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Rapid advancements in the sophistication and diffusion of advanced digital technologies such as AI warrant repose to consider their unintended consequences or ‘dark sides’. While more attention has been directed towards the ethical implications of disruptive technologies, discussions on the underlying materiality of the digital artefacts are often missing. In this article, we call for IS researchers to better conceptualise how technical objects contribute towards the emergence of negative outcomes for users, either intentionally or unintentionally. Examples are provided of conceptual and empirical papers that have sought to open the ‘black box’ of technology to elucidate this issue. We propose sociomateriality as a theoretical lens to guide studies in this area and present a future research agenda that encourages novel methodological approaches such as design science to uncover the dark side of emerging digital artefacts.

Artefacts play a vital role in software and systems development processes. Other terms like documents, deliverables, or work products are widely used in software development communities instead of the term artefact. In the following, we use the term ‘artefact’ including all these other terms. Despite its relevance, the exact denotation of the term ‘artefact’ is still not clear due to a variety of different understandings of the term and to a careless negligent usage. This often leads to approaches being grounded in a fuzzy, unclear understanding of the essential concepts involved. In fact, there does not exist a common terminology. Therefore, it is our goal that the term artefact be standardised so that researchers and practitioners have a common understanding for discussions and contributions. In this position paper, we provide a positioning and critical reflection upon the notion of artefacts in software engineering at different levels of perception and how these relate to each other. We further contribute a metamodel that provides a description of an artefact that is independent from any underlying process model. This metamodel defines artefacts at three levels. Abstraction and refinement relations between these levels allow correlating artefacts to each other and defining the notion of related, refined, and equivalent artefacts. Our contribution shall foster the long overdue and too often underestimated terminological discussion on what artefacts are to provide a common ground with clearer concepts and principles for future software engineering contributions, such as the design of artefact-oriented development processes and tools.

Estimates of functional connectivity derived from resting-state functional magnetic resonance imaging (rs-fMRI) are sensitive to artefacts caused by in-scanner head motion. This susceptibility has motivated the development of numerous denoising methods designed to mitigate motion-related artefacts. Here, we compare popular retrospective rs-fMRI denoising methods, such as regression of head motion parameters and mean white matter (WM) and cerebrospinal fluid (CSF) (with and without expansion terms), aCompCor, volume censoring (e.g., scrubbing and spike regression), global signal regression and ICA-AROMA, combined into 19 different pipelines. These pipelines were evaluated across five different quality control benchmarks in four independent datasets associated with varying levels of motion. Pipelines were benchmarked by examining the residual relationship between in-scanner movement and functional connectivity after denoising; the effect of distance on this residual relationship; whole-brain differences in functional connectivity between high- and low-motion healthy controls (HC); the temporal degrees of freedom lost during denoising; and the test-retest reliability of functional connectivity estimates. We also compared the sensitivity of each pipeline to clinical differences in functional connectivity in independent samples of people with schizophrenia and obsessive-compulsive disorder. Our results indicate that (1) simple linear regression of regional fMRI time series against head motion parameters and WM/CSF signals (with or without expansion terms) is not sufficient to remove head motion artefacts; (2) aCompCor pipelines may only be viable in low-motion data; (3) volume censoring performs well at minimising motion-related artefact but a major benefit of this approach derives from the exclusion of high-motion individuals; (4) while not as effective as volume censoring, ICA-AROMA performed well across our benchmarks for relatively low cost in terms of data loss; (5) the addition of global signal regression improved the performance of nearly all pipelines on most benchmarks, but exacerbated the distance-dependence of correlations between motion and functional connectivity; and (6) group comparisons in functional connectivity between healthy controls and schizophrenia patients are highly dependent on preprocessing strategy. We offer some recommendations for best practice and outline simple analyses to facilitate transparent reporting of the degree to which a given set of findings may be affected by motion-related artefact. [Display omitted] •We examine 19 denoising pipelines for resting-state fMRI across 4 datasets.•No single method offers perfect motion control.•Censoring and ICA-AROMA pipelines perform well across most benchmarks.•Pipeline choice impacts case-control differences in functional connectivity.

Objectives: To investigate the knowledge and awareness of ultrasound practitioners’ concerning ultrasound artefacts in evaluating the hepatobiliary system. Methods: This electronic questionnaire-based comparative study involved the ultrasound practitioners’ who work in the radiology departments in Almadinah Almunawwarah governmental hospitals during the period from 1 November 2020 to 30 April 2021. Spearman’s rho correlation test was used to correlate between knowledge and job, academic qualification, and years of experience. A T-test and cross tabulation test were done to compare the knowledge about artefacts among radiologists and radiologic technologists. Results: This study involved 94 participants distributed as 22 (23.4%) radiologists and 72 (76.6%) radiologic technologists. The results shows that 85%, 71%, 73%, 69%, 54% and 53% of the participants assigned the acoustic shadowing, acoustic enhancement, ring down, side lobe, reverberation and mirror artefacts, as artefacts respectively. However, 68%, 53%, 19%, 19%, 18%, and 40% of the participants gave correct final diagnosis of acoustic shadowing, acoustic enhancement, ring down, side lobes, reverberation, and mirror artifacts, respectively. Spearman’s rho correlation test shows significant correlation between participants with more than three years experience and knowledge related mirror artefacts (r=0.328, p=0.001). It shows significant correlation between radiologists with knowledge related mirror artefacts (r=0.367, p<0.001). A significant correlation was found between highly qualified participants and knowledge related mirror artefacts (r=0.336, p=0.001) and side lobe artefacts (r=0.237, p=0.008). Conclusion: The questionnaire-based comparative study of knowledge about artefacts of hepatobiliary ultrasound imaging reveals a high level of Ultrasound practitioners’ knowledge in differentiating artefacts from pathology with a high level of knowledge in identifying hepatobiliary acoustic shadowing and acoustic enhancement artefacts. However, insufficient knowledge was noted in identifying mirror, side lobe, reverberation and ring down artefacts. A direct link was found between academic qualification, years of experience and practioners’ knowledge among. doi: https://doi.org/10.12669/pjms.38.6.5084 How to cite this:Alsaedi HI, Krsoom AM, Alshoabi SA, Alsharif WM. Investigation Study of Ultrasound Practitioners’ Awareness about Artefacts of Hepatobiliary Imaging in Almadinah Almunawwarah. Pak J Med Sci. 2022;38(6):1526-1533.  doi: https://doi.org/10.12669/pjms.38.6.5084 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Large artefacts compromise EEG data quality during simultaneous fMRI. These artefact voltages pose heavy demands on the bandwidth and dynamic range of EEG amplifiers and mean that even small fractional variations in the artefact voltages give rise to significant residual artefacts after average artefact subtraction. Any intrinsic reduction in the magnitude of the artefacts would be highly advantageous, allowing data with a higher bandwidth to be acquired without amplifier saturation, as well as reducing the residual artefacts that can easily swamp signals from brain activity measured using current methods. Since these problems currently limit the utility of simultaneous EEG–fMRI, new approaches for reducing the magnitude and variability of the artefacts are required. One such approach is the use of an EEG cap that incorporates electrodes embedded in a reference layer that has similar conductivity to tissue and is electrically isolated from the scalp. With this arrangement, the artefact voltages produced on the reference layer leads by time-varying field gradients, cardiac pulsation and subject movement are similar to those induced in the scalp leads, but neuronal signals are not detected in the reference layer. Taking the difference of the voltages in the reference and scalp channels will therefore reduce the artefacts, without affecting sensitivity to neuronal signals. Here, we test this approach by using a simple experimental realisation of the reference layer to investigate the artefacts induced on the leads attached to the reference layer and scalp and to evaluate the degree of artefact attenuation that can be achieved via reference layer artefact subtraction (RLAS). Through a series of experiments on phantoms and human subjects, we show that RLAS significantly reduces the gradient (GA), pulse (PA) and motion (MA) artefacts, while allowing accurate recording of neuronal signals. The results indicate that RLAS generally outperforms AAS when motion is present in the removal of the GA and PA, while the combination of AAS and RLAS always produces higher artefact attenuation than AAS. Additionally, we demonstrate that RLAS greatly attenuates the unpredictable and highly variable MAs that are very hard to remove using post-processing methods. •The efficacy of RLAS was compared with standard EEG artefact removal methods.•RLAS significantly reduces the major EEG artefacts, but retains neuronal signals.•RLAS significantly attenuates the unpredictable motion artefact from the EEG data.•RLAS generally out-performs standard post-processing correction methods.•RLAS and post-processing methods combined provide the highest data quality.

The central message of this paper is that in the 20 th century there was a revolution in the way science was presented. It became most visible in a significant change in the kinds of objects used to explain science to a wider public. For centuries the objects came from the academy, university, laboratory or industry. Either directly as artefacts or as modifications and simplifications that might work better in lectures and demonstrations, they inhabited the display cases in schools, universities, science collections and museums. It was not until the 1960s in North America that science museums – or rather science centres, as they soon became known – began to build their own exhibits in such a way as to present scientific phenomena as vividly as possible. How did this turn from artefacts to ‘edufacts’ come about and what implications did it have?

A quantized quadrupole topological insulator composed of capacitively coupled microwave resonators has corner states that are protected by bulk topology and exhibit exceptional robustness against edge deformation. Topological corner states The properties of many materials with topological band structures can be understood in terms of a quantization of the electric polarization. A new class of higher-order topological insulators has recently been predicted by considering a quantization of higher-order polarizations. Christopher Peterson et al. now use a metamaterial composed of coupled microwave resonators to demonstrate such a system experimentally: a quantized quadrupole topological insulator that has corner states that are protected by topology. By deforming one of the edges from the topological to the trivial regime, they show that these corner states move inward to the corners of the newly generated boundaries, confirming that they are protected by the topology of the bulk. Such demonstrations not only provide evidence of a unique form of robustness, but also show that reconfigurable microwave circuits are a promising platform for exploring exotic topological phases of matter. The theory of electric polarization in crystals defines the dipole moment of an insulator in terms of a Berry phase (geometric phase) associated with its electronic ground state 1 , 2 . This concept not only solves the long-standing puzzle of how to calculate dipole moments in crystals, but also explains topological band structures in insulators and superconductors, including the quantum anomalous Hall insulator 3 , 4 and the quantum spin Hall insulator 5 , 6 , 7 , as well as quantized adiabatic pumping processes 8 , 9 , 10 . A recent theoretical study has extended the Berry phase framework to also account for higher electric multipole moments 11 , revealing the existence of higher-order topological phases that have not previously been observed. Here we demonstrate experimentally a member of this predicted class of materials—a quantized quadrupole topological insulator—produced using a gigahertz-frequency reconfigurable microwave circuit. We confirm the non-trivial topological phase using spectroscopic measurements and by identifying corner states that result from the bulk topology. In addition, we test the critical prediction that these corner states are protected by the topology of the bulk, and are not due to surface artefacts, by deforming the edges of the crystal lattice from the topological to the trivial regime. Our results provide conclusive evidence of a unique form of robustness against disorder and deformation, which is characteristic of higher-order topological insulators.

The electrochemical synthesis of ammonia from nitrogen under mild conditions using renewable electricity is an attractive alternative 1 – 4 to the energy-intensive Haber–Bosch process, which dominates industrial ammonia production. However, there are considerable scientific and technical challenges 5 , 6 facing the electrochemical alternative, and most experimental studies reported so far have achieved only low selectivities and conversions. The amount of ammonia produced is usually so small that it cannot be firmly attributed to electrochemical nitrogen fixation 7 – 9 rather than contamination from ammonia that is either present in air, human breath or ion-conducting membranes 9 , or generated from labile nitrogen-containing compounds (for example, nitrates, amines, nitrites and nitrogen oxides) that are typically present in the nitrogen gas stream 10 , in the atmosphere or even in the catalyst itself. Although these sources of experimental artefacts are beginning to be recognized and managed 11 , 12 , concerted efforts to develop effective electrochemical nitrogen reduction processes would benefit from benchmarking protocols for the reaction and from a standardized set of control experiments designed to identify and then eliminate or quantify the sources of contamination. Here we propose a rigorous procedure using 15 N 2 that enables us to reliably detect and quantify the electrochemical reduction of nitrogen to ammonia. We demonstrate experimentally the importance of various sources of contamination, and show how to remove labile nitrogen-containing compounds from the nitrogen gas as well as how to perform quantitative isotope measurements with cycling of 15 N 2 gas to reduce both contamination and the cost of isotope measurements. Following this protocol, we find that no ammonia is produced when using the most promising pure-metal catalysts for this reaction in aqueous media, and we successfully confirm and quantify ammonia synthesis using lithium electrodeposition in tetrahydrofuran 13 . The use of this rigorous protocol should help to prevent false positives from appearing in the literature, thus enabling the field to focus on viable pathways towards the practical electrochemical reduction of nitrogen to ammonia. A protocol for the electrochemical reduction of nitrogen to ammonia enables isotope-sensitive quantification of the ammonia produced and the identification and removal of contaminants.

Despite its great potential in studying brain anatomy and structure, diffusion magnetic resonance imaging (dMRI) is marred by artefacts more than any other commonly used MRI technique. In this paper we present a non-parametric framework for detecting and correcting dMRI outliers (signal loss) caused by subject motion. Signal loss (dropout) affecting a whole slice, or a large connected region of a slice, is frequently observed in diffusion weighted images, leading to a set of unusable measurements. This is caused by bulk (subject or physiological) motion during the diffusion encoding part of the imaging sequence. We suggest a method to detect slices affected by signal loss and replace them by a non-parametric prediction, in order to minimise their impact on subsequent analysis. The outlier detection and replacement, as well as correction of other dMRI distortions (susceptibility-induced distortions, eddy currents (EC) and subject motion) are performed within a single framework, allowing the use of an integrated approach for distortion correction. Highly realistic simulations have been used to evaluate the method with respect to its ability to detect outliers (types 1 and 2 errors), the impact of outliers on retrospective correction of movement and distortion and the impact on estimation of commonly used diffusion tensor metrics, such as fractional anisotropy (FA) and mean diffusivity (MD). Data from a large imaging project studying older adults (the Whitehall Imaging sub-study) was used to demonstrate the utility of the method when applied to datasets with severe subject movement. The results indicate high sensitivity and specificity for detecting outliers and that their deleterious effects on FA and MD can be almost completely corrected. •We present a framework for correction of distortions, subject movement and signal dropout in diffusion weighted images.•It has been validated on realistic simulated data.•One can reliably correct for signal dropout as long as the affected slices constitute no more than 10% of the total.

Image reconstruction is reformulated using a data-driven, supervised machine learning framework that allows a mapping between sensor and image domains to emerge from even noisy and undersampled data, improving accuracy and reducing image artefacts. Machine learning improves image reconstruction Reconstructing images from data, whether for medical or astronomical purposes, hinges on well-defined steps. The data sensor encodes an intermediate representation of the observed object, which is converted into an image by a mathematical operation known as the inversion of the encoding function. This inversion is often plagued by sensor imperfections and noise, requiring extra technique-specific steps to correct them. Here, Matthew Rosen and colleagues present a more unified framework termed 'automated transform by manifold approximation' (AUTOMAP). AUTOMAP tackles image reconstruction as a supervised learning task, which uses appropriate training data to link the sensor data to the output image. The authors implemented AUTOMAP with a deep neural network and tested its flexibility in learning how to reconstruct images for various magnetic resonance imaging acquisition strategies. AUTOMAP reduced artefacts and improved accuracy in images reconstructed from noisy and undersampled acquisitions. The authors expect their framework to apply to other imaging methods. Image reconstruction is essential for imaging applications across the physical and life sciences, including optical and radar systems, magnetic resonance imaging, X-ray computed tomography, positron emission tomography, ultrasound imaging and radio astronomy 1 , 2 , 3 . During image acquisition, the sensor encodes an intermediate representation of an object in the sensor domain, which is subsequently reconstructed into an image by an inversion of the encoding function. Image reconstruction is challenging because analytic knowledge of the exact inverse transform may not exist a priori , especially in the presence of sensor non-idealities and noise. Thus, the standard reconstruction approach involves approximating the inverse function with multiple ad hoc stages in a signal processing chain 4 , 5 , the composition of which depends on the details of each acquisition strategy, and often requires expert parameter tuning to optimize reconstruction performance. Here we present a unified framework for image reconstruction—automated transform by manifold approximation (AUTOMAP)—which recasts image reconstruction as a data-driven supervised learning task that allows a mapping between the sensor and the image domain to emerge from an appropriate corpus of training data. We implement AUTOMAP with a deep neural network and exhibit its flexibility in learning reconstruction transforms for various magnetic resonance imaging acquisition strategies, using the same network architecture and hyperparameters. We further demonstrate that manifold learning during training results in sparse representations of domain transforms along low-dimensional data manifolds, and observe superior immunity to noise and a reduction in reconstruction artefacts compared with conventional handcrafted reconstruction methods. In addition to improving the reconstruction performance of existing acquisition methodologies, we anticipate that AUTOMAP and other learned reconstruction approaches will accelerate the development of new acquisition strategies across imaging modalities.