Types Of Artifacts Research Articles

RAD-IQMRI: A benchmark for MRI image quality assessment

Magnetic resonance imaging (MRI) is susceptible to visual artifacts that can degrade the perceptual image quality, potentially leading to inaccurate or inefficient diagnoses in clinical practice. It is critical to evaluate the perceptual image quality and build this technique into clinical solutions. In a previous study, an MRI database was created for image quality assessment (IQA), where various types of MRI artifacts with different degrees of degradation were simulated. Application specialists assessed the image quality; however, radiologists’ perception of MRI image quality remains unknown. To make IQA clinically relevant, in this paper we conduct a new subjective experiment where 13 radiologists rated the quality of images contained in the MRI database. Based on this subjective IQA benchmark named RAD-IQMRI, we evaluate the performance of state-of-the-art objective IQA models, providing insights into their application for MRI image quality assessment in clinical settings.

Analysis and identification of ancient glass artifacts based on component data

This study explores the classification of glass artifacts based on their chemical composition. Using the stratified chi-square test method, the correlation between the chemical characteristics of glass artifacts and their susceptibility to weathering was analyzed. A decision tree model was constructed to predict and analyze the types of glass artifacts. To further study the types of glass artifacts, factor analysis was employed to reduce the dimensionality of the data, simplifying the data structure. Based on the reduced data, the K-means clustering algorithm was used to further classify the glass artifacts into three subcategories.

A community of practice approach to the management of metal resources, metalworking and hoarding in Bronze Age societies

The burial of metals in hoards is a trademark phenomenon of prehistoric Europe that may be counterintuitive to perceptions of value nowadays. For the first time here, we establish detailed biographies of a large corpus of hoarded metal objects, providing new insights into how societies in the second millennium BC engaged with their convertible material wealth. We move beyond previous research on prehistoric hoarding commonly focussing on separate questions such as what was placed in hoards, who selected the objects, what were the origins of materials, and where and when they were buried. Analysing ca. 200 metal tools and weapons, we use data reduction methods to define technological pathways in the long biographies of hoarded objects extending across the sourcing of materials, production, use, decommissioning, and deposition in the Carpathian Basin. We show how the differential treatment of materials and objects was strongly biased by social decisions across artefact types. We identify shared, standardised signature treatments that crossed over social-spatial boundaries. Our findings bring new insights on the interface between communal and elite wealth management at the intersection of technological reasoning and cultural beliefs in prehistoric communities.

Combining traceological analysis and ZooMS on Early Neolithic bone artefacts from the cave of Coro Trasito, NE Iberian Peninsula: Cervidae used equally to Caprinae.

Few studies have combined the analysis of use-wear traces, traceology, and the proteomic taxonomic identification method Zooarchaeology by Mass Spectrometry (ZooMS). Traceology provides information on the usage, in this case, of bone artefacts, while ZooMS allows for taxonomic identifications where diagnostic features are otherwise gone. The approaches therefore offer complementary information on bone artefacts, allowing for insights into species selection strategies in bone tool manufacture and their subsequent use. Here we present a case study of 20 bone artefacts, mainly bone points, from the Early Neolithic cave site of Coro Trasito located on the southern slope of the Central Pyrenees. Hitherto, studies on Early Neolithic bone artefacts from the Iberian Peninsula have suggested based on morphological assessments that Ovis aries/Capra hircus constituted the majority of the bone material selected for bone tool production. However, the taxonomic identification in this study suggests that, at this site, Cervidae was selected equally to that of O. aries/C. hircus. Furthermore, bone artefacts made from Cervidae specimens seem to be utilised in a wider range of artefact types compared to O. aries/C. hircus. Coro Trasito's bone artefact species composition is probably site-specific to some degree, however, morphological assessments of bone artefacts might not be representative and could be biased towards certain species. Therefore, research on bone artefacts' usage could possibly gain new insights by implementing ZooMS in combination with traceology.

What you see is what you trace: a two-stage interview study on traceability practices and eye tracking potential

The benefits of traceability have widely been discussed in research. However, studies have also shown that traceability practices are still not prevalent in industrial settings due to the high manual effort and lack of tool support. In this paper, we explore the feasibility of using eye tracking to automatically detect trace links to reduce manual effort and thereby increase practical applicability. We conducted a two-stage interview study in industry. In Stage 1 we interviewed 20 practitioners to provide an overview of how traceability is established in practice and how an eye tracking approach would need to be applied in order to be useful. In Stage 2 we conducted interviews with 16 practitioners from one project context to elicit role-specific workflows and analyzed which activities are suitable to obtain useful traceability links based on gaze data. As there is no one-fits-all solution to traceability, and technical limitations of eye tracking still exist, we collected information on used artifact types, tools and requirements management practices to adjust an approach to actual traceability stakeholders’ needs. We report on perspectives from different roles in software projects and give an overview of traced artifacts, current traceability experiences, as well as benefits and doubts concerned with using eye tracking to obtain links automatically. We discuss the implications for the evaluation and implementation of an automatic tracing approach in practice and how eye tracking can support requirements engineering activities.

Intan Technologies integrated circuits can produce analog-to-digital conversion artifacts that affect neural signal acquisition

Objective. Intan Technologies’ integrated circuits (ICs) are valuable tools for neurophysiological data acquisition, providing signal amplification, filtering, and digitization from many channels (up to 64 channels/chip) at high sampling rates (up to 30 kSPS) within a compact package (⩽9× 7 mm). However, we found that the analog-to-digital converters (ADCs) in the Intan RHD2000 series ICs can produce artifacts in recorded signals. Here, we examine the effects of these ADC artifacts on neural signal quality and describe a method to detect them in recorded data. Approach. We identified two types of ADC artifacts produced by Intan ICs: 1) jumps, resulting from missing output codes, and 2) flatlines, resulting from overrepresented output codes. We identified ADC artifacts in neural recordings acquired with Intan RHD2000 ICs and tested the repeated performance of 17 ICs in vitro. With the on-chip digital-signal-processing disabled, we detected the ADC artifacts in each test recording by examining the distribution of unfiltered ADC output codes. Main Results. We found larger ADC artifacts in recordings using the Intan RHX data acquisition software versions 3.0–3.2, which did not run the necessary ADC calibration command when the inputs to the Intan recording controller were rescanned. This has been corrected in the Intan RHX software version 3.3. We found that the ADC calibration routine significantly reduced, but did not fully eliminate, the occurrence and size of ADC artifacts as compared with recordings acquired when the calibration routine was not run (p < 0.0001). When the ADC calibration routine was run, we found that the artifacts produced by each ADC were consistent over time, enabling us to sort ICs by performance. Significance. Our findings call attention to the importance of evaluating signal quality when acquiring electrophysiological data using Intan Technologies ICs and offer a method for detecting ADC artifacts in recorded data.

Isolation and Identification of Carbonoclastic Fungi Causing Damage to Prehistoric Paintings in the Maros-Pangkep Karst Area

The carbonoclastic fungi known as a fungi that can produce calcium carbonate crystals (CaCO3) through the urease enzyme produced to hydrolysis of urea contained in the substrate or growth medium. The presence of carbonoclastic fungi is frequently observed in the karst areas cave wall painting, which are a type of ancient artifact. The destruction to the ancient cave artwork is brought on by the fungus. The purpose of this study was to obtain and determine the types of carbonoclastic fungi that cause damage to cave wall paintings in the Maros-Pangkep karst area. Isolation and selection of carbonoclastic fungi were carried out using Christensen Urea Agar medium. The CaCO3 precipitate potential test was carried out by calculating the mass CaCO3 precipitates formed and analysis of ammonia levels and cell biomass produced during the growth period. Fungi identification was performed using the 18S rRNA gene molecular markers. Twenty four fungi isolates obtained from swab samples in Parewe and Bulu Sipong caves, 7 positive isolated belonged to carbonoclastic fungi. The results of CaCO3 precipitates, namely isolate Ps3 producing precipitates of 80,30 mg with ammonia content value of 701,7064 ppm and cell biomass of 333,80 mg. The identification results showed that Ps3 isolate belongs to Aspergillus sp. strain BW1.

Different tools with the same functionality at Măgura-Buduiasca Neolithic settlement (Teleorman County, southern Romania)

At the Neolithic settlements from the northern Danube, three types of artefacts are constantly present in archaeological contexts: spatulas made from cattle ribs, flattened sheep/goats astragali and Unio sp. shells with traces of use. These are very different types of items, in terms of raw material and in degree of their processing. Thus, while spatulas were obtained by major transformations of ribs, the astragali suffered only moderate changes thus allowing the identification of bone type and species. The shells had no technological changes to their natural morphology. The main purpose of our analysis was to reconstruct the way in which these tools were technologically created and how they were used. For this purpose, an experimental program has been developed that allows the recording of all details: raw materials, operational sequence and processed materials. The experimental pieces were used and the use-wear evolution pattern was systematically analyzed. Comparing the use-wear marks present on the archaeological pieces with those on the experimental pieces, we conclude that such tools were used during different stages of pottery manufacturing. The archaeological assemblage from the Măgura-Buduiasca settlement was used as a case study to prove the relevance of the results.

CBCT Artifact Evaluation in a Single Device: Insights and Limitations

Objective: To classify the types of artifacts in cone-beam computed tomography (CBCT) and to evaluate them according to age and gender. Methods: CBCT images of 1500 patients (766 males and 734 females) aged 5-92 (mean age: 40.89 ± 18.82 years) were retrospectively evaluated and the patients were categorized into 4 age groups: under 20 years old, 20-39 ages, 40-59 and over 60 years old. The types of artifacts encountered in CBCT images were classified. The relationship between the artifact types with age and gender were investigated. Chi-square test was applied to analyze the relationships between variables and distribution of parameters. Results: Of the cases, 284 (18.9%) were under the age of 20, 389 (25.9%) were between the ages of 20-39, 554 (36.9%) were between the ages of 40-59 and 273 (18.2%) were over the age of 60. Moire artifact was observed at the highest rate (100%), while motion artifact was determined at the lowest rate (19.5%), and no ring artifact was detected in the analyzed images. Metallic artifact, metallic artifact removal, streak artifact and presence of dark bands were found to be statistically significant in females (p = .002, p = .001, p = .002 and p = .002, respectively). There was no statistically significant correlation between cupping artifact, metallic artifact, metallic artifact removal, streak artifact, dark band and noise, and stitched artifact (p &gt; .05). Conclusion: Both device and patient-based artifacts in CBCT images should be known, as well as the ways to prevent them.

Research on the classification of ancient silicate glass artifacts based on machine learning

AbstractClassifying cultural relics has always been a major challenge for archaeologists. Using glass artifacts as the research object, a classification model for glass artifacts was constructed using decision trees, support vector machines, and logistic regression methods based on their patterns, colors, surface weathering conditions, types, and composition ratios. Three models were used to identify the types of unknown glass artifacts. A subclassification model for high‐potassium glass and lead barium glass was established using the K‐means clustering method. The elbow method and average contour method were used to determine the optimal number of clusters, and the decision tree model was named based on the characteristics of the cluster center components. The research results indicate that the three models yield consistent identification results for unknown types of glass relics, and the classification results are good. Lead barium glass and high‐potassium glass can be divided into three and six subclasses, respectively, and the naming of the subclass decision tree is reasonable. The identification method for ancient glass relics in this article is highly practical and can provide a reference for the classification and identification of other component data.

Automated MRI Quality Assessment of Brain T1-weighted MRI in Clinical Data Warehouses: A Transfer Learning Approach Relying on Artefact Simulation

The emergence of clinical data warehouses (CDWs), which contain the medical data of millions of patients, has paved the way for vast data sharing for research. The quality of MRIs gathered in CDWs differs greatly from what is observed in research settings and reflects a certain clinical reality. Consequently, a significant proportion of these images turns out to be unusable due to their poor quality. Given the massive volume of MRIs contained in CDWs, the manual rating of image quality is impossible. Thus, it is necessary to develop an automated solution capable of effectively identifying corrupted images in CDWs. This study presents an innovative transfer learning method for automated quality con- trol of 3D gradient echo T1-weighted brain MRIs within a CDW, leveraging artefact sim- ulation. We first intentionally corrupt images from research datasets by inducing poorer contrast, adding noise and introducing motion artefacts. Subsequently, three artefact- specific models are pre-trained using these corrupted images to detect distinct types of artefacts. Finally, the models are generalised to routine clinical data through a transfer learning technique, utilising 3660 manually annotated images. The overall image quality is inferred from the results of the three models, each designed to detect a specific type of artefact. Our method was validated on an independent test set of 385 3D gradient echo T1-weighted MRIs. Our proposed approach achieved excellent results for the detection of bad quality MRIs, with a balanced accuracy of over 87%, surpassing our previous approach by 3.5 percent points. Additionally, we achieved a satisfactory balanced accuracy of 79% for the detection of moderate quality MRIs, outperforming our previous performance by 5 percent points. Our framework provides a valuable tool for exploiting the potential of MRIs in CDWs.

Deep Learning Realizes Photoacoustic Imaging Artifact Removal

Photoacoustic imaging integrates the strengths of optics and ultrasound, offering high resolution, depth penetration, and multimodal imaging capabilities. Practical considerations with instrumentation and geometry limit the number of available acoustic sensors and their “view” of the imaging target, which result in image reconstruction artifacts degrading image quality. To address this problem, YOLOv8-Pix2Pix is proposed as a hybrid artifact-removal algorithm, which is advantageous in comprehensively eliminating various types of artifacts and effectively restoring image details compared to existing algorithms. The proposed algorithm demonstrates superior performance in artifact removal and segmentation of photoacoustic images of brain tumors. For the purpose of further expanding its application fields and aligning with actual clinical needs, an experimental system for photoacoustic detection is designed in this paper to be verified. The experimental results show that the processed images are better than the pre-processed images in terms of reconstruction metrics PSNR and SSIM, and also the segmentation performance is significantly improved, which provides an effective solution for the further development of photoacoustic imaging technology.

4D thermo-reflectography of cultural heritage. The Codex 4D project: From data acquisition to the implementation in innovative virtual and mixed reality platforms

In an effort to try and improve the dissemination of cultural heritage, in this paper a novel method for the four-dimensional (4D) digitization of artefacts, based on images recorded in the mid-wave infrared spectral range, is presented. Such a method is here applied to ancient manuscripts in the frame of the “Codex 4D: journey in four dimensions into the manuscript” project. In the proposed approach, the three-dimensional geometry of the manuscript is reconstructed by processing the reflectographic images, obtained from different points of view with respect to the artefact, through Structure from Motion techniques. Thermograms obtained by means of Pulsed Thermography are also recorded since they provide a depth-resolved characterization of the artefact that is also integrated into the digital reconstruction along the orthogonal direction to the surface, hereafter referred to as the fourth dimension. The results gathered from humanities and scientific studies, are also mapped onto the 4D model in the form of interactive semantic annotations. The goal of such a reconstruction is to allow users to browse the subsurface elements into the 4D model, thus facilitating the study and the exploration of the manuscripts through the inclusion of information about their literary and art-historical contents, materials, execution techniques, and state of conservation. In addition, virtual and mixed reality environments have been developed for different kind of audience such as expert users and museum public. Despite the experimentation presented here was carried out exclusively on manuscripts, in our opinion the methodology can be successfully applied to other types of artefacts.

SpotSweeper: spatially-aware quality control for spatial transcriptomics.

Quality control (QC) is a crucial step to ensure the reliability and accuracy of the data obtained from RNA sequencing experiments, including spatially-resolved transcriptomics (SRT). Existing QC approaches for SRT that have been adopted from single-nucleus RNA sequencing (snRNA-seq) methods are confounded by spatial biology and are inappropriate for SRT data. In addition, no methods currently exist for identifying histological tissue artifacts unique to SRT. Here, we introduce SpotSweeper, spatially-aware QC methods for identifying local outliers and regional artifacts in SRT. SpotSweeper evaluates the quality of individual spots relative to their local neighborhood, thus minimizing bias due to biological heterogeneity, and uses multiscale methods to detect regional artifacts. Using SpotSweeper on publicly available data, we identified a consistent set of Visium barcodes/spots as systematically low quality and demonstrate that SpotSweeper accurately identifies two distinct types of regional artifacts, resulting in improved downstream clustering and marker gene detection for spatial domains.

Green Alternatives for Archaeological Iron Stabilization

ABSTRACT One of the most challenging types of artifact occurring within museum collections is unstable chloride-contaminated archaeological iron. A high chloride concentration causes cracking, flaking and leads to full mineralization, in effect making objects fragile. Consequently, removal of chloride ions plays a key role in stabilization treatment, while preserving the integrity of the corroded iron object. Despite the variety of stabilization methods, all have significant disadvantages, including a lack of sustainability. Within the framework of the Horizon Europe project GoGreen the potential of microbial biosorption to stabilize archaeological iron artefacts is being investigated. Dry biomass of the fungi Meyerozyma sp. and Saccharomyces cerevisiae was studied to remove chloride ions. Preliminary tests were carried out on artificially-aged steel samples. To assess the activities of the microorganisms’ functional groups and biosorption capabilities as a potential green stabilization treatment, analytical techniques including FTIR, Raman spectroscopy, and SEM-EDX were used. The results demonstrate two promising paths for the development of green stabilization treatments based on fungal biomass: passive adsorption into the cell wall and conversion of reactive corrosion products into more stable compounds. The use of microbial biomass opens up promising perspectives for the development of more sustainable solutions in archaeological iron stabilization, while avoiding the generation of toxic waste in our environment.

Home-Based EEG Hyperscanning for Infant-Caregiver Social Interactions.

Prior hyperscanning studies that record the brain activities of caregivers and children concurrently have primarily been conducted within the confines of the laboratory, thus limiting the generalizability of results to real-life settings. Here, a comprehensive protocol for capturing synchronized electroencephalography (EEG), electrocardiography (ECG), and behavioral recordings from infant-caregiver dyads during various interactive tasks at home is proposed. This protocol demonstrates how to synchronize the different data streams and report EEG data retention rates and quality checks. Additionally, critical issues and possible solutions with respect to the experimental setup, tasks, and data collection in home settings are discussed. The protocol is not limited to infant-caregiver dyads but can be applied to various dyadic constellations. Overall, we demonstrate the flexibility of EEG hyperscanning setups, which allow experiments to be conducted outside of the laboratory to capture participants' brain activities in more ecologically valid environmental settings. Yet, movement and other types of artifacts still constrain the experimental tasks that can be performed in the home setting.

Methods for Processing Physiological Artifacts in Single/Few-Channel EEG Signals

Electroencephalogram (EEG) is a non-invasive measurement method of brain electrical activity. In recent years, single/few-channel EEG has been used more and more, but various types of physiological artifacts seriously affect the analysis and wide application of single/few-channel EEG. In this paper, the regression and filtering methods, decomposition methods, blind source separation methods and machine learning methods involved in the various physiological artifacts in single/few-channel EEG are reviewed. According to the characteristics of single/few-channel EEG signals, hybrid EEG artifact removal methods for different scenarios are analyzed and summarized, mainly including single-artifact/multi-artifact scenes and online/offline scenes. In addition, the methods and metrics for validating the performance of the algorithm on semi-simulated and real EEG data are also reviewed. Finally, the development trend of single/few-channel EEG application and physiological artifact processing is briefly described.

Dividing time-An absolute chronological study of material culture from Early Iron Age urnfields in Denmark.

Chronological frameworks based on artefact typologies are essential for interpreting the archaeological record, but they inadvertently treat transitions between phases as abrupt events and disregard the temporality of transformation processes within and between individual phases. This study presents an absolute chronological investigation of a dynamic material culture from Early Iron Age urnfields in Denmark. The chronological framework of Early Iron Age in Southern Scandinavia is largely unconstrained by absolute dating, primarily due to it coinciding with the so-called 'Hallstatt calibration plateau' (c.750 to 400 cal BC), and it is difficult to correlate it with Central European chronologies due to a lack of imported artefacts. This study applies recent methodological advances in radiocarbon dating and Bayesian chronological modelling, specifically a statistical model for wood-age offsets in cremated bone and presents the first large-scale radiocarbon investigation of regional material culture from Early Iron Age in Southern Jutland, Denmark. Dated material is primarily cremated bone from 111 cremation burials from three urnfields. The study presents absolute date ranges for 16 types of pottery and 15 types of metalwork, which include most of the recognised metalwork types from the period. This provides new insights into gradual change in material culture, when certain artefact types were in production and primary use, how quickly types were taken up and later abandoned, and distinguishing periods of faster and slower change. The study also provides the first absolute chronology for the period, enabling correlation with chronologies from other regions. Urnfields were introduced at the Bronze-Iron Age transformation, which is often assumed to have occurred c.530-500 BC. We demonstrate that this transformation took place in the 7th century BC, however, which revives the discussion of whether the final Bronze Age period VI should be interpreted as a transitional phase to the Iron Age.

Pathways for Design Research on Artificial Intelligence

An expanding body of information systems research is adopting a design perspective on artificial intelligence (AI), wherein researchers prescribe solutions to problems using AI approaches rather than describing or explaining AI-related phenomena being studied. In this editorial, we address some of the challenges faced in publishing design research related to AI and articulate viable pathways for publishing such work. More specifically, we highlight six major impediments, use the explosion in the state of the art for large language models to underscore these impediments, propose some pathways for overcoming the impediments, and use several example articles to illustrate how the pathways can be followed for different types of AI-related design artifacts. History: Senior Editor; Associate Editor. Funding: A. Abbasi was funded by the National Science Foundation (NSF) [Grants 2240347 and IIS-2039915] and a Kemper Faculty Award. J. Parsons was funded by the Natural Sciences and Engineering Council of Canada (NSERC) [Grant RGPIN-2020-04916].

Research on Classification and Identification of Glass Relics Based on Pearson Clustering and Machine Learning Models

Ancient glass is very susceptible to weathering due to the influence of buried environments. In order to classify and identify glass artifacts, this article uses the Pearson cluster analysis model to classify them into four types of high potassium and three types of lead and barium. The chemical cost composition of the classification types is determined, and then the composition of each chemical component is accumulated and arranged. The proportion of each type's content is used as the basis for subclassing. Finally, multiple machine learning methods were used to identify unknown types of glass artifacts, and the results showed that the accuracy of the support vector machine model reached 100%, with a relative improvement of 2% -11%. Therefore, the support vector machine model was considered for classification.