Benchmark Measurements Research Articles

Driving Fatigue Onset and Visual Attention: An Electroencephalography-Driven Analysis of Ocular Behavior in a Driving Simulation Task.

Attentional deficits have tragic consequences on road safety. These deficits are not solely caused by distraction, since they can also arise from other mental impairments such as, most frequently, mental fatigue. Fatigue is among the most prevalent impairing conditions while driving, degrading drivers' cognitive and physical abilities. This issue is particularly relevant for professional drivers, who spend most of their time behind the wheel. While scientific literature already documented the behavioral effects of driving fatigue, most studies have focused on drivers under sleep deprivation or anyhow at severe fatigue degrees, since it is difficult to recognize the onset of fatigue. The present study employed an EEG-driven approach to detect early signs of fatigue in professional drivers during a simulated task, with the aim of studying visual attention as fatigue begins to set in. Short-range and long-range professional drivers were recruited to take part in a 45-min-long simulated driving experiment. Questionnaires were used to validate the experimental protocol. A previously validated EEG index, the MDrow, was adopted as the benchmark measure for identifying the "fatigued" spans. Results of the eye-tracking analysis showed that, when fatigued, professional drivers tended to focus on non-informative portions of the driving environment. This paper presents evidence that an EEG-driven approach can be used to detect the onset of fatigue while driving and to study the related visual attention patterns. It was found that the onset of fatigue did not differentially impact drivers depending on their professional activity (short- vs. long-range delivery).

Assessing the Feasibility of a Cancer Biomarker Testing Navigator in the Laboratory

Abstract Introduction/Objective Cancer biomarker testing is a crucial component of precision oncology, as it can guide medical treatment decisions with targeted therapies and immune checkpoint inhibitors. However, complex, fragmented, and inefficient operational processes involved in biomarker testing lead to delays, errors, and variability in testing. One potential solution is to introduce a novel role in the pathology laboratory: the cancer biomarker testing navigator (BTN). Methods/Case Report The American Society for Clinical Pathology (ASCP) conducted a mixed-methods project to assess the current state of biomarker testing operations and explore how a BTN may improve biomarker testing processes. ASCP conducted an online survey, focus groups, and worked with two hospital-based cancer centers on a feasibility pilot. Results (if a Case Study enter NA) Survey and focus group insights reveal several operational challenges around the biomarker testing process. 47 completed the survey and 16 participated in the focus groups. Participants report that biomarker test orders may be incorrect or incomplete. 37% of survey respondents experience delays preparing tissue samples for send-out cancer biomarker testing. 42% of survey respondents are unable to track the processing status of send-out tests. Excessive time is required to coordinate and facilitate send-out tests, especially when multiple reference laboratories are used since each has its own portal, requisition form, specimen requirements, etc. 87% agree they would benefit significantly from having a dedicated person to coordinate and manage cancer biomarker tests. The cancer centers that piloted this role found that they could catch and correct defects such as incorrect test ordered, incorrect specimen types marked on the test order, outdated requisition used, etc. The BTN also found instances where the test report was incomplete or had not been uploaded into the EHR. The BTN role could be justified by evaluating metrics such as task efficiency, workplace satisfaction in the lab, and levels of service satisfaction from oncologists. Patient care can be improved by reducing delays in testing and by reviewing other metrics of quality care delivery. Conclusion The BTN role in the laboratory is a novel solution for ensuring the proper collection and triage of pathology samples for biomarker testing. This role could serve as a liaison between the laboratory and ordering clinicians, provide collection and testing guidance, and help with benchmarking and quality measurement initiatives.

Gravitational waves from first-order phase transitions in LISA: reconstruction pipeline and physics interpretation

We develop a tool for the analysis of stochastic gravitational wave backgrounds from cosmological first-order phase transitions with LISA: we initiate a template databank for these signals, prototype their searches, and forecast their reconstruction.The templates encompass the gravitational wave signals sourced by bubble collisions, sound waves and turbulence. Accounting for Galactic and extra-Galactic foregrounds, we forecast the region of the parameter space that LISA will reconstruct with better than ∼ 10% accuracy, if certain experimental and theoretical uncertainties are solved by the time LISA flies.We illustrate the accuracy with which LISA can reconstruct the parameters on a few benchmark signals, both in terms of the template parameters and the phase transition ones.To show the impact of the forecasts on physics beyond the Standard Model, we map the reconstructed benchmark measurements into the parameter spaces of the singlet extension of the Standard Model and of the classically conformal invariant U(1) B-L model.

Time-Series Anomaly Detection: Overview and New Trends

Anomaly detection is a fundamental data analytics task across scientific fields and industries. In recent years, an increasing interest has been shown in the application of anomaly detection techniques to time series. In this tutorial, we take a holistic view of anomaly detection in time series and comprehensively cover detection algorithms ranging from the 1980s to the most current state-of-the-art techniques. Importantly, the scope of this tutorial extends beyond algorithmic discussion, delving into the latest advancements in benchmarking and evaluation measures for this area. In particular, our interactive systems enable the exploration of detection algorithms and benchmarking results, thereby promoting user comprehension. Driven by the absence of a one-size-fits-all anomaly detector for various time series domains and applications, we review recent advancements in automated solutions and propose a new taxonomy to motivate further research.

An Analysis of Achievement and Cost Efficiency in the Implementation of the Green Construction Concept in The Construction of a Public Service Mall in the City of Tegal

Green construction is a concept in the planning and implementation stages of construction aimed at reducing the negative environmental impacts of construction activities. The Public Service Mall Building Project in Tegal City is a building project that requires costs from planning to maintenance. The construction of a project certainly requires substantial costs. The objective of this research is to evaluate the achievements and determine the cost efficiency of using the green construction concept. This research was conducted through a preliminary survey of the involved parties who understand the green construction concept to identify the most applied categories, namely the use of refrigerants without Ozone Depleting Potential (ODP), regional materials, environmental control over cigarette smoke, and pollution from construction activities. This type of research is qualitative, with data collection techniques carried out through interviews and direct observation. The data analysis for cost efficiency involves comparing green construction concepts with non-green construction concepts. The results of the benchmark measurements for using refrigerants without Ozone Depleting Potential (ODP), regional materials, and environmental control over cigarette smoke in the green construction concept achieved 100%, while the benchmark measurement results for green construction concerning pollution from construction activities achieved 50%. Based on the cost usage analysis in the upper structure construction of the Public Service Mall Building in Tegal City, the cost usage of the GC concept is 9.25% more efficient than the conventional concept.

Who gets the flow? Financial globalisation and wealth inequality

This paper studies whether the advent of financial globalisation has contributed to increasing wealth inequality in the United States, France, and the United Kingdom. I find that (i) positive changes in the benchmark measure of financial globalisation are associated with a positive change in the top 1% and 10% wealth shares and a negative change in the wealth share of the bottom 50% of the distribution. This is equivalent to an average gain of $1 trillion for the top 10% and $1.6 trillion for the top 1%, over the period of interest. (ii) Portfolio equities and financial derivatives appear to be the driving components behind the increase in wealth shares. (iii) The implied change in wealth shares is driven by the accumulation of new financial wealth (flow) rather than the valuation of existing one. (iv) The dynamic is strengthened when a banking crisis hits the economy, possibly because people at the top of the distribution can recover their lost wealth faster than people at the bottom. The main finding is robust to an expanded country sample, albeit reducing the historical context beyond the scope of this paper.

Calibration of the online dose monitoring system at the free-electron laser SwissFEL

The latest large-scale research facility of the Paul Scherrer Institute, the x-ray free-electron laser SwissFEL, is installed in a 740 m long building. The facility accelerates electrons to a maximal energy of 5.8 GeV with repetition rates up to 100 Hz. The building is integrated in a regional recreation area with shielding of the accelerator vault dimensioned to tolerate temporary beam losses. An online dose monitoring system capable of shutting down SwissFEL ensures compliance with legal requirements. The studies present an overview of the evaluation of calibration factors for this system and results of dedicated benchmark measurements, carried out to verify underlying assumptions and simplifications.

Estimation of aggregated inertia constant and load damping: A PMU-based analytical approach

A precise, straightforward, and efficient data-driven methodology is introduced to estimate the most important aggregated generation-load parameters of the power system frequency response model i.e., the inertia constant and the load damping. The estimation method depends on defining specific base systems characterized by particular frequency features. A sensitivity analysis is carried out to determine the most suitable base system for each real-time application. The data collected from PMUs is then compared to the attributes of the base system to make accurate estimations of the parameters of interest. The effectiveness of the proposed methodologies is assessed using datasets that include both simulated data from the New York-New England benchmark and actual measurements collected from six historical power system disturbances within a national power grid.

Hospital Quality Indicators for Opioid-Exposed Infants: Results From an Expert Consensus Panel.

To elicit expert consensus on quality indicators for the hospital-based care of opioid-exposed infants. We used the ExpertLens online platform to conduct a 3-round modified Delphi panel. Expert panelists included health care providers, parents in recovery, quality experts, and public health experts. We identified 49 candidate quality indicators from a literature review and environmental scan. A total of 32 experts rated the importance and feasibility of the indicators using a 9-point Likert scale (Round 1), reviewed and discussed the initial ratings (round 2), and revised their original ratings (Round 3). Numeric scores corresponded with descriptive ratings of "low" (1-3), "uncertain" (4-6), or "high" (7-9). We measured consensus using the RAND/UCLA Appropriateness Method. Candidate quality indicators assessed structures, processes, and outcomes in multiple domains of clinical care. After the final round, 36 indicators were rated "high" on importance and feasibility. Experts had strong consensus on the importance of quality indicators to assess universal screening of pregnant people for substance use disorder, hospital staff training, standardized assessment for neonatal opioid withdrawal syndrome, nonpharmacologic interventions, and transitions of care. For indicators focused on processes and outcomes, experts saw feasibility as dependent on the information routinely documented in electronic medical records or billing records. To present a more complete picture of hospital quality, experts suggested development of composite measures that summarize quality across multiple indicators. A panel of experts reached consensus on a range of quality indicators for hospital-based care of opioid-exposed infants, with potential for use in national benchmarking, intervention studies, or hospital performance measurement.

Cross-Sectional Melt Pool Geometry of Laser Scanned Tracks and Pads on Nickel Alloy 718 for the 2022 Additive Manufacturing Benchmark Challenges

The Additive Manufacturing Benchmark Series (AM Bench) is a NIST-led organization that provides a continuing series of additive manufacturing benchmark measurements, challenge problems, and conferences with the primary goal of enabling modelers to test their simulations against rigorous, highly controlled additive manufacturing benchmark measurement data. To this end, single-track (1D) and pad (2D) scans on bare plate nickel alloy 718 were completed with thermography, cross-sectional grain orientation and local chemical composition maps, and cross-sectional melt pool size measurements. The laser power, scan speed, and laser spot size were varied for single tracks, and the scan direction was varied for pads. This article focuses on the cross-sectional melt pool size measurements and presents the predictions from challenge problems. Single-track depth correlated with volumetric energy density while width did not (within the studied parameters). The melt pool size for pad scans was greater than single tracks due to heat buildup. Pad scan melt pool depth was reduced when the laser scan direction and gas flow direction were parallel. The melt pool size in pad scans showed little to no trend against position within the pads. Uncertainty budgets for cross-sectional melt pool size from optical micrographs are provided for the purpose of model validation.

A Computational Platform for Automatic Signal Processing for Bender Element Sensors

The small strain shear modulus is an important characteristic of geomaterials that can be measured experimentally using piezoelectric sensors (bender elements). However, most conventional signal interpretation techniques are based on the visual observation of the output signal and therefore inherently subjective. Objective techniques also exist, like the cross-correlation of the input and output signals, but they lack physical insight, as they rely on the (incorrect) assumption that input and output signals are similar. This paper presents GeoHyTE, the first objective and physically consistent toolbox for the automatic processing of the output signal of bender element sensors. GeoHyTE updates a finite element model of the experiment, iteratively searching for the small strain shear modulus that maximises the correlation between the experimental and numerical output signals. The method is objective, as the results do not depend on the experience of the user, and physically consistent, as the wave propagation process is modelled in full and signals of the same nature (output) are correlated. Moreover, GeoHyTE is nearly insensitive to grossly erroneous input by the user, both in terms of the starting point of the iterative maximisation process and refinement of the finite element model. The results obtained with GeoHyTE are validated against benchmark measurements reported in the literature and experimental data obtained by the authors. A detailed statistical analysis of the results obtained with GeoHyTE and conventional interpretation techniques is also presented.

Providing a Rigorous Benchmark Measurement Foundation for Modeling-Informed Qualification and Certification of Metal Additive Manufactured Components

Metal additive manufacturing (AM) is a transformative set of technologies that are increasingly being used for demanding structural applications. However, persistent challenges regarding reliability and properties of the printed parts seriously impact qualification and certification (Q&C). Computational approaches can mitigate these challenges, but availability of benchmark measurement data for model validation is a key requirement. Q&C will be discussed in the context of the Computational Materials for Qualification and Certification (CM4QC) steering group, a tightly focused collaboration of aviation-focused companies, research and regulatory government agencies, and universities that is working to develop a roadmap for increasing the use of computational approaches in the aviation Q&C process. Benchmark measurement data will be discussed in the context of the Additive Manufacturing Benchmark Test Series (AM Bench), a broad collaboration of 10 NIST divisions and about 20 external organizations, including several that are collaborators on CM4QC, that provide rigorous measurement data for validating AM simulations for a wide range of AM technologies and material systems. Technical standards also play an important role for Q&C and the confluence between CM4QC, AM Bench, and standards organizations will be discussed.

Sub-50 fs temporal resolution in an FEL-optical laser pump-probe experiment at FLASH2.

High temporal resolution is essential for ultra-fast pump-probe experiments. Arrival time jitter and drift measurements, as well as their control, become critical especially when combining XUV or X-ray free-electron lasers (FELs) with optical lasers due to the large scale of such facilities and their distinct pulse generation processes. This paper presents the application of a laser pulse arrival time monitor that actively corrects the arrival time of an optical laser relative to the FEL's main optical clock. Combined with post-analysis single pulse jitter correction this new approach improves the temporal resolution for pump-probe experiments significantly. Benchmark measurements on photo-ionization of xenon atoms performed at FLASH beamline FL26, demonstrate a sub-50 fs FWHM overall temporal resolution.

The politicization of PISA in evidence-based policy discourses

Education reform efforts stemming from the Programme in International Student Achievement have strengthened in recent years, particularly in response to the growth of global references societies – high achieving educational jurisdictions such as Finland, Hong Kong-China, and more recently Estonia and Singapore. Despite political rhetoric, evidence-based policy development associated with this international benchmark measure is rarely, if ever, a neutral enterprise that is guided by the best available evidence. Indeed, political discourse and policy framing surrounding PISA often results in the selective use of results to justify contested policy reforms. Brief cases from Japan, Sweden, and Canada illustrate how national policies have been adopted that are not grounded, and may even run counter, to research findings. The discussion examines the politicization of PISA and its symbolic role in adding legitimacy to education reform agendas. Collectively, the analysis offers an alternative perspective to the popular notion that PISA guides evidence-based decision-making.

DBSCAN Based inductively coupled DSTATCOM for PQ improvement

This research study explains the feasibility of implementing machine learning (ML) oriented inductively coupled distributed static compensator (IC-DSTATCOM) for power quality (PQ) enhancement. The issue involved in declining the power quality (PQ) using direct coupled static compensator (DC-DSTATCOM) was identified as a hazardous disappointment. Hence, to improve the quality, coupling transformer is served in unification with DC-DSTATCOM. Also, the recent growth of machine learning (ML) systems and progression of computational resources, with unpredicted data obtainability, has inspired the researchers. In this study, density based spatial clustering of application with noise (DBSCAN) is employed by using its own learning mechanism (LM) using MATLAB/ Simulink. This controller contains six subnets. Among them, six subnets are employed for active tuned weight extraction whereas other three subnets are used for reactive part. Moreover, the abovesaid devices are triggered with the help of generated reference supply current. A case education is reviewed in detail to demonstrate the operation of both DC-DSTATCOM & IC-DSTATCOM. Finally, the IC-DSTATCOM is amplified healthier as compared to other in terms of harmonics shortening, upgrading in power factor, load balancing, and potential regulation etc. To examine the effectiveness, simulation outputs of the IC-DSTATCOM is presented by following the benchmark measure of IEEE-2030-7-2017 and IEC-61000-1 grid code.

Multi-Task Learning for Motion Analysis and Segmentation in 3D Echocardiography.

Characterizing left ventricular deformation and strain using 3D+time echocardiography provides useful insights into cardiac function and can be used to detect and localize myocardial injury. To achieve this, it is imperative to obtain accurate motion estimates of the left ventricle. In many strain analysis pipelines, this step is often accompanied by a separate segmentation step; however, recent works have shown both tasks to be highly related and can be complementary when optimized jointly. In this work, we present a multi-task learning network that can simultaneously segment the left ventricle and track its motion between multiple time frames. Two task-specific networks are trained using a composite loss function. Cross-stitch units combine the activations of these networks by learning shared representations between the tasks at different levels. We also propose a novel shape-consistency unit that encourages motion propagated segmentations to match directly predicted segmentations. Using a combined synthetic and in-vivo 3D echocardiography dataset, we demonstrate that our proposed model can achieve excellent estimates of left ventricular motion displacement and myocardial segmentation. Additionally, we observe strong correlation of our image-based strain measurements with crystal-based strain measurements as well as good correspondence with SPECT perfusion mappings. Finally, we demonstrate the clinical utility of the segmentation masks in estimating ejection fraction and sphericity indices that correspond well with benchmark measurements.

K‐Means Centroids Initialization Based on Differentiation Between Instances Attributes

The conventional K‐Means clustering algorithm is widely used for grouping similar data points by initially selecting random centroids. However, the accuracy of clustering results is significantly influenced by the initial centroid selection. Despite different approaches, including various K‐Means versions, suboptimal outcomes persist due to inadequate initial centroid choices and reliance on common normalization techniques like min‐max normalization. In this study, we propose an improved algorithm that selects initial centroids more effectively by utilizing a novel formula to differentiate between instance attributes, creating a single weight for differentiation. We introduce a preprocessing phase for dataset normalization without forcing values into a specific range, yielding significantly improved results compared to unnormalized datasets and those normalized using min‐max techniques. For our experiments, we used five real datasets and five simulated datasets. The proposed algorithm is evaluated using various metrics and an external benchmark measure, such as the Adjusted Rand Index (ARI), and compared with the traditional K‐Means algorithm and 11 other modified K‐Means algorithms. Experimental evaluations on these datasets demonstrate the superiority of our proposed methodologies, achieving an impressive average accuracy rate of up to 95.47% and an average ARI score of 0.95. Additionally, the number of iterations required is reduced compared to the conventional K‐Means algorithm. By introducing innovative techniques, this research provides significant contributions to the field of data clustering, particularly in addressing modern data‐driven clustering challenges.

Cross-section data for the reactions 12C(3He,p0)14N to 12C(3He,p6)14N at energies up to 6 MeV

The cross-sections of the 12C(3He,px)14N reactions were studied for x = 0...6 at energies from 1.5 to 6 MeV and reaction angles of 135∘ and 175∘ using thin amorphous hydrocarbon (a–C:H) film targets on silicon and chromium substrates. The cross-sections were determined relative to the Rutherford scattering cross-section of 3He from gold, the accuracy of the measured cross-sections ranges from 2 to 4% at 135∘ and 3 to 10% at 175∘. The determined cross-sections were validated using benchmark measurements from bulk carbon targets. The results are presented in graphical and tabular form.

Semi-Supervised Learning for Low-cost Personalized Obstructive Sleep Apnea Detection Using Unsupervised Deep Learning and Single-Lead Electrocardiogram.

Obstructive sleep apnea (OSA) is a common sleep-related breathing disorder that can lead to a wide range of health issues if left untreated. This study aims to address the lack of research on personalized models for single-lead electrocardiogram (ECG)-based OSA detection, by proposing an automatic semi-supervised algorithm for automated low-cost personalization fine-tuning. We utilize a convolutional neural network (CNN)-based auto-encoder (AE) with a modified training objective to detect anomalous region of OSA. An indicator based on model outputs is utilized as a benchmark measure to assign pseudo-labels with confidence to each sample. Finally, we perform validation of the semi-supervised algorithm on the same database and cross-database scenarios. By introducing semi-supervised personalization, the accuracy, AUC, and mean absolute error (MAE) of the general model (GM) of 35 subjects from the same database are improved from 86.3%, 0.915, and 5.178 to 90.3%, 0.948, and 2.593. Simultaneously, in the validation of 25 subjects from a cross-database, the accuracy, AUC, and MAE of the GM are enhanced from 75.6%, 0.800, and 9.149 to 84.3%, 0.881, and 3.509. The improved version of AE demonstrates excellent adaptability in identifying abnormal features in OSA, employing a data-driven approach to assign pseudo-labels for unknown data automatically. Additionally, leveraging the pseudo-labels through a semi-supervised fine-tuning strategy provides a solution to overcome the limitation of clinical annotations, facilitating low-cost implementation of personalized models. The semi-supervised approach proposed in this paper provides a high-performance and annotation-free solution for personalized adjustment of automatic OSA detection.

A Non-Intrusive Monitoring System on Train Pantographs for the Maintenance of Overhead Contact Lines.

The condition monitoring of an overhead contact line (OCL) is investigated by developing an innovative monitoring system for a pantograph on an electrical multiple unit of a regional line. Kinematic and dynamic modelling of the pantograph is conducted to support the designed monitoring system. The modelling is proved through rigorous test-rig experiments, while the proposed methodology is then validated through extensive field tests. The field tests serve a dual purpose: First, to validate the monitoring system using benchmark measurements of the tCat® trolley, and second, to assess the reproducibility of measurements in a realistic case. This paper presents the OCL monitoring system developed in the framework of the H2020 project SIA. The accuracy of our results is not far from that of other commercial systems, with just 12 mm of absolute error in the height measurement. Therefore, they provide reliable information about trends in various key performance indicators (KPIs) that facilitates the early detection of failures and the diagnosis of anomalies. The results highlight the importance of model calibration and validation in enabling novel health monitoring capabilities for the pantograph. By continuously monitoring the parameters and tracking their degradation trends, our approach allows for optimized scheduling of maintenance tasks for the OCL.