Source Characteristics Research Articles

Slag Inclusions in Iron Artifacts from Cemeteries at Kichigino I and Krasnaya Gorka, and the Metallurgy of the Early Iron Age Itkul Culture

Silicate slag inclusions in iron artifacts from the Trans-Urals and in iron slags from sites of the Itkul culture were analyzed to assess the geochemical characteristics of iron ore sources exploited during the Early Iron Age. Slag inclusions were found in 19 out of 25 samples from Kichigino I and Krasnaya Gorka. For comparison, we used 12 iron slag samples from Early Iron Age and medieval sites near Lake Irtysh and from Zotino mine. Via statistical analysis, four geochemical groups were separated, each including one or more Kichigino artifacts, which suggests a variety of iron ore sources used by the nomads. Slags and artifacts of the first group are associated with infiltration-sedimentary ironstone ores of the Middle Trans-Urals. Smithing slag from the Itkul site of Shatanov V suggests that these ores were already smelted in the Early Iron Age. The fact that group 1 includes only one artifact from Kichigino I demonstrates that the nomads of the Southern Trans-Urals obtained iron mainly from other sources. Group 2 is characterized by a higher content of Mn and sometimes Ba and S in inclusions. This may attest to the use of Fe-Mn ironstone associated with barite-polymetallic deposits of Central Kazakhstan. Group 3 shows an elevated content of CaO and MgO, indicating the use of ironstone from platform carbonate strata. In the fourth group, the content of K2O is high, and that of MnO, low.

Precise Relocation and Source Characterization of Two Earthquake Sequences in Yellow Sea Using Regional Lg-Wave Observations

ABSTRACT Two significant earthquakes of magnitude ML 4.6 and 4.5 occurred on 18 January and 3 December 2021 in the central region of the Yellow Sea, respectively. The earthquakes occurred beneath the Gunsan sedimentary basin at about 10 km depth with a strike-slip faulting mechanism on nodal planes striking northwest–southeast (NW–SE) and north-northeast–south-southwest (NNE–SSW). Despite a lack of close-by seismographic stations, we successfully utilized regional Lg-wave observations on both coasts of the sea—the Korean peninsula on the east and eastern China on the west. For nine earthquakes in two event sequences, the Lg-wave differential travel times of the nearby event pairs at the common station are carefully measured using the waveform cross-correlation technique. The double-difference earthquake relocation method is employed to obtain precise relative epicentral locations using the Lg correlation measurements. Relocated epicenters align along the NW–SE direction, indicating that the nodal plane striking the same direction is the likely fault plane on which both sequences occurred. This is the first case reported in the literature in which the causative fault plane has been identified for earthquakes in the central Yellow Sea region. It has an important implication for current regional tectonics; it favors neither old tectonic features trending NE–SW (Qianliyan uplift) nor the north–south alignment of significant earthquakes in the region along the Amur plate boundary. The Lg waves from the earthquake sequences are dominant seismic signals on all three-component records at stations in 160–550 km and allowed us to analyze source properties of the two largest earthquakes using the empirical Green’s function approach. Azimuthal variations of the source corner frequencies suggest that earthquake rupture likely propagated toward southeast (125°) along the fault plane, supporting the aftershock relocation results.

Molecular Characterization of a New Bacterial Source of Naringinase as Paenibacillus stellifer RAMCM-44 and Fermentative Production of Naringinase at Shake-Flask Level

Molecular Characterization of a New Bacterial Source of Naringinase as Paenibacillus stellifer RAMCM-44 and Fermentative Production of Naringinase at Shake-Flask Level

Novel methods for the control of blast noise from explosive depth hardening

DNV Spadeadam undertakes crucial major hazards testing to improve safety across a wide range of industries, including energy and infrastructure. One common form of testing is Explosive Depth Hardening (EDH) in which plastic explosives up to 10 kg TNT are used to harden pre-cast railway crossings in order to support national transport infrastructure. The operations produce extremely high intensity noise and are carried out up to 10 times per day. The source characteristics of the explosion are not well understood and vary significantly with operational demands. This has implications on occupational and environmental blast noise impacts. Quantification of the source characteristics are important for the exposures of site personnel with regards to providing adequate hearing protection. Moreover, for the purpose of managing environmental noise impacts on local communities, the uncertainties around source directivity and strength make long-range blast noise prediction difficult. This paper presents the current research on novel blast noise mitigation undertaken in the laboratory, such as experimental water curtains, water sprays, and metamaterial designs. It is proposed to implement novel methods developed in the laboratory for use during EDH shots at full-scale, and the potential improvements to occupational and environmental noise impacts will be discussed.

Towards estimating acoustic surface impedances at low frequencies in a reverberation chamber

To accurately simulate interior acoustical fields, reliable descriptions of the physical properties of the acoustical space are required. While geometry, propagation medium properties, and source characteristics are, in general, easily accessible, the absorption characteristics of the bounding surfaces of a space can be difficult to obtain. For commonly used materials, databases of absorption coefficients could be used. However, absorption coefficient databases are of limited use at low frequencies, especially below 100 Hz. Furthermore, measurement-based estimations tend to be more reliable. Thus, there is incentive to perform measurement-based estimations of the absorption characteristics of a room's bounding surfaces at low frequencies. This work considers the estimation of sound absorption, given in terms of locally-reacting surface impedance, at modal frequencies. This is made feasible by solving an inverse eigenvalue problem. The spatially-uniform, frequency-dependent impedance of the bounding surfaces of a reverberation chamber is estimated. The method is validated by comparing measured transfer functions to simulated transfer functions, which are generated using the estimated impedance. It is shown that the proposed method can be used to estimate locally-reacting surface impedance from measured room impulse responses at low frequencies.

Experience with the high mobility reception plate for source characterization according to EN 15657

EN 12354-5 describes methods for the prediction of noise from technical equipment based on the airborne sound radiation and the structure-borne sound excitation. This paper considers methods to characterize the source in terms of structure-borne-sound (sbs) emission. Laboratory methods for sbs source characterization are given in EN 15657. In current research and development projects at Rosenheim Technical University of Applied Sciences these methods are applied to fully characterize technical equipment such as heat pumps or ventilation systems. This includes the determination of the equivalent blocked force, the source single equivalent free velocity and the source single equivalent mobility. EN 15657 describes various methods to determine the required properties. Recent project experience showed that this is essentially necessary because the most suitable method has to be applied depending on the nature of the technical equipment. This paper presents experiences with indirect measurement methods on the high-mobility plate and the direct measurement method given in EN 15657.

Study on near-field noise control of stationary equipment

The noise of stationary equipment is usually controlled by the sound insulation shield with the mufflers. However, there is not enough space for the installation of the sound insulation shield in certain places or working conditions, so the noise reduction measures are difficult to apply. The noise control measures at the near-field of stationary equipment, especially those infinitely close to the surface of the equipment, can solve the problem of insufficient installation space. At this time, it is necessary to design a structure of noise control measures to ensure that it have sufficient noise reduction performance. In this paper, a noise reduction structure is designed for near-field noise control of stationary equipment. The experimental results show that the structure have a good noise reduction effect in the certain frequency range. The effective noise reduction frequency can be specially designed according to the frequency characteristics of different sound sources. In addition, a modular installation structure was designed.

Probing a planet from the subsurface to the atmosphere with infrasound data

The analysis of infrasound waves has a significant potential for retrieving a range of geophysical parameters across various scales, such as atmospheric structures, characteristics of surface and buried sources, and seismic velocity structures. This potential was recently showcased in infrasound studies that illustrated the ability to retrieve Earth's shallow velocities and Mars' near-surface atmospheric winds from remote acoustic observations. Consequently, infrasound is becoming an essential component of planetary missions, providing insights into the interiors and atmospheres of other terrestrial planets in the solar system. However, utilizing infrasound data requires efficient forward wave simulation techniques and an accurate description of waveform sensitivity to source and medium parameters. Even under ideal conditions, many inverse problems associated with infrasound-based probing are inherently ill-posed, necessitating regularization or other approaches to ensure reliable results. In this contribution, we highlight recent seismo-acoustic research findings, addressing atmospheric probing at both smaller and global scales, as well as innovative methods to accelerate infrasonic wave propagation modeling.

Sound source characteristics of heat exchanger under flow conditions

Heat exchangers are the most important equipment in ship cooling system, which generate vibration and noise due to flow excitation during operation, affecting structural safety and having a significant impact on acoustic stealth performance. Mastering the characteristics of sound source has guiding significance for noise reduction design. The acoustic signal directly obtained from the hydrophone inside the pipe including reflected sound wave, which cannot accurately characterize the sound radiation energy from heat exchanger into pipe. In order to accurately evaluate the sound source characteristics of heat exchanger under flow conditions, experimental method was used to test the sound source characteristics of heat exchanger which are not affected by connected pipelines based on the two-port sound source theory. The main factors affecting the sound source characteristics of heat exchanger were analyzed, such as flow rate, background pressure, and temperature. The research can provide reference for the vibration and noise reduction design of heat exchangers.

Systematic analysis of acoustic source localization maps for performance assessment

Noise pollution is a raising problematic mainly associated to the development of industry, cities, and transportation. In that context, the characterization of noise sources is key point to help the understanding of noise generation mechanisms and to be able to develop effective noise reduction strategies. Microphones phased array measurements can be used as input to solve the inverse problem of source localization allowing to obtain source maps for each frequency of interest. Conventional beamforming (CBF) is the most famous source localization algorithm which uses the interpretation of the propagation delays measured between each array microphones. However, the analysis of output source maps can be made difficult by the low spatial resolution in the low-frequency range and the presence of side-lobes associated with microphones distribution that can lead to additional wrong sources. This paper presents a systematic analysis of source maps to identify potential noise sources by using different methods (local maxima, Metropolis-Hastings). The set of potential noise sources can then be compared to the set of real sources to assess the CBF performances. This methodology is successfully applied to measurements performed in anechoic room for various source configurations highlighting CBF limitations (spatial resolution, correlated sources, reflective environment).

Internal gravity waves in the middle atmosphere and their impact on infrasound propagation across the International Monitoring System

Infrasound technology is used to monitor atmospheric events in order to guarantee compliance with the Comprehensive Nuclear-Test-Ban Treaty (CTBT). Over the 60 infrasound stations initially planned, 53 are operational to date as part of the International Monitoring System (IMS). As opposed to the waveform-related technologies that are used by the IMS to monitor the two other media of the geosphere (the underground and the oceans), infrasound waves are travelling in a continuously varying propagation medium. From minutes to hours and from a few hundreds of meters in the vertical to thousands of kilometers in the horizontal, atmospheric perturbations affect detection capabilities of the IMS at various scales. These perturbations need to be taken into account, for infrasound analysis to be able to provide accurate localization and characterization of sources of interest. In particular, internal gravity waves (atmospheric buoyancy waves) pose a serious challenge to atmospheric modelling because of the unresolved involved processes. They can dramatically affect transmission losses across the globe. Through numerical experiments and using high resolution modelling outputs of the atmosphere, backed up by atmospheric measurements, we demonstrate how gravity waves impact infrasound attenuation and drive signal amplification across the IMS.

An innovative approach for enclosure design of power generators to meet noise regulation targets

The use of diesel-powered engine generators as a power source is very common for various industrial and residential applications. There are strict regulatory requirements and legislations to limit the noise levels from these generators below statutory limits. The purpose of the paper is to present an approach using statistical energy analysis (SEA) augmented by experimental inputs for the prediction of noise levels for the generators and propose strategies for optimized enclosure design that effectively mitigate noise to achieve regulatory requirements. The study begins by developing an SEA model that represents the acoustic behavior of the generator and enclosure. The airborne sources such as engine, alternator, radiator fan and exhaust are modelled explicitly using experimental noise source characterization. Based on the established SEA model and excitation sources, noise levels are predicted through the calculation of energy flow between subsystems. The predicted sound levels for an existing Diesel Generator with enclosure were validated with its test data to ensure correctness of SEA model. Parametric studies were carried out using simulations on key variables, including critical noise paths, panel thickness, and noise control treatments. These investigations helped identify optimal process parameters that reduce noise levels emitted by genset in adherence to noise regulations.

Potential and application of tandem mass spectrometry (MS/MS) in the analysis and identification of novel bacteriocins: a review

SummaryBacteriocins are antimicrobial peptides synthesised ribosomally by Gram‐positive or Gram‐negative bacteria to gain a competitive advantage. The majority of bacteriocins are derived from Gram‐positive bacteria, with lactic acid bacteria being the most common source. Because they are considered ‘natural’, there is currently significant development of bacteriocins for application as food preservative agents. As a preservative agent, bacteriocin activity is highly dependent on purity, down to the amino acid profile and sequence. Therefore, bacteriocin identification is important. Currently, MS is a cutting‐edge tool in bacteriocin identification. This method has high selectivity, sensitivity and resolution. To the best of our knowledge, systematic reviews focusing on the application of MS for bacteriocin identification are currently limited. In light of this, the objective of this study is to provide a comprehensive review and summary of MS technologies in bacteriocin research, with a particular focus on the discovery and characterisation of novel sources of bacteriocin. Additionally, studies related to the discovery of bacteriocins from various sources, their role as antimicrobial agents, and their synthesis are emphasised. Thus, this study presents a comprehensive analysis of the advantages, limitations, and future perspectives of the methods employed.

Investigating device-independent quantum random number generation

Quantum random number generation (QRNG) is a resource that is a necessity in the field of cryptography. However, its certification has been challenging. In this article, we certify randomness with the aid of quantum entanglement in a device independent setting, where we choose two-photon interference for source characterisation. The CHSH inequality violation and quantum state tomography are used as independent checks on the measurement devices. These measures ensure the unpredictability of quantum random number generation. This work can be easily extended to faster randomness expansion protocols.

Ni-In-oxalate nanostructure as electrode materials for high-performance supercapacitors

Energy storage technologies play a crucial role in addressing the intermittent characteristics of renewable energy sources, improving the stability of electrical grids, and decreasing the release of greenhouse gas emissions. In this study, we presented nickel indium oxalate (Ni1-xInxC2O4) as a promising material with potential applications in the field of electrochemical energy storage. The as-prepared Ni-In- oxalate sample was subjected to different physical characterizations, including X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS). Regarding the electrochemical energy storage capability, the Ni1-xInxC2O4 electrode material exhibited a specific capacitance of 835 F g-1 (417.5 C g-1) at 1 A g-1. The incorporation of nickel into the indium oxalate nanoplates enhances their electrochemical performance. The presence of nickel in the nanoplates generated from substrate, improving the overall conductivity of the material and enhances its electrochemical reactions, thus leading to improved energy storage capabilities.

Remaining useful life prediction method for cross- condition tools based on parallel fusion

Abstract The current tool remaining useful life (RUL) prediction models do not fully consider the importance of signals from different sensors and the differences in working conditions, which leads to a decline in the model’s robust performance under new conditions. This paper introduces a novel cross-condition RUL prediction approach that integrates parallel fusion, transfer learning, and deep learning. Considering the characteristics of different data sources, correlated signals are initially selected using correlation analysis and dynamic time warping (DTW). Subsequently, multi-scale Depthwise Separable Convolution and multi-attention mechanisms are combined to capture the importance of features at various scales. Finally, a dynamic domain adaptation technique is employed to adjust the importance weights of conditional distribution discrepancy and marginal distribution discrepancy, enabling the model to adapt to cross-condition scenarios.

Modelling of Glass Soiling Due to Air Pollution Exposure at Urban and National Scales: Coimbra (Portugal) Case Study

Impacts of air pollution are not limited to human health and ecosystems, but are also important for building materials. The main objective of this study is the quantification and mapping of air pollution effects on the materials, namely the soiling effect of modern glass. An integrated modelling approach was implemented to quantify and analyze the spatial distribution of glass soiling due to exposure to air pollution. The methodology is based on an integrated modelling approach (transportation-emissions-dispersion modelling) applied with high spatial resolution for Coimbra (Portugal) urban area and compared with national scale modelling, showing the important contribution of local pollution sources affecting spatial variability in the soiling effect. Air quality data from CAMS (Copernicus Atmosphere Monitoring Service) were used to quantify the soiling effect at national scale. The results are presented and analyzed in terms of haze. The results obtained at national scale suggest that the average time to reach a 1% haze is 320 days, and this time is reduced to 180 days in the most affected areas. However, urban scale modelling applied with a detailed characterization of local pollution sources in Coimbra provides significantly different results and reveals that in the most affected areas, near road traffic, 1% haze could be reached in approximately 80 days. The methodology proposed and implemented in this study provides relevant information for the maintenance and preservation of building materials and highlights the importance of integrated modelling with high spatial resolution for the assessment of the soiling effect in the built environment.

A germanium-vacancy center in diamond as single-photon source for radiometric application

Abstract We present the metrological characterization of a single-photon source based on a germanium-vacancy center in diamond under a solid immersion lens in a confocal microscope setup at room temperature. It was characterized in terms of the emission’s spectral distribution, single-photon purity, temporal stability and the emitter’s excited state lifetime and saturation behavior. An Allan deviation analysis was performed on the emission of the single-photon source to determine the optimal averaging time of the photon flux. The single-photon source was used for the relative calibration of the detection efficiency of two single-photon avalanche diode detectors. The results were compared with measurements using attenuated laser light for the calibration of the detectors.

Self-supervised learning on MeerKAT wide-field continuum images

Context. Self-supervised learning (SSL) applied to natural images has demonstrated a remarkable ability to learn meaningful, low-dimension representations without labels, resulting in models that are adaptable to many different tasks. Until now, applications of SSL to astronomical images have been limited to Galaxy Zoo datasets, which require a significant amount of preprocessing to prepare sparse images centered on a single galaxy. With wide-field survey instruments at the forefront of the Square Kilometer Array (SKA) era, this approach to gathering training data is impractical. Aims. We demonstrate that continuum images from surveys such as the MeerKAT Galactic Cluster Legacy Survey (MGCLS) can be successfully used with SSL, without extracting single-galaxy cutouts. Methods. Using the SSL framework DINO, we experimented with various preprocessing steps, augmentations, and architectures to determine the optimal approach for this data. We trained both ResNet50 and Vision Transformer (ViT) backbones. Results. Our models match state-of-the-art results (trained on Radio Galaxy Zoo) for FRI/FRII morphology classification. Furthermore, they predict the number of compact sources via linear regression with much higher accuracy. Open-source foundation models trained on natural images such as DINOv2 also excel at simple FRI/FRII classification; the advantage of domain-specific backbones is much smaller models trained on far less data. Smaller models are more efficient to fine-tune, and doing so results in a similar performance between our models, the state-of-the-art, and open-source models on multi-class morphology classification. Conclusions. Using source-rich crops from wide-field images to train multi-purpose models is an easily scalable approach that significantly reduces data preparation time. For the tasks evaluated in this work, twenty thousand crops is sufficient training data for models that produce results similar to state-of-the-art. In the future, complex tasks like source detection and characterization, together with domain-specific tasks, ought to demonstrate the true advantages of training models with radio astronomy data over natural-image foundation models.

YOLO-CIANNA: Galaxy detection with deep learning in radio data

Context. The upcoming Square Kilometer Array (SKA) will set a new standard regarding data volume generated by an astronomical instrument, which is likely to challenge widely adopted data-analysis tools that scale inadequately with the data size. Aims. The aim of this study is to develop a new source detection and characterization method for massive radio astronomical datasets based on modern deep-learning object detection techniques. For this, we seek to identify the specific strengths and weaknesses of this type of approach when applied to astronomical data. Methods. We introduce YOLO-CIANNA, a highly customized deep-learning object detector designed specifically for astronomical datasets. In this paper, we present the method and describe all the elements introduced to address the specific challenges of radio astronomical images. We then demonstrate the capabilities of this method by applying it to simulated 2D continuum images from the SKA observatory Science Data Challenge 1 (SDC1) dataset. Results. Using the SDC1 metric, we improve the challenge-winning score by +139% and the score of the only other post-challenge participation by +61%. Our catalog has a detection purity of 94% while detecting 40–60% more sources than previous top-score results, and exhibits strong characterization accuracy. The trained model can also be forced to reach 99% purity in post-process and still detect 10–30% more sources than the other top-score methods. It is also computationally efficient, with a peak prediction speed of 500 images of 512×512 pixels per second on a single GPU. Conclusions. YOLO-CIANNA achieves state-of-the-art detection and characterization results on the simulated SDC1 dataset and is expected to transfer well to observational data from SKA precursors.