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(1) Background: Previous research has investigated the relationship between cognitive impairment, optical coherence tomography (OCT), visual fields (VF), and VF reliability in smaller patient samples using various cognitive assessment tools. This study analyzed the relationship between cognitive function scores using the Mini-Cog test and inner macular thickness (IMT) and VF sensitivity in glaucoma patients. (2) Methods: A retrospective analysis was conducted on 984 patients with 1897 eyes. Assessments included age, sex, intraocular pressure (IOP), and Mini-Cog test scores. Abnormal Mini-Cog scores were observed in 89 patients (9%). Using a mixed-effects model adjusted for background factors, the association between Mini-Cog scores and IMT, parafoveal (PF)-IMT, mean deviation (MD), pattern standard deviation, fixation losses (FL), false negatives (FN), and false positives (FP) was analyzed. (3) Results: Abnormal Mini-Cog scores (≤2) were associated with thinning of the IMT and PF-IMT, worse MDs, and higher FN and FP rates but not with PSD or FL. (4) Conclusions: Glaucoma patients with low cognitive function scores exhibited more advanced glaucoma-related changes in VF testing and morphological tests. Further longitudinal studies are needed to explore the relationship between glaucoma and cognitive impairment. [ABSTRACT FROM AUTHOR]

Algebra as a school subject is ill defined. Students experience algebra quite differently depending on the perspective of algebra taken by authors of the textbooks from which they learn. Through a content analysis of problems (n = 63,174) in the narrative and homework sections of six high school mathematics textbook series published in the U.S., we acquired systematic and reliable information about the algebra strand (i.e., symbolic algebra and functions) of each textbook series. We introduce plots to show the density, distribution, and sequencing of content, and present analyses of data for cognitive behavior, real-world context, technology, and manipulatives. Feedback on this study from an author of each textbook series is shared, and findings are discussed in terms of students' opportunities to learn. [ABSTRACT FROM AUTHOR]

This study utilized a sample of 2052 participants from government and enterprise sectors to explore the distinct effects of power and sense of power on cognitive flexibility. It also delves into how the three dimensions of reward sensitivity and the comprehensive measure of punishment sensitivity mediate this relationship. The key findings are as follows: (1) There is no significant direct correlation between power and sense of power. (2) Both power and sense of power are substantial positive predictors of cognitive flexibility, with middle- and upper-level employees demonstrating significantly greater cognitive flexibility than their lower-level counterparts, and sense of power having a more pronounced positive influence than objective power. (3) Drive and fun-seeking mediate the relationship between sense of power and cognitive flexibility, yet only when sense of power is the independent variable. (4) No mediating effects are observed for the dimensions of reward sensitivity or punishment sensitivity when power is the independent variable. Exploring reward and punishment sensitivity in the context of power's influence on cognitive flexibility in real organizational settings is of paramount importance. This enhances our understanding of the intricate ways in which power dynamics shape individual behaviors and cognition across diverse cultural landscapes and provides actionable insights for refining organizational management and leadership strategies. [ABSTRACT FROM AUTHOR]

The neural or mental simulation of actions is a powerful tool for allowing cognitive agents to develop Prospection Capabilities that are crucial for learning and memorizing key aspects of challenging skills. In previous studies, we developed an approach based on the animation of the redundant human body schema, based on the Passive Motion Paradigm (PMP). In this paper, we show that this approach can be easily extended to hyper-redundant serpentine robots as well as to hybrid configurations where the serpentine robot is functionally integrated with a traditional skeletal infrastructure. A simulation model is analyzed in detail, showing that it incorporates spatio-temporal features discovered in the biomechanical studies of biological hydrostats, such as the elephant trunk or octopus tentacles. It is proposed that such a generative internal model could be the basis for a cognitive architecture appropriate for serpentine robots, independent of the underlying design and control technologies. Although robotic hydrostats have received a lot of attention in recent decades, the great majority of research activities have been focused on the actuation/sensorial/material technologies that can support the design of hyper-redundant soft/serpentine robots, as well as the related control methodologies. The cognitive level of analysis has been limited to motion planning, without addressing synergy formation and mental time travel. This is what this paper is focused on. [ABSTRACT FROM AUTHOR]

The learning equations of an ANN are presented, giving an extremely concise derivation based on the principle of backpropagation through the descendent gradient. Then, a dual network is outlined acting between synapses of a basic ANN, which controls the learning process and coordinates the subnetworks selected by attention mechanisms toward purposeful behaviors. Mechanisms of memory and their affinity with comprehension are considered, by emphasizing the common role of abstraction and the interplay between assimilation and accommodation, in the spirit of Piaget's analysis of psychological acquisition and genetic epistemology. Learning, comprehension, and knowledge are expressed as different levels of organization of informational processes inside cognitive systems. It is argued that formal analyses of cognitive artificial systems could shed new light on typical mechanisms of "natural intelligence" and, in a specular way, that models of natural cognition processes could promote further developments of ANN models. Finally, new possibilities of chatbot interaction are briefly discussed. [ABSTRACT FROM AUTHOR]

The reconfigurable intelligent surfaces (RIS) represent a transformative technology in wireless communication, offering a novel approach to managing and enhancing radio signal propagation. By dynamically adjusting their electromagnetic properties, RIS can significantly improve the performance and efficiency of 5G and beyond communication systems. In this paper, we study a cognitive RIS-aided non-orthogonal multiple access (NOMA) network that serves multiple users and improves spectrum efficiency. Our analysis assumes a secondary network operates under multi-primary user constraints and interference from the primary source. We derive approximation closed-form formulas for outage probability (OP), and system throughput. To obtain further insights, an asymptotic expression for OP is computed by taking into account two power configurations at the source. Additionally, numerical results show the effects of important factors on performance, confirming the accuracy of the theoretical derivation. According to the simulation results, performance by the system under consideration might be improved considerably by combining a RIS and NOMA, particularly when compared to an orthogonal multiple access scheme. [ABSTRACT FROM AUTHOR]

The tourism sector plays a crucial role in the global economy, encompassing both physical infrastructure and cultural engagement. Indonesia has a wide range of attractions and has experienced remarkable growth, with Bali as a notable example of this. With the rapid advancements in technology, travelers now have the freedom to explore independently, while online travel agencies (OTAs) serve as important resources. Reviews from tourists significantly impact the service quality and perception of destinations, and text mining is a valuable tool for extracting insights from unstructured review data. This research integrates multiclass text classification and a network analysis to uncover tourists' behavioral patterns through their perceptions and movement. This study innovates beyond conventional sentiment and cognitive image analysis to the tourists' perceptions of cognitive dimensions and explores the sentiment correlation between different cognitive dimensions. We find that destinations generally receive positive feedback, with 80.36% positive reviews, with natural attractions being the most positive aspect while infrastructure is the least positive aspect. We highlight that qualitative experiences do not always align with quantitative cost-effectiveness evaluations. Through a network analysis, we identify patterns in tourist mobility, highlighting three clusters of attractions that cater to diverse preferences. This research underscores the need for tourism destinations to strategically adapt to tourists' varied expectations, enhancing their appeal and aligning their services with preferences to elevate destination competitiveness and increase tourist satisfaction. [ABSTRACT FROM AUTHOR]

This study presents a novel audio compression technique, tailored for environmental monitoring within multi-modal data processing pipelines. Considering the crucial role that audio data play in environmental evaluations, particularly in contexts with extreme resource limitations, our strategy substantially decreases bit rates to facilitate efficient data transfer and storage. This is accomplished without undermining the accuracy necessary for trustworthy air pollution analysis while simultaneously minimizing processing expenses. More specifically, our approach fuses a Deep-Learning-based model, optimized for edge devices, along with a conventional coding schema for audio compression. Once transmitted to the cloud, the compressed data undergo a decoding process, leveraging vast cloud computing resources for accurate reconstruction and classification. The experimental results indicate that our approach leads to a relatively minor decrease in accuracy, even at notably low bit rates, and demonstrates strong robustness in identifying data from labels not included in our training dataset. [ABSTRACT FROM AUTHOR]

Background and Objectives: The study aims to provide a comprehensive neuropsychological analysis of psychotic spectrum disorders, including schizophrenia, bipolar disorder, and depression. It focuses on the critical aspects of cognitive impairments, diagnostic tools, intervention efficacy, and the roles of genetic and environmental factors in these disorders. The paper emphasizes the diagnostic significance of neuropsychological tests in identifying cognitive deficiencies and their predictive value in the early management of psychosis. Materials and Methods: The study involved a systematic literature review following the PRISMA guidelines. The search was conducted in significant databases like Scopus, PsycINFO, PubMed, and Web of Science using keywords relevant to clinical neuropsychology and psychotic spectrum disorders. The inclusion criteria required articles to be in English, published between 2018 and 2023, and pertinent to clinical neuropsychology's application in these disorders. A total of 153 articles were identified, with 44 ultimately included for detailed analysis based on relevance and publication status after screening. Results: The review highlights several key findings, including the diagnostic and prognostic significance of mismatch negativity, neuroprogressive trajectories, cortical thinning in familial high-risk individuals, and distinct illness trajectories within psychosis subgroups. The studies evaluated underline the role of neuropsychological tests in diagnosing psychiatric disorders and emphasize early detection and the effectiveness of intervention strategies based on cognitive and neurobiological markers. Conclusions: The systematic review underscores the importance of investigating the neuropsychological components of psychotic spectrum disorders. It identifies significant cognitive impairments in attention, memory, and executive function, correlating with structural and functional brain abnormalities. The paper stresses the need for precise diagnoses and personalized treatment modalities, highlighting the complex interplay between genetic, environmental, and psychosocial factors. It calls for a deeper understanding of these neuropsychological processes to enhance diagnostic accuracy and therapeutic outcomes. [ABSTRACT FROM AUTHOR]

The proposed novel algorithm named decision-making algorithm with geographic mobility (DMAGM) includes detailed analysis of decision-making for cognitive radio (CR) that considers a multivariable algorithm with geographic mobility (GM). Scarce research work considers the analysis of GM in depth, even though it plays a crucial role to improve communication performance. The DMAGM considerably reduces latency in order to accurately determine the best communication channels and includes GM analysis, which is not addressed in other algorithms found in the literature. The DMAGM was evaluated and validated by simulating a cognitive radio network that comprises a base station (BS), primary users (PUs), and CRs considering random arrivals and disappearance of mobile devices. The proposed algorithm exhibits better performance, through the reduction in latency and computational complexity, than other algorithms used for comparison using 200 channel tests per simulation. The DMAGM significantly reduces the decision-making process from 12.77% to 94.27% compared with ATDDiM, FAHP, AHP, and Dijkstra algorithms in terms of latency reduction. An improved version of the DMAGM is also proposed where feedback of the output is incorporated. This version is named feedback-decision-making algorithm with geographic mobility (FDMAGM), and it shows that a feedback system has the advantage of being able to continually adjust and adapt based on the feedback received. In addition, the feedback version helps to identify and correct problems, which can be beneficial in situations where the quality of communication is critical. Despite the fact that the FDMAGM may take longer than the DMAGM to calculate the best communication channel, constant feedback improves efficiency and effectiveness over time. Both the DMAGM and the FDMAGM improve performance in practical scenarios, the former in terms of latency and the latter in terms of accuracy and stability. [ABSTRACT FROM AUTHOR]

Item quality is crucial to psychometric analyses for cognitive diagnosis. In cognitive diagnosis models (CDMs), item quality is often quantified in terms of item parameters (e.g., guessing and slipping parameters). Calibrating the item parameters with only item response data, as a common practice, could result in challenges in identifying the cause of low-quality items (e.g., the correct answer is easy to be guessed) or devising an effective plan to improve the item quality. To resolve these challenges, we propose the item explanatory CDMs where the CDM item parameters are explained with item features such that item features can serve as an additional source of information for item parameters. The utility of the proposed models is demonstrated with the Trends in International Mathematics and Science Study (TIMSS)-released items and response data: around 20 item linguistic features were extracted from the item stem with natural language processing techniques, and the item feature engineering process is elaborated in the paper. The proposed models are used to examine the relationships between the guessing/slipping item parameters of the higher-order DINA model and eight of the item features. The findings from a follow-up simulation study are presented, which corroborate the validity of the inferences drawn from the empirical data analysis. Finally, future research directions are discussed. [ABSTRACT FROM AUTHOR]

This paper presents research on determining individual perceptual thresholds in cognitive analyses and the understanding of visual patterns. Such techniques are based on the processes of cognitive resonance and can be applied to the division and reconstruction of images using threshold algorithms. The research presented here considers the most important parameters that affect the determination of visual perception thresholds. These parameters are the thematic knowledge and personal expectations that arise at the time of image observation and recognition. The determination of perceptual thresholds has been carried out using visual pattern splitting techniques through threshold methods. The reconstruction of the divided patterns was carried out by combining successive components that, as information was gathered, allowed more and more details to become apparent in the image until the observer could recognize it correctly. The study being carried out in this way made it possible to determine individual perceptual thresholds for dozens of test subjects. The results of the study also showed strong correlations between the determined perceptual thresholds and the participants' accumulated thematic knowledge, expectations and experiences from a previous recognition of similar image patterns. [ABSTRACT FROM AUTHOR]

Cognitive workload analysis is an important aspect of safety studies at the Spanish Air Force Academy where students must complete a dual academic curriculum based on military pilot training combined with an industrial engineering degree. Recently, a mental workload assessment (MWA) and forecasting model based on Shannon's law from information theory (IT) has been published; it proposes a new mathematical procedure (MWA-IT) that defines a workload index that could be extrapolated to other case studies. The aim of this study was to adapt this model to the Spanish University Centre of Defence to calculate the mental workload caused by the listening practice in English as a foreign language. In addition, a contrasting methodology, the NASA task load index (NASA-TLX), was applied to validate the proposed model using the error study provided by SMAPE and MSE. The results established an expected reference baseline for MWA-IT in English listening that is between 36 and 92 at the end of the four courses, which corresponds to the students that start with the lowest English level (higher workload = 92) and the ones with the highest English level certification (lowest workload = 36); meanwhile, the NASA-TLX result was between 49.8 and 193.7 for the same circumstances. The main difference is that MWA-IT can be predicted with 41% less deviation than can NASA-TLX and does not require the completion of a questionnaire following the activities. Finally, the study also highlights the fact that that nearly 65% of the workload was caused by the first two courses, when the advanced STEM subjects were taught and the pilot learning and practice program had not yet begun. This methodology may help the teachers in charge to redesign or add new content depending on the expected workload reference. [ABSTRACT FROM AUTHOR]

This paper focuses on achieving the low-cost coexistence of the networks in an unlicensed spectrum by making them operate on non-overlapping channels. For achieving this goal, we first give a universal convergence analysis framework for the unlicensed spectrum allocation algorithm. Then, a one-timescale iteration-adjustable unlicensed spectrum allocation algorithm is developed, where the step size and timescale parameter can be jointly adjusted based on the system performance requirement and signal overhead concern. After that, we derive the sufficient condition for the one-timescale algorithm. Furthermore, the upper bound of convergence error of the one-timescale spectrum allocation algorithm is obtained. Due to the multi-timescale evolution of the network states in the wireless network, we further propose a two-timescale iteration-adjustable joint frequency selection and frequency allocation algorithm, where the frequency selection iteration timescale is set according to the slow-changing statistical channel state information (CSI), whereas the frequency allocation iteration timescale is set according to the fast-changing local CSI. Then, we derive the convergence condition of two-timescale algorithms and the upper bound of the corresponding convergence error. The experimentalresults show that the small timescale adjustment parameter and large step size can help decrease the convergence error. Moreover, compared with traditional algorithms, the two-timescale policy can achieve throughput similar to traditional algorithms with very low iteration overhead. [ABSTRACT FROM AUTHOR]

Understanding how mild cognitive impairment affects global neural networks may explain changes in brain electrophysiology. Using graph theory and the visual oddball paradigm, we evaluated the functional connectivity of neuronal networks in brain lobes. The study involved 30 participants: 14 with mild cognitive impairment (MCI) and 16 healthy control (HC) participants. We conducted an examination using the visual oddball paradigm, focusing on electroencephalography signals with targeted stimuli. Our analysis employed functional connectivity utilizing the change point detection method. Additionally, we implemented training for linear discriminant analysis, K-nearest neighbor, and decision tree techniques to classify brain activity, distinguishing between subjects with mild cognitive impairment and those in the healthy control group. Our results demonstrate the efficacy of combining functional connectivity measurements derived from electroencephalography with machine learning for cognitive impairment classification. This research opens avenues for further exploration, including the potential for real-time detection of cognitive decline in complex real-world scenarios. [ABSTRACT FROM AUTHOR]

One's working memory process is a fundamental cognitive activity which often serves as an indicator of brain disease and cognitive impairment. In this research, the approach to evaluate working memory ability by means of electroencephalography (EEG) analysis was proposed. The result shows that the EEG signals of subjects share some characteristics when performing working memory tasks. Through correlation analysis, a working memory model describes the changes in EEG signals within alpha, beta and gamma waves, which shows an inverse tendency compared to Zen meditation. The working memory ability of subjects can be predicted using multi-linear support vector regression (SVR) with fuzzy C-mean (FCM) clustering and knowledge-based fuzzy support vector regression (FSVR), which reaches the mean square error of 0.6 in our collected data. The latter, designed based on the working memory model, achieves the best performance. The research provides the insight of the working memory process from the EEG aspect to become an example of cognitive function analysis and prediction. [ABSTRACT FROM AUTHOR]

Cognitive radio (CR) acts as a significant player in enhancing the spectral efficiency (SE) of wireless telecommunications; simultaneously, the intelligent reflecting surface (IRS) technique is a valid technique for increasing the confidentiality properties of wireless telecommunications systems through the modulation of the amplitude and phase shift of the channel. Therefore, we take into consideration an IRS-assisted multiple-input single-output (MISO) CR system to raise the confidentiality rate, which is composed of a primary network with a primary receiver (PR) and an eavesdropping link, as well as a secondary network with a secondary receiver (SR) and SR transmitter (SR-TX). In particular, we minimize the SR's transmit power under the interference temperature (IT) and confidentiality capacity constraints via the joint optimization of the beamforming vector and artificial noise (AN) constraint matrix at SR-TX together with the phase shift matrix of IRS. Numerical outcomes indicate that various transmit antenna values and the IRS element numbers at SR-TX can greatly reduce transmit power while assuring secure communication. [ABSTRACT FROM AUTHOR]

Despite evidence that it exists, short-term within-individual variability in cognitive performance has largely been ignored as a meaningful component of human cognitive ability. In this article, we build a case for why this within-individual variability should not be viewed as mere measurement error and why it should be construed as a meaningful component of an individual's cognitive abilities. We argue that in a demanding and rapidly changing modern world, between-individual analysis of single-occasion cognitive test scores does not account for the full range of within-individual cognitive performance variation that is implicated in successful typical cognitive performance. We propose that short-term repeated-measures paradigms (e.g., the experience sampling method (ESM)) be used to develop a process account of why individuals with similar cognitive ability scores differ in their actual performance in typical environments. Finally, we outline considerations for researchers when adapting this paradigm for cognitive assessment and present some initial findings from two studies in our lab that piloted the use of ESM to assess within-individual cognitive performance variation. [ABSTRACT FROM AUTHOR]

Currently, research mainly focuses on the effects of happy music on the subjective assessment of cognitive reappraisal, but relevant results of the neural mechanism are lacking. By analysing the functional connectivity of microstates based on electroencephalography (EEG), we investigated the effect of cognitive reappraisal combined with happy music on emotional regulation and the dynamic characteristics of brain functional activities. A total of 52 healthy college students were divided into music group and control group. EEG data and behavioural scores were collected during an experiment of cognitive reappraisal combined with happy music. The dynamic time window of the brain functional network was determined by microstate analysis, and the metrics of functional connectivity, clustering coefficient (Cp) and characteristic path length (Lp), were calculated based on the phase-locked value. The arousal of cognitive reappraisal significantly increased (p = 0.005) in music group, but the valence did not change significantly. This suggested that happy music did not affect emotional regulation from the behavioural perspective. Four microstate global templates (A–D) were determined. With happy music, the duration (p = 0.043) and Lp (p = 0.033) of microstate B increased significantly, indicating that the transfection efficiency of the brain network decreased, reflecting a negative effect on cognitive reappraisal. The duration (p = 0.017) of microstate D decreased and of Cp (p < 0.001) increased significantly, indicating that the local information-processing ability of the brain network increased. We conclude that happy music can change the characteristics of brain functional networks and have a positive effect on cognitive reappraisal in specific period. The research provides a certain electrophysiological basis for applying happy music to cognitive reappraisal. [ABSTRACT FROM AUTHOR]

The standard interpretation of cognitive reflection tests assumes that correct responses are reflective and lured responses are unreflective. However, prior process-tracing of mathematical reflection tests has cast doubt on this interpretation. In two studies (N = 201), we deployed a validated think-aloud protocol in-person and online to test how this assumption is satisfied by the new, validated, less familiar, and non-mathematical verbal Cognitive Reflection Test (vCRT). Verbalized thoughts in both studies revealed that most (but not all) correct responses involved reflection and that most (but not all) lured responses lacked reflection. The think-aloud protocols seemed to reflect business-as-usual performance: thinking aloud did not disrupt test performance compared to a control group. These data suggest that the vCRT usually satisfies the standard interpretation of the reflection tests (albeit not without exceptions) and that the vCRT can be a good measure of the construct theorized by the two-factor explication of 'reflection' (as deliberate and conscious). [ABSTRACT FROM AUTHOR]

Language acquisition, processing, comprehension, and production encompass a complex mechanism. Particularly, the mechanisms by which we make sense of language, including perception, conceptualization, and processing, have been controversial topics among cognitive linguists and researchers in cognitive sciences. Cognitive processes such as attention, thought, perception, and memory play a significant role in meaningful human communication. This study aimed to apply the science mapping method to detect and visualize emerging trends and patterns in literature pertaining to cognitive linguistics. In order to accomplish this, eight bibliometric and eight scientometric indicators were used in conjunction with CiteSpace 5.8.R3 and VOSviewer 1.6.18 for scientometric analysis and data visualisation. The data were collected and triangulated from three databases, including 2380 from Scopus, 1732 from WOS, and 9911 from Lens from 1969 to 2022. Among the findings were the visualization of eight bibliometric indicators regarding the knowledge production size of cognitive linguistics based on year, country, university, journal, publisher, research area, authors, and cited documents. Second, we presented scientometric indicators with regard to cognitive linguistics development, including the most important authors in the field, co-citation networks, citation networks, sigma metrics to detect works with potential citation growth, and clusters to group related topics to cognitive linguistics. We conclude the study by emphasizing that cognitive linguistics has evolved from the micro level where it focused on studying cognitive aspects of language in relation to time, language, and modality dimensions, to the macro level, which examines cognitive processes and their relationship to the construction of meaningful communication using both sensation and perception. [ABSTRACT FROM AUTHOR]

The ability to spot key ideas, trends, and relationships between them in documents is key to financial services, such as banks and insurers. Identifying patterns across vast amounts of domain-specific reports is crucial for devising efficient and targeted supervisory plans, subsequently allocating limited resources where most needed. Today, insurance supervisory planning primarily relies on quantitative metrics based on numerical data (e.g., solvency financial returns). The purpose of this work is to assess whether Natural Language Processing (NLP) and cognitive networks can highlight events and relationships of relevance for regulators that supervise the insurance market, replacing human coding of information with automatic text analysis. To this aim, this work introduces a dataset of N I D T = 829 investor transcripts from Bloomberg and explores/tunes 3 NLP techniques: (1) keyword extraction enhanced by cognitive network analysis; (2) valence/sentiment analysis; and (3) topic modelling. Results highlight that keyword analysis, enriched by term frequency-inverse document frequency scores and semantic framing through cognitive networks, could detect events of relevance for the insurance system like cyber-attacks or the COVID-19 pandemic. Cognitive networks were found to highlight events that related to specific financial transitions: The semantic frame of "climate" grew in size by +538% between 2018 and 2020 and outlined an increased awareness that agents and insurers expressed towards climate change. A lexicon-based sentiment analysis achieved a Pearson's correlation of ρ = 0.16 ( p < 0.001 , N = 829 ) between sentiment levels and daily share prices. Although relatively weak, this finding indicates that insurance jargon is insightful to support risk supervision. Topic modelling is considered less amenable to support supervision, because of a lack of results' stability and an intrinsic difficulty to interpret risk patterns. We discuss how these automatic methods could complement existing supervisory tools in supporting effective oversight of the insurance market. [ABSTRACT FROM AUTHOR]

Oculometric data, such as gaze direction, pupil size and accommodative change, play a key role nowadays in the analysis of cognitive load and attentional activities, in particular with the development of Integrated Visual Augmentation Systems in many application domains, such as health, defense and industry. Such measurements are most frequently obtained by different devices, most of them requiring steady eye and body positions and controlled lighting conditions. Recent advances in smart contact lens (SCL) technology have demonstrated the ability to achieve highly reliable and accurate measurements, preserving user mobility, for instance in measuring gaze direction. In this paper, we discuss how these three key functions can be implemented and combined in the same SCL, considering the limited volume and energy consumption constraints. Some technical options are discussed and compared in terms of their ability to be implemented, taking advantage of recent developments in the field. [ABSTRACT FROM AUTHOR]

Exercise modes can be categorized based on the skills required (open vs. closed skills), which implicates various demands on cognitive skills, especially executive functions (EFs). Thus, their practice may have varying effects on EFs. There is a lack of detailed analysis of cognitive requirements and suitable classification of sports. It is hypothesized that the amount and type of cognitive requirements of sports lead to small effect sizes when comparing open-skill exercising (OSE) and closed-skill exercising (CSE) athletes. The current meta-analysis evaluates the variances in EFs skills caused by particular sport modes. Four research databases (Web of Science, PubMed, ScienceDirect, PsychINFO) were searched for cross-sectional studies in which the authors compare the performance in EF tasks of OSE and CSE athletes. Risk of bias assessment was conducted using funnel plots and two reviewer selection process (overall and subgroup analysis; low risk of publication and selection bias). A total of 19 studies were included, revealing an overall effect size of Hedge's g = 0.174 (p = 0.157), favoring OSE for the development of EFs. The subgroup analysis revealed the effects for the subdomains of EFs (cognitive flexibility: Hedge's g = 0.210 > inhibitory control: Hedge's g = 0.191 > working memory: Hedge's g = 0.138; p > 0.05), which could be characterized as low to moderate. The hypothesis that studies with the smallest effect sizes compare sport modes with similar cognitive demands was rejected. The paper discusses the differentiation of sports into OSE and CSE and presents new approaches for their categorization. [ABSTRACT FROM AUTHOR]

A simplified approach was used to determine if "Less is More" is still a trend in comics' Peking opera characters. There were 225 website volunteers who took part in the study. Via a questionnaire survey and analysis, this study explored the feasibility of "simplification" in comedy. The results indicate that the proposed "simplifying" approach can be applied to the creation of dramatic characters, but the scale of simplification must be adjusted flexibly to suit different subjects. For audiences, there is not much recognition and sympathy for the works that are simplified in the extreme and the current symbolization. The simplification used in this study is merely the first step in testing the usefulness of simplification as an approach. It is used as a means of understanding the cognition of the audience to accept the simple features of Peking opera characters. In subsequent studies, the proposed "simplified" approach is necessary to adapt and improve with a view to practical application. It also requires an in-depth analysis of the cognitive differences of the different participants according to the cognitive and communication theories of artistic creation. [ABSTRACT FROM AUTHOR]

Featured Application: Theory of packing and covering, geometry of numbers, combinatorial geometry. A new approach to investigate the two-dimensional, regular packing of arbitrary geometric objects (GOs), using cognitive visualization, is presented. GOs correspond to congruent non-convex polygons with their associated coordinate system. The origins of these coordinate systems are accepted by object poles. The approach considered is based on cognitive processes that are forms of heuristic judgments. According to the first heuristic judgment, regular packing of congruent GOs on the plane have a honeycomb structure, that is, each GO contacts six neighboring GO, the poles of which are vertices of the pole hexagon in the honeycomb construction of packing. Based on the visualization of the honeycomb constructions a second heuristic judgment is obtained, according to which inside the hexagon of the poles, there are fragments of three GOs. The consequence is a third heuristic judgment on the plane covering density with regular packings of congruent GOs. With the help of cognitive visualization, it is established that inside the hexagon of poles there are fragments of exactly three objects. The fourth heuristic judgment is related to the proposal of a triple lattice packing for regular packing of congruent GOs. [ABSTRACT FROM AUTHOR]

Featured Application: Contactless pervasive health monitoring incl. cognitive stress analysis. Remote photo-plethysmography (rPPG) uses a camera to estimate a person's heart rate (HR). Similar to how heart rate can provide useful information about a person's vital signs, insights about the underlying physio/psychological conditions can be obtained from heart rate variability (HRV). HRV is a measure of the fine fluctuations in the intervals between heart beats. However, this measure requires temporally locating heart beats with a high degree of precision. We introduce a refined and efficient real-time rPPG pipeline with novel filtering and motion suppression that not only estimates heart rates, but also extracts the pulse waveform to time heart beats and measure heart rate variability. This unsupervised method requires no rPPG specific training and is able to operate in real-time. We also introduce a new multi-modal video dataset, VicarPPG 2, specifically designed to evaluate rPPG algorithms on HR and HRV estimation. We validate and study our method under various conditions on a comprehensive range of public and self-recorded datasets, showing state-of-the-art results and providing useful insights into some unique aspects. Lastly, we make available CleanerPPG, a collection of human-verified ground truth peak/heart-beat annotations for existing rPPG datasets. These verified annotations should make future evaluations and benchmarking of rPPG algorithms more accurate, standardized and fair. [ABSTRACT FROM AUTHOR]

As the problems of mood measurements during alcohol consumption of alcoholic beverages do not necessarily evoke interpretable physiological responses, explicit reports may be contaminated by various cognitive biases or expectations. The present study examined whether emotional responses induced by the consumption of beverages containing low concentrations of alcohol can be measured using the Implicit Association Test (IAT). The IAT can detect the estimates of internal proximity between bipolar target concepts (e.g., cheerfulness and fatigue). Participants (N = 30) received three IAT sessions, followed by drinking a beverage containing 0% (control), 1%, or 3% alcohol by volume, and three IATs (at 0, 30, and 60 min after the time of consumption). We also recorded the explicit responses regarding the extent of drunkenness. The analyses of variance with alcohol concentration and time reveal dissociation between implicit and explicit measures. The IAT scores under the alcohol conditions reflect a more cheerful mood state relative to the baseline test. This effect of enhanced cheerfulness was not observed under the non-alcohol control condition. These results demonstrate that the impact of the consumption of low-alcohol beverages on mood can be measured using the IAT. [ABSTRACT FROM AUTHOR]

Practical tools which can be quickly administered are needed for measuring subtle changes in cognitive–motor performance over time. Frailty together with cognitive impairment, or ‘cognitive frailty’, are shown to be strong and independent predictors of cognitive decline over time. We have developed an interactive instrumented trail-making task (iTMT) platform, which allows quantification of motor planning error (MPE) through a series of ankle reaching tasks. In this study, we examined the accuracy of MPE in identifying cognitive frailty in older adults. Thirty-two older adults (age = 77.3 ± 9.1 years, body-mass-index = 25.3 ± 4.7 kg/m2, female = 38%) were recruited. Using either the Mini-Mental State Examination or Montreal Cognitive Assessment (MoCA), 16 subjects were classified as cognitive-intact and 16 were classified as cognitive-impaired. In addition, 12 young-healthy subjects (age = 26.0 ± 5.2 years, body-mass-index = 25.3 ± 3.9 kg/m2, female = 33%) were recruited to establish a healthy benchmark. Subjects completed the iTMT, using an ankle-worn sensor, which transforms ankle motion into navigation of a computer cursor. The iTMT task included reaching five indexed target circles (including numbers 1-to-3 and letters A&B placed in random order) on the computer-screen by moving the ankle-joint while standing. The ankle-sensor quantifies MPE through analysis of the pattern of ankle velocity. MPE was defined as percentage of time deviation between subject’s maximum ankle velocity and the optimal maximum ankle velocity, which is halfway through the reaching pathway. Data from gait tests, including single task and dual task walking, were also collected to determine cognitive–motor performance. The average MPE in young-healthy, elderly cognitive-intact, and elderly cognitive-impaired groups was 11.1 ± 5.7%, 20.3 ± 9.6%, and 34.1 ± 4.2% (p < 0.001), respectively. Large effect sizes (Cohen’s d = 1.17–4.56) were observed for discriminating between groups using MPE. Significant correlations were observed between the MPE and MoCA score (r = −0.670, p < 0.001) as well as between the MPE and dual task stride velocity (r = −0.584, p < 0.001). This study demonstrated feasibility and efficacy of estimating MPE from a practical wearable platform with promising results in identifying cognitive–motor impairment and potential application in assessing cognitive frailty. The proposed platform could be also used as an alternative to dual task walking test, where gait assessment may not be practical. Future studies need to confirm these observations in larger samples. [ABSTRACT FROM AUTHOR]

Many Human Activity Recognition (HAR) systems are able to detect sequential executed Activity of Daily Living (ADL). However, a person is capable of doing two things in parallel or pausing one ADL and finishing it later. Thus, a HAR system must be capable of remembering and deciding which ADL is completed and which might be continued after the current ADL. We address this case by combining a stochastic Markov model and a psychological memory function to detect parallel ADL. For the evaluation, we use an input dataset and a publicly available benchmark. Our approach outperforms the leading HAR systems for the used benchmark by 5%, while using a more cost-effective installation environment. Furthermore, we address an unsupervised learning method to train the HAR system and explain the algorithm of parallel ADL detection in detail. [ABSTRACT FROM AUTHOR]

Finding the source of an accidental or deliberate release of a toxic substance into the atmosphere is of great importance for national security. The paper presents a search algorithm for turbulent environments which falls into the class of cognitive (infotaxi) algorithms. Bayesian estimation of the source parameter vector is carried out using the Rao-Blackwell dimension-reduction method, while the robots are controlled autonomously to move in a scalable formation. Estimation and control are carried out in a centralised replicated fusion architecture assuming all-to-all communication. The paper presents a comprehensive numerical analysis of the proposed algorithm, including the search-time and displacement statistics. [ABSTRACT FROM AUTHOR]

Brain-derived neurotrophic factor (BDNF) is involved in numerous cognitive functions including learning and memory. BDNF plays an important role in synaptic plasticity in humans and rats with BDNF shown to be essential for the formation of long-term memories. We previously identified a significant association between the BDNF Val66Met polymorphism (rs6265) and long-term visual memory (p-value = 0.003) in a small cohort (n = 181) comprised of healthy individuals who had been phenotyped for various aspects of memory function. In this study, we have extended the cohort to 597 individuals and examined multiple genetic variants across both the BDNF and BDNF-AS genes for association with visual memory performance as assessed by the Wechsler Memory Scale--Fourth Edition subtests Visual Reproduction I and II (VR I and II). VR I assesses immediate visual memory, whereas VR II assesses long-term visual memory. Genetic association analyses were performed for 34 single nucleotide polymorphisms genotyped on Illumina OmniExpress BeadChip arrays with the immediate and long-term visual memory phenotypes. While none of the BDNF and BDNF-AS variants were shown to be significant for immediate visual memory, we found 10 variants (including the Val66Met polymorphism (p-value = 0.006)) that were nominally associated, and three variants (two variants in BDNF and one variant in the BDNF-AS locus) that were significantly associated with long-term visual memory. Our data therefore suggests a potential role for BDNF, and its anti-sense transcript BDNF-AS, in long-term visual memory performance. [ABSTRACT FROM AUTHOR]

This paper reports on the design and evaluation of direct 3D gesture interaction with a full horizontal parallax light field display. A light field display defines a visual scene using directional light beams emitted from multiple light sources as if they are emitted from scene points. Each scene point is rendered individually resulting in more realistic and accurate 3D visualization compared to other 3D displaying technologies. We propose an interaction setup combining the visualization of objects within the Field Of View (FOV) of a light field display and their selection through freehand gesture tracked by the Leap Motion Controller. The accuracy and usefulness of the proposed interaction setup was also evaluated in a user study with test subjects. The results of the study revealed high user preference for free hand interaction with light field display as well as relatively low cognitive demand of this technique. Further, our results also revealed some limitations and adjustments of the proposed setup to be addressed in future work. [ABSTRACT FROM AUTHOR]

Characterization of cognitive and behavioral complaints is explored in Post-traumatic stress disorder (PTSD) and mild traumatic brain injury (MTBI) samples according to the severity of PTSD, depression and general anxiety conditions. Self-reported questionnaires on cognitive and behavioral changes are administered to PTSD, MTBI, MTBI/PTSD and control groups. Confounding variables are controlled. All groups report more complaints since the traumatic event. PTSD and MTBI/PTSD groups report more anxiety symptoms, depression and complaints compared to the MTBI group. Relatives of the PTSD group confirm most of the behavioral changes reported. Results suggest the utility of self-reported questionnaires to personalize cognitive and behavioral interventions in PTSD and MTBI to cope with the impacts of the traumatic event. [ABSTRACT FROM AUTHOR]

The present investigation sought to expand our understanding of the cognitive processes underlying the recognition of antonyms and to evaluate whether these processes differed in patients with schizophrenia and in healthy controls. Antonymy is the most robust of the lexico-semantic relations and is relevant to both the mental organization of the lexicon and the organization of coherent discourse, as attested by the resurgence of interest in antonymy in the linguistic and psychological domains. In contrast, the vast literature on semantic processing in schizophrenia almost ignored antonymy. In this study, we tested the online comprehension of antonyms in 39 Italian patients with paranoid schizophrenia and in an equal number of pairwise-matched healthy controls. Participants read a definitional sentence fragment (e.g., the opposite of black is), followed by the correct antonym (white) or by a semantically unrelated word (nice), and judged whether or not the target word was correct. Patients were rather accurate in identifying antonyms, but compared to controls, they showed longer response times and higher priming scores, suggesting an exaggerated contextual facilitation. Presumably, this reflects a deficient controlled semantic processing and an overreliance on stored semantic representations. [ABSTRACT FROM AUTHOR]

Knowledge structures are often represented in the form of networks or maps of concepts. The coherence and connectivity of such knowledge representations is known to be closely related to knowledge production, acquisition and processing. In this study we use network theory in making the clustering and cohesion of concept maps measurable, and show how the distribution of these properties can be interpreted through the Maximum Entropy (MaxEnt) method. This approach allows to introduce new concepts of the "energy of cognitive load" and the "entropy of knowledge organization" to describe the organization of knowledge in the concept maps. [ABSTRACT FROM AUTHOR]

The Internet of Medical Things (IoMT) paradigm is becoming mainstream in multiple clinical trials and healthcare procedures. Thanks to innovative technologies, latest-generation communication networks, and state-of-the-art portable devices, IoTM opens up new scenarios for data collection and continuous patient monitoring. Two very important aspects should be considered to make the most of this paradigm. For the first aspect, moving the processing task from the cloud to the edge leads to several advantages, such as responsiveness, portability, scalability, and reliability of the sensor node. For the second aspect, in order to increase the accuracy of the system, state-of-the-art cognitive algorithms based on artificial intelligence and deep learning must be integrated. Sensory nodes often need to be battery powered and need to remain active for a long time without a different power source. Therefore, one of the challenges to be addressed during the design and development of IoMT devices concerns energy optimization. Our work proposes an implementation of cognitive data analysis based on deep learning techniques on resource-constrained computing platform. To handle power efficiency, we introduced a component called Adaptive runtime Manager (ADAM). This component takes care of reconfiguring the hardware and software of the device dynamically during the execution, in order to better adapt it to the workload and the required operating mode. To test the high computational load on a multi-core system, the Orlando prototype board by STMicroelectronics, cognitive analysis of Electrocardiogram (ECG) traces have been adopted, considering single-channel and six-channel simultaneous cases. Experimental results show that by managing the sensory node configuration at runtime, energy savings of at least 15% can be achieved. [ABSTRACT FROM AUTHOR]

The Performance Assessment for California Teachers (PACT) is a high stakes summative assessment that was designed to measure pre-service teacher readiness. We examined the inter-rater reliability (IRR) of trained PACT evaluators who rated 19 candidates. As measured by Cohen's weighted kappa, the overall IRR estimate was 0.17 (poor strength of agreement). IRR estimates ranged from −0.29 (worse than expected by chance) to 0.54 (moderate strength of agreement); all were below the standard of 0.70 for consensus agreement. Follow-up interviews of 10 evaluators revealed possible reasons we observed low IRR, such as departures from established PACT scoring protocol, and lack of, or inconsistent, use of a scoring aid document. Evaluators reported difficulties scoring the materials that candidates submitted, particularly the use of Academic Language. Cognitive Task Analysis (CTA) is suggested as a method to improve IRR in the PACT and other teacher performance assessments such as the edTPA. [ABSTRACT FROM AUTHOR]

Research into sports coaches has identified the valuable role they play concerning social support provided to athletes together with their contribution to social and cultural interactions within both the participation and performance domains. The purpose of the present study was to qualitatively extract and examine the knowledge and on-task cognitions of high-level coaches (HLCs) within strength and conditioning (S and C). Applied cognitive task analysis (ACTA) was used to examine ten HLCs, each purposefully sampled to reflect over eight years of work in full time environments. The analysis of responses demonstrated HLCs engage in a pattern of innovative and diverse thinking, together with adaptability and multilevel planning, designed to promote an inclusive approach from performers, coaches and management. Commonality was demonstrated within the decision making of HLCs during the design of training programs. Communication was another important consideration when connecting with athletes, observing athletes, speaking to the head coach and integrating their approach with others. A confident, flexible approach to adapting to situational demands was evident and supported by the ability to recall and select from a wide range of previously learnt and tested strategies. Evidence is offered for the importance of interpersonal and social factors in HLCs' relationships with athletes and coaches. The incorporation of strategies to support versatile, dynamic decision making within future S and C coach development materials will support more impactful performances by coaches at all stages of the coaching process. [ABSTRACT FROM AUTHOR]

Performance in elementary cognitive tasks is moderately correlated with fluid intelligence and working memory capacity. These correlations are higher for more complex tasks, presumably due to increased demands on working memory capacity. In accordance with the binding hypothesis, which states that working memory capacity reflects the limit of a person's ability to establish and maintain temporary bindings (e.g., relations between items or relations between items and their context), we manipulated binding requirements (i.e., 2, 4, and 6 relations) in three choice reaction time paradigms (i.e., two comparison tasks, two change detection tasks, and two substitution tasks) measuring mental speed. Response time distributions of 115 participants were analyzed with the diffusion model. Higher binding requirements resulted in generally reduced efficiency of information processing, as indicated by lower drift rates. Additionally, we fitted bi-factor confirmatory factor analysis to the elementary cognitive tasks to separate basal speed and binding requirements of the employed tasks to quantify their specific contributions to working memory capacity, as measured by Recall−1-Back tasks. A latent factor capturing individual differences in binding was incrementally predictive of working memory capacity, over and above a general factor capturing speed. These results indicate that the relation between reaction time tasks and working memory capacity hinges on the complexity of the reaction time tasks. We conclude that binding requirements and, therefore, demands on working memory capacity offer a satisfactory account of task complexity that accounts for a large portion of individual differences in ability. [ABSTRACT FROM AUTHOR]

The relentlessly increasing number of small-sized devices with limited powering and computational capabilities requires the adoption of new approaches to spectrum access. In this paper, we analyze an underlay cooperative cognitive wireless system based on available statistical channel state information (CSI) that is applicable to the cognitive system with limited computational resources due to its low complexity. We considered the scenario where the primary and the cognitive network coexist in the same spectrum band, under the constraints of interference threshold and maximal tolerable outage permitted by the primary user. The communication in the secondary decode-and-forward (DF) relaying system is established via a self-sustainable relay, which harvests energy from both cognitive and primary transmitters. The closed-form expressions for the outage probability of the cognitive network are derived, which are valid for both time-switching relaying (TSR) and power-splitting relaying (PSR) protocols. We analyze the influence of both cognitive and primary systems as well as the impact of channel parameters on the cognitive system outage performance. The derived analytical results are corroborated by an independent simulation method. [ABSTRACT FROM AUTHOR]

In human–robot collaborative assembly tasks, it is necessary to properly balance skills to maximize productivity. Human operators can contribute with their abilities in dexterous manipulation, reasoning and problem solving, but a bounded workload (cognitive, physical, and timing) should be assigned for the task. Collaborative robots can provide accurate, quick and precise physical work skills, but they have constrained cognitive interaction capacity and low dexterous ability. In this work, an experimental setup is introduced in the form of a laboratory case study in which the task performance of the human–robot team and the mental workload of the humans are analyzed for an assembly task. We demonstrate that an operator working on a main high-demanding cognitive task can also comply with a secondary task (assembly) mainly developed for a robot asking for some cognitive and dexterous human capacities producing a very low impact on the primary task. In this form, skills are well balanced, and the operator is satisfied with the working conditions. [ABSTRACT FROM AUTHOR]

Transcriptome–wide association studies (TWAS) have identified several genes that are associated with qualitative traits. In this work, we performed TWAS using quantitative traits and predicted gene expressions in six brain subcortical structures in 286 mild cognitive impairment (MCI) samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. The six brain subcortical structures were in the limbic region, basal ganglia region, and cerebellum region. We identified 9, 15, and 6 genes that were stably correlated longitudinally with quantitative traits in these three regions, of which 3, 8, and 6 genes have not been reported in previous Alzheimer's disease (AD) or MCI studies. These genes are potential drug targets for the treatment of early–stage AD. Single–Nucleotide Polymorphism (SNP) analysis results indicated that cis–expression Quantitative Trait Loci (cis–eQTL) SNPs with gene expression predictive abilities may affect the expression of their corresponding genes by specific binding to transcription factors or by modulating promoter and enhancer activities. Further, baseline structure volumes and cis–eQTL SNPs from correlated genes in each region were used to predict the conversion risk of MCI patients. Our results showed that limbic volumes and cis–eQTL SNPs of correlated genes in the limbic region have effective predictive abilities. [ABSTRACT FROM AUTHOR]

The industry of intangible cultural heritage (ICH) tourism continues to grow, and social media can serve as an essential tool to promote this trend. Although ICH tourism development is outstanding in China, the language structure and restricted use of social media render ICH difficult for non-Chinese speakers to understand. Using content analysis, this study investigates the structure and relationships among cognitive elements of ICH tourism based on 9074 blogs posted between 2011 and 2020 on Weibo.com, one of the most popular social media platforms in China. The main analysis process consisted of matrix construction, dimension classification, and semantic network analysis. Findings indicated that the cognitive image of ICH tourism on social media can be divided into seven dimensions: institutions, ICH and inheritors, tourism products, traditional festivals and seasons, tourism facilities and services, visitors, and regions. This network vividly illustrates ICH tourism and depicts the roles of organizers, residents, inheritors, and tourists. Among these elements, institutions hold the greatest power to regulate and control ICH tourism activities, and folklore appears to be the most common type of ICH resource that can be developed into tourism activities. Practically, the results offer insight for policymakers regarding ways to better balance the relationships among heritage protection, the business economy, and people's well-being. Such strategies can promote the industrialization of ICH tourism. In addition, through content analysis, this paper confirms the effectiveness of social media in providing a richer understanding of ICH tourism. [ABSTRACT FROM AUTHOR]

Engagement in cognitively demanding activities is beneficial to preserving cognitive health. Our goal was to demonstrate the utility of frequentist, Bayesian, and fiducial statistical methods for evaluating the robustness of effects in identifying factors that contribute to cognitive engagement for older adults experiencing cognitive decline. We collected a total of 504 observations across two longitudinal waves of data from 28 cognitively impaired older adults. Participants' systolic blood pressure responsivity, an index of cognitive engagement, was continuously sampled during cognitive testing. Participants reported on physical and mental health challenges and provided hair samples to assess chronic stress at each wave. Using the three statistical paradigms, we compared results from six model testing levels and longitudinal changes in health and stress predicting changes in cognitive engagement. Findings were mostly consistent across the three paradigms, providing additional confidence in determining effects. We extend selective engagement theory to cognitive impairment, noting that health challenges and stress appear to be important moderators. Further, we emphasize the utility of the Bayesian and fiducial paradigms for use with relatively small sample sizes because they are not based on asymptotic distributions. In particular, the fiducial paradigm is a useful tool because it provides more information than p values without the need to specify prior distributions, which may unduly influence the results based on a small sample. We provide the R code used to develop and implement all models. [ABSTRACT FROM AUTHOR]

Human cognitive capabilities are under constant pressure in the modern information society. Cognitive load detection would be beneficial in several applications of human–computer interaction, including attention management and user interface adaptation. However, current research into accurate and real-time biosignal-based cognitive load detection lacks understanding of the optimal and minimal window length in data segmentation which would allow for more timely, continuous state detection. This study presents a comparative analysis of ultra-short (30 s or less) window lengths in cognitive load detection with a wearable device. Heart rate, heart rate variability, galvanic skin response, and skin temperature features are extracted at six different window lengths and used to train an Extreme Gradient Boosting classifier to detect between cognitive load and rest. A 25 s window showed the highest accury (67.6%), which is similar to earlier studies using the same dataset. Overall, model accuracy tended to decrease as the window length decreased, and lowest performance (60.0%) was observed with a 5 s window. The contribution of different physiological features to the classification performance and the most useful features that react in short windows are also discussed. The analysis provides a promising basis for future real-time applications with wearable sensors. [ABSTRACT FROM AUTHOR]

The degenerative cognitive and physical decline of Alzheimer patients, coupled with the extensive psychological and economic tolls imposed on family members that serve as caretakers, necessitate the discovery of effective cures and preventative measures for age-related cognitive depreciation. In the journey of Alzheimer's disease treatment discovery, several cross-sectional and longitudinal studies have delineated a noticeable association between the use of nonsteroidal anti-inflammatory drugs (NSAIDs), a class of low-cost drugs with minimal side effects, and the alleviation of age-related memory impairment. In this study, four datasets (two cross-sectional and two longitudinal studies) derived from the Global Alzheimer's Association Interactive Network (GAAIN) were analyzed. The significant association between the usage of NSAIDs and better cognitive status was observed. The results agree with the findings of previous studies that the use of NSAIDs may be beneficial in the early stages of Alzheimer's disease. Transcriptomic data show that ibuprofen treatment results in upregulation of several genes involved in arachidonic acid metabolism including PPARγ, Cyp4a12b, Cyp2c66, and Cyp2c37 in the hippocampus. The increase in conversion of arachidonic acid into anti-inflammatory 16C and 18C dicarboxylic acids as well as epoxyeicosatrienoic acids may play a role in reducing the risk of Alzheimer's disease development. [ABSTRACT FROM AUTHOR]

The continuing development of avionics for Unmanned Aircraft Systems (UASs) is introducing higher levels of intelligence and autonomy both in the flight vehicle and in the ground mission control, allowing new promising operational concepts to emerge. One-to-Many (OTM) UAS operations is one such concept and its implementation will require significant advances in several areas, particularly in the field of Human–Machine Interfaces and Interactions (HMI2). Measuring cognitive load during OTM operations, in particular Mental Workload (MWL), is desirable as it can relieve some of the negative effects of increased automation by providing the ability to dynamically optimize avionics HMI2 to achieve an optimal sharing of tasks between the autonomous flight vehicles and the human operator. The novel Cognitive Human Machine System (CHMS) proposed in this paper is a Cyber-Physical Human (CPH) system that exploits the recent technological developments of affordable physiological sensors. This system focuses on physiological sensing and Artificial Intelligence (AI) techniques that can support a dynamic adaptation of the HMI2 in response to the operators' cognitive state (including MWL), external/environmental conditions and mission success criteria. However, significant research gaps still exist, one of which relates to a universally valid method for determining MWL that can be applied to UAS operational scenarios. As such, in this paper we present results from a study on measuring MWL on five participants in an OTM UAS wildfire detection scenario, using Electroencephalogram (EEG) and eye tracking measurements. These physiological data are compared with a subjective measure and a task index collected from mission-specific data, which serves as an objective task performance measure. The results show statistically significant differences for all measures including the subjective, performance and physiological measures performed on the various mission phases. Additionally, a good correlation is found between the two physiological measurements and the task index. Fusing the physiological data and correlating with the task index gave the highest correlation coefficient (CC = 0.726 ± 0.14) across all participants. This demonstrates how fusing different physiological measurements can provide a more accurate representation of the operators' MWL, whilst also allowing for increased integrity and reliability of the system. [ABSTRACT FROM AUTHOR]

Current educational curricula in Spain contain few references to sustainability topics, so there is insufficient coverage of these issues in the classroom. Notably, there is a lack of reference to the concept of water from a sustainable perspective. The key aim of this study was to analyze the presence of the concept of water in the curriculum that regulates secondary education in Spain using a previously established system of categories. An exploratory and descriptive research methodology was followed, in which we carried out a qualitative lexicographical analysis of the concept of water in the Spanish secondary education curriculum. An analysis of the cognitive demand required of students to learn about water, according to Bloom's taxonomy, was also conducted. The results show that the concept of water appears moderately in the curriculum focusing on some aspects of Sustainable Development. Likewise, the analysis of the cognitive demand required of students for learning about water reveals that lower levels of knowledge and comprehension predominate based on Bloom's taxonomy. We consider that teaching water from a sustainable perspective can generate in students awareness and values about nature and the environment, knowledge that contributes to sensible use of water and involvement for sustainable development. [ABSTRACT FROM AUTHOR]

This study presents ambient backscatter communication (AmBC) network as a concept of "modulation in the air" that has drawn growing interest by both academia and industry recently. In particular, we investigate and analyze an AmBC system relying on cognitive radio, where the primary destination is equipped with multiple antennas and maximum ratio combining (MRC). A wireless powered relay is necessary to serve both primary and secondary destinations. Benefiting from the surrounding radio frequency (RF) source, the relay can support the backscattering signal. To facilitate the performance analysis of received nodes, this study presents exact closed-form expressions of the outage probability. For comparison, the outage and throughput performance of these nodes are considered in numerical simulation. Taking advantage of the AmBC technology, the impact of the backscatter ratio on system performance is carefully studied considering various other parameters. Simulation results demonstrate the exactness of the derived outage probabilities and show that the optimal throughput performance can be achieved at specific parameters. [ABSTRACT FROM AUTHOR]