Physikalisch-Technische Bundesanstalt Research Articles

Spectral aerosol optical depth from SI-traceable spectral solar irradiance measurements

Abstract. Spectroradiometric measurements of direct solar irradiance traceable to the SI were performed by three spectroradiometer systems during a 3-week campaign in September 2022 at the Izaña Atmospheric Observatory (IZO) located on the island of Tenerife, Canary Islands, Spain. The spectroradiometers provided direct spectral irradiance measurements in the spectral ranges 300 to 550 nm (QASUME), 550 to 1700 nm (QASUME-IR), 300 to 2150 nm (BiTec Sensor, BTS), and 316 to 1030 nm (Precision Solar Spectroradiometer, PSR), with relative standard uncertainties of 0.7 %, 0.9 %, and 1 % for QASUME/QASUME-IR, the PSR, and the BTS respectively. The calibration of QASUME and QASUME-IR was validated prior to this campaign at Physikalisch-Technische Bundesanstalt (PTB) by measuring the spectral irradiance from two spectral irradiance sources, the high-temperature blackbody BB3200pg as a national primary standard and the tuneable laser facility TULIP. The top-of-atmosphere (ToA) solar irradiance spectra from the spectroradiometers were retrieved from direct solar irradiance measurements using zero-air-mass extrapolation during cloud-free conditions, which were then compared to the TSIS-1 Hybrid Solar Reference Spectrum (HSRS). These ToA solar spectra agreed to within 1 % for spectral ranges longer than 400 nm (for QASUME also at shorter wavelengths) in the spectral regions free of significant trace gas absorption and were well within the combined uncertainties over the full investigated spectral range. Using the results from the comparison with QASUME, the relative standard uncertainty of the TSIS-1 HSRS ToA solar spectrum in the spectral range 308 to 400 nm could be reduced from its nominal 1.3 % to 0.8 %, representing the relative standard uncertainty of the QASUME ToA solar spectrum in this spectral range. The spectral aerosol optical depth (AOD) retrieved from the solar irradiance measurements of these spectroradiometers using the TSIS-1 HSRS as the reference ToA solar spectrum agreed to within 0.01 in optical depth in nearly all common spectral channels of two narrowband filter radiometers belonging to the Global Atmosphere Watch Precision Filter Radiometer (GAW-PFR) network and Aerosol Robotic Network (AERONET). This study shows that it is now possible to retrieve spectral AOD over the extended spectral range from 300 to 1700 nm using solar irradiance measurements traceable to the SI using laboratory-calibrated spectroradiometers with similar quality to that from traditional Langley-based calibrated instruments. The main improvement to previous investigations is the recent availability of the high-spectral-resolution TSIS-1 HSRS with very low uncertainties, which provides the top-of-atmosphere reference for the spectral atmospheric transmission measurements obtained from ground-based solar irradiance measurements.

A Cost-Based Dual ConvNet-Attention Transfer Learning Model for ECG Heartbeat Classification

The heart is a very crucial organ of the body. Concerted efforts are constantly put forward to provide adequate monitoring of the heart. A heart disorder is reported to cause a lot of hidden ailments resulting in numerous deaths. Early heart monitoring using an electrocardiogram (ECG) through the advancement of computer-aided diagnostic (CAD) systems is widely used. Meanwhile, the use of human reading of ECG results are faced with many challenges of inaccurate and unreliable interpretations. Over two decades, studies provided artificial intelligence (AI) technique using machine learning (ML) algorithms as a fast and reliable technique for ECG heartbeat classification. Moreover, in recent times, deep learning (DL) techniques have been focused on providing automatic feature extraction and better classification performance. On the other hand, the challenge with the ECG data is its imbalance nature. Therefore, this paper proposes a cost-based dual convolutional attention transfer DL model for ECG classification. The proposed model uses PhysionNet-MIT-BIH and Physikalisch-Technische Bundesanstalt (PTB) Diagnostics datasets. The first part uses the MIT-BIH for ECG categorization, while representations learned from the first classifier are used for PTB analysis through transfer learning (TL). The proposed model is evaluated and compared with well-performing conventional ML models based on their F1-score and accuracy scores. Our experimental finding show that the proposed model outperformed the well-performing ML models as well as competitive with past studies for both the classification and TL part, having obtained 98.45% for both F1-score and accuracy. The proposed model is applicable to real-life trials and experiments for ECG heartbeat and other similar domains.

Development of primary reference facilities and measurement comparison of standard artifacts for the bidirectional transmittance distribution function

To determine the bidirectional transmittance distribution function (BTDF) of diffusely transmitting materials, two new primary facilities have been developed at the Physikalisch-Technische Bundesanstalt (PTB) and Aalto University (Aalto). A detailed description of both facilities and the different approaches used are presented in this paper. The performance of both facilities is compared by determining the BTDF of two different diffuser types in both in-plane and out-of-plane bidirectional geometries at four different wavelengths in the visible spectral range. Due to delayed completion of PTB’s primary facility, the measured BTDF values are compared between Aalto’s primary facility and another PTB setup, whose measurement scales are successfully transferred to PTB’s primary facility by an internal comparison. A thorough analysis of the measurement uncertainty is presented, leading to a combined k = 1 standard uncertainty of 0.8%–1.2% for PTB’s primary facility and 1.3%–1.7% for Aalto’s primary facility. The BTDF results obtained agree well within their expanded k = 2 uncertainty. This indirect bilateral comparison shows that Aalto’s and PTB’s new facilities are suited as primary reference setups for the determination of the BTDF. These studies also reveal action points to improved measurement capabilities and for a reduction of the measurement uncertainty, depending on the type of diffuser under test.

Bilateral comparison between viscosity measurement standard systems developed by KRISS and PTB

The viscosity measurement standard systems developed by the Korea Research Institute of Standards and Science (KRISS) and Physikalisch–Technische–Bundesanstalt (PTB) were compared, and the international equivalence of the standard viscosity measurement system built by KRISS was confirmed. In the system developed by KRISS, the measurement range of the viscosity measurement standard system was 0.3–100000 mm2/s with 0.17–0.5% uncertainty (U, k = 2). The density of a liquid was measured using a pycnometer, and an absolute viscosity of 0.3–8000 mPas was measured with an uncertainty of 0.18–0.6%. The viscosity measurement standard systems of KRISS and PTB were bilaterally compared, using three different viscosity standard liquids synthesized by PTB, under six experimental conditions. Considering the uncertainty, the calculated En was < 1 (0.17–0.72) for all experimental cases. Therefore, we confirmed the viscosity standard system created recently by KRISS to be significantly equivalent to that developed by PTB.

Optimized Solutions of Electrocardiogram Lead and Segment Selection for Cardiovascular Disease Diagnostics.

Most of the existing multi-lead electrocardiogram (ECG) detection methods are based on all 12 leads, which undoubtedly results in a large amount of calculation and is not suitable for the application in portable ECG detection systems. Moreover, the influence of different lead and heartbeat segment lengths on the detection is not clear. In this paper, a novel Genetic Algorithm-based ECG Leads and Segment Length Optimization (GA-LSLO) framework is proposed, aiming to automatically select the appropriate leads and input ECG length to achieve optimized cardiovascular disease detection. GA-LSLO extracts the features of each lead under different heartbeat segment lengths through the convolutional neural network and uses the genetic algorithm to automatically select the optimal combination of ECG leads and segment length. In addition, the lead attention module (LAM) is proposed to weight the features of the selected leads, which improves the accuracy of cardiac disease detection. The algorithm is validated on the ECG data from the Huangpu Branch of Shanghai Ninth People's Hospital (defined as the SH database) and the open-source Physikalisch-Technische Bundesanstalt diagnostic ECG database (PTB database). The accuracy for detection of arrhythmia and myocardial infarction under the inter-patient paradigm is 99.65% (95% confidence interval: 99.20-99.76%) and 97.62% (95% confidence interval: 96.80-98.16%), respectively. In addition, ECG detection devices are designed using Raspberry Pi, which verifies the convenience of hardware implementation of the algorithm. In conclusion, the proposed method achieves good cardiovascular disease detection performance. It selects the ECG leads and heartbeat segment length with the lowest algorithm complexity while ensuring classification accuracy, which is suitable for portable ECG detection devices.

#EXSA2021, EXSA first virtual conference in X‐ray Spectrometry

As the third wave of the COVID-19 pandemic was sweeping across Europe, many researchers, and especially young scientists, were struggling to disseminate their work, discussing new projects, and finding their place in science. The X-ray Spectrometry community was no exception. True to its purposes and objectives, the European X-ray Spectrometry Association (EXSA) decided to organize its first “virtual conference on X-ray Spectrometry,” #EXSA2021, to tighten and consolidate the links in between its members, and to propose a collaborating and networking platform where representatives from academia, industrial and private sector, and metrology institutes, could meet and exchange ideas and progresses. In a nutshell, the main objective of EXSA (www.exsa.hu) is to promote innovation and cooperation of X-ray spectroscopists and analysts within Europe by supporting talented young scientists; by fostering the interface between the academic and industrial sector (via activities such as the Fundamental Parameter Initiative); promoting X-ray Spectrometry courses and providing high-quality training via for instance the (co) organization of schools and workshops on novel and emerging X-ray Spectrometry topics. The next event will be held in Paris (France) in late October 2023. If one wanted to learn about the latest research and technologies in X-ray Spectrometry on the following topics: XRS in environmental applications, XRS for novel materials including, for example, batteries, nanostructures, instrumental development for XRS and its applications, and fundamental parameters, #EXSA2021 was the conference to join. Not only the event featured a collection of exclusive talks from leading scientists, but also contributions from young scientists, lively poster sessions, and in-depth thoughts during round tables and discussions. It also concluded with the meeting of the International Initiative on Fundamental Parameter. With 5 invited talks, 26 oral contributions, and 22 posters, #EXSA2021 went beyond our expectations. Please refer to our website https://www.exsa.hu/conf2021/ for more information. Open to all, and widely disseminated by word of mouth by EXSA members, #EXSA2021 was a true success with 128 participants, among which 20% were from the industry, 40% were non-EXSA members and with, on average, 72 persons attending per session. Indeed, to allow for a worldwide participation, the conference was organized as long sessions taking place from noon to late afternoon European time, which offered the possibility to join for attendees from different time zones. Despite a very lively event, and dynamics in EXSA community, only a few manuscripts were received to contribute to #EXSA2021 proceedings. These are as eclectic in contents as the X-ray Spectrometry fields of research are, and we are happy to share those with you in this special issue. We would like to express our appreciation and sincere thanks to those who made the #EXSA2021 possible. Our sponsors, for their great support to EXSA throughout all the years, and we would also like to thank the PTB (Physikalisch Technische Bundesanstalt, Germany) for providing us with the online platform and technical assistance, and Wiley X-ray Spectrometry journal, for providing awards to the best poster and best oral presentation of young scientists, and supporting the publication of these proceedings. The help by the program committee, the involvement of the moderators and speakers of the round tables, session chairs, and award committee was unvaluable to insure a well-balanced, interactive, and dynamic #EXSA2021. Finally, we are grateful to everyone who participated to #EXSA2021. We hope that you have as good memories of our #EXSA2021 virtual conference as we do! #EXSA2021 Chairs, Diane Eichert and Michael Kolbe.

International Comparison Exercise for Biological Dosimetry after Exposures with Neutrons Performed at Two Irradiation Facilities as Part of the BALANCE Project

In the case of a radiological or nuclear event, biological dosimetry can be an important tool to support clinical decision-making. During a nuclear event, individuals might be exposed to a mixed field of neutrons and photons. The composition of the field and the neutron energy spectrum influence the degree of damage to the chromosomes. During the transatlantic BALANCE project, an exposure similar to a Hiroshima-like device at a distance of 1.5 km from the epicenter was simulated, and biological dosimetry based on dicentric chromosomes was performed to evaluate the participants ability to discover unknown doses and to test the influence of differences in neutron spectra. In a first step, calibration curves were established by irradiating blood samples with 5 doses in the range of 0–4 Gy at two different facilities in Germany (Physikalisch-Technische Bundesanstalt [PTB]) and the USA (the Columbia IND Neutron Facility [CINF]). The samples were sent to eight participating laboratories from the RENEB network and dicentric chromosomes were scored by each participant. Next, blood samples were irradiated with 4 blind doses in each of the two facilities and sent to the participants to provide dose estimates based on the established calibration curves. Manual and semiautomatic scoring of dicentric chromosomes were evaluated for their applicability to neutron exposures. Moreover, the biological effectiveness of the neutrons from the two irradiation facilities was compared. The calibration curves from samples irradiated at CINF showed a 1.4 times higher biological effectiveness compared to samples irradiated at PTB. For manual scoring of dicentric chromosomes, the doses of the test samples were mostly successfully resolved based on the calibration curves established during the project. For semiautomatic scoring, the dose estimation for the test samples was less successful. Doses >2 Gy in the calibration curves revealed nonlinear associations between dose and dispersion index of the dicentric counts, especially for manual scoring. The differences in the biological effectiveness between the irradiation facilities suggested that the neutron energy spectrum can have a strong impact on the dicentric counts.

Fast neutron response characterization of an EJ-276 plastic scintillator for use as a neutron monitor

EJ-276 is a recent development in plastic scintillators that comes with improved pulse shape discrimination over its predecessor EJ-299-33. In this work, the neutron response functions, proton light output function, and detection efficiency of EJ-276 were measured using mono-energetic neutron reference fields at the national metrology laboratory, Physikalisch-Technische Bundesanstalt, in the neutron energy range 1.5–19 MeV. In particular, the proton light output in the range 14–19 MeV is presented for the first time. The optimization of Pulse Shape Discrimination has also been investigated using an 241Am-Be source and results are presented. Finally, we study how, using a shadow-cone method and time of flight method, we can discriminate scattered and other spurious neutrons from the mono-energetic beam. The characterized EJ-276 detector is to be used in the monitor of the neutron fields and gamma background at the new Neutron Irradiation Laboratory for Electronics facility at the Rutherford Appleton Laboratory.

Traceable S-parameter measurements up to 90 GHz in 1.35 mm coaxial

As part of a joint program three National Measurement Institutes around Europe—the National Physical Laboratory in the UK, the Physikalisch-Technische Bundesanstalt in Germany and the Laboratoire National de métrologie et d’essais in France—have each established new capabilities for the traceable measurement of S-parameters up to 90 GHz in the new 1.35 mm (E band) coaxial connector. Each institution has established their own measurement systems to access the full frequency band for the connector, utilising a variety of commercially available equipment and measurement setups. Each have also developed their own calibration techniques and verification methods using different vector network analyser calibration routines. These include optimised multi-line Through Reflect Line, multiple Offset Shorts Through and Short Open Load Reciprocal (SOLR) schemes. Uncertainties for each of these setups and calibration schemes were determined utilising theoretical models of the systems and calibration standards, or through verification methods, with different effects being derived from the dimensional characteristics for each of the different standards or verification artifacts. The different dimensional systems used to make these measurements were also developed and established previously as part of the same program. The last part of this exercise was a three-way intercomparison between the laboratories. The comparison involved the measurement of a set of four traveling standards, including both 1-port and 2-port as well as plug (male) and jack (female) devices, each with different reflection and transmission characteristics to better test and demonstrate the equivalence of the capabilities.

Bilateral comparison of irradiance scales between PMOD/WRC and PTB for longwave downward radiation measurements

In this work, comparison measurements are presented between two independently realised and characterised blackbody cavities which serve as irradiance standards, namely the well-established Tilted Bottom Cavity BB2007 and the new Hemispherical Blackbody (HSBB). Both are used to realise the unit for thermal infrared irradiance. The BB2007 at the Physikalisch-Meteorologisches Observatorium Davos/World Radiation Center has long provided the reference for longwave downward radiation within the Baseline Surface Radiation Network. Longwave downward radiation is constantly measured at multiple stations around the world using specific broadband infrared radiometers with a hemispherical acceptance angle, for example pyrgeometers. The HSBB, developed at the Physikalisch-Technische Bundesanstalt (PTB) in recent years, was specifically designed to calibrate radiometers with a hemispherical acceptance angle which measure longwave downward radiation. The HSBB is directly traceable to the Radiation Temperature Scale of PTB and, in turn, via this scale to the SI. Comparison measurements between the BB2007 and HSBB in this work were carried out with three transfer instruments: the dedicated Temperature Stabilised Radiation Thermometer, an Infrared Integrating Sphere (IRIS) instrument and a Kipp and Zonen CG4 pyrgeometer. The results show good agreement with respect to the target irradiance uncertainty of W m−2 provided by the HSBB. This study thus supports and validates the traceability of atmospheric longwave downward radiation to the SI linking measurements performed with the World Infrared Standard Group of pyrgeometers to the traceability of the reference blackbody BB2007 using IRIS instruments as the transfer standard.

Hypergraph and cross-attention-based unsupervised domain adaptation framework for cross-domain myocardial infarction localization

Solving individual differences between subjects is critical for the promotion of electrocardiogram (ECG) classification algorithms in the intelligent health monitoring industry. Popular inter-subject-based solutions usually require the manual labeling of heartbeats and frequent updating of the model for new subjects. To track these problems, we propose a hypergraph and cross-attention-based unsupervised domain adaptation (HGCA-UDA) framework for the myocardial infarction localization. Specifically, we first build a hypergraph-based dual-channel network, that can simultaneously learn specific feature representations from an ECG lead and disease category levels for samples from different domains. We then design a cross-attention module to align cross-domain locally similar samples. Subsequently, a domain alignment strategy based on the Wasserstein distance is proposed to align the global edge feature distribution. Finally, a pseudo-label generation scheme is proposed to further align fine-grained category information. We conduct extensive experiments on two public benchmark datasets (the Physikalisch-Technische Bundesanstalt (PTB) and PTB_XL database), and the results show that the proposed HGCR-UDA (with unlabeled patients) achieves comparable results compared with state-of-the-art inter-patient-based methods (with labeled patients) and has excellent applications prospects in the field of intelligent health monitoring.

Automated Localization of Myocardial Infarction from Vectorcardiographic via Tensor decomposition.

Myocardial infarction (MI) causes rapid and permanent damage to the heart muscle. Therefore, it can deteriorate the myocardial structure and function if not timely diagnosed and treated. However, it is difficult to determine the precise localization of MI based on vectorcardiogram (VCG) due to the existing studies ignore the spatiotemporal features of VCG. In this paper, a precise MI localization method was proposed based on Tucker decomposition. The multi-scale characteristics of wavelet transform and the spatiotemporal characteristics of VCG were used to construct the VCG tensor containing the local and the spatiotemporal information. The VCG tensor was compressed in the time dimension based on Tucker decomposition to remove redundant information and extract the local spatiotemporal features. The features were fed back to the TreeBagger classifier. The proposed method achieved a total accuracy of 99.80% for 11 types of MI on the benchmark Physikalisch-Technische Bundesanstalt database. The area under the receiver operating characteristic curves and precision-recall curves of each kind of ECG signal was more than 0.88. The proposed algorithm effectively realized the classification of normal ECG and 11 categories of MI using VCG. Therefore, this study provides new ideas for the intelligent diagnosis of MI based on VCG.

Electrocardiogram (ECG)-Based User Authentication Using Deep Learning Algorithms.

Personal authentication security is an essential area of research in privacy and cybersecurity. For individual verification, fingerprint and facial recognition have proved particularly useful. However, such technologies have flaws such as fingerprint fabrication and external impediments. Different AI-based technologies have been proposed to overcome forging or impersonating authentication concerns. Electrocardiogram (ECG)-based user authentication has recently attracted considerable curiosity from researchers. The Electrocardiogram is among the most reliable advanced techniques for authentication since, unlike other biometrics, it confirms that the individual is real and alive. This study utilizes a user authentication system based on electrocardiography (ECG) signals using deep learning algorithms. The ECG data are collected from users to create a unique biometric profile for each individual. The proposed methodology utilizes Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) to analyze the ECG data. The CNNs are trained to extract features from the ECG data, while the LSTM networks are used to model the temporal dependencies in the data. The evaluation of the performance of the proposed system is conducted through experiments. It demonstrates that it effectively identifies users based on their ECG data, achieving high accuracy rates. The suggested techniques obtained an overall accuracy of 98.34% for CNN and 99.69% for LSTM using the Physikalisch-Technische Bundesanstalt (PTB) database. Overall, the proposed system offers a secure and convenient method for user authentication using ECG data and deep learning algorithms. The approach has the potential to provide a secure and convenient method for user authentication in various applications.

Efficient Clustering-Based electrocardiographic biometric identification

The correct identification of individuals through different biometric traits is becoming increasingly important. Apart from traditional biomarkers (like fingerprints), many alternative measures have been proposed during the last two decades: electrocardiogram (ECG) and electroencephalogram (EEG) signals, iris or facial recognition, conductual traits, etc. Several works have shown that ECG-based recognition is a feasible alternative, either for stand-alone or multi-biometric recognition systems. In this paper, we propose a novel framework for ECG-based biometric identification, consisting of a simple and robust feature extraction approach and a clustering-based feature reduction method, that enables for an efficient and scalable biometric identification. The proposed feature reduction approach is a two phase method: it uses a clustering algorithm to group features according to their similarities first, and then clusters are represented in terms of a prototype vector and associated to the available subjects. On its side, the proposed time-domain feature extraction method is a semi-fiducial procedure, where the well-known Pan–Tompkins algorithm is first used to detect the R wave peaks of the QRS complexes, and then fixed-width time segments are selected for further dimensionality reduction and feature extraction. The resulting combined methods are efficient, robust, scalable and attain excellent results (with up-to 98.6% sensitivity) on all the subjects of the Physikalisch-Technische Bundesanstalt (PTB) database, regardless of their pathological or healthy status. Additionally, we also show how the existing Auto Correlation/Discrete Cosine Transform (AC/DCT)-based non-fiducial feature extraction method can be integrated within our framework, allowing us to attain up to 90.6% sensitivity on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia database. Since this database is much noisier and has a much lower sampling rate (360 Hz instead of 1000 Hz), we claim that this is a very good result.

Recent Progress on Combustion Characteristics of Ammonia-Based Fuel Blends and Their Potential in Internal Combustion Engines

Review Recent Progress on Combustion Characteristics of Ammonia-Based Fuel Blends and Their Potential in Internal Combustion Engines Denghao Zhu , and Bo Shu * Department of Physical Chemistry, Physikalisch-Technische Bundesanstalt, Bundesallee 100, DE-38116 Braunschweig, Germany * Correspondence: bo.shu@ptb.de Received: 21 November 2022 Accepted: 22 December 2022 Published: 18 January 2023 Abstract: Ammonia has recently attracted numerous attentions from researchers and policy makers as promising energy and hydrogen carrier for mitigating the carbon footprints in the energy sector. The mature infrastructure for the production, storage, and transportation of ammonia allows a quick role of ammonia in energy systems. By applying green hydrogen and renewable energies, green ammonia can be produced, which makes ammonia even more attractive. Prior to the commercial use of ammonia for large-scale energy systems, e.g., internal combustion engines and stationary gas turbines, a fundamental understanding of the ignition and combustion behaviors of ammonia is essential. In the past decades, a lot of studies on ammonia combustion have been published. Those studies covered broad topics including experimental and numerical investigations which were either fundamental or practical oriented. To continuously follow state-of-the-art and to provide a brief overview of the most recent research on ammonia combustion so that others can easily identify the most relevant work, this review summarizes the recent progress on combustion characteristics of ammonia and ammonia fuel blends as well as their potential use in internal combustion engines. Combining the advantages and drawbacks identified in both fundamental and practical studies, a clearer road map for ammonia application is given.

TinyML-Based Classification in an ECG Monitoring Embedded System

Recently, the development of the Internet of Things (IoT) has enabled continuous and personal electrocardiogram (ECG) monitoring. In the ECG monitoring system, classification plays an important role because it can select useful data (i.e., reduce the size of the dataset) and identify abnormal data that can be used to detect the clinical diagnosis and guide further treatment. Since the classification requires computing capability, the ECG data are usually delivered to the gateway or the server where the classification is performed based on its computing resource. However, real-time ECG data transmission continuously consumes battery and network resources, which are expensive and limited. To mitigate this problem, this paper proposes a tiny machine learning (TinyML)-based classification (i.e., TinyCES), where the ECG monitoring device performs the classification by itself based on the machine-learning model, which can reduce the memory and the network resource usages for the classification. To demonstrate the feasibility, after we configure the convolutional neural networks (CNN)-based model using ECG data from the Massachusetts Institute of Technology (MIT)-Beth Israel Hospital (BIH) arrhythmia and the Physikalisch Technische Bundesanstalt (PTB) diagnostic ECG databases, TinyCES is validated using the TinyML-supported Arduino prototype. The performance results show that TinyCES can have an approximately 97% detection ratio, which means that it has great potential to be a lightweight and resource-efficient ECG monitoring system.

Application of Convolutional Dendrite Net for Detection of Myocardial Infarction Using ECG Signals

Myocardial infarction (MI) is a sudden-onset medical emergency. Without timely diagnosis and treatment, mortality is very high. The mining of deep features increases the computational burden while improving the accuracy of MI detection. So, there is a trade-off between speed and accuracy. However, considering our own classification process, we are more inclined to integrate the logic between shallow features and deep features. Therefore, this article is dedicated to transforming the high computational cost deep features into low computational cost shallow feature logic, so as to improve the speed without affecting the accuracy. The method proposed in this article mainly includes artifact removal, waveform location, coding, and classification. Hilbert curve completes the coding, which efficiently converts the time-domain features of electrocardiogram (ECG) signals into image features through a double recursive algorithm. Convolutional Dendrite Net (CDD Net) realizes the classification of images by extracting the logical relationship between features. The method is verified by Physikalisch Technische Bundesanstalt (PTB) dataset. The results show that the average detection accuracy and time of one patient are 98.95% and 2.09 ms, respectively.

Copper isotope delta measurements in high purity materials: CCQM-P213 pilot study

Main textAccurate and precise isotope ratio measurements of heavy elements are playing an increasingly important role in modern analytical sciences and have numerous applications. Today, isotope ratio measurements are typically performed with two principal techniques: thermal ionization mass spectrometry (TIMS) and multiple collector-inductively coupled plasma mass spectrometry (MC-ICP-MS). To obtain accurate results by mass spectrometry, isotopic certified reference materials (iCRMs) are needed for mass bias correction and for the validation of the method used for analysis. Thus, it is of paramount importance to achieve measurement comparability of all data reported, and to assess measurement capability of each CRM producer/National Metrology Institute (NMI). Therefore, the international comparison (CCQM-P213) was performed to assess the analytical capabilities of NMIs for the accurate determination of copper isotope ratio delta values in high purity materials. The study was proposed by the coordinating laboratories National Research Council Canada (NRC), National Institute of Standards and Technology (NIST), Bundesanstalt für Materialforschung und -prüfung (BAM) and Physikalisch-Technische Bundesanstalt (PTB) as an activity of the Isotope Ratio Working Group (IRWG) of the Consultative Committee for Amount of Substance - Metrology in Chemistry and Biology (CCQM). Participants included six NMIs and one designated institute (DI) from the six countries. Although no measurement method was prescribed by the coordinating laboratories, MC-ICP-MS with either standard-sample bracketing (SSB) or combined SSB with internal normalization (C-SSBIN) models for mass bias correction were recommended. Results obtained from the six NMIs and one DI were in good agreement.To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/.The final report has been peer-reviewed and approved for publication by the CCQM, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

Neutron-induced charged-particle reaction studies in nuclear astrophysics with a Micromegas based gaseous detector

Neutron-induced charged-particle reaction studies on various unstable nuclei play an important role in understanding various nucleosynthesis processes occurring in explosive astrophysical scenarios. We are pursuing a novel experimental approach to study neutron-induced charge particle cross-sections for various unstable nuclei at effective temperatures of 1.5-3.5 GK using the 7Li(p, n)7Be reaction as a neutron source with three orders of magnitude higher neutron intensities with respect to currently available neutron time-of-flight facilities. We plan to perform our experiments with a 10-µA proton beam at the Physikalisch Technische Bundesanstalt facility (PTB, Germany), with a Micromegas based gaseous detector being developed.

Qualification of Fe0.95Co0.05Si2 as a reference material for high temperature measurement of the thermoelectric power factor

Precise characterization of thermoelectric (TE) materials is mandatory for a meaningful validation of scientific results, focusing on traceable determination of the Seebeck coefficient (S), the electric (σ) and thermal (κ) conductivity. These properties form the thermoelectric figure of merit zT = (S2σ/κ)T, which is used for assessment of TE conversion efficiency with respect to the absolute temperature T. The level of metrological standardization is characterized to date by available reference samples for the Seebeck coefficient, publications on best practice measurement approaches, and accomplished international round robin (RR) campaigns on transport property measurements.We report on the qualification of β-Fe0.95Co0.05Si2 (FeSi2) as the first semiconducting high temperature reference material for the TE power factor (PF = S2σ), which is currently developed by the German Aerospace Center (DLR) and the Physikalisch-Technische Bundesanstalt (PTB). The relation between the size of FeSi2 compacts obtained by current-assisted short time sintering and the resulting functional homogeneity of the material is presented, which is studied by spatially resolved Seebeck coefficient measurements at room temperature. Data from temperature dependent characterization of the PF is provided to assess functional stability and homogeneity under integral measurement conditions. The outcome of certification measurements at PTB report on individual uncertainty contributions as the base for a later testimony of FeSi2 as a metrological reference for the PF. Integral measurements have been expanded to further comparative test results obtained by the Korea Electrotechnology Research Institute (KERI), the Indian Institute of Technology Bombay (IITB), and the Netzsch Gerätebau GmbH. The findings give evidence of an excellent homogeneity, reproducibility and stability with low standard deviations for S and σ far below typical measurement uncertainties, which effectively demonstrates the suitability of FeSi2 as a future reference material for TE metrology at elevated temperatures up to 1000 K.