Representation Of Structure Research Articles

How Students Use Cognitive Structures to Process Information in the Algebraic Reasoning?

Cognitive processes are procedures for using existing knowledge to combine it with new knowledge and make decisions based on that knowledge. This study aims to identify the cognitive structure of students during information processing based on the level of algebraic reasoning ability. This type of research is qualitative with exploratory methods. The data collection technique used began by providing a valid and reliable test instrument for algebraic reasoning abilities for six mathematics education student programs at the Islamic University of Sultan Agung Indonesia. Subjects were selected based on the level of upper, middle, and lower algebraic reasoning abilities. The results showed that (1) students with the highest level of algebraic reasoning ability meet the logical structure of Logical Reasoning which shows that students at the upper level can find patterns and can generalize; (2) Students at the intermediate level understand the cognitive structure of Symbolic Representations, where students can make connections between knowledge and experience and look for patterns and relationships but have difficulty making rules and generalizations; (3) students at lower levels understand the cognitive structure of Comparative Thinking, where students are only able to make connections between prior knowledge and experience.

3D Seismic Attribute Conditioning Using Multiscale Sheet-Enhancing Filtering

Seismic coherence attributes are valuable for identifying structural features, but they often face challenges due to significant background noise and non-feature-related stratigraphic discontinuities. To address this, it is necessary to apply attribute conditioning to the coherence to enhance the visibility of these structures. The primary challenge of attribute conditioning lies in finding a concise structural representation that isolates only the true interpretive features while effectively removing noise and stratigraphic interference. In this study, we choose sheet-like structures as this concise structural representation, as faults are typically characterized by their thin and narrow profiles. Inspired by multiscale Hessian-based filtering (MHF) and its application on vascular structure detection, we propose a method called anisotropic multiscale Hessian-based sheet-enhancing filtering (AMHSF). This method is specifically designed to extract and magnify sheet-like structures from noisy coherence images, with a novel enhancement function distinct from those traditionally used in vascular enhancement. The effectiveness of our AMHSF is demonstrated through experiments on both synthetic and real datasets, showcasing its potential to improve the identification of structural features in coherence images.

THE NON-COPRIME GRAPHS OF UPPER UNITRIANGULAR MATRIX GROUPS OVER THE RING OF INTEGER MODULO WITH PRIME ORDER AND THEIR TOPOLOGICAL INDICES

In its application graph theory is widely applied in various fields of science, including scheduling, transportation, industry, and structural chemistry, such as topological indexes. The study of graph theory is also widely applied as a form of representation of algebraic structures, including groups. One form of graph representation that has been studied is non-coprime graphs. The upper unitriangular matrix group is a form of group that can be represented in graph form. This group consists of upper unitriangular matrices, which are a special form of upper triangular matrix with entries in a ring R and all main diagonal entries have a value of one. In this research, we look for the form of a non-coprime graph from the upper unitriangular matrix group over a ring of prime modulo integers and several topological indexes, namely the Harmonic index, Wiener index, Harary index, and First Zagreb index. The findings of this research indicate that the structure of the graph and the general formula for the Harmonic index, Wiener index, Harary index, and First Zagreb index were successfully obtained.

Large-scale-aware data augmentation for reduced-order models of high-dimensional flows

Convolutional autoencoders have proven to be an adequate tool to perform reduced-order modeling for high-dimensional nonlinear dynamical systems. Their goal is to reduce dimensionality strongly while preserving the most characteristic features of the system. Here, we show that these models rely sensitively on the completeness of the provided data. This is particularly challenging for fully turbulent flows with their coherent structures ranging from large-scale superstructures to dissipative eddies over orders of magnitude in time and space. As a result, an unrealistically large number of data snapshots would be required to properly cover all the essential dynamics, whereas the features on small time and length scales require only a small number of snapshots of the respective flow, especially the long lasting large-scale structures that are difficult to characterize either numerically or experimentally. We demonstrate for three types of flows that a missing representation of large-scale turbulent structures leads to failures in the training process. We suggest a method to mitigate this shortcoming. This includes the transformation of data samples to new large-scale structures, which enhance the data. Furthermore, we skip augmentations that are more detrimental to the model performance. We evaluate our method for three datasets, two from numerical simulations of turbulent Rayleigh–Bénard convection flows and one from laboratory experiment for the flow past an array of cylinders. We show that the method can substantially improve model utility for high-dimensional data. In this way, we avoid an intensive grid search through possible augmentation combinations without further knowledge about the underlying system.

Systematic Review and Framework for AI-Driven Tacit Knowledge Conversion Methods and Machine Learning Algorithms for Ontology-Based Chatbots in E-Learning Platforms

The conversion of tacit knowledge, which is deeply rooted in personal experience and often difficult to articulate, presents a significant challenge within knowledge management systems. Ontology-based chatbots offer a promising solution by leveraging structured knowledge representations and advanced natural language processing (NLP) techniques to facilitate this transformation. This paper explores the various methods and algorithms used in developing ontology-based chatbots, with a particular focus on their role in converting tacit knowledge into more accessible forms. Additionally, it provides a comparative analysis of the algorithms employed, highlighting their respective strengths and weaknesses. Ultimately, this study addresses the critical challenge of managing and converting tacit knowledge, with the aim of enhancing the overall effectiveness of knowledge management systems.

Cortical encoding of spatial structure and semantic content in 3D natural scenes.

Our visual system enables us to effortlessly navigate and recognize real-world visual environments. Functional magnetic resonance imaging (fMRI) studies suggest a network of scene-responsive cortical visual areas, but much less is known about the temporal order in which different scene properties are analysed by the human visual system. In this study, we selected a set of 36 full-colour natural scenes that varied in spatial structure and semantic content that our male and female human participants viewed both in 2D and 3D while we recorded magnetoencephalography (MEG) data. MEG enables tracking of cortical activity in humans at millisecond timescale. We compared the representational geometry in the MEG responses with predictions based on the scene stimuli using the representational similarity analysis framework. The representational structure first reflected the spatial structure in the scenes in time-window 90-125 ms, followed by the semantic content in time-window 140-175 ms after stimulus onset. The 3D stereoscopic viewing of the scenes affected the responses relatively late, from around 140 ms from stimulus onset. Taken together, our results indicate that the human visual system rapidly encodes a scene's spatial structure and suggest that this information is based on monocular instead of binocular depth cues.Significance statement Our visual system enables us to recognize and navigate our visual surroundings seemingly effortlessly, but what exactly happens in our brains remains poorly understood. With the help of time-resolved brain imaging (magnetoencephalography, MEG), we found that the brain first encodes the spatial structure of a scene (e.g., cluttered or navigable) before its semantic content (e.g., a car park or farm). Brain imaging studies typically use 2D pictures as stimuli. Here we asked whether binocular disparity, a depth cue which arises from our two eyes seeing the scene from slightly different angles, aids the coding of the spatial structure. Our results suggest that this 3D depth cue plays little role in the rapid, initial sensing of our spatial surroundings.

A Symbolic AI Approach to Medical Training

In traditional medical education, learners are mostly trained to diagnose and treat patients through supervised practice. Artificial Intelligence and simulation techniques can complement such an educational practice. In this paper, we present GLARE-Edu, an innovative system in which AI knowledge-based methodologies and simulation are exploited to train learners “how to act” on patients based on the evidence-based best practices provided by clinical practice guidelines. GLARE-Edu is being developed by a multi-disciplinary team involving physicians and AI experts, within the AI-LEAP (LEArning Personalization of AI and with AI) Italian project. GLARE-Edu is domain-independent: it supports the acquisition of clinical guidelines and case studies in a computer format. Based on acquired guidelines (and case studies), it provides a series of educational facilities: (i) navigation, to navigate the structured representation of the guidelines provided by GLARE-Edu, (ii) automated simulation, to show learners how a guideline would suggest to act, step-by-step, on a specific case, and (iii) (self)verification, asking learners how they would treat a case, and comparing step-by-step the learner’s proposal with the suggestions of the proper guideline. In this paper, we describe GLARE-Edu architecture and general features, and we demonstrate our approach through a concrete application to the melanoma guideline and we propose a preliminary evaluation.

Screening Cyclodextrin Complexes for Bisphenols with High Binding Performance Based on the Data-Driven Model.

The distinctive cavity structure of cyclodextrin, which results in binding properties, is credited with its application prospects in chemical, pharmacy, and material fields. The binding capacity can be regulated by substituting the hydroxyl groups on the cyclodextrins. It is possible to acquire anticipated binding properties by designing the modified groups on cyclodextrins. In this article, a data-driven model is proposed with a novel cyclodextrin/guest structure representation method to assist the cyclodextrin design. The model's performance is verified via several validations, as the squared correlation coefficients for cross-validation (Q2) and test set (R2test) are 0.801 and 0.841, respectively. With the proposed model and fluorescence experiments for cyclodextrin/bisphenol complexes, several cyclodextrin hosts, which have a strong binding capacity for bisphenols, are screened, synthesized, and characterized. The results show a controlled average absolute error of 0.605 M-1, suggesting the feasibility of data supplementation and molecular design. It is believed that the data-driven model can serve as theoretical assistance and a driving tool for the cyclodextrin complexes design, potentially leading to advancements in cyclodextrin's industrial applications and scientific research.

Mapping Mental Representations With Free Associations: A Tutorial Using the R Package associatoR.

People's understanding of topics and concepts such as risk, sustainability, and intelligence can be important for psychological researchers and policymakers alike. One underexplored way of accessing this information is to use free associations to map people's mental representations. In this tutorial, we describe how free association responses can be collected, processed, mapped, and compared across groups using the R package associatoR. We discuss study design choices and different approaches to uncovering the structure of mental representations using natural language processing, including the use of embeddings from large language models. We posit that free association analysis presents a powerful approach to revealing how people and machines represent key social and technological issues.

Mathematical modeling and QSPR analysis of hepatitis treatment drugs through connection indices an innovative approach.

Chemical structures may be defined based on their topology, which allows for the organization of molecules and the representation of new structures with specific properties. We use topological indices, which are precise numerical measurements independent of structure, to measure the bonding arrangement of a chemical network. An essential objective of studying topological indices is to collect and alter chemical structure data to develop a mathematical relationship between structures and physico-chemical properties, bio-activities, and associated experimental factors. Topological indices establish the correlation between molecular characteristics, such as physical, chemical, thermodynamic, and biological activity, and their corresponding chemical structures in Quantitative Structure-Property Relationships (QSPR) and Quantitative Structure-Activity Relationships (QSAR). Hepatitis, in its advanced stages, can lead to the development of mental illnesses. Effective management of symptoms frequently entails regular use of medicine and therapy for a prolonged period. Assurance of the safety and efficacy of drug design is of utmost importance. Various parameters, including solubility, metabolic stability, toxicity, permeability, and transporter effects, depending on the physical and chemical properties of the treatment, impact the efficacy of biopharmacological design. Increasingly, computational approaches have become indispensable in the discipline of pharmaceutical discoveries and development. The major objective of this work is to examine the chemical appropriateness of connection indices in evaluating six physico-chemical properties of the 14 medicines used for the treatment of hepatitis. This work conducts QSPR analysis to obtain the most accurate approximations for the physico-chemical attributes of pharmacological agents employed in the treatment of hepatitis.

Design, development, and evaluation of the organic chemistry representational competence assessment (ORCA)

This paper describes the design and evaluation of the O̲rganic chemistry R̲epresentational C̲ompetence A̲ssessment (ORCA). Grounded in Kozma and Russell's representational competence framework, the ORCA measures the learner's ability to interpret, translate, and use six commonly used representations of molecular structure (condensed structures, Lewis structures, skeletal structures, wedge-dash diagrams, Newman projections, and chair conformations). Semi-structured interviews with 38 first-semester organic chemistry learners informed the development of the ORCA items. The ORCA was developed and refined through three pilot administrations involving a total of 3477 first-semester organic chemistry students from multiple institutions. The final version of the ORCA was completed by 1494 students across five institutions. Various analyses provided evidence for the validity and reliability of the data generated by the assessment. Both one-factor and three-factor correlated structures were explored via confirmatory factor analysis. The one-factor model better captured the underlying structure of the data, which suggests that representational competence is better evaluated as a unified construct rather than as distinct, separate skills. The ORCA data reveal that the representational competence skills are interconnected and should consistently be reinforced throughout the organic chemistry course.

A Topology-Enhanced Multi-Viewed Contrastive Approach for Molecular Graph Representation Learning and Classification.

In recent times, graph representation learning has been becoming a hot research topic which has attracted a lot of attention from researchers. Graph embeddings have diverse applications across fields such as information and social network analysis, bioinformatics and cheminformatics, natural language processing (NLP), and recommendation systems. Among the advanced deep learning (DL) based architectures used in graph representation learning, graph neural networks (GNNs) have emerged as the dominant and highly effective framework. The recent GNN-based methods have demonstrated state-of-the-art performance on complex supervised and unsupervised tasks at both the node and graph levels. In recent years, to enhance multi-view and structured graph representations, contrastive learning-based techniques have been developed, introducing models known as graph contrastive learning (GCL) models. These GCL approaches leverage unsupervised contrastive methods to capture multi-view graph representations by comparing node and graph embeddings, yielding significant improvements in both graph-level representations and task-specific applications, such as molecular embedding and classification. However, as most GCL techniques are primarily designed to focus on the explicit graph structure through GNN-based encoders, they often overlook critical topological insights that could be provided through topological data analysis (TDA). Given the promising research indicating that topological features can greatly benefit various graph learning tasks, we propose a novel topology-enhanced, multi-view graph contrastive learning model called TMGCL. Our TMGCL model is designed to capture and utilize both comprehensive multi-scale topological and global structural information from graphs. This enhanced representation capability positions TMGCL to directly support a range of applications, such as molecular classification, with improved accuracy and robustness. Extensive experiments within two real-world datasets proved the effectiveness and outperformance of our proposed TMGCL in comparing with state-of-the-art GNN/GCL-based baselines.

Building Detection and Outlining in Multi-Modal Remote Sensor Data: A Stratified Approach

Exploitation of aerial sensor data for vectorized representation of urban structures is of great importance for urban modeling. Even if elevation data is available, extracting complex and irregular building shapes would become challenging. In this paper, we present a stratified approach for the extraction of building outlines consisting of two steps: Classification and vectorization. For classification, we use training data transfer to evaluate a dataset with no or little labeled data. Since the available reference data is also flawed, we use a self-developed interactive tool to adjust and improve the building shape before contrasting it with the classification results. Initial building polygons are slightly generalized and refined considering building shape characteristics. Hereby, Least-Squares adjustment is implemented to solve the best-fit problem of building edges with the input data by applying the Gauss-Helmert and Gauss-Markov Models. On the raster level, the resulting polygons achieved an accuracy of over 99% in the Potsdam dataset and almost 98% in the Munich dataset while on the vector level, the median building-wise deviations lie in sub-meter range. Although there were few mis-detections and, sometimes, considerable peak deviations for the Hausdorff distance, the compelling qualitative results attest to the robustness and validity of the proposed procedure.

STOUT V2.0: SMILES to IUPAC name conversion using transformer models

Naming chemical compounds systematically is a complex task governed by a set of rules established by the International Union of Pure and Applied Chemistry (IUPAC). These rules are universal and widely accepted by chemists worldwide, but their complexity makes it challenging for individuals to consistently apply them accurately. A translation method can be employed to address this challenge. Accurate translation of chemical compounds from SMILES notation into their corresponding IUPAC names is crucial, as it can significantly streamline the laborious process of naming chemical structures. Here, we present STOUT (SMILES-TO-IUPAC-name translator) V2, which addresses this challenge by introducing a transformer-based model that translates string representations of chemical structures into IUPAC names. Trained on a dataset of nearly 1 billion SMILES strings and their corresponding IUPAC names, STOUT V2 demonstrates exceptional accuracy in generating IUPAC names, even for complex chemical structures. The model's ability to capture intricate patterns and relationships within chemical structures enables it to generate precise and standardised IUPAC names. While established deterministic algorithms remain the gold standard for systematic chemical naming, our work, enabled by access to OpenEye’s Lexichem software through an academic license, demonstrates the potential of neural approaches to complement existing tools in chemical nomenclature.Scientific contribution STOUT V2, built upon transformer-based models, is a significant advancement from our previous work. The web application enhances its accessibility and utility. By making the model and source code fully open and well-documented, we aim to promote unrestricted use and encourage further development.Graphical

Open-set fault diagnosis based on dynamic triple multivariate guided structural constraints

Abstract Existing deep learning-based models for mechanical fault diagnosis perform well in identifying predefined faults, but these models substantially degrade in performance when they encounter unknown faults. Thus, it is crucial to investigate open-set fault diagnosis that can handle unknown faults more efficiently. Current methods for open-set fault diagnosis in machinery face challenges by the lack of hierarchical structure in feature representation and the overlapping regions of known and unknown sample distributions. To solve these problems, we propose a composite dual-branching dynamic triplet multivariate constrained (CDDTMC) model for mechanical open-set fault diagnosis. The CDDTMC framework consists of three main core modules: a feature extraction module, a structural constraint module and a fault diagnosis module. In the feature extraction module a composite two-branch network is designed to extract hierarchical feature representations from known samples. After extracting the sample features, it represents the samples with structural constraints using multivariate constraints based on bidirectional dynamic triplet loss to achieve discriminativeness and compactness. Determining the optimal decision boundary for each category based on the structural constraints and uses a distance-based diagnostic algorithm to identify fault diagnosis. We conducted experiments on two publicly available bearing datasets to validate the performance of the model. The results show that the model improves the average accuracy classification by 10.73% and 13.84%, respectively, compared to other comparative model.

Digital transformation of cinematograph as a factor in forming a new visual aesthetic: an analysis of the foreign film discourse at the beginning of the XXI century

The purpose of the article is to consider the process of the digital cinematograph transformation as a factor in forming a new visual aesthetics through the analysis of the foreign cinema discourse at the beginning of the XXI century. The methodology of the research is based on a holistic approach, which involves formulating a picture of cinema in the digital world, based on different points of view of directors and film critics on the results of the impact of new technologies on reforming the foundations of cinema, resulting in the formation of a new visual aesthetic. This dictates the use of discourse analysis (a method of studying the content) of reviews, comments, surveys, etc. related to innovative approaches in the film industry. An attempt to define the influence of new media on cinematic aesthetics involves the use of the principle of techno(media)determinism, which helps to reveal the interconnections of technological, stylistic, programmatic, and socio-cultural processes. The results. It is confirmed that the computerization at the beginning of the XXI century actively contributed to the formation of new structures of representation and content, and also initiated a rethinking of the role of cinema in society. Digital technologies have freed traditional cinema from its aesthetic and stylistic tropes and languages, thus opening up cinematic aesthetics to the influences of automatisms integrated into digital tools and computer software. It is emphasized that, along with the choice of long shooting, deep focus and A. Bazin’s theory of the objective machine, where the filmmaker’s prejudices are minimized, proleptic editing and indexicality are also perceived as a choice, not as format-driven limitations. The computerization and dematerialization of the camera turns it into a co-creator and co-director, thereby dictating a new logic of seeing the mise-en-scene and the nature of authorship, and also determines the development of aesthetic styles that, thanks to the hybridity of digital images, go beyond the boundaries of visual realism. It is found out that the optimization and further spread of compositing, as well as digital intermediaries, changes the indexical nature of cinema and creates conditions for the consistent hybridization of visual forms and the transformation of aesthetic styles that become more similar in their characteristics to the visuals of video games and graphic novels than to photography. The scientific novelty. The article for the first time analyzes different points of view of foreign directors and film critics in the first decade of the XXI century on the consequences and prospects of the impact of digital technologies on reforming the foundations of cinema, and also considers several creative experiments within the framework of cinema 3.0 with new influences and genre hybridity that have influenced the formation of a new visual aesthetic. The practical significance. The results of this research can be used in the educational process when teaching a general course on the history of cinema and special courses on the digital transformation of audio-visual art and cinematograph in particular.

Distributed Representations for Cognitive Control in Frontal Medial Cortex.

In natural and artificial neural networks, modularity and distributed structure afford complementary but competing benefits. The former allows for hierarchical representations that can flexibly recombine modules to address novel problems, whereas the latter can benefit from less constrained training, potentially uncovering fruitful statistical regularities. Here, we investigate these competing demands in the context of human sequential behavior. First, we explore this setting by comparing the properties of several recurrent neural network models. We find that explicit hierarchical structure by itself fails to provide a critical performance advantage when compared with a "flat" model that does not incorporate hierarchical structure. However, hierarchy appears to facilitate cognitive control processes that support nonroutine behaviors and behaviors that are carried out under computational stress. Second, we compare these models against fMRI data using representational similarity analysis. We find that a model that incorporates so-called wiring costs in the cost function, which produces a hierarchically organized gradient of representational structure across the hidden layer of the neural network, best accounts for fMRI data collected from human participants in a previous study [Holroyd, C. B., Ribas-Fernandes, J. J. F., Shahnazian, D., Silvetti, M., & Verguts, T., Human midcingulate cortex encodes distributed representations of task progress. Proceedings of the National Academy of Sciences, U.S.A., 115, 6398-6403, 2018]. The results reveal that the ACC encodes distributed representations of sequential task context along a rostro-caudal gradient of abstraction: Rostral ACC encodes relatively abstract and temporally extended patterns of activity compared with those encoded by caudal ACC. These results provide insight into the role of ACC in motivation and cognitive control.

Assessing Forest Resources with Terrestrial and Backpack LiDAR: A Case Study on Leaf-On and Leaf-Off Conditions in Gari Mountain, Hongcheon, Republic of Korea

In Republic of Korea, the digital transformation of forest data has emerged as a critical priority at the governmental level. To support this effort, numerous case studies have been conducted to collect and analyze forest data. This study evaluated the accuracy of forest resource assessment methods using terrestrial laser scanning (TLS) and backpack personal laser scanning (BPLS) under Leaf-on and Leaf-off conditions in the Gari Mountain Forest Management Complex, Hongcheon, Republic of Korea. The research was conducted across six sample plots representing low, medium, and high stand densities, dominated by Larix kaempferi and Pinus koraiensis. Conventional field survey methods and LiDAR technologies were used to compare key forest attributes such as tree height and volume. The results revealed that Leaf-off LiDAR data exhibited higher accuracy in capturing tree height and canopy structures, particularly in high-density plots. In contrast, during the Leaf-on season, measurements of understory vegetation and lower canopy were hindered by foliage obstruction, reducing precision. Seasonal differences significantly impacted LiDAR measurement accuracy, with Leaf-off data providing a clearer and more reliable representation of forest structures. This study underscores the necessity of considering seasonal conditions to improve the accuracy of LiDAR-derived metrics. It offers valuable insights for enhancing forest inventory practices and advancing the application of remote sensing technologies in forest management.

Beauty and the Beast: Tracing Patriarchal Panopticism in Louise O’Neill’s Only Ever Yours

Only Ever Yours is a dystopian narrative underlining a feminist discourse that critiques the impractical obsessions of the masculine culture over the beauty standards of women. Set in an intensely regimented institutional structure in which young girls are tamed by an androcentric authority, the novel magnifies the gravity of patriarchal practices in reducing the status of women into mere objects by overemphasising their physical appearance and sensuality. This paper interrogates the living condition of the female characters that are subject to rigorous control mechanisms of the institution that seeks to mould them into ideal companions for young men adhering to patriarchal stereotypes. Drawing on Michel Foucault’s concept of ‘Panopticism’, this paper critically examines the representation of control structure in the select text, revealing its profound entrenchment of patriarchal ideologies. Moreover, it highlights the reciprocity between the panoptic system and the patriarchal gaze in enforcing a self-disciplinary inclination in women and examines its psychological repercussions on them.

Radiation dose and image quality in pediatric bitewing imaging.

Bitewing examinations are used to examine the pediatric dental status. The aim of this study was to compare the image quality and radiation dose between two different X-ray systems used for pediatric bitewing imaging. Data were obtained from both pediatric in vivo bitewing studies and phantom studies. Two X-ray systems were used: Trophy CCX digital (TCCX) (Trophy Irix 70, Marne-la-Vallee, France) and Planmeca Pro X (PPX) (Planmeca, Helsinki, Finland). An Unfors dosimeter (Unfors EDD-30, Billdal, Sweden) was used to measure the skin dose. Image quality was evaluated blindly by three experienced dentists using a Likert scale of 1-5 based on the visual representation of the anatomical structures with emphasis on caries. The highest scoring in vivo bitewing images were obtained with TCCX, but the skin dose was 36% higher (mean 3029±613 μGy) and 113% higher (mean 1364±276 μGy) with standard settings than with compared to PPX. The evaluation of image quality revealed a higher median value for all ratings of TCCX compared to PPX for both the patient and phantom studies, meaning that the images of TCCX were rated as higher quality than the images from PPX. No correlation was found between the median score and the skin dose or between the median score and the exposure times. A significant difference in radiation dose and image quality was found between TCCX and PPX in bitewing imaging. TCCX generally produced higher doses and better graded images, although all images were suitable for diagnosis.