA blocks using relevant tools of a software package in the off-line mode. The data blocks need to be manually cut-and-pasted between these tools. This working steps tend to be error-prone and time consuming. In addition, improper algorithm selection for tree inferring without applying an MSA similarity score tends to generate the phylogenetic tree with low accuracy and also take much more time. In this work, we present a new practical approach for the phylogenetic tree analysis applying our enhancement for the similarity score estimation and gaps trimming of the MSA blocks. We propose \textit{in-silico} algorithms for automating the concerned similarity score estimation and gaps trimming, and deploy them as web services. We demonstrate the web services utilized by composing them into an integrated stateful WSDL workflow. Our case study datasets are a complete coding sequences (CDS) and sets of complete genome of Dengue Viruses - 2, fetched from the NCBI RefSeq nucleotide database. Our proposed algorithms have correctly returned results, verified and satisfied by our bioinformaticians. Our distributions, user manuals and endpoints of the web services, and the open source programs are available at https://bioservices.sci.psu.ac.th.

Designing and Implementing a Network for Sensing Water Quality and Hydrology across Mountain to Urban Transitions

AbstractWater resources are increasingly impacted by growing human populations, land use, and climate changes, and complex interactions among biophysical processes. In an effort to better understand these factors in semiarid northern Utah, United States, we created a real‐time observatory consisting of sensors deployed at aquatic and terrestrial stations to monitor water quality, water inputs, and outputs along mountain to urban gradients. The Gradients Along Mountain to Urban Transitions (GAMUT) monitoring network spans three watersheds with similar climates and streams fed by mountain winter‐derived precipitation, but that differ in urbanization level, land use, and biophysical characteristics. The aquatic monitoring stations in the GAMUT network include sensors to measure chemical (dissolved oxygen, specific conductance, pH, nitrate, and dissolved organic matter), physical (stage, temperature, and turbidity), and biological components (chlorophyll‐a and phycocyanin). We present the logistics of designing, implementing, and maintaining the network; quality assurance and control of numerous, large datasets; and data acquisition, dissemination, and visualization. Data from GAMUT reveal spatial differences in water quality due to urbanization and built infrastructure; capture rapid temporal changes in water quality due to anthropogenic activity; and identify changes in biological structure, each of which are demonstrated via case study datasets.

An integrated calcium imaging processing toolbox for the analysis of neuronal population dynamics.

The development of new imaging and optogenetics techniques to study the dynamics of large neuronal circuits is generating datasets of unprecedented volume and complexity, demanding the development of appropriate analysis tools. We present a comprehensive computational workflow for the analysis of neuronal population calcium dynamics. The toolbox includes newly developed algorithms and interactive tools for image pre-processing and segmentation, estimation of significant single-neuron single-trial signals, mapping event-related neuronal responses, detection of activity-correlated neuronal clusters, exploration of population dynamics, and analysis of clusters' features against surrogate control datasets. The modules are integrated in a modular and versatile processing pipeline, adaptable to different needs. The clustering module is capable of detecting flexible, dynamically activated neuronal assemblies, consistent with the distributed population coding of the brain. We demonstrate the suitability of the toolbox for a variety of calcium imaging datasets. The toolbox open-source code, a step-by-step tutorial and a case study dataset are available at https://github.com/zebrain-lab/Toolbox-Romano-et-al.