Representações sociais da autoproteção profissional e pessoal para Enfermeiros no contexto da COVID-19 / Social Representations of Professional and Personal Self-Protection for Nurses in the COVID-19 Context / Representaciones sociales de la autoprotección profesional y personal para enfermeros en el contexto del COVID-19

Objetivo: analisar as representações sociais da autoproteção profissional e pessoal dos enfermeiros no contexto da Covid-19. Método: estudo descritivo, com abordagem qualitativa, pautado na Teoria das Representações Sociais, abordagem processual. Participaram 30 enfermeiros que realizaram cuidados de enfermagem a pacientes com Covid-19, no Estado do Rio de Janeiro. A coleta de dados ocorreu por meio de entrevista semiestruturada entre abril e maio 2021, realizada em plataforma online. O tratamento dos dados foi realizado com a técnica de análise de conteúdo temático-categorial. Resultado: a análise resultou em quatro categorias: Representações sociais e práticas relativas a Covid-19; Representações sociais da autoproteção; Desafios no enfrentamento da Covid-19: autoproteção pessoal e profissional; e Estrutura e fluxo de atendimento e sua relação com a autoproteção profissional. Considerações finais: houve engajamento dos profissionais no uso dos EPI na pandemia, o que aponta uma preocupação quanto à autoproteção na prestação de cuidados à pacientes com a Covid-19.

Objective: to analyze the social representations of professional and personal self-protection among nurses in the context of Covid-19. Method: a descriptive study with a qualitative approach, based on the Social Representations Theory, using a procedural approach. Thirty nurses who provided nursing care to Covid-19 patients in the State of Rio de Janeiro participated in the study. Data collection took place through semi-structured interviews between April and May 2021, conducted on an online platform. Data analysis was performed using the thematic-categorical content analysis technique. Results: The analysis resulted in four categories of social representations and practices related to challenges in coping with Covid-19: personal and professional self-protection; Care structure and flow and its relationship with professional self-protection. Final considerations: There was engagement of professionals in the use of PPE during the pandemic, indicating a concern regarding self-protection in providing care to patients with Covid-19.

Objetivo: analizar las representaciones sociales de la autoprotección profesional y personal de los enfermeros en el contexto del Covid-19. Método: estudio descriptivo, con enfoque cualitativo, basado en la Teoría de las Representaciones Sociales, enfoque procesal. Participaron 30 enfermeros que brindaron cuidados de enfermería a pacientes con Covid-19 en el Estado de Río de Janeiro. La recolección de datos se llevó a cabo a través de entrevistas semiestructuradas entre abril y mayo de 2021, realizadas en una plataforma online. El procesamiento de los datos se realizó mediante la técnica de análisis de contenido temático categorial. Resultado: del análisis surgieron cuatro categorías: Representaciones sociales y prácticas relacionadas con el Covid-19; Representaciones sociales de la autoprotección; Desafíos de enfrentar el Covid-19: autoprotección personal y profesional; y Estructura y flujo de atención y su relación con la autoprotección profesional. Consideraciones finales: los profesionales demostraron compromiso en el uso de EPP durante la pandemia, lo que indica que se preocupaban por autoprotegerse durante la atención a pacientes con Covid-19.

Adult attachment assessed via the ASA and AAI: Empirical convergence and links with autonomic physiological responding during attachment assessments.

This study examined the empirical convergence of Attachment Script Assessment (ASA) deactivation, hyperactivation, and anomalous scripts with conceptually corresponding attachment patterns assessed via the Adult Attachment Interview (AAI), and the significance of ASA dimensions for autonomic physiological reactivity during adult attachment assessments. Young adults' (50% male; Mage = 19 years; 80% White/European American) ASA deactivation, hyperactivation, and anomalous content were significantly associated with AAI dismissing (r = .26-.38), preoccupied (r = .31-.35), and unresolved (r = .37) states of mind, respectively. ASA hyperactivation and anomalous content were associated with heightened RSA reactivity to the AAI and ASA, aligning with expectations that these attachment patterns capture the tendency to heighten expressions of negative, traumatic experiences. ASA deactivation was associated with smaller increases in electrodermal activity to the ASA-indicative of less sympathetic arousal-converging with the tendency of individuals higher in deactivation to avoid discussing attachment themes in the ASA.

The Attachment Script Assessment: Introduction of a coding system to evaluate deactivation, hyperactivation, and anomalous content.

Although research on adult attachment has yielded insight into the legacy of attachment for functioning in adulthood, methodological challenges persist in the assessment of adult attachment. The Adult Attachment Interview (AAI) offers a rich assessment of secure, insecure, and unresolved states of mind. However, it is resource intensive to administer and code. Attachment Script Assessment (ASA) offers a resource-effective alternative to the AAI. However, the ASA coding system only yields a single, security-like dimension: secure base script knowledge. Here, we introduce a complementary coding system for the ASA to assess attachment deactivation (i.e. script characterized by limited interpersonal connection and minimization of attachment problems/emotions), hyperactivation (i.e. script in which attachment-relevant problems and negative emotions are heightened), and anomalous content (i.e. script in which attachment problems contain elements of fear and/or disorientation); and we discuss the conceptual convergence of these scripts with corresponding patterns of attachment insecurity and disorganization.

A mathematical theory of relational generalization in transitive inference.

Humans and animals routinely infer relations between different items or events and generalize these relations to novel combinations of items. This allows them to respond appropriately to radically novel circumstances and is fundamental to advanced cognition. However, how learning systems (including the brain) can implement the necessary inductive biases has been unclear. We investigated transitive inference (TI), a classic relational task paradigm in which subjects must learn a relation ([Formula: see text] and [Formula: see text]) and generalize it to new combinations of items ([Formula: see text]). Through mathematical analysis, we found that a broad range of biologically relevant learning models (e.g. gradient flow or ridge regression) perform TI successfully and recapitulate signature behavioral patterns long observed in living subjects. First, we found that models with item-wise additive representations automatically encode transitive relations. Second, for more general representations, a single scalar "conjunctivity factor" determines model behavior on TI and, further, the principle of norm minimization (a standard statistical inductive bias) enables models with fixed, partly conjunctive representations to generalize transitively. Finally, neural networks in the "rich regime," which enables representation learning and improves generalization on many tasks, unexpectedly show poor generalization and anomalous behavior on TI. We find that such networks implement a form of norm minimization (over hidden weights) that yields a local encoding mechanism lacking transitivity. Our findings show how minimal statistical learning principles give rise to a classical relational inductive bias (transitivity), explain empirically observed behaviors, and establish a formal approach to understanding the neural basis of relational abstraction.

The Wise Mind Balances the Abstract and the Concrete.

We explored how individuals' mental representations of complex and uncertain situations impact their ability to reason wisely. To this end, we introduce situated methods to capture abstract and concrete mental representations and the switching between them when reflecting on social challenges. Using these methods, we evaluated the alignment of abstractness and concreteness with four integral facets of wisdom: intellectual humility, open-mindedness, perspective-taking, and compromise-seeking. Data from North American and UK participants (N = 1,151) revealed that both abstract and concrete construals significantly contribute to wise reasoning, even when controlling for a host of relevant covariates and potential response bias. Natural language processing of unstructured texts among high (top 25%) and low (bottom 25%) wisdom participants corroborated these results: semantic networks of the high wisdom group reveal greater use of both abstract and concrete themes compared to the low wisdom group. Finally, employing a repeated strategy-choice method as an additional measure, our findings demonstrated that individuals who showed a greater balance and switching between these construal types exhibited higher wisdom. Our findings advance understanding of individual differences in mental representations and how construals shape reasoning across contexts in everyday life.

Three-dimensional representation of the pure electric-dipole and the mixed first hyperpolarizabilities: The modified unit sphere representation.

In this work, the theory of the modified unit sphere representation (mUSR) has been proposed as a computational tool suitable for the three-dimensional representation of the pure electric-dipole [ ß λ µ ν ( - 2 ω ; ω , ω ) $$ {\beta}_{\lambda \mu \nu}\left(-2\omega; \omega, \omega \right) $$ ] as well as of the mixed electric-dipole/magnetic-dipole [ α J λ µ ν ( - 2 ω ; ω , ω ) $$ {}^{\alpha }{J}_{\lambda \mu \nu}\left(-2\omega; \omega, \omega \right) $$ and ß J λ µ ν ( - 2 ω ; ω , ω ) $$ {}^{\beta }{J}_{\lambda \mu \nu}\left(-2\omega; \omega, \omega \right) $$ ] or electric-dipole/electric-quadrupole [ α K λ µ ν o ( - 2 ω ; ω , ω ) $$ {}^{\alpha }{K}_{\lambda \mu \nu o}\left(-2\omega; \omega, \omega \right) $$ and ß K λ µ ν o ( - 2 ω ; ω , ω ) $$ {}^{\beta }{K}_{\lambda \mu \nu o}\left(-2\omega; \omega, \omega \right) $$ ] first hyperpolarizabilities. These five quantities are Cartesian tensors and they are responsible for the chiral signal in the chiroptical version of the hyper-Rayleigh scattering (HRS) spectroscopy, namely the HRS optical activity (HRS-OA) spectroscopy. For the first time, for each hyperpolarizability, alongside with the three-dimensional representation of the whole (i.e., reducible) Cartesian tensors, the mUSRs are developed for each of the irreducible Cartesian tensors (ICTs) that constitute them. This scheme has been applied to a series of three (chiral) hexahelicene molecules containing different degrees of electron-withdrawing (quinone) groups and characterized by the same (positive) handedness. For these molecules, the mUSR shows that, upon substitution, the most remarkable qualitative and semi-quantitative (enhancement of the molecular responses) effects are obtained for the pure electric-dipole and for the mixed electric-dipole/magnetic-dipole hyperpolarizabilities.

Distractor similarity and category variability effects in search.

Categorical search involves looking for objects based on category information from long-term memory. Previous research has shown that search efficiency in categorical search is influenced by target/distractor similarity and category variability (i.e., heterogeneity). However, the interaction between these factors and their impact on different subprocesses of search remains unclear. This study examined the effects of target/distractor similarity and category variability on processes of categorical search. Using multidimensional scaling, we manipulated target/distractor similarity and measured category variability for target categories that participants searched for. Eye-tracking data were collected to examine attentional guidance and target verification. The results demonstrated that the effect of category variability on response times (RTs) was dependent on the level of target/distractor similarity. Specifically, when distractors were highly similar to target categories, there was a negative relation between RTs and variability, with low variability categories producing longer RTs than higher variability categories. Surprisingly, this trend was only present in the eye-tracking measures of target verification but not attentional guidance. Our results suggest that searchers more effectively guide attention to low-variability categories compared to high-variability categories, regardless of the degree of similarity between targets and distractors. However, low category variability interferes with target match decisions when distractors are highly similar to the category, thus the advantage that low category variability provides to searchers is not equal across processes of search.

Parental prenatal representations of the child are related to 18-month-old children's social-emotional competence.

Parental representations of the child are linked to positive developmental outcomes in children, but the impact of prenatal representations on early social-emotional development, particularly from fathers, is less understood. This study explores how fathers' and mothers' prenatal representations within two-parent families are associated with early social-emotional development. Prenatal representations of fathers (n = 88) and mothers (n = 92) were assessed between 28 and 32 weeks of gestation using the Working Model of the Child Interview, categorizing them as balanced or nonbalanced. The children's (n = 97; 49.5% girls) social-emotional and behavioral problems and competencies were measured at 18 months using the Brief Infant-Toddler Social and Emotional Assessment. Balanced prenatal representations of both parents were related to higher social-emotional competence in toddlers. However, prenatal representations were not related to social-emotional and behavioral problems. The results highlight the benefits of balanced prenatal representations in promoting early social-emotional competence in children.

Representações culturais do câncer de mama: uma revisão de escopo / Breast cancer cultural representations: a scoping review

Resumo O objetivo deste artigo é mapear a produção científica global sobre representações sociais ou culturais e câncer de mama no campo da saúde coletiva e discutir como esse fenômeno se apresenta na literatura. Foi realizada uma revisão de escopo, tendo como norte a seguinte pergunta: "Como representações culturais ou sociais no contexto do câncer de mama são descritas na produção científica global no âmbito da saúde coletiva?". As buscas foram realizadas em cinco fontes de literatura científica, sendo incluídos 45 estudos. O tratamento analítico seguiu a técnica de análise de conteúdo na modalidade temática. O acervo analisado pode ser tematizado nas seguintes categorias: (1) Comprometimento na imagem corporal e nas interações; (2) Espiritualidade; (3) Perda do controle da vida; (4) Seguir com a vida e (5) Associação a questões étnico-raciais. Apesar dos avanços da biomedicina, observa-se que nas representações do câncer de mama ainda permanecem metáforas associadas ao câncer no século passado. Conclui-se que, dentre outros aspectos, a atenção a mulheres com câncer de mama não pode ser pautada apenas pelas abordagens biomédica e epidemiológica, uma vez que essa doença é atravessada por saberes que competem com essas abordagens.

Abstract This article aims to map the global scientific production on social or cultural representations and breast cancer in Public Health and discuss how it is presented in the literature. We conducted a scoping review guided by the question: "How are cultural or social representations in the context of breast cancer described in the global scientific Public Health production?". We searched for works in five scientific literature sources and included 45 studies. The analytical process followed the content analysis technique in the thematic modality. The analyzed collection can be thematized into the following categories: (1) Compromised body image and interactions, (2) Spirituality, (3) Loss of control over life, (4) Going on with life, and (5) Association with ethnic-racial issues. Despite advances in biomedicine, we observed that representations of breast cancer still have metaphors associated with cancer in the last century. We conclude that, among other aspects, care for women with breast cancer cannot be guided only by biomedical and epidemiological approaches since this disease is traversed by knowledge that competes with these approaches.

The time course of person perception from voices in the brain.

When listeners hear a voice, they rapidly form a complex first impression of who the person behind that voice might be. We characterize how these multivariate first impressions from voices emerge over time across different levels of abstraction using electroencephalography and representational similarity analysis. We find that for eight perceived physical (gender, age, and health), trait (attractiveness, dominance, and trustworthiness), and social characteristics (educatedness and professionalism), representations emerge early (~80 ms after stimulus onset), with voice acoustics contributing to those representations between ~100 ms and 400 ms. While impressions of person characteristics are highly correlated, we can find evidence for highly abstracted, independent representations of individual person characteristics. These abstracted representationse merge gradually over time. That is, representations of physical characteristics (age, gender) arise early (from ~120 ms), while representations of some trait and social characteristics emerge later (~360 ms onward). The findings align with recent theoretical models and shed light on the computations underpinning person perception from voices.

MvMRL: a multi-view molecular representation learning method for molecular property prediction.

Effective molecular representation learning is very important for Artificial Intelligence-driven Drug Design because it affects the accuracy and efficiency of molecular property prediction and other molecular modeling relevant tasks. However, previous molecular representation learning studies often suffer from limitations, such as over-reliance on a single molecular representation, failure to fully capture both local and global information in molecular structure, and ineffective integration of multiscale features from different molecular representations. These limitations restrict the complete and accurate representation of molecular structure and properties, ultimately impacting the accuracy of predicting molecular properties. To this end, we propose a novel multi-view molecular representation learning method called MvMRL, which can incorporate feature information from multiple molecular representations and capture both local and global information from different views well, thus improving molecular property prediction. Specifically, MvMRL consists of four parts: a multiscale CNN-SE Simplified Molecular Input Line Entry System (SMILES) learning component and a multiscale Graph Neural Network encoder to extract local feature information and global feature information from the SMILES view and the molecular graph view, respectively; a Multi-Layer Perceptron network to capture complex non-linear relationship features from the molecular fingerprint view; and a dual cross-attention component to fuse feature information on the multi-views deeply for predicting molecular properties. We evaluate the performance of MvMRL on 11 benchmark datasets, and experimental results show that MvMRL outperforms state-of-the-art methods, indicating its rationality and effectiveness in molecular property prediction. The source code of MvMRL was released in https://github.com/jedison-github/MvMRL.

Unsupervised EEG-Based Seizure Anomaly Detection with Denoising Diffusion Probabilistic Models.

While many seizure detection methods have demonstrated great accuracy, their training necessitates a substantial volume of labeled data. To address this issue, we propose a novel method for unsupervised seizure anomaly detection called SAnoDDPM, which uses denoising diffusion probabilistic models (DDPM). We designed a novel pipeline that uses a variable lower bound on Markov chains to identify potential values that are unlikely to occur in anomalous data. The model is first trained on normal data, then anomalous data is input to the trained model. The model resamples the anomalous data and converts it to normal data. Finally, the presence of seizures can be determined by comparing the before and after data. Moreover, the input 2D spectrograms are encoded into vector-quantized representations, which enables powerful and efficient DDPM while maintaining its quality. Experimental comparisons on the publicly available datasets, CHB-MIT and TUH, show that our method delivers better results, significantly reduces inference time, and is suitable for deployment in a clinical environments. As far as we are aware, this is the first DDPM-based method for seizure anomaly detection. This novel approach significantly contributes to the progression of seizure detection algorithms, thereby augmenting their practicality in clinical settings.

Evaluation of upper limb perception after stroke with the new Affected Limb Perception Questionnaire (ALPQ): a study protocol.

BACKGROUND: Following a stroke, patients may suffer from alterations in the perception of their own body due to an acquired deficit in body representations. While such changes may impact their quality of life as well as recovery, they are not systematically assessed in clinical practice. This study aims at providing a better understanding of the rate, evolution, and impact on recovery of upper limb (UL) body perceptions (BPs) alterations following stroke. In addition, we will investigate associations among BPs alterations items, their associations with the sensorimotor functions, UL activity, damages in brain structure and connectivity. METHODS: We developed a new tool named ALPQ (for Affected Limb Perception Questionnaire) to address the present study objectives. It assesses subjective alterations in the perception of the affected UL following stroke, by measuring several dimensions, namely: anosognosia for hemiplegia, anosodiaphoria for hemiplegia, hemiasomatognosia, somatoparaphrenia, personification of the affected limb, illusion of modification of physical characteristics (temperature, weight, length), illusory movements, super- or undernumerary limb, UL disconnection, misoplegia, and involuntary movement. This study combines a cross-sectional and longitudinal design. The completed data sample will include a minimum of 60 acute and 100 sub-acute stroke patients. When possible, patients are followed up to the chronic stage. Complementary evaluations are administered to assess patients' sensorimotor and cognitive functions as well as UL activity, and brain lesions will be analysed. DISCUSSION: This study will provide a better understanding of BPs alterations following stroke: their rate and evolution, as well as their associations with sensorimotor deficit, cognitive profile and UL activity, brain lesions and recovery. Ultimately, the results could support the personalization of rehabilitation strategy according to patients' UL perception to maximize their recovery. STUDY REGISTRATION: The protocol for this study has been pre-registered on the Open Science Framework on July the 7th, 2021: https://osf.io/p6v7f .

Flexible Conceptual Representations.

A view that has been gaining prevalence over the past decade is that the human conceptual system is malleable, dynamic, context-dependent, and task-dependent, that is, flexible. Within the flexible conceptual representation framework, conceptual representations are constructed ad hoc, forming a different, idiosyncratic instantiation upon each occurrence. In this review, we scrutinize the neurocognitive literature to better understand the nature of this flexibility. First, we identify some key characteristics of these representations. Next, we consider how these flexible representations are constructed by addressing some of the open questions in this framework: We review the age-old question of how to reconcile flexibility with the apparent need for shareable stable definitions to anchor meaning and come to mutual understanding, as well as some newer questions we find critical, namely, the nature of relations among flexible representations, the role of feature saliency in activation, and the viability of all-or-none feature activations. We suggest replacing the debate about the existence of a definitional stable core that is obligatorily activated with a question of the degree and probability of activation of the information constituting a conceptual representation. We rely on published works to suggest that (1) prior featural salience matters, (2) feature activation may be graded, and (3) Bayesian updating of prior information according to current demands offers a viable account of how flexible representations are constructed. This proposal provides a theoretical mechanism for incorporating a changing momentary context into a constructed representation, while still preserving some of the concept's constituent meaning.

Prospective Associations Between Personality Traits and Major Depressive Disorder Symptom Severity: The Mediating Role of Illness Representations.

Major depressive disorder (MDD) patients' personality traits and illness representations are linked to MDD severity. However, the associations between personality and illness representations in MDD and the mediating role of illness representations between personality and MDD severity have not been investigated. This study aimed to prospectively investigate the aforementioned associations and the possible mediating role of illness representations between personality and MDD severity. One hundred twenty-five patients with a MDD diagnosis, aged 48.18 ± 13.92 (84% females), participated in the study. Personality traits were measured with the Traits Personality Questionnaire at baseline. Illness representations were measured with the Illness Perception Questionnaire-Mental Health about five months later (mean = 5.08 ± 1.14 months). MDD severity was measured about 10 months after the baseline assessment (mean = 9.53 ± 2.36 months) with the Beck Depression Inventory. SPSS 29 and AMOS 27 were used to conduct correlational and parallel mediation analyses. According to the results, Neuroticism was positively and Extraversion was negatively linked to MDD severity. Negative MDD impact representations fully mediated these associations. Neuroticism and Extraversion are linked to future MDD severity through patients' representations of MDD's impact. Restructuring maladaptive representations about MDD's impact can be a promising way to reduce symptom severity in patients with high Neuroticism and low Extraversion levels.

Representations of illness and treatments in patients with desmoid tumors: A thematic content analysis of a qualitative study.

PURPOSE: Desmoid tumors are a rare and complex disease characterized by a great diversity in its forms, localizations, and prognosis. Both the disease and the treatment can have a significant impact on quality of life in patients. Given the complexity of the disease and its rarity, the literature on patients' experience with the disease scarce. The purpose of this study is to investigate illness representations and subjective experience in participants affected with desmoid tumors. METHODS: Telephonic semi-directive interviews were used in French patients over 18 years, diagnosed with desmoid tumor. Data were analyzed through a general inductive method to identify emergent general themes in participants' discourse. RESULTS: Participants (8 women, 7 men) in this study were aged between 27 and 71. The analysis revealed eight major themes relative to representations of illness and treatment, live with the illness, the impact of illness on relationships with others, the illness and medical pathways, and the identity changes caused by the illness. The two most salient themes were illness and treatment representations and life with the illness. Those themes were chosen for this study. CONCLUSIONS: The results provide new insights on representation of and experience with desmoid tumors in patients. It brings arguments for the necessity of development wider systematic study to explore those variables in a larger sample during all the illness pathway. Indeed, this population meets particular issues appealing for the development of a specific psychosocial support.

s2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer's disease solely from structural MRI.

OBJECTIVE: To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD. METHODS: A total of 3377 participants' sMRI from four independent databases were retrospectively identified to construct an interpretable deep learning model that integrated multi-dimensional representations of AD solely on sMRI (called s2MRI-ADNet) by a dual-channel learning strategy of gray matter volume (GMV) from Euclidean space and the regional radiomics similarity network (R2SN) from graph space. Specifically, the GMV feature map learning channel (called GMV-Channel) was to take into consideration spatial information of both long-range spatial relations and detailed localization information, while the node feature and connectivity strength learning channel (called NFCS-Channel) was to characterize the graph-structured R2SN network by a separable learning strategy. RESULTS: The s2MRI-ADNet achieved a superior classification accuracy of 92.1% and 91.4% under intra-database and inter-database cross-validation. The GMV-Channel and NFCS-Channel captured complementary group-discriminative brain regions, revealing a complementary interpretation of the multi-dimensional representation of brain structure in Euclidean and graph spaces respectively. Besides, the generalizable and reproducible interpretation of the multi-dimensional representation in capturing complementary group-discriminative brain regions revealed a significant correlation between the four independent databases (p < 0.05). Significant associations (p < 0.05) between attention scores and brain abnormality, between classification scores and clinical measure of cognitive ability, CSF biomarker, metabolism, and genetic risk score also provided solid neurobiological interpretation. CONCLUSION: The s2MRI-ADNet solely on sMRI could leverage the complementary multi-dimensional representations of AD in Euclidean and graph spaces, and achieved superior performance in the early diagnosis of AD, facilitating its potential in both clinical translation and popularization.

Automating Fault Test Cases Generation and Execution for Automotive Safety Validation via NLP and HIL Simulation.

The complexity and the criticality of automotive electronic implanted systems are steadily advancing and that is especially the case for automotive software development. ISO 26262 describes requirements for the development process to confirm the safety of such complex systems. Among these requirements, fault injection is a reliable technique to assess the effectiveness of safety mechanisms and verify the correct implementation of the safety requirements. However, the method of injecting the fault in the system under test in many cases is still manual and depends on an expert, requiring a high level of knowledge of the system. In complex systems, it consumes time, is difficult to execute, and takes effort, because the testers limit the fault injection experiments and inject the minimum number of possible test cases. Fault injection enables testers to identify and address potential issues with a system under test before they become actual problems. In the automotive industry, failures can have serious hazards. In these systems, it is essential to ensure that the system can operate safely even in the presence of faults. We propose an approach using natural language processing (NLP) technologies to automatically derive the fault test cases from the functional safety requirements (FSRs) and execute them automatically by hardware-in-the-loop (HIL) in real time according to the black-box concept and the ISO 26262 standard. The approach demonstrates effectiveness in automatically identifying fault injection locations and conditions, simplifying the testing process, and providing a scalable solution for various safety-critical systems.

Effects of Lexical Properties in L2 Chinese Compound Processing: A Multivariate Approach.

Previous studies on L2 (i.e., second language) Chinese compound processing have focused on the relative efficiency of two routes: holistic processing versus combinatorial processing. However, it is still unclear whether Chinese compounds are processed with multilevel representations among L2 learners due to the hierarchical structure of the characters. Therefore, taking a multivariate approach, the present study evaluated the relative influence and importance of different grain sizes of lexical information in an L2 Chinese two-character compound decision task. Results of supervised component generalized linear regression models with random forests analysis revealed that the orthographic, phonological and semantic information all contributed to L2 compound processing, but the L2 learners used more orthographic processing strategies and fewer phonological processing strategies compared to the native speakers. Specifically, the orthographic information was activated at the whole-word, the character and the radical levels in orthographic processing, and the phonological information at the whole-word, the syllable, and the phoneme levels all exerted contributions in phonological processing. Furthermore, the semantic information of the whole words and the constituents was accessed in semantic processing. These findings together suggest that the L2 learners are able to use cues at all levels simultaneously to process Chinese compound words, supporting a multi-route model with a hierarchical morphological structure in such processing.

The Role of Large Language Models in Transforming Emergency Medicine: Scoping Review.

BACKGROUND: Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM. OBJECTIVE: Given the absence of a comprehensive framework for exploring the roles of LLMs in EM, this scoping review aims to systematically map the existing literature on LLMs' potential applications within EM and identify directions for future research. Addressing this gap will allow for informed advancements in the field. METHODS: Using PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) criteria, we searched Ovid MEDLINE, Embase, Web of Science, and Google Scholar for papers published between January 2018 and August 2023 that discussed LLMs' use in EM. We excluded other forms of AI. A total of 1994 unique titles and abstracts were screened, and each full-text paper was independently reviewed by 2 authors. Data were abstracted independently, and 5 authors performed a collaborative quantitative and qualitative synthesis of the data. RESULTS: A total of 43 papers were included. Studies were predominantly from 2022 to 2023 and conducted in the United States and China. We uncovered four major themes: (1) clinical decision-making and support was highlighted as a pivotal area, with LLMs playing a substantial role in enhancing patient care, notably through their application in real-time triage, allowing early recognition of patient urgency; (2) efficiency, workflow, and information management demonstrated the capacity of LLMs to significantly boost operational efficiency, particularly through the automation of patient record synthesis, which could reduce administrative burden and enhance patient-centric care; (3) risks, ethics, and transparency were identified as areas of concern, especially regarding the reliability of LLMs' outputs, and specific studies highlighted the challenges of ensuring unbiased decision-making amidst potentially flawed training data sets, stressing the importance of thorough validation and ethical oversight; and (4) education and communication possibilities included LLMs' capacity to enrich medical training, such as through using simulated patient interactions that enhance communication skills. CONCLUSIONS: LLMs have the potential to fundamentally transform EM, enhancing clinical decision-making, optimizing workflows, and improving patient outcomes. This review sets the stage for future advancements by identifying key research areas: prospective validation of LLM applications, establishing standards for responsible use, understanding provider and patient perceptions, and improving physicians' AI literacy. Effective integration of LLMs into EM will require collaborative efforts and thorough evaluation to ensure these technologies can be safely and effectively applied.