Full Data Research Articles

InSPECtor: an end-to-end design framework for compressive pixelated hyperspectral instruments.

Classic designs of hyperspectral instrumentation densely sample the spatial and spectral information of the scene of interest. Data may be compressed after the acquisition. In this paper, we introduce a framework for the design of an optimized, micropatterned snapshot hyperspectral imager that acquires an optimized subset of the spatial and spectral information in the scene. The data is thereby already compressed at the sensor level but can be restored to the full hyperspectral data cube by the jointly optimized reconstructor. This framework is implemented with TensorFlow and makes use of its automatic differentiation for the joint optimization of the layout of the micropatterned filter array as well as the reconstructor. We explore the achievable compression ratio for different numbers of filter passbands, number of scanning frames, and filter layouts using data collected by the Hyperscout instrument. We show resulting instrument designs that take snapshot measurements without losing significant information while reducing the data volume, acquisition time, or detector space by a factor of 40 as compared to classic, dense sampling. The joint optimization of a compressive hyperspectral imager design and the accompanying reconstructor provides an avenue to substantially reduce the data volume from hyperspectral imagers.

Analysis of structure and data validation processes of energy management system in designated facilities in Thailand

The Energy Conservation and Promotion Act have been promulgated, focused on designated factory and building, in Thailand since 1992. Therefore, the eight-step Energy Management System (EnMS), simplified from ISO50001, have been fully enforced since 2010. A Total of 9360 facilities in 2020 have to annually submitted the EnMS report with full production, energy consumption and conservation measures data. The structure and data validation to the EnMS have been designed and constructed based on the statistical theory for verify the incoming annual data from facilities to the national database. This paper focused on validation procedure analysis including the major obstacles to this EnMS database system by using the TACCC concept. The validation process consists of three main parts; (i) energy consumption by using the SEC, (ii) thermal and electrical energy ratio, and (iii) energy conservation measure by using the payback period which can be disaggregated into housekeeping, process improvement and machine change. All three parts are well explained in the paper. We also found that major erratum are on the inconsistency and completeness issues which both are from the incomplete data input from the facilities and the improper performance from some external energy auditors. More capacity building have to be set for the facility’s responsible person and external auditors along with the strong enforcement from the responsible government agency. Policy recommendation have also been discussed.

Type material in the Hieracium (Compositae: Cichorieae) collection of Joseph Bornmüller

The herbarium of Joseph Bornmüller (1862–1948) was acquired by the Berlin Botanical Museum in 1938 and belongs today to its most valuable possessions. Parts of Bornmüller´s Hieracium collection remained in the museum´s backlog and have now been incorporated in the general herbarium. A list of Hieracium names published by various authors on the basis of plant material collected by Bornmüller or deposited in Bornmüller´s herbarium is presented here, including information on the type localities given in the protologues. The type material available is listed with full label data and other particulars to be found on the specimens. The status of the type specimens is discussed and the currently accepted names are provided. The names Hieracium fridae-bornmuelleriae and H. scitulum subsp. aquae-albae are validated, the new combinations H. bifidum subsp. arnstadtense, H. diaphanoides subsp. leptodermum, H. glaucinum subsp. chrysoprasicola, and H. schmidtii subsp. trichocyaneum are proposed, and 95 names are lectotypified.

Improving Worst-case TSN Communication Times of Large Sensor Data Samples by Exploiting Synchronization

Higher levels of automated driving also require a more sophisticated environmental perception. Therefore, an increasing number of sensors transmit their data samples as frame bursts to other applications for further processing. As a vehicle has to react to its environment in time, such data is subject to safety-critical latency constraints. To keep up with the resulting data rates, there is an ongoing transition to a Time-Sensitive Networking (TSN)-based communication backbone. However, the use of TSN-related industry standards does not match the automotive requirements of large timely sensor data transmission, nor it offers benefits on time-critical transmissions of single control data packets. By using the full data rate of prioritized IEEE 802.1Q Ethernet, giving time guarantees on large data samples is possible, but with strongly degraded results due to data collision. Resolving such collisions with time-aware shaping comes with significant overhead. Hence, rather than optimizing the parameters of the existing protocol, we propose a system design that synchronizes the transmission times of sensor data samples. This limits network protocol complexity and hardware requirements by avoiding tight time synchronization and time-aware shaping. We demonstrate that individual sensor data samples are transmitted without significant interference, exclusively at full Ethernet data rate. We provide a synchronous event model together with a straightforward response time analysis for synchronous multi-frame sample transmissions. The results show that worst-case latencies of such sample communication, in contrast to non-synchronized approaches, are close to their theoretical minimum as well as to simulative results while keeping the overall network utilization high.

Using the theory of planned behavior model to predict factors influencing breastfeeding behavior among preterm mothers at week 6 postpartum: the mediating effect of breastfeeding intention.

Exclusive breastfeeding (EBF) in the first 6 weeks postpartum is key to continued breastfeeding. This study aimed to explore the role of EBF-related predictors (particularly breastfeeding intention) in breastfeeding behavior among preterm mothers at week 6 postpartum based on the theory of planned behavior (TPB). A total of 352 mothers of preterm infants were recruited, 340 of whom participated in this study. Prior to discharge, participants completed the Chinese versions of the modified Breastfeeding Attrition Predictive Tool, the Breastfeeding Knowledge Questionnaire (BKQ), the Infant Feeding Intention, and the Edinburgh Postnatal Depression Scale. Responses to the items of the Breastfeeding Behavioral Questionnaire (BBQ) were also collected by telephone at week 6 postpartum. The final analyses included 321 participants who completed the full two-wave data collection. The fitness indices of the modified TPB model were acceptable. Breastfeeding knowledge and EBF before discharge positively impacted breastfeeding intention, whereas depression had a negative impact. Before discharge, breastfeeding intention fully mediated the impacts of breastfeeding attitude, social and professional support, knowledge, depression, and EBF on breastfeeding behavior and partially mediated the influence of perceived breastfeeding control on breastfeeding behavior. These findings indicate that TPB accurately predicts breastfeeding behavior among preterm mothers at week 6 postpartum, and breastfeeding intention is key to the above-mentioned EBF-related factors and breastfeeding behavior. The findings underline the need for further longitudinal studies and corresponding interventions for preterm mothers with a high risk of EBF attrition.

Extremely missing numerical data in Electronic Health Records for machine learning can be managed through simple imputation methods considering informative missingness: A comparative of solutions in a COVID-19 mortality case study

Background and objectiveReusing Electronic Health Records (EHRs) for Machine Learning (ML) leads on many occasions to extremely incomplete and sparse tabular datasets, which can hinder the model development processes and limit their performance and generalization. In this study, we aimed to characterize the most effective data imputation techniques and ML models for dealing with highly missing numerical data in EHRs, in the case where only a very limited number of data are complete, as opposed to the usual case of having a reduced number of missing values. MethodsWe used a case study including full blood count laboratory data, demographic and survival data in the context of COVID-19 hospital admissions and evaluated 30 processing pipelines combining imputation methods with ML classifiers. The imputation methods included missing mask, translation and encoding, mean imputation, k-nearest neighbors’ imputation, Bayesian ridge regression imputation and generative adversarial imputation networks. The classifiers included k-nearest neighbors, logistic regression, random forest, gradient boosting and deep multilayer perceptron. ResultsOur results suggest that in the presence of highly missing data, combining translation and encoding imputation—which considers informative missingness—with tree ensemble classifiers—random forest and gradient boosting—is a sensible choice when aiming to maximize performance, in terms of area under curve. ConclusionsBased on our findings, we recommend the consideration of this imputer-classifier configuration when constructing models in the presence of extremely incomplete numerical data in EHR.

National validation of the Centers for Medicare & Medicaid Services strategy for identifying potential surgical-site infections following colon surgery and abdominal hysterectomy.

National validation of claims-based surveillance for surgical-site infections (SSIs) following colon surgery and abdominal hysterectomy. Retrospective cohort study. US hospitals selected for data validation by Centers for Medicare & Medicaid Services (CMS). The study included 550 hospitals performing colon surgery and 458 hospitals performing abdominal hysterectomy in federal fiscal year 2013. We requested 1,200 medical records from hospitals selected for validation as part of the CMS Hospital Inpatient Quality Reporting program. For colon surgery, we sampled 60% with a billing code suggestive of SSI during their index admission and/or readmission within 30 days and 40% who were readmitted without one of these codes. For abdominal hysterectomy, we included all patients with an SSI code during their index admission, all patients readmitted within 30 days, and a sample of those with a prolonged surgical admission (length of stay > 7 days). We calculated sensitivity and positive predictive value for the different groups. We identified 142 colon-surgery SSIs (46 superficial SSIs and 96 deep and organ-space SSIs) and 127 abdominal-hysterectomy SSIs (58 superficial SSIs and 69 deep and organ-space SSIs). Extrapolating to the full CMS data validation cohort, we estimated an SSI rate of 8.3% for colon surgery and 3.0% for abdominal hysterectomy. Our colon-surgery surveillance codes identified 93% of SSIs, with 1 SSI identified for every 2.6 patients reviewed. Our abdominal-hysterectomy surveillance codes identified 73% of SSIs, with 1 SSI identified for every 1.6 patients reviewed. Using claims to target record review for SSI validation performed well in a national sample.

Demonstrating paths for unlocking the value of cloud genomics through cross cohort analysis

Recently, large scale genomic projects such as All of Us and the UK Biobank have introduced a new research paradigm where data are stored centrally in cloud-based Trusted Research Environments (TREs). To characterize the advantages and drawbacks of different TRE attributes in facilitating cross-cohort analysis, we conduct a Genome-Wide Association Study of standard lipid measures using two approaches: meta-analysis and pooled analysis. Comparison of full summary data from both approaches with an external study shows strong correlation of known loci with lipid levels (R2 ~ 83–97%). Importantly, 90 variants meet the significance threshold only in the meta-analysis and 64 variants are significant only in pooled analysis, with approximately 20% of variants in each of those groups being most prevalent in non-European, non-Asian ancestry individuals. These findings have important implications, as technical and policy choices lead to cross-cohort analyses generating similar, but not identical results, particularly for non-European ancestral populations.

Predictive modelling for airline technical operations

The digital platform AVIATAR leverages aircraft and maintenance data as well as advanced algorithms to optimize technical operations. Optimization is achieved by utilizing predictive maintenance algorithms which lead to a reduction of costs and operational incidences. The rise of AI algorithms combined with an increase of the available data to feed those algorithms provides both an opportunity as well as challenge to the traditional approaches which were in use in the aviation industry for decades. We demonstrate how AVIATAR’s data science team develops state of the art predictive maintenance models by combining advanced statistical models and AI with the unique engineering know how of Lufthansa Technik. We will show the immense value of full flight data in order to achieve this and provide an outlook into the potential future role of algorithms in the aviation industry.
 

Shortfalls in tracking data available to inform North American migratory bird conservation

Animal tracking has become an effective way to identify where and when migratory species encounter threats throughout their annual cycle. Yet, untracked or poorly tracked species and undiscoverable or inaccessible data for the species that have been tracked mean that gaps in the knowledge of where and when species occur are still an issue for conservation. These gaps in knowledge of species movements have been termed the “movement shortfall”. Here, we quantify the movement shortfall for North American migratory birds by comprehensively reviewing full annual cycle tracking data and identifying gaps and biases in how, where, and what species are tracked with electronic tracking devices. We found 30 species for which tracking is not feasible given body size constraints, no data for 291 trackable species, and restricted or reduced data accessibility for an additional 59 species. Thus, despite the ability to track most species, the movement shortfall remains a constraint to informing conservation strategies for 56 % of North American migratory bird species. The number increases to 65 % when considering species with restricted or reduced data accessibility, further limiting access to this information. Moreover, 23 % of the tracking data stems from low precision tracking technologies reducing the implementation and effectiveness to conservation actions. A lack of species and population data hinders conservation and biases management decisions, ultimately making inefficient use of conservation resources. We encourage researchers to consider these gaps in their decisions about future tracking efforts, conservation management, and data archiving practices.

Lessons Learned from Aircraft Component Failure Prediction using Full Flight Sensor Data

Successful aircraft predictive maintenance relies on the accurate prediction of major aircraft component failures for operators to schedule and carry out maintenance operations before failure actually happens. In this paper, we share important lessons learned from our development of prognostics alerts using full flight sensor data, including various challenges of using big data, data quality issues, failure identification for data labeling, engineering-driven vs. data-driven methods, and aggregating alerts into actionable alerts. We also provide recommendations based on our experience with prognostic alerts developed and deployed for many airline operators.

Forest terrain and canopy height estimation using stereo images and spaceborne LiDAR data from GF-7 satellite

ABSTRACT Accurate estimation of forest terrain and canopy height is crucial for timely understanding of forest growth. Gao Fen-7 (GF-7) Satellite is China’s first sub-meter-level three-dimensional (3D) mapping satellite for civilian use, which was equipped with a two-line-array stereo mapping camera and a laser altimeter system that can provide stereo images and full waveform LiDAR data simultaneously. Most of the existing studies have concentrated on evaluating the accuracy of GF-7 for topographic survey in bare land, but few have in-depth studied its ability to measure forest terrain elevation and canopy height. The purpose of this study is to evaluate the potential of GF-7 LiDAR and stereo image for forest terrain and height measurement. The Airborne Laser Scanning (ALS) data were utilized to generate reference terrain and forest vertical information. The validation test was conducted in Pu’er City, Yunnan Province of China, and encouraging results have obtained. The GF-7 LiDAR data obtained the accuracy of forest terrain elevation with RMSE of 8.01 m when 21 available laser footprints were used for results verification; meanwhile, when it was used to calculate the forest height, R2 of 0.84 and RMSE of 3.2 m were obtained although only seven effective footprints were used for result verification. The canopy height values obtained from GF-7 stereo images have also been proven to have high accuracy with the resolution of 20 m × 20 m compared with ALS data (R2 = 0.88, RMSE = 2.98 m). When the results were verified at the forest sub-compartment scale that taking into account the forest types, further higher accuracy (R2 = 0.96, RMSE = 1.23 m) was obtained. These results show that GF-7 has considerable application potential in forest resources monitoring.

Sectorial Plane Wave Imaging for Weld Inspection Studies

Advanced ultrasonic array–based imaging techniques that rely on A-scan data acquired through individual parallel reception of array elements and subsequent post-processing have shown promising performance by generating high-resolution images. An advanced array-based imaging algorithm called sectorial plane wave imaging (SPWI) has been recently proposed with multimode imaging and rapid inspection capabilities. In this work, the efficiency of the SPWI algorithm is demonstrated and compared to the total focusing method (TFM) and plane wave imaging (PWI) algorithms in detecting different types of common weld defects: lack of sidewall fusion, slag, porosity, toe crack, centerline crack, and root crack. The full matrix capture data acquired over each weld defect using a 32-element linear array on an angular wedge configuration is converted to the PWI matrix data to perform SPWI. The corner and indirect mode SPWI inspection results are able to detect all the weld defects with image quality comparable to that of TFM and PWI, but at a higher processing speed due to the slight compromise over reception focusing, making SPWI a viable advanced ultrasonic array–based imaging technique for inspecting welds.

Exploring experiences of times without care and encounters in dementia: protocol for a living and adaptive evidence map

IntroductionIndividuals with dementia spend most of the day without care, without encounters, and usually without activity. Although this has been proven in studies, there is a knowledge gap on how...

On the Cover: 2020 Murray Lecture: Quantitative Full Field Data Fusion for Evaluation of Complex Structures

On the Cover: 2020 Murray Lecture: Quantitative Full Field Data Fusion for Evaluation of Complex Structures

Learning data science methods through a mobile device and full body motion data

This study presents a mobile app that facilitates undergraduate students to learn data science through their own full body motions. Leveraging the built-in camera of a mobile device, the proposed app captures the user and feeds their images into an open-source computer-vision algorithm that localizes the key joint points of human body. As students can participate in the entire data collection process, the obtained motion data is context-rich and personally relevant to them. The app utilizes the collected motion data to explain various concepts and methods in data science under the context of human movements. The app also visualizes the geometric interpretation of data through various visual aids, such as interactive graphs and figures. In this study, we use principal component analysis, a commonly used dimensionality reduction method, as an example to demonstrate the proposed learning framework. Strategies to encompass other learning modules are also discussed for further improvement.

Ideational Metaphor in Saudi EFL Students’ Academic Writing: A Systemic-Functional Linguistic Analysis

Over the past decade, there has been an upsurge in studies on the quality of EFL students' English academic writing. However, research on grammatical metaphors in academic writing by EFL students is limited. As such, the purpose of this research is to identify the occurrences of ideational metaphors in students' academic writing essays. In this research, the qualitative descriptive approach was applied. This study included 20 undergraduate students from the English Language and Literature Department at the College of Science and Humanities in Shaqra University, Saudi Arabia, Essay Writing course in the fourth level. Students were to write 350-word essay on the subject of "Learning Difficulties During the Coronavirus Pandemic". The data corpus for the study totals 8,482 words. According to the findings, the students' essays include 408 sentences that comprise the ideational metaphor. It is considered insignificant in contrast to the corpus's full data set of 8,482 words. The occurrence of ideational metaphors is dominated by material processes, followed by mental processes, relational processes, behavioral processes, and existential processes. This conclusion shows that EFL students' essay structures are unlikely to have a high number of good grammatical metaphor clauses. For EFL students, lecturers, teachers, and other educational professionals involved in instructing English academic writing, this study has pedagogical implications; it sheds light on how grammatical metaphors in writing texts can serve as a stand-in for the caliber of writing used in academic and scientific literature.

Machine Learning Applications in Nondestructive Testing of Concrete Structures

AbstractMachine Learning bears great potential for data‐driven solutions in the field of nondestructive testing (NDT) of concrete structures. The analysis of the data collected with NDT methods, such as ultrasonics, impact‐echo and ground penetrating radar, can be complex and requires experience. The expected benefit of Machine Learning applications in this context goes beyond the increase of efficiency obtained by automating the analysis process. While traditional analysis approaches are usually solely based on key features according to the basic principles, Machine Learning algorithms can consider the full data content and reveal hidden correlations. For an organized approach to Machine Learning on NDT data, an analysis tool has been developed. The tool provides a graphical user interface to manage and label training/test data and interactively define the Deep Neural Network architecture. In particular, Convolutional Neural Networks, as proven successful in various image recognition tasks, are implemented. The Machine Learning concepts are demonstrated in show cases, comprising ultrasonic as well as impact‐echo applications. In particular, the relevance of a targeted preprocessing is addressed, comparing the effectiveness of time‐, frequency‐ and joint‐time‐frequency‐representations.

Genomic prediction of seasonal forage yield in perennial ryegrass

AbstractBackgroundGenomic selection has the potential to accelerate genetic gain in perennial ryegrass breeding, provided complex traits such as forage yield can be predicted with sufficient accuracy.MethodsIn this study, we compared modelling approaches and feature selection strategies to evaluate the accuracy of genomic prediction models for seasonal forage yield production.ResultsOverall, model selection had limited impact on predictive ability when using the full data set. For a baseline genomic best linear unbiased prediction model, the highest mean predictive accuracy was obtained for spring grazing (0.78), summer grazing (0.62) and second cut silage (0.56). In terms of feature selection strategies, using uncorrelated single‐nucleotide polymorphisms (SNPs) had no impact on predictive ability, allowing for a potential decrease of the data set dimensions. With a genome‐wide association study, we found a significant SNP marker for spring grazing, located in the genic region annotated as coding for an enzyme responsible for fucosylation of xyloglucans—major components of the plant cell wall. We also presented an approach to increase interpretability of genomic prediction models with the use of Gene Ontology enrichment analysis.ConclusionsApproaches for feature selection will be relevant in development of low‐cost genotyping platforms in support of routine and cost‐effective implementation of genomic selection.

The impact of globalization on domestic growth in Africa

Research into the impacts of globalization on domestic growth in Africa has been scarce and the results of the research that does exist have been mixed. This research addresses this gap in the literature by using the newly revised KOF Globalization Index to determine the impact of social, political, and financial globalization on African economies. The KOF Index was revised substantially in 2019. Our full data set includes 40 years of data, from 1980-2019. Findings indicate that the relationship between globalization and GDP is best represented by a non-linear cubic model. With that model, social globalization has become Africa’s most important predictor of GDP, particularly in the most recent ten-year period. Economic globalization was also a small, but significant, predictor. Implications for policymaking are also discussed.