Measures Of Intelligence Research Articles

Broadband Internet and Cognitive Functioning*

We empirically examine the impact of high‐speed internet access on cognitive functioning using data from the Household, Income and Labour Dynamics in Australia survey and crystallised and fluid intelligence measures. Leveraging differences in the National Broadband Network rollout across Australia, we find that high‐speed internet access causes a decline in crystallised intelligence and that the effects are mediated by social capital and moderated by age and gender. Although no overall effect on fluid intelligence was observed, a decline was noted in young adults. These findings underline the nuanced influence of high‐speed internet access on different aspects of cognitive function.

SDPS-09 MIXED RESPONSE OF BRAIN METASTASES TREATED WITH OSIMERTINIB PREDICTS INFERIOR SURVIVAL OUTCOMES: AN ARTIFICIAL INTELLIGENCE-BASED LESION-LEVEL ASSESSMENT FOR CRANIAL CONTROL OF EGFRMUTANT NONSMALL CELL LUNG CANCER

Abstract Osimertinib has been found to be effective for intracranial control in patients with metastatic EGFR-mutant non-small cell lung cancer (NSCLC). However, the lesion-level response of brain metastases to osimertinib has not been thoroughly evaluated. This retrospective study included 73 patients with EGFR-mutant NSCLC and brain metastases who were treated with osimertinib at a single institution from 2016 to 2021. Patients with the leptomeningeal disease were excluded. The study used an FDA-approved brain tumor management artificial intelligence (AI) platform, VBrain, to identify, track, and measure brain metastases on the baseline and follow-up MRI brain scans. Mixed response (MiR) was defined as the occurrence of progressive or new lesions along with synchronous responsive shrinking intracranial lesions at the first follow-up scan. K-means grouping was used to partition patients into two clusters according to their response heterogeneity. With a median follow-up of 23.8 months, a high MiR score (higher than 103%, n=25) was associated with worse cranial-progression-free survival (3.2 vs. 17.9 months, p<0.0001) and significantly inferior overall survival (12.4 vs. 30.1 months, HR=2.35, p=0.016). This poses a similar negative impact on the survivals of 18 patients with pure RANO-BM progression (HR=2.52, p=0.025). Eight patients had the highest MiR score (MiRmax), in whom synchronous new lesions and completely responsive tumors were observed. In a multivariate Cox proportional-hazards regression involving performance status, extracranial failure, tumor volume, lesion number, and RANO-BM classification as variables, MiRmax remained an independent prognosticator for inferior survival (HR=5.20, 95%CI; 2.21–12.23, p=0.002). Our study demonstrates that MiR in brain metastases from EGFR-mutant NSCLC treated with osimertinib is associated with inferior survival outcomes and a higher risk of local progression. Lesion-level response assessment using AI may provide important prognostic information and aid in treatment decision-making for these patients.

Attention and neurodevelopment in young children who are HIV-exposed uninfected

ABSTRACT Children HIV-exposed, uninfected (CHEU) are at risk for compromised developmental outcomes. Attention is important for behavioural, cognitive and academic skills, yet has not been thoroughly investigated compared to children HIV-unexposed uninfected (CHUU). Fifty-five CHEU and 51 CHUU children were recruited at 5.5 years of age. Measures of inattention (IA), hyperactivity/impulsivity (HI) and total scores were collected using the parent-reported ADHD-Rating-Scale-IV. Measures of intelligence, visuomotor skills, academics and adaptive functioning were obtained. Analyses of between-group differences were performed as were correlational and multiple regression models, accounting for maternal education, employment and delivery type. Few children met clinical cut-offs for probable ADHD (3.6% CHEU, 2.0% CHUU), and no group differences in measures of IA, HI and combined scores were found. CHEU scored significantly lower than CHUU on intelligence, visuomotor function, academic skills and aspects of adaptive behaviour, though within age expectations. Lower Full-Scale IQ and Processing Speed were associated with higher IA in CHEU and lower adaptive functioning with higher IA in CHUU. Across both groups, children of unemployed mothers had more HI symptoms. CHEU were not at increased risk for attention difficulties at 5.5 years of age. Maternal employment status highlights the contribution of sociodemographic factors in shaping behaviour and neurodevelopment.

Shifting as an executive function separate from updating and inhibition in old age: Behavioral and genetic evidence

This study aimed to examine the organization of executive functions (EFs), specifically working memory updating, prepotent response inhibition, and mental-set shifting in old age, with a particular focus on determining whether the shifting function was behaviorally and genetically separated from the other functions. A total of 248 healthy older Chinese individuals participated, and multiple measures of executive functions were collected. Additionally, measures of fluid intelligence were included to explore the varying relationships between the three executive functions and this higher-order cognitive ability. Furthermore, genetic data were gathered and analyzed to investigate the associations between EFs and six candidate single-nucleotide polymorphisms (SNPs) mapped to dopaminergic, serotonergic, or glutamatergic genes. The results indicated that both the three-factor model and the two-factor model, which combined updating and inhibition, demonstrated a good fit. Furthermore, shifting was found to be behaviorally separated from the other two functions, and the correlation between shifting and fluid intelligence was smaller compared to the correlations between updating and inhibition with fluid intelligence. Moreover, the DRD2 SNPs showed significant associations with shifting, rather than with updating and inhibition. These findings provide evidence that shifting is distinct and separate from updating and inhibition, highlighting the diversity of EFs among older adults.

Can Computational Intelligence Model Phenomenal Consciousness?

Consciousness and intelligence are properties that can be misunderstood as necessarily dependent. The term artificial intelligence and the kind of problems it managed to solve in recent years has been shown as an argument to establish that machines experience some sort of consciousness. Following Russell’s analogy, if a machine can do what a conscious human being does, the likelihood that the machine is conscious increases. However, the social implications of this analogy are catastrophic. Concretely, if rights are given to entities that can solve the kind of problems that a neurotypical person can, does the machine have potentially more rights than a person that has a disability? For example, the autistic syndrome disorder spectrum can make a person unable to solve the kind of problems that a machine solves. We believe the obvious answer is no, as problem-solving does not imply consciousness. Consequently, we will argue in this paper how phenomenal consciousness, at least, cannot be modeled by computational intelligence and why machines do not possess phenomenal consciousness, although they can potentially develop a higher computational intelligence than human beings. In order to do so, we try to formulate an objective measure of computational intelligence and study how it presents in human beings, animals, and machines. Analogously, we study phenomenal consciousness as a dichotomous variable and how it is distributed in humans, animals, and machines.

Assessing the role of an artificial intelligence assessment tool for thoracic aorta diameter on routine chest CT.

To assess the diagnostic accuracy and clinical impact of automated artificial intelligence (AI) measurement of thoracic aorta diameter on routine chest CT. A single-centre retrospective study involving three cohorts. 210 consecutive ECG-gated CT aorta scans (mean age 75 ± 13) underwent automated analysis (AI-Rad Companion Chest CT, Siemens) and were compared to a reference standard of specialist cardiothoracic radiologists for accuracy measuring aortic diameter. A repeated measures analysis tested reporting consistency in a second cohort (29 patients, mean age 61 ± 17) of immediate sequential pre-contrast and contrast CT aorta acquisitions. Potential clinical impact was assessed in a third cohort of 197 routine CT chests (mean age 66 ± 15) to document potential clinical impact. AI analysis produced a full report in 387/436 (89%) and a partial report in 421/436 (97%). Manual vs AI agreement was good to excellent (ICC 0.76-0.92). Repeated measures analysis of expert and AI reports for the ascending aorta were moderate to good (ICC 0.57-0.88). AI diagnostic performance crossed the threshold for maximally accepted limits of agreement (>5 mm) at the aortic root on ECG-gated CTs. AI newly identified aortic dilatation in 27% of patients on routine thoracic imaging with a specificity of 99% and sensitivity of 77%. AI has good agreement with expert readers at the mid-ascending aorta and has high specificity, but low sensitivity, at detecting dilated aortas on non-dedicated chest CTs. An AI tool may improve the detection of previously unknown thoracic aorta dilatation on chest CTs vs current routine reporting.

An Automatically Adaptive Digital Health Intervention to Decrease Opioid-Related Risk While Conserving Counselor Time: Quantitative Analysis of Treatment Decisions Based on Artificial Intelligence and Patient-Reported Risk Measures.

Some patients prescribed opioid analgesic (OA) medications for pain experience serious side effects, including dependence, sedation, and overdose. As most patients are at low risk for OA-related harms, risk reduction interventions requiring multiple counseling sessions are impractical on a large scale. This study evaluates whether an intervention based on reinforcement learning (RL), a field of artificial intelligence, learned through experience to personalize interactions with patients with pain discharged from the emergency department (ED) and decreased self-reported OA misuse behaviors while conserving counselors' time. We used data representing 2439 weekly interactions between a digital health intervention ("Prescription Opioid Wellness and Engagement Research in the ED" [PowerED]) and 228 patients with pain discharged from 2 EDs who reported recent opioid misuse. During each patient's 12 weeks of intervention, PowerED used RL to select from 3 treatment options: a brief motivational message delivered via an interactive voice response (IVR) call, a longer motivational IVR call, or a live call from a counselor. The algorithm selected session types for each patient each week, with the goal of minimizing OA risk, defined in terms of a dynamic score reflecting patient reports during IVR monitoring calls. When a live counseling call was predicted to have a similar impact on future risk as an IVR message, the algorithm favored IVR to conserve counselor time. We used logit models to estimate changes in the relative frequency of each session type as PowerED gained experience. Poisson regression was used to examine the changes in self-reported OA risk scores over calendar time, controlling for the ordinal session number (1st to 12th). Participants on average were 40 (SD 12.7) years of age; 66.7% (152/228) were women and 51.3% (117/228) were unemployed. Most participants (175/228, 76.8%) reported chronic pain, and 46.2% (104/225) had moderate to severe depressive symptoms. As PowerED gained experience through interactions over a period of 142 weeks, it delivered fewer live counseling sessions than brief IVR sessions (P=.006) and extended IVR sessions (P<.001). Live counseling sessions were selected 33.5% of the time in the first 5 weeks of interactions (95% CI 27.4%-39.7%) but only for 16.4% of sessions (95% CI 12.7%-20%) after 125 weeks. Controlling for each patient's changes during the course of treatment, this adaptation of treatment-type allocation led to progressively greater improvements in self-reported OA risk scores (P<.001) over calendar time, as measured by the number of weeks since enrollment began. Improvement in risk behaviors over time was especially pronounced among patients with the highest risk at baseline (P=.02). The RL-supported program learned which treatment modalities worked best to improve self-reported OA risk behaviors while conserving counselors' time. RL-supported interventions represent a scalable solution for patients with pain receiving OA prescriptions. Clinicaltrials.gov NCT02990377; https://classic.clinicaltrials.gov/ct2/show/NCT02990377.

Identifying Measures of Emotional Intelligence and Dangerous Driving

Dangerous driving behaviors are a public safety concern. These behaviors are heavily influenced and can be triggered by emotions. Emotions, and their resultant behaviors, are influenced by emotional intelligence (EI), defined as an individual’s ability to identify and differentiate between emotions, and make decisions based on them. The objective of this study was to determine the best EI and dangerous driving behavior measures. This exploratory study examined methods to measure EI and its possible relationship with various measures of dangerous driving behavior among 155 adult drivers in the United States with a valid driver’s license. Participants completed an online survey, which included three scales for EI assessment (TEIQue, GENOS, WLEIS) and three dangerous driving behavior scales (DDDI, DBQ, DBI), along with questions about past crashes and citations. Responses from the survey were analyzed using Pearson’s correlation, linear regression, and binary logistic regression. Significant correlations were observed between all driving behaviors and the TEIQue EI scales. Notably, the TEIQue total EI and emotionality subscales emerged as the most influential predictors of dangerous driving behaviors. Participants with higher scores in total EI and emotionality engaged in fewer dangerous driving behaviors. Furthermore, the GENOS and WLEIS EI scales failed to predict dangerous driving behaviors. This study suggests that driver EI should be analyzed using the TEIQue EI scale and driving behaviors should be measured using the DDDI and DBQ scales. Moreover, promoting and improving EI could be useful in preventing dangerous driving behaviors among drivers and reducing their fatality rates on the road.

Emotional Intelligence as Evaluative Activity: Theory, Findings, and Future Directions

The question of whether ability-related emotional intelligence (ability EI) predicts important life outcomes has attracted considerably more attention than the question of what ability EI consists of. In the present paper, the authors draw from the attitude and emotion literatures to suggest that the evaluation dimension of meaning is likely key in understanding how ability EI operates. Measures of ability EI predict the extent to which individuals can accurately evaluate words and measures of the latter type act as emotional intelligence measures. Extending this analysis, the paper reviews recent sources of data linking ability EI to attitudinal processes, such as those involved in attitude–behavior relationships and affective bipolarity. Individuals with high EI appear to experience their affect in more bipolar terms and they display evidence of greater decisiveness in their evaluations. Pursuing links of the present type will allow researchers to generate new predictions concerning the ability EI construct.

Language Abilities are Associated with Both Verbal and Nonverbal Intelligence in Children on the Autism Spectrum

ABSTRACT Intellectual abilities factor into levels of functioning used to characterize autism. Language difficulties are highly prevalent in autism and may impact performance on measures of intellectual abilities. As such, nonverbal tests are often prioritized in classifying intelligence in those with language difficulties and autism. However, the relationship between language abilities and intellectual performance is not well characterized, and the superiority of tests with nonverbal instructions is not well established. The current study evaluates verbal and nonverbal intellectual abilities in the context of language abilities in autism and the potential benefit of tests with nonverbal instructions. Participants were 55 children and adolescents on the autism spectrum who underwent a neuropsychological evaluation as part of a study examining language functioning in autism. Correlation analyses were performed to examine relations between expressive and receptive language abilities. Language abilities (CELF−4) were significantly correlated with all measures of both verbal (WISC-IV VCI) and nonverbal intelligence scores (WISC-IV PRI and Leiter-R). There were no significant differences between nonverbal intelligence measures with verbal or nonverbal instructions. We further discuss the role of assessment of language abilities in interpreting results of intelligence testing in populations with higher prevalence of language difficulties

Pengelolaan Kecerdasan Emosi Pendidik TK Azhari Islamic School Jakarta

This research concludes that the management and measurement of emotional intelligence (EQ) of Azhari Islamic School Kindergarten educators experience many obstacles in carrying out online (online) learning through smartphone applications during lockdown, such as: teaching loss, stress-inducing boredom, limited skills in using gadgets and learning applications, internet network disturbances and funds, and lack of understanding of the character and characteristics of students. This is motivated by problematic emotional problems that arise as a result of the impact of the Covid-19 pandemic in Indonesia which affects all aspects of life including the education process in PAUD. This research shows that there is a process of managing and measuring emotional intelligence (EQ) of Azhari Islamic School Jakarta Kindergarten educators based on Daniel Goleman's emotional intelligence theory. The process of managing the EQ of educators at the Azhari Islamic School Jakarta Kindergarten through intensive training and self-development programs from foundations and independently. The results of measuring the emotional intelligence of educators at Azhari Islamic School Jakarta Kindergarten show that overall the five aspects of Goleman's emotional intelligence (self-awareness, self-regulation/emotional management, motivation, empathy and social skills) have high and good emotional intelligence with an average percentage of 80.4% . Aspects of the ability to manage emotions produce the highest score of 94.79%. And on the results of measuring the aspect of fostering external relations which has a percentage of 72.42% (enough category)

The Current and Retrospective Cognitive Reserve (2CR) survey and its relationship with cognitive and mood measures

Cognitive Reserve (CR) is often assessed with surveys spanning demographic, lifestyle, and socio-behavioral variables. The role of both past and current life experiences on CR has, however, rarely been examined. We developed the Current and Retrospective Cognitive Reserve (2CR) survey to assess classical CR proxies (socio-economic status, engagement in leisure and social activities) and other dimensions of potential importance (family engagement, religious/spiritual activity) both currently (CRc; in later adulthood) and retrospectively (CRr; as recalled from younger adulthood). We administered the 2CR, measures of general cognitive functioning, working memory (WM), crystallized—vocabulary— and fluid—reasoning—intelligence, and depressive symptoms (DS) to 235 community-dwelling Italian adults (ages 55–90 years). We used exploratory and confirmatory factor analyses to examine the 2CR latent structure, and we estimated correlations of its dimensions with cognitive abilities and DS. Analyses confirmed a three-level factor structure with two global CR factors (CRc and CRr) at the top level, dimensional CR factors (socio-economic status, family engagement, leisure activity, social engagement, and religious/spiritual activity) at mid-level and observed items at the lowest level. Item-factor representations partially differed across CRc and CRr. Both CRc and CRr were positively correlated with measures of intelligence, WM and DS, but associations of measures of intelligence were stronger for CRr, whereas associations of WM and DS were slightly stronger for CRc. The 2CR can be considered a reliable survey for assessing CR proxies within a multidimensional, “life stage-dependent” framework insofar as CRc are CRr closely related but also differently associated with intelligence, WM, and DS.

Towards a Heuristic Neuropsychological Model of Adjudicative Competency.

This study sought to delineate the neuropsychological processes that undergird the psycho-legal concept of competency to stand trial (CST). Accordingly, we retrospectively examined the relationship between clinical judgments of competence or incompetence of defendants committed to a maximum-security psychiatric facility and neuropsychological measures of cognitive and social intelligence and declarative memory. Results indicated that both groups (competent and incompetent) showed similar levels of depressed cognitive intelligence with Wechsler full-scale IQ levels falling in the upper end of the borderline range. Compared with defendants clinically judged as incompetent, defendants recommended as competent scored significantly higher on measures of social intelligence and episodic memory, with the most pronounced advantage occurring on tests of verbal memory that place heavy demands on encoding, consolidation, and retrieval of aurally presented narrative material. Cognitive capacities in areas of social intelligence and episodic memory may play critical roles in developing a heuristic neuropsychological model of CST. The evaluation of these domains offers implications for the assessment, restoration, and understanding of CST.

Measuring effectiveness of AI-based virtual service assistants by user mental health improvement: A research agenda

This paper argues that traditional performance measures of artificial intelligence (AI)-based virtual service assistants (VSAs) fall short of capturing the human-like attributes of VSAs, their influences on human partners, and hence a VSA’s true potential. The current study suggests that the effectiveness of VSAs can be measured by examining the variables related to mental health improvements of the consumers or human partners as a direct result of their conversations, companionship and engagement with the VSAs. The paper presents a research agenda, highlighting the role of perceived leadership of the AI-based VSAs, consumer engagement with VSAs and the subsequent social support of the VSAs to their consumers. It suggests that the aforementioned will result in the enhancement of a variety of coping skills such as problem solving, emotional and time-focused coping, and a subsequent overall increase of trust in AI-based VSAs among their consumers.

Artificial intelligence, language and Neuropsychological measures in Alzheimer’s disease

AbstractBackgroundOne of the current challenges in the field of Alzheimer’s disease (AD) is to identify patients with mild cognitive impairment (MCI) that will convert to AD. Artificial intelligence, in particular machine learning (ML), has established as one of more powerful approach to extract reliable predictors and to automatically classify different AD phenotypes. It is time to accelerate the translation of this knowledge in clinical practice, mainly by using low‐cost features originating from the language and neuropsychological assessment.MethodWe aim at performing a meta‐analytic evaluation of the contribution of ML and language/neuropsychological measures for the automated classification of AD and MCI patients and the prediction of MCIs’ conversion to AD‐type dementia. Specifically, we study, from the results reported by independent studies, whether ML algorithms, trained on a set of language and neuropsychological measures, could be used for the automatic diagnosis of MCI and AD‐type dementia. To tackle the issue of cross‐linguistic sensitivity, this work spans cohorts of speakers from widely different languages. The advantages and issues that should be taken under consideration for translating these approaches into reliable clinical studies are discussed.ResultAlthough a high heterogeneity was observed, this meta‐analysis shows that ML applied to language and neuropsychological measures can lead to a successful automatic classification, being more specific as screening rather than prognosis tool. Relevant categories of language and neuropsychological tests can be extracted by ML that maximize the classification accuracy.ConclusionML applied on language and neuropsychological measures can automatically classify AD patients, even at an early stage of the disease. This brings several advantages, such as the development of more objective and efficient language and neuropsychological batteries for improving the early diagnosis of AD. Future studies should empirically test the combination of methodological features necessary to improve patients’ classification also at the preclinical stages.

The Adelaide Score: An artificial intelligence measure of readiness for discharge after general surgery.

This study aimed to examine the performance of machine learning algorithms for the prediction of discharge within 12 and 24 h to produce a measure of readiness for discharge after general surgery. Consecutive general surgery patients at two tertiary hospitals, over a 2-year period, were included. Observation and laboratory parameter data were stratified into training, testing and validation datasets. Random forest, XGBoost and logistic regression models were evaluated. Each ward round note time was taken as a different event. Primary outcome was classification accuracy of the algorithmic model able to predict discharge within the next 12 h on the validation data set. 42 572 ward round note timings were included from 8826 general surgery patients. Discharge occurred within 12 h for 8800 times (20.7%), and within 24 h for 9885 (23.2%). For predicting discharge within 12 h, model classification accuracies for derivation and validation data sets were: 0.84 and 0.85 random forest, 0.84 and 0.83 XGBoost, 0.80 and 0.81 logistic regression. For predicting discharge within 24 h, model classification accuracies for derivation and validation data sets were: 0.83 and 0.84 random forest, 0.82 and 0.81 XGBoost, 0.78 and 0.79 logistic regression. Algorithms generated a continuous number between 0 and 1 (or 0 and 100), representing readiness for discharge after general surgery. A derived artificial intelligence measure (the Adelaide Score) successfully predicts discharge within the next 12 and 24 h in general surgery patients. This may be useful for both treating teams and allied health staff within surgical systems.

The Relative Strengths of Relationships Between Fine Motor Skills, Working Memory, Processing Speed and Fluid Intelligence in Early Elementary School Children.

The goal in the present paper was to examine the combined and relative impact of fine motor ability, auditory working memory, and processing speed on fluid intelligence in a sample of early elementary school students. Our participant sample was 145 children (Mage = 9.1years, SD = 1.1; 80 boys, 65 girls). We used the Raven's Colored Progressive Matrices Test as a measure of fluid intelligence and five other measures to represent the three predictor variables: the Grooved Pegboard Test as a measure of fine motor skills, Digit Span Forwards and Digit Span Backwards tests as measures of working memory, and Rapid Automatized Naming and Letter-Digit Substitution tasks as measures of processing speed. Regression analyses indicated that only two of these measures had a statistically significant association with the fluid intelligence test scores, namely, scores on the Grooved Pegboard (fine motor skills) and Digit Span Backwards (working memory) tests, with these two measures explaining 35% of the variance in the fluid intelligence test scores. Thus, fine motor skills and working memory were correlated with fluid intelligence in early elementary-grade students. Until the directions of these relationships are better understood, we might assume that interventions aiming to increase young children's fluid intelligence, or at least their intelligence scores, might partly target working memory and fine motor skills.

A measure of leadership's spiritual intelligence from the perspectives of Brazilian and Portuguese workers

This paper aims to assess the psychometric quality of the Spiritual Intelligence Self-Report Inventory (SISRI), a scale adapted to measure leaders' spiritual intelligence (SI) from the perspective of Brazilian and Portuguese workers. This scale initially measures an individual's spiritual intelligence, and its adaptation opens up the opportunity to perceive the leader's SI from the point of view of his work team. In order to compare possible cultural differences, the research was carried out in hospital organizations in two countries: Brazil and Portugal. The Total sample is 434 workers. The statistical results indicate that the adapted SISRI is a reliable instrument that can measure the leader's spiritual intelligence. The four-factor structure is supported, as well as the construct´s invariance and convergence are maintained. As for cultural differences, it was observed that Brazilian workers have higher scores in the leader's total spiritual intelligence score, specifically in the Existential Critical Thinking (CET) and Awareness Expansion (CSE) dimensions.

Pulse repetition interval modulation recognition using deep CNN evolved by extreme learning machines and IP-based BBO algorithm

Pulse repetition interval modulation (PRIM) recognition is a critical task in electronic intelligence (ELINT) and electronic support measure (ESM) systems for detecting radar threats accurately. However, PRI recognition is a complex issue due to missing and spurious pulses, resulting in noisy PRI pattern changes in real environments. To address this problem, this paper proposes a novel approach that recognizes the five common types of PRIM through a four-phase process. In the first phase, a deep convolutional neural network (DCNN) is used as a feature extractor. Then, extreme learning machines (ELMs) are used for real-time recognition of the PRIM patterns in the second phase. In the third phase, we employ the biogeography-based optimizer (BBO) to enhance the network’s robustness by optimizing the connection weights and biases. To address the increasing complexity of the model, we introduce an optimized variable-length internet protocol-based BBO (VBBO) in the fourth phase. In this approach (i.e., DCNN-VBBO-ELM), each layer of DCNN is encoded by an IP address into a habitat of VBBO in the same sequence as the DCNN layers. To evaluate the proposed method, we develop a real experimental dataset consisting of five common PRI patterns. Our approach achieves a final accuracy of 97.05%, which is better than other ELM-based benchmark models. Moreover, the proposed model requires only 27 s of training time to process 50,000 training images, confirming its real-time capabilities. In conclusion, our proposed approach improves PRI recognition by leveraging DCNN, ELM, and VBBO, resulting in a more accurate and robust real-time radar PRI classifier.

Common Factor Structure of the Ten Subtest Wechsler Adult Intelligence Scale-Fourth Edition in a Clinical Sample and 15 Subtest Version in the Standardization Sample.

The 10 core subtests of the Wechsler Adult Intelligence Scale-IV (WAIS-IV) suffice to produce the 4 index scores for clinical assessments. Factor analytic studies with the full complement of 15 subtests reveal a 5-factor structure that aligns with Cattell-Horn-Carroll taxonomy of cognitive abilities. The current study investigates the validity of 5-factor structure in a clinical setting with reduced number of 10 subtests. Confirmatory factor analytic models were fitted to a clinical neurosciences archival data set (n_Male = 166, n_Female = 155) and to 9 age-group samples of the WAIS-IV standardization data (n = 200 for each group). The clinical and the standardization samples differed as (a) the former comprised scores from patients, aged 16 to 91, with disparate neurological diagnosis whereas the latter was demographically stratified, (b) only the 10 core subtests in the former but all 15 subtests in the latter were administered, and (c) the former had missing data, but the latter was complete. Despite empirical constraints to eliciting 5 factors with only 10 indicators, the well-fitting, 5-factor (acquired knowledge, fluid intelligence, short-term memory, visual processing, and processing speed) measurement model evinced metric invariance between the clinical and standardization samples. The same cognitive constructs are measured on the same metrics in every sample examined and provide no reason to reject the assumption that the 5 underlying latent abilities of the 15 subtest version in the standardization samples can also be inferred from the 10 subtest version in clinical populations.