Representação social da Covid-19 para a população de uma cidade de pequeno porte / Social representation of Covid-19 for the population of a small-sized city / Representación social del Covid-19 para la población de un pequeño pueblo

Objetivo: analisar a representação social da Covid-19 para a população geral de uma cidade de pequeno porte do Estado do Rio de Janeiro. Método: estudo qualitativo, apoiado na abordagem estrutural das representações sociais. Participaram 100 usuários de serviços de saúde. Os dados foram coletados por questionário sociodemográfico de evocações livres de palavras e roteiro de entrevista semiestruturada. Os dados foram analisados com o auxílio dos softwares Excel, EVOC 2005 e análise de conteúdo temático-categorial para contextualização das evocações respectivamente. Resultados: os termos do possível núcleo central foram: morte, sofrimento, cuidados, ansiedade-angústia e vacina. Na primeira periferia: medo e prevenção. À segunda periferia: informação-desinformação; desgoverno; ter-fé e proteção. A zona de contrate: doença; isolamento-social; dificuldades; catástrofe-mundial; desemprego e pandemia. Considerações finais: marcaram essa representação os impactos psicossociais negativos resultantes da desestruturação da vida e das mortes ocasionadas pela nova doença, no entanto o grupo aderiu as medidas de cuidados de proteção.

Objective: to analyze the social representation of Covid-19 among the general population of a small-sized city in the State of Rio de Janeiro. Method: Qualitative study, based on the structural approach of social representations. One hundred healthcare service users participated. Data were collected through a sociodemographic questionnaire, free word evocation, and a semi-structured interview guide. The data were analyzed using Excel software, EVOC 2005, and thematic-categorical content analysis for contextualization of the evocations, respectively. Results: the terms of the possible central core were: death, suffering, care, anxiety-distress, and vaccine. In the first periphery: fear and prevention. In the second periphery: information-misinformation; mismanagement; having faith and protection. The contrast zone: disease; social isolation; difficulties; global catastrophe; unemployment; and pandemic. Final considerations: this representation was marked by the negative psychosocial impacts resulting from the disruption of life and the deaths caused by the new disease; however, the group adhered to protective care measures.

Objetivo: analizar la representación social del Covid-19 para la población general de una pequeña ciudad del Estado de Río de Janeiro. Método: estudio cualitativo, basado en el enfoque estructural de las representaciones sociales. Participaron 100 usuarios de servicios de salud. Los datos se recolectaron mediante un cuestionario sociodemográfico con evocación libre de palabras y una guía de entrevista semiestructurada. Los datos fueron analizados utilizando lo software Excel y EVOC 2005 y análisis de contenido temático-categórico para contextualizar las evocaciones respectivamente. Resultados: los términos del posible núcleo central eran: muerte, sufrimiento, cuidados, ansiedad-angustia y vacuna. En la primera periferia: miedo y prevención. En la segunda periferia: información-desinformación; desgobierno; tener fe y protección. La zona de contraste: enfermedad; aislamiento-social; dificultades; catástrofe-mundial; desempleo y pandemia. Consideraciones finales: esta representación se caracterizó por los impactos psicosociales negativos derivados de la desestructuración de la vida y de las muertes causada por la nueva enfermedad, sin embargo, el grupo adhirió a las medidas de protección.

Object permanence in rooks (Corvus frugilegus): Individual differences and behavioral considerations.

Piagetian object permanence (OP) refers to the ability to know that an object continues to exist when out of sight: In humans, it develops in six stages. Species of great apes, other mammals, and birds (parrots, corvids, and pigeons) have been shown to possess partial or full OP, which is a prerequisite for more complex physical cognition abilities they may possess. In birds, the greatest variation is in Stage 6 (invisible displacements) and in "A-not-B" errors-incorrectly persevering in searching an empty location rewarded previously. Caching abilities have been invoked as holding explanatory power over results in corvids, for which this error is sometimes completely absent. The rook (Corvus frugilegus), a cognitively advanced, social, caching corvid, has not yet been studied for OP. This study applies tasks of one OP scale commonly adapted for nonhuman animals, Uzgiris and Hunt's Scale 1, as well as later-conceived tasks 16 and S, to a sample of adult, captive rooks. One rook demonstrated full OP (Stage 6b, multiple invisible displacements), whereas other individuals varied, attaining between Stages 5a (single visible displacements) and 6a (single invisible displacements). Like some corvids, a few made transient "A-not-B" errors. Behavioral considerations potentially underlying observed individual variation in results in rooks, including dominance, neophobia, past experiences, and individual idiosyncrasies, are examined. Rooks, like other corvids, possess well-developed OP abilities, and these results support the idea that exertion of executive control is required to avoid "A-not-B" errors, rather than caching abilities or developmental age, as previously suggested.

Joint learning of nonlinear representation and projection for fast constrained MRSI reconstruction.

PURPOSE: To develop and evaluate a novel method for computationally efficient reconstruction from noisy MR spectroscopic imaging (MRSI) data. METHODS: The proposed method features (a) a novel strategy that jointly learns a nonlinear low-dimensional representation of high-dimensional spectroscopic signals and a neural-network-based projector to recover the low-dimensional embeddings from noisy/limited data; (b) a formulation that integrates the forward encoding model, a regularizer exploiting the learned representation, and a complementary spatial constraint; and (c) a highly efficient algorithm enabled by the learned projector within an alternating direction method of multipliers (ADMM) framework, circumventing the computationally expensive network inversion subproblem. RESULTS: The proposed method has been evaluated using simulations as well as in vivo 1 $$ {}^1 $$ H and 31 $$ {}^{31} $$ P MRSI data, demonstrating improved performance over state-of-the-art methods, with about 6 × $$ \times $$ fewer averages needed than standard Fourier reconstruction for similar metabolite estimation variances and up to 100 × $$ \times $$ reduction in processing time compared to a prior neural network constrained reconstruction method. Computational and theoretical analyses were performed to offer further insights into the effectiveness of the proposed method. CONCLUSION: A novel method was developed for fast, high-SNR spatiospectral reconstruction from noisy MRSI data. We expect our method to be useful for enhancing the quality of MRSI or other high-dimensional spatiospectral imaging data.

How pain and body representations transform each other: A narrative review.

Pain, as a multidimensional and subjective experience, intertwines with various aspects of body representation, involving sensory, affective and motivational components. This review explores the bidirectional relationship between pain and body representations, emphasizing the impact of the sense of ownership on pain perception, the transformative impact of pain on motor imagery, the effects associated with vicarious pain perception on body representations and the role of pain in the maintenance of body representations in specific clinical conditions. Literature indicates complex interactions between pain and body representations, with the sense of ownership inducing analgesic effects in some cases and hyperalgesia in others, contingent upon factors such as the appearance of the affected limb. Pain sensations inform the body on which actions might be executed without harm, and which are potentially dangerous. This information impacts on motor imagery too, showing reduced motor imagery and increased reaction times in tasks where motor imagery involves the painful body parts. Finally, contrary to the conventional view, according to which pain impairs body representation, evidence suggests that pain can serve as an informative somatosensory index, preserving or even enhancing the representation of the absent or affected body parts. This bidirectional relationship highlights the dynamic and multifaceted nature of the interplay between pain and body representations, offering insights into the adaptive nature of the central nervous system in response to perceived bodily states.

Multi-angle speed-of-sound imaging with sparse sampling to characterize medical tissue properties.

Medical Speed-of-sound (SoS) imaging, which can characterize medical tissue properties better by quantifying their different SoS, is an effective imaging method compared with conventional B-mode ultrasound imaging. As a commonly used diagnostic instrument, a hand-held array probe features convenient and quick inspection. However, artifacts will occur in the single-angle SoS imaging, resulting in indistinguishable tissue boundaries. In order to build a high-quality SoS image, a number of raw data are needed, which will bring difficulties to data storage and processing. Compressed sensing (CS) theory offers theoretical support to the feasibility that a sparse signal can be rebuilt with random but less sampling data. In this study, we proposed an SoS reconstruction method based on CS theory to process signals obtained from a hand-held linear array probe with a passive reflector positioned on the opposite side. The SoS reconstruction method consists of three parts. Firstly, a sparse transform basis is selected appropriately for a sparse representation of the original signal. Then, considering the mathematical principles of SoS imaging, the ray-length matrix is used as a sparse measurement matrix to observe the original signal, which represents the length of the acoustic propagation path. Finally, the orthogonal matching pursuit algorithm is introduced for image reconstruction. The experimental result of the phantom proves that SoS imaging can clearly distinguish tissues that show similar echogenicity in B-mode ultrasound imaging. The simulation and experimental results show that our proposed method holds promising potential for reconstructing precision SoS images with fewer signal samplings, transmission, and storage.

An Explainable Graph Neural Framework to Identify Cancer-Associated Intratumoral Microbial Communities.

Microbes are extensively present among various cancer tissues and play critical roles in carcinogenesis and treatment responses. However, the underlying relationships between intratumoral microbes and tumors remain poorly understood. Here, a MIcrobial Cancer-association Analysis using a Heterogeneous graph transformer (MICAH) to identify intratumoral cancer-associated microbial communities is presented. MICAH integrates metabolic and phylogenetic relationships among microbes into a heterogeneous graph representation. It uses a graph transformer to holistically capture relationships between intratumoral microbes and cancer tissues, which improves the explainability of the associations between identified microbial communities and cancers. MICAH is applied to intratumoral bacterial data across 5 cancer types and 5 fungi datasets, and its generalizability and reproducibility are demonstrated. After experimentally testing a representative observation using a mouse model of tumor-microbe-immune interactions, a result consistent with MICAH's identified relationship is observed. Source tracking analysis reveals that the primary known contributor to a cancer-associated microbial community is the organs affected by the type of cancer. Overall, this graph neural network framework refines the number of microbes that can be used for follow-up experimental validation from thousands to tens, thereby helping to accelerate the understanding of the relationship between tumors and intratumoral microbiomes.

Multimodal fusion network for ICU patient outcome prediction.

Over the past decades, massive Electronic Health Records (EHRs) have been accumulated in Intensive Care Unit (ICU) and many other healthcare scenarios. The rich and comprehensive information recorded presents an exceptional opportunity for patient outcome predictions. Nevertheless, due to the diversity of data modalities, EHRs exhibit a heterogeneous characteristic, raising a difficulty to organically leverage information from various modalities. It is an urgent need to capture the underlying correlations among different modalities. In this paper, we propose a novel framework named Multimodal Fusion Network (MFNet) for ICU patient outcome prediction. First, we incorporate multiple modality-specific encoders to learn different modality representations. Notably, a graph guided encoder is designed to capture underlying global relationships among medical codes, and a text encoder with pre-fine-tuning strategy is adopted to extract appropriate text representations. Second, we propose to pairwise merge multimodal representations with a tailored hierarchical fusion mechanism. The experiments conducted on the eICU-CRD dataset validate that MFNet achieves superior performance on mortality prediction and Length of Stay (LoS) prediction compared with various representative and state-of-the-art baselines. Moreover, comprehensive ablation study demonstrates the effectiveness of each component of MFNet.

ST-SCSR: identifying spatial domains in spatial transcriptomics data via structure correlation and self-representation.

Recent advances in spatial transcriptomics (ST) enable measurements of transcriptome within intact biological tissues by preserving spatial information, offering biologists unprecedented opportunities to comprehensively understand tissue micro-environment, where spatial domains are basic units of tissues. Although great efforts are devoted to this issue, they still have many shortcomings, such as ignoring local information and relations of spatial domains, requiring alternatives to solve these problems. Here, a novel algorithm for spatial domain identification in Spatial Transcriptomics data with Structure Correlation and Self-Representation (ST-SCSR), which integrates local information, global information, and similarity of spatial domains. Specifically, ST-SCSR utilzes matrix tri-factorization to simultaneously decompose expression profiles and spatial network of spots, where expressional and spatial features of spots are fused via the shared factor matrix that interpreted as similarity of spatial domains. Furthermore, ST-SCSR learns affinity graph of spots by manipulating expressional and spatial features, where local preservation and sparse constraints are employed, thereby enhancing the quality of graph. The experimental results demonstrate that ST-SCSR not only outperforms state-of-the-art algorithms in terms of accuracy, but also identifies many potential interesting patterns.

What we mean when we say semantic: Toward a multidisciplinary semantic glossary.

Tulving characterized semantic memory as a vast repository of meaning that underlies language and many other cognitive processes. This perspective on lexical and conceptual knowledge galvanized a new era of research undertaken by numerous fields, each with their own idiosyncratic methods and terminology. For example, "concept" has different meanings in philosophy, linguistics, and psychology. As such, many fundamental constructs used to delineate semantic theories remain underspecified and/or opaque. Weak construct specificity is among the leading causes of the replication crisis now facing psychology and related fields. Term ambiguity hinders cross-disciplinary communication, falsifiability, and incremental theory-building. Numerous cognitive subdisciplines (e.g., vision, affective neuroscience) have recently addressed these limitations via the development of consensus-based guidelines and definitions. The project to follow represents our effort to produce a multidisciplinary semantic glossary consisting of succinct definitions, background, principled dissenting views, ratings of agreement, and subjective confidence for 17 target constructs (e.g., abstractness, abstraction, concreteness, concept, embodied cognition, event semantics, lexical-semantic, modality, representation, semantic control, semantic feature, simulation, semantic distance, semantic dimension). We discuss potential benefits and pitfalls (e.g., implicit bias, prescriptiveness) of these efforts to specify a common nomenclature that other researchers might index in specifying their own theoretical perspectives (e.g., They said X, but I mean Y).

The role of culture in the representation of probiotic foods.

Functional foods, and specifically probiotics, are important products present in retail worldwide. Probiotics comprise "live microorganisms that, when administered in adequate amounts, can confer a health benefit on the host". This study aimed to investigate the effect of culture on probiotic foods social representation. A total of 818 consumers from seven countries (Argentina, Brazil, Honduras, Mauritius, Mexico, Peru, and Slovenia) participated in the study. A free word association task was performed with the inductor term "probiotic food," followed by lemmatization, categorization, and prototypical analysis of the social representation. The results indicated that the term health was common in all countries studied, as well as other positive aspects such as benefits, well-being, and functional foods. This study helped to shed light and better understand the role of culture in the social representation of probiotics.

Linking the inner and outer mental representations of the body to social cognition skills: a Systematic Review and Meta-analysis.

An interesting interpretation of embodiment highlights the critical role of mental body representations (BR), including motor, somatosensory, and interoceptive formats, for social cognition. However, the nature of this relationship is still debated at the empirical level, with various studies arriving at different conclusions. Here, we present a systematic review and meta-analysis aimed to summarize data from 3466 participants in 21 studies to provide a more comprehensive understanding of the relationship between inner and outer BR and social cognition. We focused on two core social cognition aspects: empathy and Theory of Mind (ToM). Concerning the inner BR, our meta-analytic findings reveal a significant correlation between specific interoceptive dimensions (i.e., interoceptive sensibility and accuracy) and social cognition, which was stronger for empathy than ToM. Conversely, although further research is needed, functional BR that mainly involve the outer body processing (i.e., nonaction-oriented BR) may show positive links with ToM. These findings point to specific interactions between BR and social cognition skills, supporting multi-faceted and embodied social cognition models. However, we also identified critical knowledge gaps and highlighted the need for further investigation to deepen our theoretical understanding of these relationships and their implications for clinical practice.

Reduced representation bisulfite sequencing (RRBS) analysis reveals variation in distribution and levels of DNA methylation in white birch (Betula papyrifera) exposed to nickel.

Research in understanding the role of genetics and epigenetics in plant adaptations to environmental stressors such as metals is still in its infancy. The objective of the present study is to assess the effect of nickel on DNA methylation level and distribution in white birch (Betula papyrifera Marshall) using reduced representation bisulfite sequencing (RRBS). The distribution of methylated C sites of each sample revealed that the level of methylation was much higher in CG context varying between 54% and 65%, followed by CHG (24%-31.5%), and then CHH with the methylation rate between 3.3% and 5.2%. The analysis of differentially methylated regions (DMR) revealed that nickel induced both hypermethylation and hypomethylation when compared to water. Detailed analysis showed for the first time that nickel induced a higher level of hypermethylation compared to controls, while potassium triggers a higher level of hypomethylation compared to nickel. Surprisingly, the analysis of the distribution of DMRs revealed that 38%-42% were located in gene bodies, 20%-24% in exon, 19%-20% in intron, 16%-17% in promoters, and 0.03%-0.04% in transcription start site. RRBS was successful in detecting and mapping DMR in plants exposed to nickel.

Portrayal or Betrayal: Heteronormativity and the Realism Burden in the 2022 Disney+ Pride Collection.

The presence and quality of LGBTQ+ representation in film and television has important implications for viewers, especially LGBTQ+ youth. This study conducted a content analysis of the 2022 Disney+ Pride Collection, a list of movies and episodes designated by Disney as including LGBTQ+ characters and stories. This analysis examined the depiction of LGBTQ+ fictional characters, finding that Black LGBTQ+ characters were more likely to be presented as adults and in romantic relationships compared to characters of other races, suggesting that their intersecting identities influence their portrayal toward more heteronormative scripts. In what we argue is a heteronormative evolution of the butch/femme binary, lesbian characters were frequently presented on a light/dark binary. Further, in what we describe as a "realism burden," portrayals of discrimination and LGBTQ+ identity labeling were limited to realistic fiction and adult media, and were absent from the post-gay worlds of sci-fi and fantasy.

Harnessing collective structure knowledge in data augmentation for graph neural networks.

Graph neural networks (GNNs) have achieved state-of-the-art performance in graph representation learning. Message passing neural networks, which learn representations through recursively aggregating information from each node and its neighbors, are among the most commonly-used GNNs. However, a wealth of structural information of individual nodes and full graphs is often ignored in such process, which restricts the expressive power of GNNs. Various graph data augmentation methods that enable the message passing with richer structure knowledge have been introduced as one main way to tackle this issue, but they are often focused on individual structure features and difficult to scale up with more structure features. In this work we propose a novel approach, namely collective structure knowledge-augmented graph neural network (CoS-GNN), in which a new message passing method is introduced to allow GNNs to harness a diverse set of node- and graph-level structure features, together with original node features/attributes, in augmented graphs. In doing so, our approach largely improves the structural knowledge modeling of GNNs in both node and graph levels, resulting in substantially improved graph representations. This is justified by extensive empirical results where CoS-GNN outperforms state-of-the-art models in various graph-level learning tasks, including graph classification, anomaly detection, and out-of-distribution generalization.

Ontology of clinical practice guidelines for Integrated Traditional Chinese and Western Medicine.

OBJECTIVE: Clinical practice guidelines (CPGs) for Integrated Traditional Chinese and Western Medicine (TCM and WM) are important medical documents used to assist medical decision-making and are of great significance for standardizing clinical pathways. However, due to the constraints of text format, it is difficult for Integrated TCM and WM CPGs to play a real role in medical practice. In addition, how to standardize the structure and semantic relationships between Integrated TCM and WM CPG knowledge, and realize the construction of computable, sharable and reliable CPGs, remains an urgent issue to be addressed. Therefore, we are proposing an ontology of CPGs for Integrated TCM and WM. METHODS: We first initialized domain concepts and relationships to ensure the accuracy of the ontology knowledge structure. We then screened CPGs that meet the standards for Integrated TCM and WM, analyzed and classified the contents, and extracted the common structures. Based on the seven-step ontology construction method combined with inference-complement, referring to the representation methods and hierarchical relationships of terms and concepts in MeSH, ICD-10, SNOMED-CT, and other ontologies and terminology sets, we formed the concept structure and semantic relationship tables for the ontology. We also achieved the matching and mapping between the ontology and reference ontologies and term sets. Next, we defined the aspects and constraints of properties, selected multiple Integrated TCM and WM CPGs as instances to populate, and used ontology reasoning tools and formulated defined inference rules to reason and extend the ontology. Finally, we evaluated the performance of the ontology. RESULTS: The content of the Integrated TCM and WM CPGs is divided into nine parts: basic information, background, development method, clinical question, recommendation, evidence, conclusion, result, and reason for recommendations. The Integrated TCM and WM CPG ontology has 152 classes and defines 90 object properties and 114 data properties, with a maximum classification depth of 4 layers. The terms of disease, drug and examination item names in the ontology have been standardized. CONCLUSIONS: This study proposes an Integrated TCM and WM CPG ontology. The ontology adopts a modular design, which has both sharing and scaling ability, and can express rich guideline knowledge. It provides important support for the semantic processing and computational application of guideline documents.

Neighborhood structure-guided brain functional networks estimation for mild cognitive impairment identification.

The adoption and growth of functional magnetic resonance imaging (fMRI) technology, especially through the use of Pearson's correlation (PC) for constructing brain functional networks (BFN), has significantly advanced brain disease diagnostics by uncovering the brain's operational mechanisms and offering biomarkers for early detection. However, the PC always tends to make for a dense BFN, which violates the biological prior. Therefore, in practice, researchers use hard-threshold to remove weak connection edges or introduce l 1-norm as a regularization term to obtain sparse BFNs. However, these approaches neglect the spatial neighborhood information between regions of interest (ROIs), and ROI with closer distances has higher connectivity prospects than ROI with farther distances due to the principle of simple wiring costs in resent studies. Thus, we propose a neighborhood structure-guided BFN estimation method in this article. In detail, we figure the ROIs' Euclidean distances and sort them. Then, we apply the K-nearest neighbor (KNN) to find out the top K neighbors closest to the current ROIs, where each ROI's K neighbors are independent of each other. We establish the connection relationship between the ROIs and these K neighbors and construct the global topology adjacency matrix according to the binary network. Connect ROI nodes with k nearest neighbors using edges to generate an adjacency graph, forming an adjacency matrix. Based on adjacency matrix, PC calculates the correlation coefficient between ROIs connected by edges, and generates the BFN. With the purpose of evaluating the performance of the introduced method, we utilize the estimated BFN for distinguishing individuals with mild cognitive impairment (MCI) from the healthy ones. Experimental outcomes imply this method attains better classification performance than the baselines. Additionally, we compared it with the most commonly used time series methods in deep learning. Results of the performance of K-nearest neighbor-Pearson's correlation (K-PC) has some advantage over deep learning.

The allocentric nature of ground-surface representation: A study of depth and location perception.

When observers perceive 3D relations, they represent depth and spatial locations with the ground as a reference. This frame of reference could be egocentric, that is, moving with the observer, or allocentric, that is, remaining stationary and independent of the moving observer. We tested whether the representation of relative depth and of spatial location took an egocentric or allocentric frame of reference in three experiments, using a blind walking task. In Experiments 1 and 2, participants either observed a target in depth, and then straightaway blind walked for the previously seen distance between the target and the self; or walked to the side or along an oblique path for 3 m and then started blind walking for the previously seen distance. The difference between the conditions was whether blind walking started from the observation point. Results showed that blind walking distance varied with the starting locations. Thus, the represented distance did not seem to go through spatial updating with the moving observer and the frame of reference was likely allocentric. In Experiment 3, participants observed a target in space, then immediately blind walked to the target, or blind walked to another starting point and then blind walked to the target. Results showed that the end location of blind walking was different for different starting points, which suggested the representation of spatial location is likely to take an allocentric frame of reference. Taken together, these experiments convergingly suggested that observers used an allocentric frame of reference to construct their mental space representation.

Knowledge-based representation: Patient engagement in drug development.

INTRODUCTION: Recently, different actors have intensified their efforts to make drug development more participatory. They have produced many frameworks, tools and dedicated fora, where patients are portrayed as relevant stakeholders to be involved throughout the entire drug development trajectory. To better understand what such participatory efforts entail, in this article, we investigate how patient representation is configured in drug development and what patients can engage as representatives in this field. METHODS: This is a qualitative study based on the thematic analysis of 40 semistructured interviews with different stakeholders in the field and three patient engagement How-To guides (HTGs) complemented by observations of two sessions of the Patient Engagement Open Forum (PEOF) and a patient expert training of the European Patients' Academy on Therapeutic Innovation (EUPATI). FINDINGS: The emerging practices of patient engagement in drug development configure representation as hinging upon three types of knowledge-drug development knowledge, autobiographical knowledge and community knowledge-and a specific set of skills. We discern a new kind of representation based on these findings, termed 'knowledge-based representation', which appears to more accurately describe how patients are expected to represent others in drug development. CONCLUSION: Even though knowledge-based representation may be understood as an attempt to downplay the political aspects of representation in favour of its epistemic elements, the political processes involved in patient representation in drug development cannot be ignored. The extent to which reliance on knowledge-based representation will contribute to democratic decision-making is likely to depend on the resources needed to develop the types of knowledge relevant to representation work and on how these types of knowledge are determined. PATIENT OR PUBLIC CONTRIBUTION: Patient representatives and practitioners in the field of patient engagement (including 13 interviewees, representatives of EUPATI and HTG developers) gave feedback on the interpretation of the findings during a multistakeholder workshop we organised. We also sent an interviewee an extended draft and discussed it during an online meeting. Claudia Egher presented these findings at a PEOF session in June 2023, which further contributed to their validation.

Augmented reality for chemistry education to promote the use of chemical terminology in teacher training.

Chemistry as a whole is divided into three levels. The macroscopic level describes real, observable phenomena of the material world. The submicroscopic level focuses on particles. The representative level includes pictorial and symbolic representations to visualize substance in its nature. Students often have problems separating these levels and conceptually transfer each of the three levels to the other. Therefore, teachers need to use chemical terminology correctly when teaching the substance-particle concept. Augmented Reality (AR) connects real and virtual world. The observer physically moves in a real environment that integrates virtual elements. The AR technology has great potential for learning in the subject chemistry, especially when it comes to making the "invisible" visible and illustrating scientific phenomena at particle level. The simultaneous presentation should avoid split-attention and offers new possibilities to interactively deal with (M)ER. The question arises whether AR has a positive effect on the use of technical language and the associated understanding of the concept of dealing with (M)ER at the substance and particle levels. With an AR app on the tablet and the AR glasses, the chemical processes of a real experiment are represented by AR visualizations. Therefore, the AR app was piloted. This study captured the chemistry handling with (M)ER of chemistry teachers (N = 30) using a pre-post survey. The participating preservice teachers are described below. Each test includes five tasks elaborated by thinking aloud. The thinking-aloud protocols to acquire the use of the chemical terminology are evaluated in MAXQDA.

State Space Representation of Jiles-Atherton Hysteresis Model and Application for Closed-Loop Control.

Hysteresis is a fundamental characteristic of magnetic materials. The Jiles-Atherton (J-A) hysteresis model, which is known for its few parameters and clear physical interpretations, has been widely employed in simulating hysteresis characteristics. To better analyze and compute hysteresis behavior, this study established a state space representation based on the primitive J-A model. First, based on the five fundamental equations of the J-A model, a state space representation was established through variable substitution and simplification. Furthermore, to address the singularity problem at zero crossings, local linearization was obtained through an approximation method based on the actual physical properties. Based on these, the state space model was implemented using the S-function. To validate the effectiveness of the state space model, the hysteresis loops were obtained through COMSOL finite element software and tested on a permalloy toroidal sample. The particle swarm optimization (PSO) method was used for parameter identification of the state space model, and the identification results show excellent agreement with the simulation and test results. Finally, a closed-loop control system was constructed based on the state space model, and trajectory tracking experiments were conducted. The results verify the feasibility of the state space representation of the J-A model, which holds significant practical implications in the development of magnetically shielded rooms, the suppression of magnetic interference in cold atom clocks, and various other applications.