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1.
An. psicol ; 40(1): 38-43, Ene-Abri, 2024. tab
Artigo em Inglês | IBECS | ID: ibc-229025

RESUMO

El objetivo del presente estudio fue el de examinar la fiabilidad, validez y estructura factorial de la adaptación española de la Clance Impostor Phenomenon Scale (CIPS). Para ello, un total de 271 estudiantes españoles completaron una versión traducida de la escala original de 20 ítems. En nuestra muestra, el instrumento mostró una alta fiabilidad, medida como consistencia interna, (ωTotal =.90) y correlaciones moderadas-altas con medidas de depresión (r =.633), autoestima (r = -.754) y miedo a las evaluaciones negativas (r = .666), lo cual sugiere tanto una validez nomológica como discriminante. Aunque en la validación original se propuso una estructura de tres factores, otros estudios han encontrado ajuste a estructuras de uno y dos factores. Aquí, utilizamos un análisis factorial confirmatorio (AFC) para probar el ajuste de estos tres modelos. Nuestros resultados muestran que, en la adaptación a español, el modelo con dos factores es el preferido. Esta adaptación al español de la CIPS provee a los profesionales clínicos una de una nueva herramienta para poder investigar los mecanismos que subyacen al síndrome del impostor, así como futuros tratamientos.(AU)


The aim of this study was to examine the reliability, validity, and factorial structure of the Spanish version of the Clance Impostor Phenom-enon Scale (CIPS). A sample of 271 Spanish students was recruited to complete a translated version of the original 20-item CIPS. In our sample, the instrument showed high internal consistency reliability (ωTotal=.90) and a moderate-to-strong correlation with measures of depression (r= .633), self-esteem (r= -.754) and fear of negative evaluation (r= .666), suggesting both nomological and discriminant validity. Althoughthe original valida-tion of the CIPS proposed a factorial structure with three factors, subse-quent validations also revealed adjustment to two-and one-factor struc-tures. Here, we used confirmatory factor analysis (CFA) to test the three different models. The results showed that in our adaptation, a 2-factor structure might be preferred. This adaptation of the CIPS to Spanish pro-vides clinicians with a new method to gain insight into the psychological mechanisms behind the Impostor phenomenon and suitable treatments.(AU)


Assuntos
Humanos , Masculino , Feminino , Adulto Jovem , Estudantes/psicologia , Reprodutibilidade dos Testes , Inteligência , Psicologia , Espanha , Análise Fatorial
2.
Hum Brain Mapp ; 45(4): e26633, 2024 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-38433682

RESUMO

Most neuroimaging studies linking regional brain volumes with cognition correct for total intracranial volume (ICV), but methods used for this correction differ across studies. It is unknown whether different ICV correction methods yield consistent results. Using a brain-wide association approach in the MRI substudy of UK Biobank (N = 41,964; mean age = 64.5 years), we used regression models to estimate the associations of 58 regional brain volumetric measures with eight cognitive outcomes, comparing no correction and four ICV correction approaches. Approaches evaluated included: no correction; dividing regional volumes by ICV (proportional approach); including ICV as a covariate in the regression (adjustment approach); and regressing the regional volumes against ICV in different normative samples and using calculated residuals to determine associations (residual approach). We used Spearman-rank correlations and two consistency measures to quantify the extent to which associations were inconsistent across ICV correction approaches for each possible brain region and cognitive outcome pair across 2320 regression models. When the association between brain volume and cognitive performance was close to null, all approaches produced similar estimates close to the null. When associations between a regional volume and cognitive test were not null, the adjustment and residual approaches typically produced similar estimates, but these estimates were inconsistent with results from the crude and proportional approaches. For example, when using the crude approach, an increase of 0.114 (95% confidence interval [CI]: 0.103-0.125) in fluid intelligence was associated with each unit increase in hippocampal volume. However, when using the adjustment approach, the increase was 0.055 (95% CI: 0.043-0.068), while the proportional approach showed a decrease of -0.025 (95% CI: -0.035 to -0.014). Different commonly used methods to correct for ICV yielded inconsistent results. The proportional method diverges notably from other methods and results were sometimes biologically implausible. A simple regression adjustment for ICV produced biologically plausible associations.


Assuntos
Encéfalo , Cognição , Humanos , Pessoa de Meia-Idade , Encéfalo/diagnóstico por imagem , Hipocampo , Inteligência , Neuroimagem
3.
PLoS One ; 19(3): e0298020, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38457397

RESUMO

In previous magnetoencephalography (MEG) studies, children with autism spectrum disorder (ASD) have been shown to respond differently to speech stimuli than typically developing (TD) children. Quantitative evaluation of this difference in responsiveness may support early diagnosis and intervention for ASD. The objective of this research is to investigate the relationship between syllable-induced P1m and social impairment in children with ASD and TD children. We analyzed 49 children with ASD aged 40-92 months and age-matched 26 TD children. We evaluated their social impairment by means of the Social Responsiveness Scale (SRS) and their intelligence ability using the Kaufman Assessment Battery for Children (K-ABC). Multiple regression analysis with SRS score as the dependent variable and syllable-induced P1m latency or intensity and intelligence ability as explanatory variables revealed that SRS score was associated with syllable-induced P1m latency in the left hemisphere only in the TD group and not in the ASD group. A second finding was that increased leftward-lateralization of intensity was correlated with higher SRS scores only in the ASD group. These results provide valuable insights but also highlight the intricate nature of neural mechanisms and their relationship with autistic traits.


Assuntos
Transtorno do Espectro Autista , Criança , Humanos , Transtorno do Espectro Autista/diagnóstico , Magnetoencefalografia , Inteligência/fisiologia , Testes de Inteligência , Grupo Associado
4.
Sci Rep ; 14(1): 6412, 2024 03 17.
Artigo em Inglês | MEDLINE | ID: mdl-38494508

RESUMO

Opinion diversity is crucial for collective decision-making, but maintaining it becomes challenging in the face of social influence. We propose selective exposure as an endogenous mechanism that preserves opinion diversity by forming exclusive subgroups of like-minded individuals, or echo chambers, which have been often perceived as an obstacle to achieving collective intelligence. We consider situations where a group of agents collectively make decisions about the true state of nature with the assumption that agents update their opinions by adopting the aggregated opinions of their information sources (i.e., naïve learning), or alternatively, replace incongruent sources with more like-minded others without adjusting their opinions (i.e., selective exposure). Individual opinions at steady states reached under these dynamics are then aggregated to form collective decisions, and their quality is assessed. The results suggest that the diversity-reducing effects of social influence are effectively confined within subgroups formed by selective exposure. More importantly, strong propensities for selective exposure maintain the quality of collective decisions at a level as high as that achieved in the absence of social influence. In contrast, naïve learning allows groups to reach consensuses, which are more accurate than initial individual opinions, but significantly undermines the quality of collective decisions.


Assuntos
Emoções , Inteligência , Humanos , Consenso , Aprendizagem
5.
PLoS One ; 19(3): e0300214, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38483877

RESUMO

With the development of the new generation communication system in China, the application of intelligent transportation system is more extensive, which brings higher demands for vehicle flow detection and monitoring. Traditional traffic flow detection modes often cannot meet the high statistical accuracy requirement and high-speed detection simultaneously. Therefore, an improved Inception module is integrated into the single shot multi box detector algorithm. An intelligent vehicle flow detection model is constructed based on the improved single shot multi box detector algorithm. According to the findings, the convergence speed of the improved algorithm was the fastest. When the test sample was the entire test set, the accuracy and precision values of the improved method were 93.6% and 96.0%, respectively, which were higher than all comparison target detection algorithms. The experimental results of traffic flow statistics showed that the model had the highest statistical accuracy, which converged during the training phase. During the testing phase, except for manual statistics, all methods had the lowest statistical accuracy on motorcycles. The average accuracy and precision of the designed model for various types of images were 96.9% and 96.8%, respectively. The calculation speed of this intelligent model was not significantly improved compared to the other two intelligent models, but it was significantly higher than manual monitoring methods. Two experimental data demonstrate that the intelligent vehicle flow detection model designed in this study has higher detection accuracy. The calculation speed has no significant difference compared with the traditional method, which is helpful to the traffic flow management in intelligent transportation system.


Assuntos
Algoritmos , Inteligência , China , Motocicletas
6.
Sci Rep ; 14(1): 6758, 2024 03 21.
Artigo em Inglês | MEDLINE | ID: mdl-38514808

RESUMO

In this work, we use a simple multi-agent-based-model (MABM) of a social network, implementing selfish algorithm (SA) agents, to create an adaptive environment and show, using a modified diffusion entropy analysis (DEA), that the mutual-adaptive interaction between the parts of such a network manifests complexity synchronization (CS). CS has been shown to exist by processing simultaneously measured time series from among organ-networks (ONs) of the brain (neurophysiology), lungs (respiration), and heart (cardiovascular reactivity) and to be explained theoretically as a synchronization of the multifractal dimension (MFD) scaling parameters characterizing each time series. Herein, we find the same kind of CS in the emergent intelligence of groups formed in a self-organized social interaction without macroscopic control but with biased self-interest between two groups of agents playing an anti-coordination game. This computational result strongly suggests the existence of the same CS in real-world social phenomena and in human-machine interactions as that found empirically in ONs.


Assuntos
Algoritmos , Inteligência , Humanos , Entropia
7.
Cereb Cortex ; 34(3)2024 03 01.
Artigo em Inglês | MEDLINE | ID: mdl-38436466

RESUMO

The debate on whether computer gaming enhances players' cognitive function is an ongoing and contentious issue. Aiming to delve into the potential impacts of computer gaming on the players' cognitive function, we embarked on a brain imaging-derived phenotypes (IDPs)-wide Mendelian randomization (MR) study, utilizing publicly available data from a European population. Our findings indicate that computer gaming has a positive impact on fluid intelligence (odds ratio [OR] = 6.264, P = 4.361 × 10-10, 95% confidence interval [CI] 3.520-11.147) and cognitive function (OR = 3.322, P = 0.002, 95% CI 1.563-7.062). Out of the 3062 brain IDPs analyzed, only one phenotype, IDP NET100 0378, was significantly influenced by computer gaming (OR = 4.697, P = 1.10 × 10-5, 95% CI 2.357-9.361). Further MR analysis suggested that alterations in the IDP NET100 0378 caused by computer gaming may be a potential factor affecting fluid intelligence (OR = 1.076, P = 0.041, 95% CI 1.003-1.153). Our MR study lends support to the notion that computer gaming can facilitate the development of players' fluid intelligence by enhancing the connectivity between the motor cortex in the resting-state brain and key regions such as the left dorsolateral prefrontal cortex and the language center.


Assuntos
Análise da Randomização Mendeliana , Jogos de Vídeo , Encéfalo/diagnóstico por imagem , Cognição , Computadores , Inteligência , Fenótipo , Neuroimagem
8.
PLoS One ; 19(3): e0299027, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38442120

RESUMO

High-precision waveform identification and measurement are effective for waveform detection and evaluation in signal processing. The accuracy of waveform identification, precision of measurement, and speed of response are important indicators of waveform measurement instruments. To detect the waveform accurately, a hold and attenuation circuit divided into two is designed, and the STM32F4 microcontroller is used to accurately capture and perform spectrum analysis using a high-precision analog-to-digital converter based on fast Fourier transform technology to identify key parameters, such as waveform type, frequency, peak-to-peak value, and duty cycle. To improve the recognition accuracy and response speed, technical solutions, such as high-frequency sampling and over-zero detection, are used to improve the system efficiency. Algorithm simulation, circuit simulation, and physical testing show that the high-precision waveform synchronization recognition circuit and algorithm can accurately recognize various essential waveforms in the voltage and frequency ranges of 50 mV ≤ VPP ≤ 10 V and 1 Hz ≤ f ≤ 50 kHz, respectively, and simultaneously measure important parameters, such as frequency, peak-to-peak value, and duty cycle with an accuracy within ±1%. Intelligent linkage, no intermediate parameter setting, and a response speed of approximately 0.3 s make it suitable for such applications as fast and high-precision waveform intelligent detection and display. The method is highly integrated, simple to operate, cost-effective, and practical.


Assuntos
Algoritmos , Inteligência , Simulação por Computador , Exame Físico , Tecnologia
9.
Elife ; 122024 Mar 05.
Artigo em Inglês | MEDLINE | ID: mdl-38441539

RESUMO

In children, psychotic-like experiences (PLEs) are related to risk of psychosis, schizophrenia, and other mental disorders. Maladaptive cognitive functioning, influenced by genetic and environmental factors, is hypothesized to mediate the relationship between these factors and childhood PLEs. Using large-scale longitudinal data, we tested the relationships of genetic and environmental factors (such as familial and neighborhood environment) with cognitive intelligence and their relationships with current and future PLEs in children. We leveraged large-scale multimodal data of 6,602 children from the Adolescent Brain and Cognitive Development Study. Linear mixed model and a novel structural equation modeling (SEM) method that allows estimation of both components and factors were used to estimate the joint effects of cognitive phenotypes polygenic scores (PGSs), familial and neighborhood socioeconomic status (SES), and supportive environment on NIH Toolbox cognitive intelligence and PLEs. We adjusted for ethnicity (genetically defined), schizophrenia PGS, and additionally unobserved confounders (using computational confound modeling). Our findings indicate that lower cognitive intelligence and higher PLEs are significantly associated with lower PGSs for cognitive phenotypes, lower familial SES, lower neighborhood SES, and less supportive environments. Specifically, cognitive intelligence mediates the effects of these factors on PLEs, with supportive parenting and positive school environments showing the strongest impact on reducing PLEs. This study underscores the influence of genetic and environmental factors on PLEs through their effects on cognitive intelligence. Our findings have policy implications in that improving school and family environments and promoting local economic development may enhance cognitive and mental health in children.


Childhood is a critical period for brain development. Difficult experiences during this developmental phase may contribute to reduced intelligence and poorer mental health later in life. Genetics and environmental factors also play roles. For example, having family support or a higher family income has been linked to better brain health outcomes for children. Delusions or hallucinations, or other psychotic-like experiences during childhood, are linked with poor mental health later in life. Children who experience psychotic-like episodes between the ages of nine and eleven have a higher risk of developing schizophrenia or related conditions. Environmental circumstances during childhood also appear to play a crucial role in shaping the risk of schizophrenia or related conditions. Park, Lee et al. show that positive parenting and supportive school and neighborhood environments boost child intelligence and mental health. In the experiments, Park, Lee et al. analyzed data on 6,602 children to determine how genetics and environmental factors shaped their intelligence and mental health. The models show that children with higher intelligence have a lower risk of psychosis. Both genetics and supportive environments contribute to higher intelligence. Complex interactions between biology and social factors shape children's intelligence and mental health. Beneficial genetics and coming from a family with more financial resources are helpful. Yet, social environments, such as having parents who use positive child-rearing practices, or having supportive schools or neighborhoods, have protective effects that can offset other disadvantages. Policies that help parents, encourage supportive school environments, and strengthen neighborhoods may boost children's intelligence and mental health later in life.


Assuntos
Transtornos Mentais , Transtornos Psicóticos , Adolescente , Criança , Humanos , Transtornos Psicóticos/genética , Saúde Mental , Cognição , Inteligência/genética
10.
Sensors (Basel) ; 24(5)2024 Feb 20.
Artigo em Inglês | MEDLINE | ID: mdl-38474890

RESUMO

RF-based gesture recognition systems outperform computer vision-based systems in terms of user privacy. The integration of Wi-Fi sensing and deep learning has opened new application areas for intelligent multimedia technology. Although promising, existing systems have multiple limitations: (1) they only work well in a fixed domain; (2) when working in a new domain, they require the recollection of a large amount of data. These limitations either lead to a subpar cross-domain performance or require a huge amount of human effort, impeding their widespread adoption in practical scenarios. We propose Wi-AM, a privacy-preserving gesture recognition framework, to address the above limitations. Wi-AM can accurately recognize gestures in a new domain with only one sample. To remove irrelevant disturbances induced by interfering domain factors, we design a multi-domain adversarial scheme to reduce the differences in data distribution between different domains and extract the maximum amount of transferable features related to gestures. Moreover, to quickly adapt to an unseen domain with only a few samples, Wi-AM adopts a meta-learning framework to fine-tune the trained model into a new domain with a one-sample-per-gesture manner while achieving an accurate cross-domain performance. Extensive experiments in a real-world dataset demonstrate that Wi-AM can recognize gestures in an unseen domain with average accuracy of 82.13% and 86.76% for 1 and 3 data samples.


Assuntos
Gestos , Reconhecimento Automatizado de Padrão , Humanos , Reconhecimento Psicológico , Tecnologia da Informação , Inteligência , Algoritmos
11.
Artigo em Inglês | PAHO-IRIS | ID: phr-59318

RESUMO

[ABSTRACT]. This article points out deficiencies in present-day definitions of public health surveillance, which include data collection, analysis, interpretation and dissemination, but not public health action. Controlling a public health problem of concern requires a public health response that goes beyond information dissemination. It is unde- sirable to have public health divided into data generation processes (public health surveillance) and data use processes (public health response), managed by two separate groups (surveillance experts and policy-makers). It is time to rethink the need to modernize the definition of public health surveillance, inspired by the authors’ enhanced Data, Information, Knowledge, Intelligence and Wisdom model. Our recommendations include expanding the scope of public health surveillance beyond information dissemination to comprise actionable knowledge (intelligence); mandating surveillance experts to assist policy-makers in making evidence-informed decisions; encouraging surveillance experts to become policy-makers; and incorporating public health literacy training – from data to knowledge to wisdom – into the curricula for all public health professionals. Work on modernizing the scope and definition of public health surveillance will be a good starting point.


[RESUMEN]. En este artículo se señalan las deficiencias de las definiciones actuales de la vigilancia de salud pública, que incluyen la recopilación, el análisis, la interpretación y la difusión de los datos, pero no las medidas de salud pública. El control de un problema de salud pública de interés exige una respuesta de salud pública que vaya más allá de la difusión de información. No es deseable que la salud pública esté dividida por un lado en procesos de generación de datos (vigilancia de salud pública) y por otro en procesos de uso de datos (respuesta de salud pública), gestionados por dos grupos diferentes (expertos en vigilancia y responsables de la formulación de políticas). Ha llegado el momento de replantear la necesidad de modernizar la definición de la vigilancia de salud pública tomando como referencia el modelo mejorado de Datos, Información, Cono- cimiento, Inteligencia y Sabiduría de los autores. Entre las recomendaciones que se proponen se encuentran las de ampliar el alcance de la vigilancia de salud pública más allá de la difusión de información para que incluya también el conocimiento aplicable (inteligencia); instar a los expertos en vigilancia a que presten ayuda a los responsables de la formulación de políticas en la toma de decisiones basadas en la evidencia; alentar a los expertos en vigilancia a que se conviertan en responsables de la formulación de políticas; e incorporar la formación en conocimientos básicos de salud pública (desde los datos hasta los conocimientos y la sabiduría) en los planes de estudio de todos los profesionales de la salud pública. Un buen punto de partida será trabajar en la modernización del alcance y la definición de la vigilancia de salud pública.


[RESUMO]. Este artigo aponta deficiências nas definições atuais de vigilância em saúde pública, que incluem coleta, análise, interpretação e disseminação de dados, mas não ações de saúde pública. O controle de um prob- lema preocupante de saúde pública exige uma resposta de saúde pública que vá além da disseminação de informações. A saúde pública não deve ser dividida em processos de geração de dados (vigilância em saúde pública) e processos de uso de dados (resposta de saúde pública) gerenciados por dois grupos distintos (especialistas em vigilância e formuladores de políticas). É hora de repensar a necessidade de modernizar a definição de vigilância em saúde pública, inspirada no modelo aprimorado de Dados, Informações, Con- hecimento, Inteligência e Sabedoria dos autores. Nossas recomendações incluem: expansão do escopo da vigilância em saúde pública para além da disseminação de informações, de modo a abranger conhecimentos acionáveis (inteligência); obrigatoriedade de que os especialistas em vigilância auxiliem os formuladores de políticas na tomada de decisões baseadas em evidências; incentivo para que os especialistas em vigilân- cia se tornem formuladores de políticas; e incorporação de capacitação em letramento em saúde pública (partindo dos dados para o conhecimento e em seguida para a sabedoria) nos currículos de todos os profis- sionais de saúde pública. O trabalho de modernizar o escopo e a definição de vigilância em saúde pública será um bom ponto de partida.


Assuntos
Vigilância em Saúde Pública , Coleta de Dados , Gestão da Informação em Saúde , Gestão da Saúde da População , Letramento em Saúde , Sistema de Aprendizagem em Saúde , Inteligência , Vigilância em Saúde Pública , Coleta de Dados , Gestão da Informação em Saúde , Gestão da Saúde da População , Letramento em Saúde , Sistema de Aprendizagem em Saúde , Inteligência , Vigilância em Saúde Pública , Coleta de Dados , Gestão da Informação em Saúde , Gestão da Saúde da População , Letramento em Saúde , Sistema de Aprendizagem em Saúde , Inteligência
12.
J Med Internet Res ; 26: e48324, 2024 Feb 22.
Artigo em Inglês | MEDLINE | ID: mdl-38386404

RESUMO

BACKGROUND: Allergic rhinitis (AR) is a chronic disease, and several risk factors predispose individuals to the condition in their daily lives, including exposure to allergens and inhalation irritants. Analyzing the potential risk factors that can trigger AR can provide reference material for individuals to use to reduce its occurrence in their daily lives. Nowadays, social media is a part of daily life, with an increasing number of people using at least 1 platform regularly. Social media enables users to share experiences among large groups of people who share the same interests and experience the same afflictions. Notably, these channels promote the ability to share health information. OBJECTIVE: This study aims to construct an intelligent method (TopicS-ClusterREV) for identifying the risk factors of AR based on these social media comments. The main questions were as follows: How many comments contained AR risk factor information? How many categories can these risk factors be summarized into? How do these risk factors trigger AR? METHODS: This study crawled all the data from May 2012 to May 2022 under the topic of allergic rhinitis on Zhihu, obtaining a total of 9628 posts and 33,747 comments. We improved the Skip-gram model to train topic-enhanced word vector representations (TopicS) and then vectorized annotated text items for training the risk factor classifier. Furthermore, cluster analysis enabled a closer look into the opinions expressed in the category, namely gaining insight into how risk factors trigger AR. RESULTS: Our classifier identified more comments containing risk factors than the other classification models, with an accuracy rate of 96.1% and a recall rate of 96.3%. In general, we clustered texts containing risk factors into 28 categories, with season, region, and mites being the most common risk factors. We gained insight into the risk factors expressed in each category; for example, seasonal changes and increased temperature differences between day and night can disrupt the body's immune system and lead to the development of allergies. CONCLUSIONS: Our approach can handle the amount of data and extract risk factors effectively. Moreover, the summary of risk factors can serve as a reference for individuals to reduce AR in their daily lives. The experimental data also provide a potential pathway that triggers AR. This finding can guide the development of management plans and interventions for AR.


Assuntos
Rinite Alérgica , Humanos , Análise por Conglomerados , Inteligência , Rememoração Mental , Fatores de Risco
13.
Am J Trop Med Hyg ; 110(3): 518-528, 2024 Mar 06.
Artigo em Inglês | MEDLINE | ID: mdl-38320317

RESUMO

Current modeling practices for environmental and sociological modulated infectious diseases remain inadequate to forecast the risk of outbreak(s) in human populations, partly due to a lack of integration of disciplinary knowledge, limited availability of disease surveillance datasets, and overreliance on compartmental epidemiological modeling methods. Harvesting data knowledge from virus transmission (aerosols) and detection (wastewater) of SARS-CoV-2, a heuristic score-based environmental predictive intelligence system was developed that calculates the risk of COVID-19 in the human population. Seasonal validation of the algorithm was uniquely associated with wastewater surveillance of the virus, providing a lead time of 7-14 days before a county-level outbreak. Using county-scale disease prevalence data from the United States, the algorithm could predict COVID-19 risk with an overall accuracy ranging between 81% and 98%. Similarly, using wastewater surveillance data from Illinois and Maryland, the SARS-CoV-2 detection rate was greater than 80% for 75% of the locations during the same time the risk was predicted to be high. Results suggest the importance of a holistic approach across disciplinary boundaries that can potentially allow anticipatory decision-making policies of saving lives and maximizing the use of available capacity and resources.


Assuntos
COVID-19 , SARS-CoV-2 , Humanos , COVID-19/epidemiologia , Estações do Ano , Águas Residuárias , Vigilância Epidemiológica Baseada em Águas Residuárias , Inteligência
14.
J Alzheimers Dis ; 98(1): 109-117, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38363609

RESUMO

Background: The mechanism(s) of cognitive impairment remains complex, making it difficult to confirm the factors influencing poststroke cognitive impairment (PSCI). Objective: This study quantitatively investigated the degree of influence and interactions of clinical indicators of PSCI. Methods: Information from 270 patients with PSCI and their Wechsler Adult Intelligence Scale (WAIS-RC) scores, totaling 18 indicators, were retrospectively collected. Correlations between the indicators and WAIS scores were calculated. Multiple linear regression model(MLR), genetic algorithm modified Back-Propagation neural network(GA-BP), logistic regression model (LR), XGBoost model (XGB), and structural equation model were used to analyze the degree of influence of factors on the WAIS and their mediating effects. Results: Seven indicators were significantly correlated with the WAIS scores: education, lesion side, aphasia, frontal lobe, temporal lobe, diffuse lesions, and disease course. The MLR showed significant effect of education, lesion side, aphasia, diffuse lesions, and frontal lobe on the WAIS. The GA-BP included five factors: education, aphasia, frontal lobe, temporal lobe, and diffuse lesions. LR predicted that the lesion side contributed more to mild cognitive impairment, while education, lesion side, aphasia, and course of the disease contributed more to severe cognitive impairment. XGB showed that education, side of the lesion, aphasia, and diffuse lesions contributed the most to PSCI. Aphasia plays a significant mediating role in patients with severe PSCI. Conclusions: Education, lesion side, aphasia, frontal lobe, and diffuse lesions significantly affected PSCI. Aphasia is a mediating variable between clinical information and the WAIS in patients with severe PSCI.


Assuntos
Afasia , Disfunção Cognitiva , Acidente Vascular Cerebral , Humanos , Estudos Retrospectivos , Acidente Vascular Cerebral/complicações , Acidente Vascular Cerebral/psicologia , Cognição , Disfunção Cognitiva/diagnóstico , Disfunção Cognitiva/etiologia , Disfunção Cognitiva/psicologia , Inteligência
15.
J Chem Inf Model ; 64(5): 1568-1580, 2024 Mar 11.
Artigo em Inglês | MEDLINE | ID: mdl-38382011

RESUMO

Atomic structure prediction and associated property calculations are the bedrock of chemical physics. Since high-fidelity ab initio modeling techniques for computing the structure and properties can be prohibitively expensive, this motivates the development of machine-learning (ML) models that make these predictions more efficiently. Training graph neural networks over large atomistic databases introduces unique computational challenges, such as the need to process millions of small graphs with variable size and support communication patterns that are distinct from learning over large graphs, such as social networks. We demonstrate a novel hardware-software codesign approach to scale up the training of atomistic graph neural networks (GNN) for structure and property prediction. First, to eliminate redundant computation and memory associated with alternative padding techniques and to improve throughput via minimizing communication, we formulate the effective coalescing of the batches of variable-size atomistic graphs as the bin packing problem and introduce a hardware-agnostic algorithm to pack these batches. In addition, we propose hardware-specific optimizations, including a planner and vectorization for the gather-scatter operations targeted for Graphcore's Intelligence Processing Unit (IPU), as well as model-specific optimizations such as merged communication collectives and optimized softplus. Putting these all together, we demonstrate the effectiveness of the proposed codesign approach by providing an implementation of a well-established atomistic GNN on the Graphcore IPUs. We evaluate the training performance on multiple atomistic graph databases with varying degrees of graph counts, sizes, and sparsity. We demonstrate that such a codesign approach can reduce the training time of atomistic GNNs and can improve their performance by up to 1.5× compared to the baseline implementation of the model on the IPUs. Additionally, we compare our IPU implementation with a Nvidia GPU-based implementation and show that our atomistic GNN implementation on the IPUs can run 1.8× faster on average compared to the execution time on the GPUs.


Assuntos
Aceleração , Redes Neurais de Computação , Algoritmos , Comunicação , Inteligência
16.
Med Eng Phys ; 124: 104060, 2024 02.
Artigo em Inglês | MEDLINE | ID: mdl-38418032

RESUMO

On the basis of extracting mechanomyography (MMG) signal features, the classification of hand movements has certain application values in human-machine interaction systems and wearable devices. In this paper, pattern recognition of hand movements based on MMG signal is studied with swarm intelligence algorithms introduced to optimize support vector machine (SVM). Time domain (TD) features, wavelet packet node energy (WPNE) features, frequency domain (FD) features, convolution neural network (CNN) features were extracted from each channel to constitute different feature sets. Three novel swarm intelligence algorithms (i.e., bald eagle search (BES), sparrow search algorithm (SSA), grey wolf optimization (GWO)) optimized SVM is proposed to train the models and recognition of hand movements are tested for each MMG feature extraction method. Using GWO as the optimization algorithm, time consumption is less than using the other two swarm algorithms. Using GWO with TD+FD features can obtain the classification accuracy of 93.55 %, which is higher than other methods while using CNN to extract features can be independent of domain knowledge. The results confirm GWO-SVM with TD + FD features is superior to some other methods in the classification problem for tiny samples based on MMG.


Assuntos
Algoritmos , Máquina de Vetores de Suporte , Humanos , Redes Neurais de Computação , Inteligência , Aceleração
17.
PLoS One ; 19(2): e0297513, 2024.
Artigo em Inglês | MEDLINE | ID: mdl-38324594

RESUMO

Hidden Markov Models (HMMs) have become an immensely popular tool for health assessment and fault diagnosis of rolling element bearings. The advantages of an HMM include its simplicity, robustness, and interpretability, while the generalization capability of the model still needs to be enhanced. The Dempster-Shafer theory of evidence can be used to conduct a comprehensive evaluation, and Stacking provides a novel training strategy. Therefore, the HMM-based fusion method and ensemble learning method are proposed to increase the credibility of quantitative analysis and optimize classifiers respectively. Firstly, vibration signals captured from bearings are decomposed into intrinsic mode functions (IMFs) using ensemble empirical mode decomposition (EEMD), and then the Hilbert envelope spectra of main components are obtained; Secondly, multi-domain features are extracted as model input from preprocessed signals; Finally, HMM-based intelligent health assessment framework and fault diagnosis framework are established. In this work, the life cycle health assessment modeling is performed using a few training samples, the bearing degradation state is quantitatively evaluated, normal and abnormal samples are effectively distinguished, and the accuracy of fault diagnosis is significantly improved.


Assuntos
Generalização Psicológica , Inteligência , Vibração
18.
Sci Rep ; 14(1): 3440, 2024 02 10.
Artigo em Inglês | MEDLINE | ID: mdl-38341459

RESUMO

The emergence of publicly accessible artificial intelligence (AI) large language models such as ChatGPT has given rise to global conversations on the implications of AI capabilities. Emergent research on AI has challenged the assumption that creative potential is a uniquely human trait thus, there seems to be a disconnect between human perception versus what AI is objectively capable of creating. Here, we aimed to assess the creative potential of humans in comparison to AI. In the present study, human participants (N = 151) and GPT-4 provided responses for the Alternative Uses Task, Consequences Task, and Divergent Associations Task. We found that AI was robustly more creative along each divergent thinking measurement in comparison to the human counterparts. Specifically, when controlling for fluency of responses, AI was more original and elaborate. The present findings suggest that the current state of AI language models demonstrate higher creative potential than human respondents.


Assuntos
Inteligência Artificial , Idioma , Humanos , Comunicação , Inteligência , Fenótipo
19.
Medicina (B Aires) ; 84 Suppl 1: 72-78, 2024 Mar.
Artigo em Espanhol | MEDLINE | ID: mdl-38350628

RESUMO

INTRODUCTION: Executive functions and Metacognition are integrated for the management of intellectual resources in close relation to intelligence its functioning and results; they are specially interesting for understanding the expression and development of high intellectual abilility (HIA). The aim of the study is to find out the relationship between executive functions (and components) and metacognition (and components) in schoolchildren with HIA. MATERIALS AND METHOD: Measures of executive and metacognitive functioning and perfectionism were extracted from a sample of n= 147 schoolchildren with HIA. RESULTS: statistical analyses using Path analysis, offered an adjusted model in which the different executive components are related to the metacognitive components. DISCUSSION: We conclude and discuss the integrative model between executive function and metacognition and its mediating role as an endophenotype between genetic endowment and the expression of resource performance, suggesting the transfer of results to the education of high intellectual ability for the optimal and ethical expression of high potential.


Introducción: Las funciones ejecutivas y la metacognición se integran para la gestión de recursos intelectuales, en estrecha relación con la inteligencia, su funcionamiento y resultados, especialmente interesante para comprender la expresión y desarrollo, más o menos óptimos, de la alta capacidad intelectual (ACI). El Objetivo del trabajo es conocer la relación entre las funciones ejecutivas (y componentes) y la metacognición (y componentes) en escolares con ACI. Materiales y Métodos: Las medidas de funcionamiento y ejecutivo, metacognitivo y de perfeccionismo extraídas en una muestra de n= 147 escolares con ACI son analizadas estadísticamente mediante el Path análisis. Resultados: Se obtiene un modelo ajustado en el que se relacionan los distintos componentes ejecutivos con los metacognitivos. Discusión: Se concluye y discute el modelo integrador entre función ejecutiva y metacognición y su papel mediador, como endofenotipo entre la dotación genética y la expresión de rendimiento de los recursos, sugiriendo la transferencia de resultados a la educación de la alta capacidad intelectual para la óptima y ética expresión del alto potencial.


Assuntos
Metacognição , Humanos , Criança , Função Executiva , Cognição , Inteligência
20.
Sci Rep ; 14(1): 3761, 2024 02 14.
Artigo em Inglês | MEDLINE | ID: mdl-38355614

RESUMO

Research on implicit theories of intelligence (a.k.a. intelligence mindset) has shown that endorsing a stronger growth mindset (the belief that intelligence can be improved) is adaptive in the face of difficulties. Although the theory presumes implicit processes (i.e., unaware beliefs, guiding behaviors and actions automatically), the concept is typically assessed with self-reports. In this project we brought together research on intelligence mindset with research on implicit social cognition. Harnessing recent innovations from research on implicit measures, we assessed intelligence mindsets on an implicit level with a mousetracking Propositional Evaluation Paradigm. This measure captures the spontaneous truth evaluation of growth- and fixed-mindset statements to tap into implicit beliefs. In two preregistered laboratory studies (N = 184; N = 193), we found that implicitly measured growth mindsets predicted learning engagement after an experience of failure above and beyond the explicitly measured growth mindset. Our results suggest that implicit and explicit aspects of intelligence mindsets must be differentiated. People might be in a different mindset when making learning-related decisions under optimal conditions (i.e., with ample time and capacity) or under suboptimal conditions (i.e., when time pressure is high). This advancement in the understanding of implicit theories of intelligence is accompanied with substantial implications for theory and practice.


Assuntos
Inteligência , Aprendizagem , Humanos , Autorrelato
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