Elemental Images Research Articles

Exploring Rare Earth Elements in complex microscopic mineral phases: Inputs from μLIBS imaging

Understanding the distribution and concentration of Rare Earth Elements (REE) within various minerals and ores is crucial in mineralogy and geology for comprehending the micro-scale processes that contribute to the formation of REE-bearing minerals. Laser Induced Breakdown Spectroscopy elemental imaging emerges as a valuable tool due to its spatial resolution (15 μm) and ability for blind and rapid identification of elements within thin rock sections. Geological samples from the metamorphic core of the Zagros orogen (Iran) prepared as thin rock sections have been analyzed by LIBS imaging. Mineralogical phases have been determined from LIBS images of major elements. We first found that the distribution of Yttrium and REEs varies depending on the metamorphic stage. Then, while garnets may exhibit zoning in Mn, we observed that not all of them incorporate Yttrium. Finally, we demonstrated that REEs carriers include bastnaesite (Y Ce La F) and xenotime-like minerals primarily composed of Y with varying amounts of Yb, Gd, and Nd. We also demonstrated that the REEs minerals can exhibit either millimetric size when embedded within andalusite phases, or micrometric size when randomly scattered throughout the matrix. And we have also observed that some samples feature abundant Y-enriched Zircon phases. This study demonstrates that μLIBS imaging enables drawing a comprehensive picture of geological samples down to the micrometric scale. It has become a key technique in this field, providing access to the mineralogy of REE minerals, which is paramount for optimizing the final exploitation process compared to other analytical techniques.

CA-YOLO: Ensembled Model Optimization for Remote Sensing Image Object Detection

Multiple object retrieval methods are solved by the CA-YOLO model to find elements in complex spatial images. This addresses weak multiscale feature learning and the delicate balance between model complexity and performance. CA-YOLO is based on YOLOv5 and uses a small coordinate attention module at the lower layer to recover selected features and reduce data. A fast spatial pyramid with tandem design modules at the bottom uses stochastic pooling to speed up thinking and merge features of different sizes. Anchor box interactions and missing features have been updated to make it easier to find items of different sizes and shapes. CA-YOLO outperforms YOLO in multiple object detection and average inference speed of 125 frames/sec. CA-YOLO is a great option in the same conditions and difficulties. The study also investigated V3 tiny, V4, V5s, V8s, CA Yolos, and V5x6 YOLO models. This suggests that YOLO V5x6 can achieve more than 95% mAP on remote sensing object identification datasets. Index terms - Object detection, attention mechanism, coordinate attention, SPPF, SIoU loss.

Absorption Correction for 3D Elemental Distributions of Dental Composite Materials Using Laboratory Confocal Micro-X-ray Fluorescence Spectroscopy.

Confocal micro-X-ray fluorescence (micro-XRF) spectroscopy facilitates three-dimensional (3D) elemental imaging of heterogeneous samples in the micrometer range. Laboratory setups using X-ray tube excitation render the method accessible for diverse research fields but interpretation of results and quantification remain challenging. The attenuation of X-rays in composites depends on the photon energy as well as on the composition and density of the material. For confocal micro-XRF, attenuation severely impacts elemental distribution information, as the signal from deeper layers is distorted by superficial layers. Absorption correction and quantification of fluorescence measurements in heterogeneous composite samples have so far not been reported. Here, an absorption correction approach for confocal micro-XRF combining density information from microcomputed tomography (micro-CT) data with laboratory X-ray absorption spectroscopy (XAS) and synchrotron transmission measurements is presented. The energy dependency of the probing volume is considered during the correction. The methodology is demonstrated on a model composite sample consisting of a bovine tooth with a clinically used restoration material.

UFBSM: unmixing fusion and background sparse dictionary Model for hyperspectral anomaly detection

ABSTRACT In recent years, low-rank sparse representation (LRR) schemes have been applied to the field of hyperspectral image (HSI) anomaly detection (AD) and received attention. However, it is often accompanied by a small amount of noise in the AD, which leads to a high false alarm rate of detection results. In this paper, the HSI-AD algorithm is proposed to address this problem. First, the HSI is unmixed using the deep network to obtain the endmembers and abundance, and the abundance matrix is fused with the HSI to obtain the mixed data, which to a certain extent complements the spatial information of the HSI because the abundance matrix can reflect the subpixel-level information of the target image elements. Then, the mixed data are decomposed based on the model proposed in this paper. To overcome the situation that the sparse part of the traditional model is prone to mix with a small amount of noise, the sparse decomposition of the mixed data into anomaly and noise is used to remove the small amount of noise from the sparse part. A joint dictionary consisting of background and anomaly is used in the above model. In addition, to address the problem of information loss caused by the uniform accumulation of kernel norm over all singular values in the process of model decomposition, this paper designs the γ norm decomposition method to replace the general kernel norm decomposition for a more effective extraction of useful information.

Interfacial stress and crack propagation experimental study in mini-LED chip debonding

The ultra-miniaturization trend of chips presents new challenges for non-destructive peeling-off techniques. This study models the mini-LED chip-UV film adhesive system as a micro laminated structure and conducts the interfacial stresses and crack propagation analysis of the structure using theoretical, finite element and experimental methods. The innovations of this paper are the illustration of the negligible effect of size effect on 125 μm x 75 μm x 60 μm chips, and the determination of the technology parameter tuning method to promote chip peeling-off. Firstly, applying the first-order shear deformation theory, and considering the UV-curable acrylic adhesive layer and the UV film as elastic materials, Lamé coefficients are introduced to explore the free vibration of the micro laminated structure. The analyses indicate that the scale effect has an extremely small impact, within 0.5%, on natural frequencies of the 100-micron scale laminated structure with a small length-to-thickness ratio. Thereafter, a three parameter elastic foundation (3-PEF) model of the chip-UV film adhesive structure is established and the interfacial stresses distributions are parametrically analyzed. Additionally, an experimental platform for needle-ejecting chip peeling-off is designed and constructed. Finite element simulation, experimental observations, and image recognition of crack generation and propagation during the peeling process were performed. Results show that cracks are generated at both free ends of the adhesive layer and progressively extended inwards. This is consistent with the 3-PEF analytical solution, in which the stress concentration also occurs at free ends of the adhesive layer. The agreement between theoretical, simulation and experimental results regarding interfacial stresses provides a solid foundation for further analysis of chip peeling-off mechanism.

The Influence of the Soil Profile on the Formation of the Elemental Image of Grapes and Wine of the Cabernet Sauvignon Variety.

The features for assessing the authenticity of wines by region of origin are studied, based on the relationship between the mineral composition of the wine, the grapes, and the soil profile (0 to 160 cm) from the place of growth of Cabernet Sauvignon grapes. Soil, grape, and wine samples were taken from the territories of six vineyards in the Anapa district of Krasnodar Territory, Russia. Using the methods of ICP-OES, thermal, and X-ray phase analysis, the soils were differentiated into three groups, differing in mineralogical and mineral compositions. The soil samples of the first group contained up to 31% quartz, the second group up to 25% quartz and 19% mixed calcite, and the third group up to 32% calcite and 15% quartz. The formation of the elemental image of the grapes was studied, taking into account the total content and mobile forms of metals in the soil. The territorial proximity of the vineyards did not affect the extraction of elements from the soil into the grape berry, and the migration of metals for each territory was selective. According to the values of the biological absorption coefficient, the degree of transition of metals from the soil to a berry was estimated. For K, Ti, Zn, Rb, Cu, and Fe in all berries, the coefficient was higher than 1.00, which means that the berry extracts contained not only mobile-form, but also difficult-to-dissolve metal compounds. The migration of macro-components from the soil to the berry was low, and amounted to 6-7% for Ca, 0.8-3.0% for Na, and 25-70% for Mg of the concentration of their mobile forms. For all territories, the maximum correlation between metal concentrations in grapes and soil was observed for samples from a depth of 0-40 cm. The discriminant model based on concentrations of Rb, Al, K, Sr, Co, Na, Pb, Ca, and Ni showed the formation of clusters in the territories of vineyard cultivation. The developed model allow the problems of identifying wines by region to be solved with high accuracy, using their elemental image.

USE OF INTELLIGENT FUZZY IMAGE SEGMENTATION SYSTEMS IN THE PROFESSIONAL TRAINING OF FUTURE SPECIALISTS IN ENGINEERING AND PEDAGOGICAL FIELDS

Relevance: The article addresses the critical issue of integrating intelligent image segmentation systems that utilize fuzzy logic into the training processes for future specialists in engineering and pedagogical fields. This integration is a significant aspect of the digitization of higher education. Aim: The goal is to implement intelligent vehicle image segmentation systems using fuzzy logic to train specialists in engineering and pedagogical fields. Methods: The preliminary processing of the images of the studied objects (vehicles) involved digital filtering methods, contour detection, profile analysis, and contrast enhancement. Image segmentation was performed using watershed methods, contour lines, and region growing. After segmentation, the obtained segments were selected based on size. Fuzzy membership functions were then applied to determine the degree of affiliation of the segments to the meaningful parts of the studied objects, ensuring reliable recognition of these parts and stable operation of the intelligent system despite external influences on the acquired images. Results: A computer system has been developed for the segmentation of vehicle images using fuzzy logic, which has been integrated into the training of specialists in engineering and pedagogical fields. The segmentation methods isolate objects within the images, which are then recognized using fuzzy logic. Thanks to the fuzzy membership functions, elements of vehicle images are reliably recognized even when there is some ambiguity in the shapes of the segments. The practical significance of the developed system is demonstrated through the processing of car images. Conclusions: The integration of the developed system into the educational process provides students with both theoretical knowledge and practical skills related to intelligent image processing systems.

A Qualitative Study of Water Quality Using Landsat 8 and Station Water Quality-Monitoring Data to Support SDG 6.3.2 Evaluations: A Case Study of Deqing, China

Facing the challenge of the degradation of global water quality, it is urgent to realize the Sustainable Development Goal 6.3.2 (SDG 6.3.2), which focuses on improving global water quality. Currently, remote sensing technology is widely used for water quality monitoring. Existing water quality-monitoring studies have been conducted based on quantitative water quality inversion. It requires a high degree of the synchronization of the time and location of the collection of station monitoring data and remote sensing data (air–ground spatiotemporal synchronization), which can be resource intensive and time consuming. However, policymakers and the public are more interested in the quality of water (good or poor) than in the specific values of the water quality parameters, as evidenced by the emergence of SDG 6.3.2. In this study, we change the traditional idea of quantitative water quality research, focus on water quality qualitative research combined with the characteristics of water pollution, propose a remote sensing water quality sample enhancement method under the condition of “air–ground spatiotemporal asynchrony”, and construct a remote sensing water quality sample library. On the basis of this sample library, a random forest water quality classification model was constructed to classify water quality qualitatively. We obtained the distribution of good water bodies in Deqing County, China, for example, from 2013 to 2022. The results show that the model has high accuracy (Kappa = 0.6004, OA = 0.8387), and we found that the water quality in Deqing County improved in the order of “major rivers, lakes, and tributaries” during the period from 2013 to 2015. This also verifies the feasibility of using this sample enhancement method to conduct qualitative research on water quality. Based on this water quality classification model, a set of spatial-type evaluation processes of SDG 6.3.2 based on image elements was designed. The evaluation results show that the water quality situation in Deqing County can be divided into two stages: there is a trend of substantial improvement from 2013 (evaluated value of SDG 6.3.2 = 63.25) to 2015 (evaluated value of SDG 6.3.2 = 83.16); and it has remained stable and fluctuating after reaching the good environmental water quality since 2015. This study proposes a simple method for rapidly evaluating SDG 6.3.2 via utilizing easily accessible Landsat 8 and water quality-monitoring data to classify water quality. The method can directly obtain water quality category information without the need for additional sampling, thus saving costs. It is a very simple process that is easy to implement, while also providing a high level of accuracy. This significantly reduces the barriers to evaluating SDG 6.3.2, supports the realization of the sustainable management of water resources globally, and is highly generalizable.

The use of pretrained neural networks for solving the problem of reverse searching of X-ray images of prohibited items and substances

The paper considers the application of pretrained neural networks to solve the problem of reverse searching of X-ray images of prohibited items and substances. The purpose of the work is to conduct an analysis and substantiate ways to improve the efficiency of baggage and passenger hand luggage X-ray image recognition systems. An analysis of existing domestic and foreign works in the field of baggage and passenger hand luggage X-ray image recognition is presented. It has been revealed that, despite the achieved results in the development of algorithms for recognizing prohibited items and substances, they do not fully cope with such a complexity factor as the overlay of objects. To solve this problem, the paper proposes to additionally analyze X-ray images with low confidence in object recognition. This stage includes the following steps: image segmentation, extraction of features of segmented image elements; search for similar images in the database; decision-making on the class of segmented image elements. This article discusses the last three steps. Variants of approaches to feature extraction from images are analyzed, particularly those based on the application of convolutional autoencoders and pretrained neural networks. The approach based on the application of pretrained neural networks is chosen. The ResNet-50 architecture neural network, pretrained on the ImageNet collection, is used during the work. In order to apply this model to extract image feature vectors, the last classification layer was preliminarily removed. All the previous layers of the model encode the image into a vector. ResNet-50 generates a 2048-dimensional feature vector of images. The principal component analysis is used to reduce the dimensionality of the image feature vectors. The decision of whether the segmented image element is a prohibited item or substance is considered as a reverse search problem using the k-nearest neighbor algorithm. In this case, the class of the X-ray image element is the class most frequently encountered among the K nearest neighbors. In order to test the proposed approach, a training dataset, including 4,635 images of individual items and substances that may be encountered in baggage and passenger hand luggage, was generated. A comparative analysis of image indexing and image search under different algorithms and feature number is presented. A comparative analysis of the model accuracy is provided. It is concluded that the most acceptable is the “Brute force” algorithm in combination with the principal component analysis.

Constructing elements of a destination's image depending on the measurement and development of travel and tourism competitiveness indicators

Constructing elements of a destination's image depending on the measurement and development of travel and tourism competitiveness indicators

Zircon petrochronological evidences for diachronous tectonic switch and orogenic keel collapse of the Tongbai-Dabie-Sulu orogens

Migmatites can be produced in different stages of orogeny and thus have a great significance to constrain the evolution of an orogenic belt. Migmatites widely occur in the Tongbai orogen, but their formation ages and geological significance have not been well constrained. In this study, a combination of cathodoluminescence (CL) imaging, U-Pb-Hf-O isotopes and trace elements of zircons were carried for migmatites in the Tongbai orogen. Most inherited cores in a melanosome show variable UPb ages and Hf isotope compositions with high δ18O values, suggesting they might be detrital zircons from its protolith. Whereas other relict cores in this sample have nebulously zoning or no zoning, flat HREE patterns with insignificant Eu anomalies and relatively low formation temperatures, indicating their formation under eclogite-facies conditions. They yield UPb ages of 228 ± 3 Ma, providing direct evidence for the involvement of Triassic subducted continental materials. The overgrowth rims and some new grown grains in the migmatites show euhedral shapes, low CL luminescence, high Th/U ratios, clear negative Eu anomalies and steep HREE patterns, suggesting they are anatectic zircons formed during partial melting. They yield UPb ages from 139 ± 2 to 132 ± 1 Ma. Combined with previous studies, we suggest that migmatization mainly occurred at 139–130 Ma in the Tongbai orogen. The anatectic zircons in the leucosomes show much lower δ18O values (5.79 ± 0.23‰ vs. 8.94 ± 0.16‰) and higher 176Hf/177Hf ratios (0.282302–0.282648 vs. 0.281728–0.282504) than the zircons in adjacent melanosomes, suggesting that the leucosomes might be externally sourced. According to a compilation of the geochronological information of migmatite and magmatism, the Tongbai-Dabie-Sulu orogens have a spatial and temporal variation of partial melting and magma activities in the post-collisional stage. These might result from the diachronical tectonic switch and collapse of the orogenic lithospheric keels caused by the subduction and rollback of the Paleo-Pacific slab.

Development of an atmospheric pressure plasma-based OES device for in-situ mapping of Cd and related elements in plants

We have developed an innovative optical emission spectrometry imaging device integrating a diode laser for sample introduction and an atmospheric pressure plasma based on dielectric barrier discharge for atomization and excitation. By optimizing the device parameters and ensuring appropriate leaf moisture, we achieved effective imaging with a lateral resolution as low as 50 μm. This device allows for tracking the accumulation of Cd and related species such as K, Zn, and O2+∙, in plant leaves exposed to different Cd levels and culture times. The results obtained are comparable to established in-lab imaging and quantitative methods. With its features of compact construction, minimal sample preparation, ease of operation, and low limit of detection (0.04 μg/g for Cd), this novel methodology shows promise as an in-situ elemental imaging tool for interdisciplinary applications.

HUBUNGAN PENGETAHUAN IBU HAMIL TENTANG ANEMIA DENGAN KEJADIAN ANEMIA DI PUSKESMAS PORIAHA KABUPATEN TAPANULI TENGAH TAHUN 2020

Counseling with Audio Visual Media is a very important means of information because it contains elements of sound and images, which in the process of absorbing material involves the senses of sight and hearing. Lack of knowledge of elementary school age children about oral health is influenced by the lack of information obtained, including in the form of counseling, especially Audio Visual Media. This type of research was descriptive survey method with the aim to find out the description of dental and oral health counseling by using audio-visual media on the knowledge of students of Class VIII-1 at SMP Mulia Pratama Private Medan, with a sample size of 30 people. Data collection was carried out by distributing questionnaires before and after counseling using audio visual media. The results obtained by the data that the level of knowledge of students about oral health before counseling the majority of poor criteria, amounting to 11 people (36.6%). Whereas after counseling, the level of knowledge of students increased to a majority of good criteria, amounting to 27 people (90%), and none of them had bad criteria. The conclusion of this study is an increase in student knowledge about oral health after counseling with audio-visual media and in line with the objectives of counseling. It is expected that schools will provide communication media to increase knowledge, especially dental and oral health for students.

PENYULUHAN KESEHATAN GIGI DAN MULUT DENGAN MENGGUNAKAN MEDIA AUDIO VISUAL TERHADAP PENGETAHUAN PADA KELAS VIII-1

Counseling with Audio Visual Media is a very important means of information because it contains elements of sound and images, which in the process of absorbing material involves the senses of sight and hearing. Lack of knowledge of elementary school age children about oral health is influenced by the lack of information obtained, including in the form of counseling, especially Audio Visual Media. This type of research was descriptive survey method with the aim to find out the description of dental and oral health counseling by using audio-visual media on the knowledge of students of Class VIII-1 at SMP Mulia Pratama Private Medan, with a sample size of 30 people. Data collection was carried out by distributing questionnaires before and after counseling using audio visual media. The results obtained by the data that the level of knowledge of students about oral health before counseling the majority of poor criteria, amounting to 11 people (36.6%). Whereas after counseling, the level of knowledge of students increased to a majority of good criteria, amounting to 27 people (90%), and none of them had bad criteria. The conclusion of this study is an increase in student knowledge about oral health after counseling with audio-visual media and in line with the objectives of counseling. It is expected that schools will provide communication media to increase knowledge, especially dental and oral health for students.

A comparative Analysis of Image Enhancement Using Autoencoders and Generative Adversarial Networks

The purpose of this study, which focuses on image enhancement methods, is to investigate how various methods contribute to higher-quality photos in particular applications. Tracing the history of image improvement, encompassing the technological evolution of early photography from the 19th century to the present. The foundation of conventional image enhancement technology, which directly affects image elements and transformation coefficients, respectively, emphasizes frequency domain and spatial domain processing. It is noted that the application of autoencoders and generative adversarial networks to image enhancement as a deep learning training strategy has shown effectiveness in picture classification, target recognition, and image segmentation. This article primarily examines and contrasts autoencoders and generative adversarial networks, summarizing their applicability, constraints, benefits, and drawbacks and talking about the patterns in their upcoming development. The study has guiding value for advancing the development of picture technology and offers significant theoretical and empirical support for the field of image enhancement.

Viewing resolution and depth-of-field enhancement for a digital 3D display based on neural network-enabled multilayer view perspective fitting

The traditional digital three-dimensional (3D) display suffers from low resolution and a narrow depth of field (DoF) due to the lack of planar pixels transformed into view perspectives and the limitation of the diffraction effect of the lens, respectively, which are the main drawbacks to restrict the commercial application of this display technology. Here, the neural network-enabled multilayer view perspective fitting between the reconstructed and original view perspectives across the desired viewing depth range is proposed to render the optimal elemental image array (EIA) for enhancing the viewing resolution as well as the DoF of the digital 3D display. Actually, it is an end-to-end result-oriented coding method to render the fusion EIA with optimal multidepth fusion and resolution enhancement with high registration accuracies for both view perspective and depth reconstructions by using a depth-distributed fitting neural network paradigm. The 3D images presented in the simulations and optical experiments with improved viewing resolution and extended viewing depth range are demonstrated, verifying the feasibility of the proposed method.

Integrating Explainable Machine Learning in Clinical Decision Support Systems: Study Involving a Modified Design Thinking Approach.

Though there has been considerable effort to implement machine learning (ML) methods for health care, clinical implementation has lagged. Incorporating explainable machine learning (XML) methods through the development of a decision support tool using a design thinking approach is expected to lead to greater uptake of such tools. This work aimed to explore how constant engagement of clinician end users can address the lack of adoption of ML tools in clinical contexts due to their lack of transparency and address challenges related to presenting explainability in a decision support interface. We used a design thinking approach augmented with additional theoretical frameworks to provide more robust approaches to different phases of design. In particular, in the problem definition phase, we incorporated the nonadoption, abandonment, scale-up, spread, and sustainability of technology in health care (NASSS) framework to assess these aspects in a health care network. This process helped focus on the development of a prognostic tool that predicted the likelihood of admission to an intensive care ward based on disease severity in chest x-ray images. In the ideate, prototype, and test phases, we incorporated a metric framework to assess physician trust in artificial intelligence (AI) tools. This allowed us to compare physicians' assessments of the domain representation, action ability, and consistency of the tool. Physicians found the design of the prototype elegant, and domain appropriate representation of data was displayed in the tool. They appreciated the simplified explainability overlay, which only displayed the most predictive patches that cumulatively explained 90% of the final admission risk score. Finally, in terms of consistency, physicians unanimously appreciated the capacity to compare multiple x-ray images in the same view. They also appreciated the ability to toggle the explainability overlay so that both options made it easier for them to assess how consistently the tool was identifying elements of the x-ray image they felt would contribute to overall disease severity. The adopted approach is situated in an evolving space concerned with incorporating XML or AI technologies into health care software. We addressed the alignment of AI as it relates to clinician trust, describing an approach to wire framing and prototyping, which incorporates the use of a theoretical framework for trust in the design process itself. Moreover, we proposed that alignment of AI is dependent upon integration of end users throughout the larger design process. Our work shows the importance and value of engaging end users prior to tool development. We believe that the described approach is a unique and valuable contribution that outlines a direction for ML experts, user experience designers, and clinician end users on how to collaborate in the creation of trustworthy and usable XML-based clinical decision support tools.

Holoscopic Elemental-Image-Based Disparity Estimation Using Multi-Scale, Multi-Window Semi-Global Block Matching

In Holoscopic imaging, a single aperture is used to acquire full-colour spatial images like a fly’s eye by gently altering angles between nearby lenses with a micro-lens array. Due to its simple data collection and visualisation methods, which provide robust and scalable spatial information, and its motion parallax, binocular disparity, and convergence, this technique may be able to overcome traditional 2D imaging issues like depth, scalability, and multi-perspective problems. A novel disparity-map-generating method uses angular information from a single Holoscopic image’s micro-images, or Elemental Images (EIs), to create a scene’s disparity map. Not much research has used EIs instead of Viewpoint Images (VPIs) for disparity estimation. This study investigates whether angular perspective data may replace spatial orthographic data. Using noise reduction and contrast enhancement, EIs with a low resolution and lack of texture are pre-processed to calculate the disparity. The Semi-Global Block Matching (SGBM) technique is used to calculate the disparity between EI pixels. A multi-resolution approach overcomes EIs’ resolution constraints, and a content-aware analysis dynamically modifies the SGBM window size settings to generate disparities across different texture and complexity levels. A background mask and nearby EIs with accurate backgrounds detect and rectify EIs with erroneous backgrounds. Our method generates disparity maps that outperform two state-of-the-art deep learning algorithms and VPIs in real images.

Judging athletic movement in moving images: a critique of agonic reason in representations of alpine sport, seen through the Paltrow v. Sanderson ski crash trial

ABSTRACT This paper concerns the judgement and critique of athletic movement in moving images. Inspired by the ski crash trial case of Paltrow v. Sanderson, and by comparing different media representations of downhill skiing, the essay outlines a framework that discerns as well as connects elements of movement and images, developing the concept of the ‘diorama’ in relation to Deleuze’s notion of the diagram and Kant’s idea of critique. Thus, moving images featuring elite alpine skier Mikaela Shiffrin, fictional character James Bond, and an official skiing game for the international ski federation (World Cup Ski Racing, FIS) figure as comparative material to the animation that played a central role in the celebrity trial. Diorama and diagram are posited on a continuum to assess when and how judgement takes place in each of the exhibits. The essay concludes by discussing how the judging of athletic movement in moving images contributed to Paltrow winning the case, and theoretically by connecting this finding to dioramas and diagrams as tools apt for a framework aiming at the critique of athletic movement in moving images.

Retraction: "Evaluation of the neuroprotective effects of electromagnetic fields and coenzyme Q10 on hippocampal injury in mouse".

Retraction: "Evaluation of the neuroprotective effects of electromagnetic fields and coenzyme Q10 on hippocampal injury in mouse" by Mansoureh Soleimani, Fereshteh Golab, Akram Alizadeh, Sara Rigi, Zeinab Nazarian Samani, Gelareh Vahabzadeh, Tahmineh Peirovi, Maryam Sarbishegi, Majid Katebi, Fereshteh Azedi, J Cell Physiol 2019, 234: 18720-18730. The above article, published online on 1 April 2019 in Wiley Online Library (https://onlinelibrary.wiley.com/doi/full/10.1002/jcp.28512) has been retracted by agreement between the journal's Editor in Chief, Alexander Hutchison, and the Wiley Periodicals LLC. The retraction has been agreed following an investigation based on allegations raised by third parties. Image elements in Figures 3 and 5 were found to have been duplicated and/or published elsewhere in a different scientific context by the same author group. The authors have admitted significant errors in figure compilation due to the simultaneous execution of experiments for both this article and their previous publication, resulting in complications with organization and storage of data. Accordingly, the editors consider the conclusions of this article to be invalid. The authors have been informed of the decision to retract the article but disagree with it.