Measurement Data Research Articles

A framework for studying environmental statistics in developmental science.

Psychologists tend to rely on verbal descriptions of the environment over time, using terms like "unpredictable," "variable," and "unstable." These terms are often open to different interpretations. This ambiguity blurs the match between constructs and measures, which creates confusion and inconsistency across studies. To better characterize the environment, the field needs a shared framework that organizes descriptions of the environment over time in clear terms: as statistical definitions. Here, we first present such a framework, drawing on theory developed in other disciplines, such as biology, anthropology, ecology, and economics. Then we apply our framework by quantifying "unpredictability" in a publicly available, longitudinal data set of crime rates in New York City (NYC) across 15 years. This case study shows that the correlations between different "unpredictability statistics" across regions are only moderate. This means that regions within NYC rank differently on unpredictability depending on which definition is used and at which spatial scale the statistics are computed. Additionally, we explore associations between unpredictability statistics and measures of unemployment, poverty, and educational attainment derived from publicly available NYC survey data. In our case study, these measures are associated with mean levels in crime rates but hardly with unpredictability in crime rates. Our case study illustrates the merits of using a formal framework for disentangling different properties of the environment. To facilitate the use of our framework, we provide a friendly, step-by-step guide for identifying the structure of the environment in repeated measures data sets. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Reflections on the challenges of conducting an international multicentre randomized controlled trial of balance training in addition to pulmonary rehabilitation and its impact on fall incidence in people with COPD

BackgroundPulmonary rehabilitation (PR) is accepted as standard care for individuals with COPD. We conducted an international, multi-centred randomized controlled trial (RCT) to determine if adding balance training to PR would reduce the incidence of falls in people with COPD. While there have been many trials investigating the effectiveness of PR, few have involved international collaboration. Successful execution of rehabilitation trials requires a significant investment of time, staffing, and resources. With the recent completion of the Balance Training for Fall Reduction in COPD RCT, we report on the design, implementation, and execution of our trial using project management phases. We also highlight our lessons learned for consideration in future multi-centre rehabilitation trials.MethodsThis was a retrospective review of the planning, preparation, timelines, and personnel training involved in the execution of this study using four of the five project management phases described by Farrell et al. in 2010: (1) initiation, (2) planning, (3) execution, and (4) monitoring and controlling. We report descriptive statistics as percentages and counts and summarize our lessons learned.ResultsTen outpatient PR programs in three continents participated. Thirty-one personnel worked on the trial across all sites. Enrolment began in January 2017 and was suspended in March 2020 due to the COVID-19 pandemic. Approximately 1275 patients were screened, 455 (36%) were eligible, 258 (57%) consented, 243 (53%) participated, and 130 (61%) completed the 12-month follow-up assessment. Lessons learned through our experience included (1) ensuring awareness of funder policies and considering the impact on collaborating sites; (2) preparing for the possibility of human resource and program disruptions; (3) anticipating site dropout and having a contingency plan in place; (4) planning and monitoring process measure data before, during, and after trial initiation; (5) ensuring frequent and consistent communication with and between collaborating sites; (6) maximizing features of database platform to ensure data set completeness and controlled data access; and (7) identifying strategies for increasing patient engagement in a high-demand study.ConclusionsWe identify seven lessons learned through our experience conducting an international, multicentre rehabilitation-based RCT. These lessons can provide guidance to other trialists conducting studies with similar logistics and may assist with future trial planning and implementation.

Mapping reservoir water quality from Sentinel-2 satellite data based on a new approach of weighted averaging: Application of Bayesian maximum entropy

In regions like Oman, which are characterized by aridity, enhancing the water quality discharged from reservoirs poses considerable challenges. This predicament is notably pronounced at Wadi Dayqah Dam (WDD), where meeting the demand for ample, superior water downstream proves to be a formidable task. Thus, accurately estimating and mapping water quality indicators (WQIs) is paramount for sustainable planning of inland in the study area. Since traditional procedures to collect water quality data are time-consuming, labor-intensive, and costly, water resources management has shifted from gathering field measurement data to utilizing remote sensing (RS) data. WDD has been threatened by various driving forces in recent years, such as contamination from different sources, sedimentation, nutrient runoff, salinity intrusion, temperature fluctuations, and microbial contamination. Therefore, this study aimed to retrieve and map WQIs, namely dissolved oxygen (DO) and chlorophyll-a (Chl-a) of the Wadi Dayqah Dam (WDD) reservoir from Sentinel-2 (S2) satellite data using a new procedure of weighted averaging, namely Bayesian Maximum Entropy-based Fusion (BMEF). To do so, the outputs of four Machine Learning (ML) algorithms, namely Multilayer Regression (MLR), Random Forest Regression (RFR), Support Vector Regression (SVRs), and XGBoost, were combined using this approach together, considering uncertainty. Water samples from 254 systematic plots were obtained for temperature (T), electrical conductivity (EC), chlorophyll-a (Chl-a), pH, oxidation–reduction potential (ORP), and dissolved oxygen (DO) in WDD. The findings indicated that, throughout both the training and testing phases, the BMEF model outperformed individual machine learning models. Considering Chl-a, as WQI, and R-squared, as evaluation indices, BMEF outperformed MLR, SVR, RFR, and XGBoost by 6%, 9%, 2%, and 7%, respectively. Furthermore, the results were significantly enhanced when the best combination of various spectral bands was considered to estimate specific WQIs instead of using all S2 bands as input variables of the ML algorithms.

Exploration on the protective effect of liraglutide on renal injury in diabetic nephropathy rats based on ROS-NLRP3 inflammasomes

Objective: To investigate the effect of liraglutide on diabetic nephropathy in rats and its regulatory mechanism. Methods: The diabetic nephropathy rat model was constructed with high-glucose-high-fat diet in combination with STZ, and was randomly divided into normal saline group and liraglutide group. The rats in liraglutide group were given sc 200 μg/kg of liraglutide, and the rats in normal saline group were given sc 20 mg/kg of normal saline twice a day for 4 weeks. The normal control group was not treated with any treatment. The biochemical indexes such as rat body weight, 24-hour urine total protein quantification (UTP), fasting blood glucose (FPG), triglyceride (TG), cholesterol (TC), blood urea nitrogen (BUN) and serum creatinine (Scr) were measured. HE staining, Masson staining and PAS staining were used to observe the pathologically morphological changes in renal tissues. Western-blot was used to detect the expression of NLRP3 inflammasome-related protein in renal tissues. Elisa was used to measure the serum levels of interleukin-18 (IL-18) and IL-1β. SPSS 26.0 statistical software and Graph Prism 9.0 software were used for analysis and mapping. The t-test was used for the comparison of measurement data between two groups, one-way ANOVA was used for the comparison among multiple groups, and Tukey’s test was used for the comparison in the same group. Results: Compared with the normal saline group, FBG, UTP, BUN, Scr, TC and TG in the liraglutide group were significantly decreased (p < .01), the glomerular basement membrane was slightly thickened, with the tubular lumen slightly dilated and the lesion damage alleviated; the expression levels of NLRP3, ASC and caspase-1 inflammasome-related proteins were decreased (p < .01), and the levels of IL-18 and IL-1β were decreased (p < .01).Conclusion: Liraglutide can inhibit the activation of NLRP3 inflammasome mediated by oxidative stress in renal tissues through ROS-NLRP3 inflammasome pathway, thereby inhibiting the inflammation, and finally playing an anti-diabetic nephropathy renal injury role.

Iterative Stress Reconstruction Algorithm to Estimate Three-Dimensional Residual Stress Fields in Manufactured Components

Abstract Residual stress (RS) significantly impacts the mechanical performance of components. Measurement of RS often provides incomplete data in terms of components of stress and spatial density. Employing such fields in finite element simulations results in significant modification of the field to achieve equilibrium and compatibility among strains. To overcome this, an iterative stress reconstruction algorithm (ISRA) is developed to estimate 3D RS fields that satisfy equilibrium, are stress component-wise complete, and represent the characterized data sampled. An Al 7075-T651 plate and an additively manufactured (AM) A36 steel wall are considered for RS reconstruction using measurement data from the literature. A maximum variation of ∼2.5 MPa in the Al plate, and ∼10 MPa in the steel wall are observed between the reconstructed and measured stresses. Furthermore, unknown stress components emerge and reach significant magnitudes (upto ∼2.3 MPa in the Al plate and ∼45 MPa in the AM wall) during ISRA. Indeed, it is found that minor errors in measurement or data processing are eliminated through the physical requirements during ISRA. Employing a reconstructed RS field is hence not just more accurate given its compatibility, but it additionally corrects for minor errors in measurement. Furthermore, it is found that spatially dense measurement data result in convergence with fewer iterations. Finally, although ISRA yields a nonunique solution dependent on boundary conditions, measurement errors, fitting errors, and mesh density, it accommodates for uncertainties and inaccuracies in measurement, as opposed to failing to reach a physically realistic converged solution.

Joint k-ω Space Image Reconstruction and Data Fitting for Chemical Exchange Saturation Transfer Magnetic Resonance Imaging

Chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is a novel MRI technology to image certain compounds at extremely low concentrations. Long acquisition time to measure signals at a set of offset frequencies of the Z-spectra and to repeat measurements to reduce noise pose significant challenges to its applications. This study explores correlations of CEST MR images along the spatial and Z-spectral dimensions to improve MR image quality and robustness of magnetization transfer ratio (MTR) asymmetry estimation via a joint k-ω reconstruction model. The model was formulated as an optimization problem with respect to MR images at all frequencies ω, while incorporating regularizations along the spatial and spectral dimensions. The solution was subject to a self-consistency condition that the Z-spectrum of each pixel follows a multi-peak data fitting model corresponding to different CEST pools. The optimization problem was solved using the alternating direction method of multipliers. The proposed joint reconstruction method was evaluated on a simulated CEST MRI phantom and semi-experimentally on choline and iopamidol phantoms with added Gaussian noise of various levels. Results demonstrated that the joint reconstruction method was more tolerable to noise and reduction in number of offset frequencies by improving signal-to-noise ratio (SNR) of the reconstructed images and reducing uncertainty in MTR asymmetry estimation. In the choline and iopamidol phantom cases with 10.5% noise in the measurement data, our method achieved an averaged SNR of 31.0 dB and 32.2 dB compared to the SNR of 24.7 dB and 24.4 dB in the conventional reconstruction approach. It reduced uncertainty of the MTR asymmetry estimation over all regions of interest by 54.4% and 43.7%, from 1.71 and 2.38 to 0.78 and 1.71, respectively.

Location-Specific Microstructure Characterization Within AM Bench 2022 Nickel Alloy 718 3D Builds

AbstractThe Additive Manufacturing Benchmark Test Series (AM Bench) is a broad effort to produce rigorous measurement datasets for validating AM computer simulations across the range of processing, structure, and properties, for many additive manufacturing (AM) build methods and material classes. Here, the microstructures of nickel alloy 718 AM Bench 2022 test artifacts produced using laser-based powder bed fusion (PBF-LB), in both as-built and fully heat-treated conditions, are examined. Cross sections are primarily characterized using large area scanning electron microscopy (SEM) electron backscatter diffraction (EBSD) and example analyses of the crystallographic textures are described. These data are part of a large set of in situ and ex situ measurements from both three-dimensional builds and laser tracks on bare plates. All the measurement data are available online with download links at www.nist.gov/ambench.

Shedding Light on Luminescence Lifetime Measurement and Associated Data Treatment

Luminescence lifetime is a crucial parameter in photophysical studies that bears essential physical and chemical information and that is used to quantify a variety of phenomena, from the determination of quenching mechanisms to temperature sensing and bioimaging. The current perception of lifetime measurement is that it is a trivial and fast experiment. However, despite this apparent simplicity, measuring luminescence decay and fitting the obtained data to a suitable model can be far more intricate. In this perspective, the influence of experimental parameters and fitting procedures on the determination of lifetimes are investigated and, through carefully chosen examples, it is shown that large variations, up to 10%, can be induced by varying parameters such as the data acquisition time, the baseline evaluation, or the mathematical fitting model. In order to present to a wider audience, detailed mathematical descriptions are kept out of the manuscript.

Observational study of missing SOFA score data frequency in RCTs relative to ICU length of stay

The Sequential Organ Failure Assessment, also known as SOFA score, was introduced to assess organ dysfunction of critical ill patients. However, understanding the impact of missing SOFA scores in randomized controlled trials and how this affect the validity and applicability of the SOFA score as a surrogate endpoint for predicting mortality has been a matter of interest. To address this, a secondary analysis of a systematic review was conducted to quantify the relationship between SOFA scores and the prediction of mortality in critically ill adults in randomized controlled trials (RCTs). The systematic review being referred to included 87 RCTs with a total of 12,064 critically ill patients. This analysis focused on missing SOFA score data in relation to the length of stay on the intensive care unit (ICU) and the methods used to handle missing data. SOFA score measurements from the included studies were categorized into three time frames: Early (t ≤ 4 days), Intermediate (t = 5–10 days) and Late (t > 10 days) measurement. Only one study reported a complete data set for calculating the SOFA score for an Early measurement. When considering all methods used to address missing data, 32% of studies still had missing data for Early measurements, and this percentage increased to 64% for Late measurements. These findings suggested that, over time, the number of studies with incomplete data sets has been increasing. The longer a patient is treated on the ICU, the higher the number of missing data which can impact the validity of SOFA score analyses. There was no clear trend towards a specific method for compensating missing data. An exemplary calculation demonstrated that ignoring missing data may lead to an underestimated variability of the treatment effect. This, in turn, could bias the interpretation of study results by policy- and clinical decision-makers. Overall, there are several limitations that need to be considered when using SOFA score as a surrogate endpoint for mortality. When employed as an outcome, the SOFA score is frequently missing and most studies do not adequately describe the amount or nature of missing data, or the methods used to handle missing data in the analysis.

A shared-aperture pentaband antenna with high impedance surface for CubeSat application

A circular polarized (CP) pentaband antenna based on the aperture-in-aperture (AIA) concept is presented for CubeSat applications. This AIA consists of five different bands ranging from L-band to Ka-band. Four different antennas, each operating at a specific frequency band, namely 12 GHz, 18.5 GHz, 26 GHz, and 32 GHz, were incorporated into an L-band (viz. 1.5 GHz) antenna. Notably, the five antennas can operate simultaneously for a CubeSat downlink operation with a frequency ratio of 21.3:1. The antenna structure shows a realized gain of 5–10 dBi with good CP bandwidth (< 3 dB) across the overall operational frequency range. That is, the realized gain of L-band (1.5 GHz), X-band (12.5 GHz), K-band1 (18.5 GHz), K-band2 (26 GHz), and Ka-bands (32 GHz) are 5.05 dBi, 8.21 dBi, 7.33 dBi, 7.97 dBi, and 8.56 dBi. A high impedance surface (HIS) is incorporated with the Ka-band antenna to mitigate the ripples in the radiation pattern created by the interference of surface waves. A prototype was fabricated and tested. The measurement data agrees well with the simulation.

An accelerated inexact Newton regularization scheme with a learned feature-selection rule for non-linear inverse problems

With computational inverse problems, it is desirable to develop an efficient inversion algorithm to find a solution from measurement data through a mathematical model connecting the unknown solution and measurable quantity based on the first principles. However, most of mathematical models represent only a few aspects of the physical quantity of interest, and some of them are even incomplete in the sense that one measurement corresponds to many solutions satisfying the forward model. In this paper, in light of the recently developed iNETT method in (2023 Inverse Problems 39 055002), we propose a novel iterative regularization method for efficiently solving non-linear ill-posed inverse problems with potentially non-injective forward mappings and (locally) non-stable inversion mappings. Our approach integrates the inexact Newton iteration, the non-stationary iterated Tikhonov regularization, the two-point gradient acceleration method, and the structure-free feature-selection rule. The main difficulty in the regularization technique is how to design an appropriate regularization penalty, capturing the key feature of the unknown solution. To overcome this difficulty, we replace the traditional regularization penalty with a deep neural network, which is structure-free and can identify the correct solution in a huge null space. A comprehensive convergence analysis of the proposed algorithm is performed under standard assumptions of regularization theory. Numerical experiments with comparisons with other state-of-the-art methods for two model problems are presented to show the efficiency of the proposed approach.

Precisely identifying and mitigating shallow reading behaviors through online reading data: exploring quantitative reading behavior methods and precise behavioral intervention

ABSTRACT Online learning has become a part of daily life. However, it has fostered a tendency to shallow reading. Notably, little attention has been paid to precisely identifying shallow reading behaviors using quantified education big data in favor of methods such as conceptual descriptions, opinions, and questionnaires, resulting in an imprecise analysis of its impact on learning and a lack of strategies to mitigate its causative factors. This study addresses the issue by introducing a behavior-identifying model grounded in measurable data to precisely identify shallow reading behaviors. Initially, three key dimensions of shallow reading behaviors were proposed, along with corresponding mathematical indicators. Subsequently, the k-means algorithm and Pearson correlation coefficient were employed to evaluate the relationship between shallow reading and learning performance, underscoring its significance. The analysis showed that shallow reading behaviors are highly associated with poor learning performance, thus hindering critical reading. These findings further validate the predominant influence of reading speed on learning performance. Consequently, a strategy was developed to intentionally decrease reading speed. Following that, an experiment was conducted to assess the effectiveness of this strategy in mitigating shallow reading. The results revealed improved learning performance and a significant reduction in shallow reading behaviors without increasing cognitive load.

Experimental Validation of Radiance-Based Methods for Simulating Solar Penumbras and Pinhole Projections

ABSTRACT The sun’s vast size creates two distinct shadow phenomena on Earth: solar penumbras and pinhole projections. These sunlight effects are particularly important in daylight perception research and exacting daylighting design. As such, a method to generate accurate and efficient lighting simulations of these sunlight effects is needed to facilitate research and design applications. The present study is the first to investigate the accuracy of three Radiance-based methods for generating solar penumbras and pinhole projections against real-world measurements using various semi-controlled scenarios. In addition, we examine the applicability of these methods in terms of simulation time and file size. These methods involve the use of Radiance’s direct jittering algorithm, utilizing solely Radiance’s stochastic calculation, and subdividing a Radiance sun into many small suns. The simulation results of solar penumbras and pinhole projections per method are compared to real-world measurement data and assessed on their applicability. The results show that employing a subdivided sun offers an accurate and efficient method for simulating scenes with solar penumbras and pinhole projections using Radiance.

Comparative analysis of different treatment strategies for septic spondylitis: a retrospective study of one hundred and twelve patients.

To compare the clinical efficacy and prognosis differences between conservative treatment and surgical treatment in patients with non-serious neurologically intact pyogenic spondylitis (Nsi-Nsni-PS), and to provide theoretical reference for the clinical treatment of Nsi-Nsni-PS patients. A retrospective analysis was conducted on 112 cases of Nsi-Nsni-PS patients treated in our hospital from June 2016 to June 2021. According to different treatment methods, they were divided into conservative treatment group (53 cases) and surgical treatment group (59 cases). The general data, laboratory tests, imaging examinations, length of hospital stay, duration of antibiotic use, VAS for pain before and after treatment, ODI, local kyphotic angle correction of diseased vertebrae, and recurrence rate were collected and analyzed in both groups. SPSS 26.0 statistical software was used for analysis. Measurement data were expressed as mean ± standard deviation, and independent sample t-test or rank sum test was used for comparison between groups, while variance analysis was used for intra-group comparison. Count data were expressed as number (%) and compared between groups using chi-square test or Fisher's exact test. Mann-Whitney U test was used to evaluate the changes in local kyphotic angle between the two groups. A p value < 0.05 was considered statistically significant. There were no significant differences in general data and imaging characteristics between the two groups (P > 0.05); there were no statistically significant differences in the positive culture rate of pathogens, length of hospital stay, duration of antibiotic use, treatment complications, WBC, CRP, ESR levels at admission and discharge, VAS and ODI at admission and last follow-up between the two groups (P > 0.05). The WBC and CRP levels of patients in the conservative group at discharge were lower than those in the surgical group (P < 0.05), and there was no significant difference in the decrease in inflammatory indicators (WBC, CRP, ESR) between the two groups (P > 0.05). By the last follow-up, the neurological function of patients in both groups had significantly improved compared to admission (P < 0.05), with 12 out of 15 ASIA grade D patients in the conservative group recovering to grade E, and 21 out of 25 grade D patients in the surgical group recovering to grade E, with no worsening of neurological function in either group. The differences in VAS and ODI scores at the last follow-up compared to before treatment were statistically significant in both groups (P < 0.05), and all patients regained normal activity. Compared with before treatment, the correction degree of local kyphotic angle in the surgical group at the last follow-up was 0.93 ± 4.94°, slightly higher than that in the conservative group (-0.83 ± 3.37°), and the difference was statistically significant(P < 0.05). During our follow-up, we found that both conservative and surgical treatments achieved satisfactory clinical outcomes in patients with Nsi-Nsni-PS. Compared to conservative treatment, surgical intervention did not demonstrate significant advantages in reducing hospitalization time and antibiotic usage duration, increasing pathogen culture positivity rate, lowering treatment complications, or controlling recurrence. However, surgical intervention showed superiority in correcting the local kyphotic angle of spinal lesions, albeit with relatively increased surgical trauma, risks, and treatment costs. At the last follow-up, the surgical group did not exhibit better long-term efficacy. Therefore, when formulating clinical treatment strategies for patients with Nsi-Nsni-PS, it may be preferable to prioritize conservative treatment, supplemented by the use of sensitive or empiric antibiotics for infection management, to improve patient prognosis.

High spatial resolution inversion of chromophoric dissolved organic matter (CDOM) concentrations in Ebinur Lake of arid Xinjiang, China: Implications for surface water quality monitoring

Utilizing satellite remote sensing for the assessment and temporal-spatial analysis of Chromophoric Dissolved Organic Matter (CDOM) is vital for overseeing lake water health and devising management plans. This study focused on the saline, turbid and arid Ebinur Lake, located in China’s northwestern region. The Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) model algorithms were compared to select the one with the highest accuracy. It combined Sentinel-2 remote sensing data and in situ measurement data for the quantitative inversion of CDOM. Monthly CDOM distribution maps were generated with a 10 m resolution for the non-frozen months of May to October from 2018 to 2022, followed by a comprehensive analysis of temporal trends. The primary conclusions are: (1) The XGBoost model yielded highly accurate CDOM estimates, with a training set coefficient of determination (R2) of 0.94, a Root Mean Square Error (RMSE) of 0.06 mg/L, Mean Absolute Percentage Error (MAPE) of 6.05 %, Relative Percent Difference (RPD) of 4.07; the test set demonstrated an R2 of 0.41 with an RMSE of 0.22 mg/L, MAPE of 22.74 %, RPD of 1.35; (2) Throughout the study period, the main lake portion displayed variable CDOM spatial patterns and trends. The inversion indicated higher CDOM concentrations in the central part than nearshore areas and decreasing CDOM in tandem with seasonable water-surface shrinkage. The findings offer hints for an accurate evaluation of water color parameters of Ebinur Lake and practical references for monitoring arid-region lake water quality via remote sensing.

Leveraging measurement data quality by adoption of the FAIR guiding principles

Abstract The analysis and reuse of measured process data are enablers for sustainable and resilient manufacturing in the future. Maintaining high measurement data quality is vital for maximising the usage and value of the data at hand. To ensure this data quality, the data management must be applied consequently throughout the complete Data Life-Cycle (DLC) and adhere to the FAIR guiding principles. In the two research consortia NFDI4Ing and the Cluster of Excellence “Internet of Production,” we investigate approaches to increase the measurement of data quality by integrating the FAIR guiding principles in all data management activities of the DLC. To facilitate the uptake of the FAIR guiding principles, we underline the significance of FAIR data for the reuse of high-quality data. Second, we are introducing a harmonised DLC to streamline data management activities. Third, we concisely review current trends and best practices in FAIR-aware data management and give suggestions for implementing the FAIR guiding principles.

Incoming Inspection of Lithium‐Ion Batteries Based on Multi‐cell Testing

Incoming inspections of battery cells prior to module assembly help to ensure the quality of the battery system and prevent the installation of anomalous cells. Depending on the area of application, identifying deviations in the electrical behavior of the battery cells under test can be essential for downstream assembly processes like cell matching and algorithm adaptations of the battery management software. In this work, the use of a multi‐cell testing procedure involving differential voltage analysis, incremental capacity analysis, direct current internal resistance tests, and electrochemical impedance spectroscopy is investigated to reveal differences in cell properties and identify anomalous cells while economizing on the required cell test channels. The results obtained from 20 model‐identical 21700 cylindrical cells from four different batches demonstrate that this methodology can detect material variations, such as differing silicon and graphite content, which are not disclosed by the supplier or indicated in the data sheet. A teardown with elemental analysis of two cells from different batches is carried out as verification. Finally, prospects for potential application scenarios and raw measurement data are provided.

Leveraging measurement data quality by adoption of the FAIR guiding principles

Abstract The analysis and reuse of measured process data are enablers for sustainable and resilient manufacturing in the future. Maintaining high measurement data quality is vital for maximising the usage and value of the data at hand. To ensure this data quality, the data management must be applied consequently throughout the complete Data Life-Cycle (DLC) and adhere to the FAIR guiding principles. In the two research consortia NFDI4Ing and the Cluster of Excellence “Internet of Production,” we investigate approaches to increase the measurement of data quality by integrating the FAIR guiding principles in all data management activities of the DLC. To facilitate the uptake of the FAIR guiding principles, we underline the significance of FAIR data for the reuse of high-quality data. Second, we are introducing a harmonised DLC to streamline data management activities. Third, we concisely review current trends and best practices in FAIR-aware data management and give suggestions for implementing the FAIR guiding principles.

Extraction of nanometer-scale displacements from noisy signals at frequencies down to 1 mHz obtained by differential laser Doppler vibrometry

Abstract. A method is presented by which very small, slow, anharmonic signals can be extracted from measurement data overlaid with noise that is orders of magnitude larger than the signal of interest. To this end, a multi-step filtering process is applied to a time signal containing the time-dependent displacement of the surface of a sample, which is determined with a contactless measurement method, differential laser Doppler vibrometry (D-LDV), at elevated temperatures. The time signal contains the phase difference of the measurement and reference laser beams of the D-LDV, already greatly reducing noise from, e.g., length fluctuations, heat haze, and mechanical vibrations. In postprocessing of the data, anharmonic signal contributions are identified and extracted to show the accurate displacement originating from thickness changes of thin films and related sample bending. The approach is demonstrated on a Pr0.1Ce0.9O2−δ (PCO) thin film deposited on a single-crystalline ZrO2-based substrate. The displacement extracted from the data is ca. 38 % larger and the uncertainty ca. 35 % lower than those calculated directly from the D-LDV spectrum.

CFDI: Coordinated false data injection attack in active distribution network

AbstractThe active distribution network (ADN) can obtain measurement data, estimate system states, and control distributed energy resources (DERs) and flexible loads to ensure voltage stability. However, the ADN is more vulnerable to cyber attacks due to the recent wave of digitization and automation efforts. In this article, false data injection (FDI) attacks are focused on and they are classified into two types, that is, type I attacks on measurement data and type II attacks on control commands. After studying the impact of these two FDI attacks on the ADN, a new threat is revealed called coordinated FDI attack, which can maximize the voltage deviation by coordinating type I and type II FDI attacks. From the attacker's perspective, the scheme of CFDI is proposed and an algorithm is developed to find the optimal attack strategy. The feasibility of CFDI attacks has been validated on a smart distribution testbed. Moreover, simulation results on an ADN benchmark have demonstrated that CFDI attacks could cause remarkable voltage deviation that may deteriorate the stability of the distribution network. Moreover, the impact of CFDI attacks is higher than pure type I or type II attacks. To mitigate the threat, some countermeasures against CFDI attacks are also proposed.