Processing Speed Research Articles

Cumulative blood pressure load and cognitive decline in older adults: An observational analysis of two large cohorts.

Cumulative blood pressure metrics may provide greater precision for measuring temporal risk exposure, especially in later life where data are mixed regarding associations of high blood pressure (BP) on cognitive function. We examined the relationship between greater cumulative exposure to high BP in later life and several domains of cognitive function. Individual cognitive assessment scores and BP measurements in older adults (age ≥70 years) at baseline and over approximately 8 years of follow-up were available in the population-based Canadian Victoria Longitudinal Study (VLS) and Swedish Gothenburg H70 Birth Cohort Studies (H70). Linear mixed models were used to quantify associations between cumulative systolic and diastolic BP and change in cognitive scores. Each additional 100mmHg increase in cumulative BP was related to greater decline in the Rey Auditory Verbal Learning Test (RAVLT) List A, trials 1-5 total score over follow-up: -0.23 (95% confidence interval [CI] -0.32, -0.13) for systolic BP and -0.41 (95%CI -0.58, -0.23) for diastolic BP. Similarly increases cumulative systolic and diastolic BP were related to greater declines Digit Symbol Substitution Task (DSS) scores: -0.59 (95%CI -0.80, -0.38) and -1.04 (95% CI -1.40, -0.67), respectively. There were no associations of cumulative BP and temporal changes in general cognition, other measures of verbal episodic memory, or semantic fluency. Higher cumulative BP is associated with greater declines in RAVLT measured immediate memory span and complex attention, information processing speed and visuospatial scanning in older adults, but the scale of change is small. Additional research is required to further define these relationships and identify opportunities for prevention.

Global functional connectivity reorganization reflects cognitive processing speed deficits and fatigue in multiple sclerosis.

Cognitive impairment (CI) in multiple sclerosis (MS) is associated with bidirectional changes in resting-state centrality measures. However, practicable functional magnetic resonance imaging (fMRI) biomarkers of CI are still lacking. The aim of this study was to assess the graph-theory-based degree rank order disruption index (kD) and its association with cognitive processing speed as a marker of CI in patients with MS (PwMS) in a secondary cross-sectional fMRI analysis. Differentiation between PwMS and healthy controls (HCs) using kD and its correlation with CI (Symbol Digit Modalities Test) was compared to established imaging biomarkers (regional degree, volumetry, diffusion-weighted imaging, lesion mapping). Additional associations were assessed for fatigue (Fatigue Scale for Motor and Cognitive Functions), gait and global disability. Analysis in 56 PwMS and 58 HCs (35/27 women, median age 45.1/40.5 years) showed lower kD in PwMS than in HCs (median -0.30/-0.06, interquartile range 0.55/0.54; p = 0.009, Mann-Whitney U test), yielding acceptable yet non-superior differentiation (area under curve 0.64). kD and degree in medial prefrontal cortex (MPFC) correlated with CI (kD/MPFC Spearman's ρ = 0.32/-0.45, p = 0.019/0.001, n = 55). kD also explained fatigue (ρ = -0.34, p = 0.010, n = 56) but neither gait nor disability. kD is a potential biomarker of CI and fatigue warranting further validation.

Women Have the Power: When Motor Efficiency Makes the Difference in Older Individuals of the Sardinian Blue Zone.

This study was mainly aimed at exploring the effect of gender on the patterns of Physical Activty (PA) in older people living in an area of exceptional longevity, the so-called Sardinian Blue Zone. Furthermore, the study intended to investigate the nature of the relationships among PA metrics, cognitive measures, and age. One hundred and nine community-dwelling participants (Mage = 81.7 years old, SD = 8.1 years) completed a battery of cognitive tests (i.e., Mini-Mental State Examination and Trail Making Test) and were provided a wearable accelerometer to collect data for 7 consecutive days about amount and intensity of the performed PA on a daily basis. A series of Analyses of Covariance was conducted to examine the impact of gender on PA variables, whereas the global cognitive functioning measure was used as a covariate. Thus, it was found that women outperformed men in terms of a more active lifestyle, as indicated by the significantly reduced sedentary behavior and increased percentage of time spent in PA of moderate to vigorous intensity (MVPA). Moreover, significant associations were found among age, PA, and cognitive measures. Finally, global cognitive functioning was significantly associated with MVPA and overall accelerometric counts, as well as significant relationships were found between time spent in sedentary behavior and MVPA, accelerometric counts, speed of processing, and cognitive flexibility, respectively. Overall, these outcomes suggest that older individuals, especially women, exhibiting more preserved cognitive efficiency also reported superior levels of PA. Therefore, the promotion of a more active lifestyle is essential to achieve better functional and cognitive health in late adulthood.

Associations between cytokine levels and cognitive function among individuals at clinical high risk for psychosis

ObjectiveTo explore the intricate interplay among cytokines, cognitive functioning, and conversion to psychosis in individuals at clinical high-risk (CHR) for psychosis. MethodWe initially enrolled 385 individuals at CHR and 95 healthy controls (HCs). Subsequently, 102 participants at CHR completed the 1-year follow-up assessments, and 47 participants transitioned to psychosis. We assessed the levels of interleukins (IL-1β, IL-2, IL-6, IL-8, IL-10), tumor necrosis factor-α (TNF-α), granulocyte-macrophage colony-stimulating factor (GM-CSF), and vascular endothelial growth factor (VEGF). We comprehensively evaluated cognitive performance across six domains, including speed of processing (SP), attention/vigilance (AV), working memory (WM), verbal learning (VeL), visual learning (ViL), and reasoning and problem-solving (RPS). ResultsHigher baseline cognitive domain scores were associated with elevated GM-CSF and reduced VEGF levels. In the follow-up analysis, significant time effects were observed for IL-1β and IL-2. We also observed significant interaction effects between specific cognitive domains (AV, WM, VeL, and OCS) and levels of cytokine (GM-CSF, IL-1β, IL-6, and TNF-α). Changes in WM were negatively correlated with changes in TNF-α levels and positively correlated with changes in VEGF levels. Variations in VeL were inversely correlated with changes in GM-CSF and IL-10 levels, whereas changes in RPS were positively associated with changes in GM-CSF and IL-8 levels. ConclusionsOur results revealed intricate associations among cytokine levels, cognitive performance, and psychosis progression.

Evidence for the dependence of visual and kinesthetic motor imagery on isolated visual and motor practice.

Motor imagery (MI) is a cognitive process believed to rely on the representation developed through experience. The equivalence between MI and execution has been questioned and the relationship between experience types and MI is unclear. We tested how observational and physical practice of hand gesture sequences impacted visual and kinesthetic MI and transfer to the unpracticed effector. Three groups (n = 22/gp.); no-vision physical practice, observational practice and no-practice control, practiced and visually and kinesthetically imagined performing the sequences. MI was assessed using mental chronometry, a movement time (MT) congruency measure and subjective ratings. Physical practice improved kinesthetic MI ratings and observational practice improved visual MI ratings. Contrary to predictions, physical practice did not enhance timing congruency. Imagined MTs were longer in transfer after physical practice, suggesting MI was not based on the same representation. These data question ideas of equivalence, with poor temporal matching after no-vision physical practice.

Cognitive Performance of Latino and White Non-Latino Individuals With Parkinson's Disease.

Cognitive impairment is a common nonmotor symptom in Parkinson's disease (PD). Individuals of Latino background are traditionally underrepresented in research on PD. Despite the fact that Latinos comprise 18% of the U.S. population, they commonly make up less than 5% of samples in studies of PD. Emerging evidence suggests that Latino individuals with PD may experience disparities relative to White non-Latinos in terms of having more severe motor symptoms, more severe depressive symptoms, and worse health-related quality of life. The purpose of the present study was to investigate differences in cognitive performance between Latino and White non-Latino individuals with PD and examine correlates of cognitive performance. Data were obtained from the Parkinson's Progression Markers Initiative. Participants included 60 Latino individuals with PD and 1,009 White non-Latino individuals with PD, all of whom were followed annually for up to 5 years. Participants completed neuropsychological tests of attention and working memory, processing speed, visuospatial functioning, verbal fluency, and immediate and delayed memory and recall. Relative to White non-Latino individuals with PD, Latino individuals with PD had significantly lower scores on the global measure of cognitive functioning, a test of processing speed, and tests of working memory and attention. Years of education was the strongest correlate of performance in these three cognitive domains among individuals in the Latino group. These findings provide initial evidence of disparities in cognitive functioning among Latino individuals with PD. Educational disadvantages may be one potential driver of these disparities.

Effects of diesel exhaust inhalation on cognitive performance in human volunteers: A randomized controlled crossover study.

Mounting evidence links exposure to traffic-related air pollution (TRAP) to impairment in cognitive functioning. To determine if short-term, controlled exposure to diesel exhaust (DE) adversely affects one or more cognitive function domains. We carried out a double-blinded crossover design with 28 healthy, adult volunteers. Volunteers were exposed to two conditions for 120min each, on separate order-randomized occasions: filtered air (FA) and DE (300µg/m3 PM2.5) at the Air Pollution Exposure Laboratory (APEL) at Vancouver General Hospital (VGH). Volunteers were blinded to the two exposure conditions. Volunteers completed five computerised neuropsychological tests of the Cambridge Neuropsychological Test Automated Battery (CANTAB) prior to (2-hr before) and at three timepoints following each exposure condition (0-hr, 3-h post-, and 24-h post-exposure). The selected CANTAB tests were related to five cognitive domains - attention, spatial working memory, strategy use, executive function, and processing speed. We hypothesized that short-term diesel exposure would adversely affect one or more cognitive function domains. Following screening, 15 volunteers were randomized to receive FA followed by DE and 14 volunteers were randomized to receive the exposures in the reverse sequence. A total of 28 volunteers contributed to the final analysis. Short-term exposure to DE was associated with slower reaction times in the Reaction Time Index task. DE was associated with a decrease of 18.2ms (p=0.05) in simple reaction time and 23.5ms (p=0.04) in five-choice reaction time. This first study to investigate the effects of TRAP on the cognitive performance of humans in a controlled environment shows slowed reaction times similar to those previously demonstrated with blood alcohol levels of 0.05%. Important implications exist for workers in occupations where attention and reaction time are connected to safety and performance.

Reduced P300 amplitude in children and adolescents with autism is associated with slowed processing speed, executive difficulties, and social-communication problems.

Selective attention to auditory input is reflected in the brain by an electric amplitude called the P3b amplitude, which is measured using electroencephalography. Previous research has shown that children and adolescents with autism have an attenuated P3b amplitude when they have to attend specific sounds while ignoring other sounds. However, it is unknown whether a reduced P3b amplitude in autistic children and adolescents is associated with their autism features, daily functioning and/or cognitive functions. This study aimed to examine these questions. Therefore, we assessed selective attention to auditory input in 57 children with autism aged 7-14 years and 57 neurotypically developing controls while measuring their brain activity with electroencephalography. Participants further underwent cognitive assessment, and parents reported on autistic traits and daily functioning. As expected, children with autism had lower P3b amplitude compared to their neurotypical peers. Importantly, an attenuated P3b amplitude was associated with more parent-reported social-communication problems and difficulties with daily functioning. Children with autism further had reduced processing speed of visual input, which also was coupled to a lower P3b amplitude. In conclusion, we found attenuated P3b amplitude in children with autism performing an auditory selective attention task, which was related to difficulties with processing visual input and allocating attentional resources critical for social and daily functioning. The results suggest that autistic children are more vulnerable to being disturbed when the environment is filled with conflicting sensory input.

Deep learning methods for improving the accuracy and efficiency of pathological image analysis.

This study presents a novel integration of two advanced deep learning models, U-Net and EfficientNetV2, to achieve high-precision segmentation and rapid classification of pathological images. A key innovation is the development of a new heatmap generation algorithm, which leverages meticulous image preprocessing, data enhancement strategies, ensemble learning, attention mechanisms, and deep feature fusion techniques. This algorithm not only produces highly accurate and interpretatively rich heatmaps but also significantly improves the accuracy and efficiency of pathological image analysis. Unlike existing methods, our approach integrates these advanced techniques into a cohesive framework, enhancing its ability to reveal critical features in pathological images. Rigorous experimental validation demonstrated that our algorithm excels in key performance indicators such as accuracy, recall rate, and processing speed, underscoring its potential for broader applications in pathological image analysis and beyond.

Computerized cognitive testing to capture cognitive decline in Alzheimer's disease: Longitudinal findings from the ARMADA study.

Timely detection and tracking of Alzheimer's disease (AD) -related cognitive decline has become a public health priority. We investigated whether the NIH Toolbox for Assessment of Neurological and Behavioral Function-Cognition Battery (NIHTB-CB) detects AD-related cognitive decline. N=171 participants (age 76.5±8; 53% female, 34% Aβ-positive) from the ARMADA study completed the NIHTB-CB at baseline, 12 months, and 24 months. Linear mixed-effect models correcting for demographics were used to examine cross-sectional and longitudinal NIHTB-CB scores in individuals across the clinical AD spectrum. Compared to Aβ-negative healthy controls, Aβ-positive individuals with amnestic MCI or mild AD performed worse on all NIHTB-CB measures and showed an accelerated decline in processing speed, working memory, and auditory word comprehension tests. These findings support the use of the NIHTB-CB in early AD, but also imply that the optimal NIHTB-CB composite score to detect change over time may differ across clinical stages of AD. Future directions include replication of these findings in larger and more demographically diverse samples. We examined NIH Toolbox-Cognition Battery scores across the clinical AD spectrum.All NIH Toolbox tests detected cross-sectional cognitive impairment in MCI-to-mild AD.Three NIH Toolbox tests captured further decline over time in MCI-to-mild AD.The NIH Toolbox can facilitate timely detection of AD-related cognitive decline.

An end-to-end scheduling digital twin for multistage batch plants considering safety

Modern multistage batch plants, characterized by products processed through a series of stages with competing processing units, face challenges in optimizing scheduling and considering inserted events such as breakdowns and maintenance operations simultaneously in current highly dynamic and volatile market. It necessitates robust scheduling tools for efficiency, flexibility, and safety. Previous mathematical programming and meta-heuristic methods, while effective, are time-consuming for large or complex problems. Hybrid models and machine learning approaches have shown promise but often lack robustness against real-world disruptions. In response to these challenges, this work proposes an end-to-end digital twin (DT) solution based on a deep reinforcement learning model for multiproduct multistage batch plants. This model provides a versatile solution for scheduling processes of varying sizes without the need for extensive retraining, seamlessly incorporating dynamic factors. Tests on the model demonstrate its rapid interaction and robust solution capabilities, highlighting its comprehensive advantages in processing speed and result quality.

Cognitive changes in post-stroke depression patients undergoing treatment with transcranial direct current stimulation (tDCS): an exploratory, ancillary analysis of a randomized, sham-controlled clinical trial.

Post-stroke depression (PSD) affects approximately 40% of stroke survivors, with cognitive deficits being frequently observed. Transcranial Direct Current Stimulation (tDCS) has shown promise in improving cognitive performance in stroke patients. We explored the effects of tDCS on cognitive performance in PSD. An exploratory analysis was conducted in 48 patients from a double-blinded, sham-controlled, randomized clinical trial that investigated the effects of tDCS for treating PSD. A neuropsychological battery was applied at baseline and endpoint. We assessed three key domains: (1) Stroop effect, measured by the Stroop test components (color naming, word-reading and word-color interference); (2) processing speed, assessed using the Trail Making Test and the Digit Symbol coding test; (3) executive function, evaluated with the Digit Span test and the Frontal Assessment Battery (FAB). A Linear mixed regression models were used to evaluate changes according to groups. We found that the active tDCS group worsened slightly, while the sham group improved in Executive Function for the adjusted models. Significant interactions were also found for FAB. We found no consistent evidence that tDCS significantly improved the cognitive domains. The bidirectional association with cognition analysis suggest that tDCS effects may vary based on depression severity and task complexity.

Automating International Human Rights Adjudication

International human rights courts and treaty bodies are increasingly turning to automated decision-making (“ADM”) technologies to expedite and enhance their review of individual complaints. These tribunals have yet to consider many of the legal, normative, and practical issues raised by the use of different types of automation technologies for these purposes. This article offers a comprehensive and balanced assessment of the benefits and challenges of introducing ADM into international human rights adjudication. We argue in favor of using ADM to digitize documents and for internal case management purposes and to make straightforward recommendations regarding registration, inadmissibility, and the calculation of damages. In contrast, we reject the use of algorithms or artificial intelligence (“AI”) to predict whether a state has violated a human rights treaty. In between these polar categories we discuss semi-automated programs that cluster similar cases together, summarize and translate key texts, and recommend relevant precedents. We weigh the benefits of introducing these tools to improve international human rights adjudication—which include greater speed and efficiency in processing and sorting cases, identifying patterns in jurisprudence, and enabling judges and staff to focus on more complex responsibilities— against two types of cognitive biases—biases inherent in the datasets on which ADM is trained and biases arising from interactions between humans and machines. We also introduce a framework for enhancing the accountability that mitigates the potential harms caused by ADM technologies.

Baseline predictors of cross-sectional and longitudinal performance on the symbol digit modalities test in individuals with multiple sclerosis.

Cognitive impairment occurs frequently in persons with multiple sclerosis (PwMS) at some point in the course of the disease. However, not all PwMS develop cognitive difficulties suggesting a role for important moderating factors. We examined baseline predictors of cross-sectional and longitudinal change in cognitive performance in PwMS. 680 PwMS enrolled in the Comprehensive Longitudinal Investigation of Multiple Sclerosis at the Brigham and Women's Hospital who completed the Symbol Digit Modalities Test (SDMT), a brief measure of speed of information processing, at least twice during a 10-year period were identified. Potential baseline demographic (age, education, and sex), clinical (disability, disease duration, and disease category), and patient-reported outcome (PRO) (fatigue, depression, and quality of life) predictors were examined in cross-sectional analyses using linear regression and in longitudinal analyses using linear mixed effects models. In cross-sectional analyses, age, disease duration, and disability each showed associations with SDMT. Group differences were observed between females and males, subjects with and without college degrees, and subjects with relapsing and progressive MS. All PRO measures showed associations with SDMT, and the strongest association was with fatigue. In the longitudinal model, increased baseline age and increased baseline disability were each associated with a greater decline in SDMT performance. None of the baseline PROs were associated with longitudinal change in SDMT. We observed strong associations between baseline demographic, clinical, and PRO measures and concurrent SDMT, but more limited associations between these measures and longitudinal change in SDMT.

Neuropsychological and clinical indicators of Lewy body and Alzheimer's pathology

Background Clinical distinction between Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) poses significant challenges due to pathological comorbidity. Similar ages of onset and overlapping cognitive and psychiatric symptoms can lead to diagnostic inaccuracy and inappropriate treatment recommendations. Objective Identify the best combination of clinical and neuropsychological predictors of AD, DLB, and mixed DLB/AD neuropathology in dementia patients. Methods Using the National Alzheimer's Coordinating Center dataset, we selected either pure AD ( n = 189), DLB ( n = 21), or mixed DLB/AD ( n = 42) patients on autopsy. Neuropsychological and clinical predictors, including core clinical features of DLB, were entered into multivariable logistic regressions. Results Gait disturbances (odds ratio (OR) = 19.32; p = 0.01), visual-spatial complaints (OR = 6.06; p = 0.03), and visual hallucinations (OR = 31.06; p = 0.002) predicted DLB compared to AD, along with better memory (OR = 3.42; p = 0.003), naming (OR = 3.35; p = 0.002), and worse processing speed (OR = 0.51; p = 0.01). When comparing DLB to DLB/AD, gait disturbances (OR = 6.33; p = 0.01), increased depressive symptoms (OR = 1.44; p = 0.03), and better memory (OR = 3.01; p = 0.004) predicted DLB. Finally, rapid eye movement sleep behavior disorder (RBD) (OR = 6.44; p = 0.004), parkinsonism severity (OR = 1.07; p = 0.02), and lower depressive symptoms (OR = 0.70; p = 0.006) and memory impairment (OR = 0.57; p = 0.02) distinguished DLB/AD from AD. Conclusions Our study converges with prior research suggesting specific neuropsychological and clinical features can help distinguish DLB from AD. Neuropsychological differentiation becomes more challenging among mixed pathologies and in advanced cognitive impairment, although the presence of RBD and parkinsonism distinguished DLB. Earlier clinical assessment and incorporation of in vivo and postmortem biomarkers should enhance diagnostic accuracy and understanding of disease characteristics, offering significant relevance for disease-modifying treatments.

Obesity Affects Disease Activity and Progression, Cognitive Functioning, and Quality of Life in People With Multiple Sclerosis.

While obesity is a known risk factor of the development of multiple sclerosis (MS), its impact on MS disease progression remains unclear. We aimed to investigate the influence of body mass index (BMI) on disease activity and progression, cognitive performance, and health-related quality of life in patients with MS. Patients from an incident population-based case-control study (n = 3,249) were categorized based on BMI status at diagnosis and followed up after diagnosis through the Swedish MS registry. Outcomes included changes in the Expanded Disability Status Scale (EDSS), Multiple Sclerosis Impact Scale 29, and Symbol Digit Modalities Test. The mean follow-up time was 10.6 years (SD 6.1). Linear mixed models were used to analyze long-term changes while Cox regression models assessed the risk of 24-week confirmed disability worsening, time to reach EDSS score 3 and EDSS score 4, the appearance of new lesions on MRI, patient-reported physical and psychological worsening, and processing speed worsening. Obesity, compared with healthy weight, was associated with a 0.02-point faster annual increase in the EDSS score (β for EDSS score x time 0.02, 95% CI 0.00-0.04). In addition, obesity was linked to a higher risk of reaching EDSS score 3 (HR 1.43, 95% CI 1.17-1.75) and EDSS score 4 (HR 1.40, 95% CI 1.07-1.73) and an increased risk of physical and psychological worsening. New lesions on MRI were more frequent among those with overweight and obesity, compared with those with healthy weight (HR 1.21, 95% CI 1.02-1.44 and HR 1.29, 95% CI 1.03-1.62, respectively). Among those who had not changed BMI group during follow-up, the associations between obesity and unfavorable outcomes became more pronounced, and the HR of cognitive disability worsening was 1.51 (95% CI 1.09-2.09) among those with obesity, compared with nonobese participants. In participants with MS, obesity was associated with faster disease progression, poorer health-related quality of life, and more rapid cognitive decline. Both overweight and obesity were associated with higher MRI activity.

When the days are(n’t) numbered: Calendar calculations in transparent and opaque systems

Abstract Calendar calculations, the process of computing the target day or month, exhibit peculiar differences across languages. In systems like English, calendar labels are largely opaque (Tuesday, August), which invites calculations to rely more heavily on verbal listing. In transparent systems, like Chinese, habitual labeling of calendar terms numerically (Tuesday = Day 2, August = Month 8) facilitates fast numerical operations instead of verbal listing. This study examines the effects that different levels of transparency of the calendar naming system may have on calculations in the speakers’ first and second language. Chinese–English bilinguals were tested alongside English and Chinese controls. Forced-choice calendar calculations (day, month, hour and year) and self-reported strategies were used as tasks to tap into participants’ calculation speed, accuracy and temporal reasoning. In the calculation questions, we manipulated Distance (short/long), Direction (forward/backward), Input (linguistic/numerical) and Boundary (within/across). More complex Month calculations significantly differed across groups while easier day calculations did not. The English group reported reliance on verbal listing while the Chinese and the Bilingual groups preferred numerical reasoning. These findings bring new evidence for linguistic relativity in the form of modulations of calendar processing speed changing as a function of linguistic transparency, input type and task demand.

Enhancing Network Security using Possibility Neutrosophic Hypersoft Set for Cyberattack Detection

Network security is any endeavor intended to defend the integrity and usability of the data and network. Fast development in network technology and the scope and amount of information transported on a network is gradually growing. Based on these situations, the complexity and density of cyber-attacks and threats are also increasing. The constantly expanding connectivity makes it more difficult for cyber-security specialists to monitor all the movements on the network. More complex and frequent cyber-attack makes anomaly identification and detection in network events challenging. Machine learning (ML) provides different techniques and tools to automate cyber-attack detection and for prompt prognosis and analysis of attack types. The model of a neutrosophic hypersoft set (NHSS) is a combination of a neutrosophic set with a hypersoft set. It is a useful structure to handle multi-objective problems and multi-attributes with disjoint attributable values. This study derives the Possibility Neutrosophic Hypersoft Set for Cyberattack Detection (pNHSS-CAD) technique to improve network security. The pNHSS-CAD method has its formation in feature selection with the Whale Optimization Algorithm (WOA), which successfully recognizes the important features from the data, thus improving processing speed and reducing dimensionality. Following feature selection, the pNHs-set classifier is employed for the robust detection and identification of cyber-attacks, which leverages the power of the neutrosophic set to deal with ambiguity and uncertainty in the information. The Firefly (FF) technique is applied for hyperparameter fine-tuning, which ensures the model operates at maximum effectiveness to enhance the performance of the classification. This wide-ranging method leads to a very efficient cyberattack recognition method, which can able to accurately mitigate and identify risks in the real world

Neurocognitive Profiles of Early Adulthood Attention-Deficit/Hyperactivity Disorder.

This study examined the neurocognitive profiles of early adulthood attention-deficit/hyperactivity disorder (ADHD) patients using the Korean version of the Wechsler Adult Intelligence Scale, 4th Edition (K-WAIS-IV) and Continuous Performance Test 3rd Edition (CPT-3) assessment results. A total of 105 individuals underwent the K-WAIS-IV assessment, and 68 participants completed the CPT-3. We examined the differences between intelligence subindex scores using paired t-tests and applied Pearson's correlation analysis to determine the correlation between the K-WAIS-IV and CPT-3 scores. Working Memory Index scores were significantly lower than Verbal Comprehension Index scores, whereas Processing Speed Index (PSI) scores were significantly lower than all three other subindex scores. Significant negative correlations were found between all four K-WAIS-IV subindex scores and the CPT-3 scores for Detectability, Omissions, Commissions, Perseverations, Hit Reaction Time, Hit Reaction Time Standard Deviation, and Variability. The PSI of the K-WAIS-IV can be considered a useful predictor in early adulthood ADHD patients combined with the CPT-3 examination.

UAV target tracking method based on global feature interaction and anchor-frame-free perceptual feature modulation.

Target tracking techniques in the UAV perspective utilize UAV cameras to capture video streams and identify and track specific targets in real-time. Deep learning UAV target tracking methods based on the Siamese family have achieved significant results but still face challenges regarding accuracy and speed compatibility. In this study, in order to refine the feature representation and reduce the computational effort to improve the efficiency of the tracker, we perform feature fusion in deep inter-correlation operations and introduce a global attention mechanism to enhance the model's field of view range and feature refinement capability to improve the tracking performance for small targets. In addition, we design an anchor-free frame-aware feature modulation mechanism to reduce computation and generate high-quality anchors while optimizing the target frame refinement computation to improve the adaptability to target deformation motion. Comparison experiments with several popular algorithms on UAV tracking datasets, such as UAV123@10fps, UAV20L, and DTB70, show that the algorithm balances speed and accuracy. In order to verify the reliability of the algorithm, we built a physical experimental environment on the Jetson Orin Nano platform. We realized a real-time processing speed of 30 frames per second.