Processing Ability Research Articles

Different facets of age perception in people with developmental prosopagnosia and “super-recognisers”

AbstractThe interplay between facial age and facial identity is evident from several scenarios experienced in daily life, such as when recognising a face several decades after the last exposure. However, the link between age and identity processing, and how age perception abilities might diverge in individuals with different face processing abilities, has scarcely been considered. Furthermore, the approach used to test age perception ability may also influence outcome, but the effect of different paradigms on performance is not yet known. Across three studies, we compare super-recognisers (SRs), people with developmental prosopagnosia (DPs), and a group of neurotypical controls, on three age perception paradigms. There were no differences on the numeric age estimation task (i.e. providing precise age estimates for a series of faces; Study 1), and numeric age estimation task with added noise-distortion to stimuli (Study 2). However, SRs were more accurate when instructed to classify ambient faces as either over- or under- the age of 18 compared to both DPs and controls (Study 3). Thus, there may be nuanced differences in age processing which can be tapped into using separate paradigms; however, given that the difference is only with SRs it remains unclear to what extent these are linked to facial identity processing.

Cholinergic regulation of dendritic Ca2+ spikes controls firing mode of hippocampal CA3 pyramidal neurons

Active dendritic integrative mechanisms such as regenerative dendritic spikes enrich the information processing abilities of neurons and fundamentally contribute to behaviorally relevant computations. Dendritic Ca2+ spikes are generally thought to produce plateau-like dendritic depolarization and somatic complex spike burst (CSB) firing, which can initiate rapid changes in spatial coding properties of hippocampal pyramidal cells (PCs). However, here we reveal that a morpho-topographically distinguishable subpopulation of rat and mouse hippocampal CA3PCs exhibits compound apical dendritic Ca2+ spikes with unusually short duration that do not support the firing of sustained CSBs. These Ca2+ spikes are mediated by L-type Ca2+ channels and their time course is restricted by A- and M-type K+ channels. Cholinergic activation powerfully converts short Ca2+ spikes to long-duration forms, and facilitates and prolongs CSB firing. We propose that cholinergic neuromodulation controls the ability of a CA3PC subtype to generate sustained plateau potentials, providing a state-dependent dendritic mechanism for memory encoding and retrieval.

Raising the performances of copoly(ether-imide) films by structural design modulation towards energy storage applications

The present study aims to shed light onto the influence of structural design on the overall properties of a series of aliphatic–aromatic CN-based copoly(ether-imide)s, with emphasis on energy storage performance, with the support provided by selected benchmarks. To boost the energy storage capability of the free-standing films made therefrom, three molar ratios of a CN-aromatic hard segment and a Jeffamine-based soft component were used. Thus, through different chemical strategies and their synergism, the films morphology and their physico-chemical properties like wetting, optical, mechanical, thermal, dielectric and, particularly breakdown strength and energy storage were modulated. The results showed high thermal stability, single glass transition temperatures, good mechanical properties, and relative low dielectric constants of the copolymers. The absence of crystallization demonstrated the amorphous nature of the films due to the good interpenetration of soft and hard components, although some phase separations were observed in some copolymers with no clearly defined boundaries. Due to the balanced thermal stability, dielectric behavior, mechanical features, and film processing ability, these copoly(ether-imide)s display potential applications in high-temperature energy storage field, reaching breakdown strength and energy storage density values up to 459 kV/mm and 2.70 J/cm3, respectively.

Implementation of a STEM-Based Project-Based Learning Model to Improve Students' Science Process Skills on Alternative Energy Material

Science process skills are skills that students must have in learning Physics because students are required to be able to apply scientific methods and knowledge and develop the knowledge skills they already have, so that science process skills are needed by students to face the real world which is dominated by technology and science. The purpose of this study is to ascertain how students' science process abilities in studying physics are affected by a STEM (science, technology, engineering, and mathematics) project-based learning approach. The study's sample was selected through the use of purposeful sampling. The sample consisted of thirty-four X grade students from a public high school in Bandung City. Formative pretest-posttest testing was used in this study as part of a quantitative pre-experiment methodology. Twelve multiple-choice questions were utilized in a formative evaluation of the abilities of the science process to collect research data. The results revealed that the moderate group had an N-Gain score of 0.61. A high category for observing and classifying indicators and a medium category for forecasting, measuring, drawing conclusions, and presenting indicators are used to categorize the improvement in science process skills indicators. Thus, the STEM-based learning model can improve students' science process skills.

Auditory perceptual ability affects dichotic listening performance in older adults

ABSTRACT Age-related changes pose challenges in speech processing for older adults. However, little is known about the role of auditory perceptual ability in their performance in dichotic listening tasks. The present study investigated how older adults’ auditory perceptual abilities affected their correct rates and the right ear advantage (REA) in the dichotic listening tasks in two experiments. In Experiment 1, older adults’ performance was assessed using dichotic listening tasks based on consonant–vowel (CV) words varying in consonants, vowels, and lexical tones, each presenting distinct auditory perceptual demands. It was found that older adults exhibited decreased correct rates as auditory perceptual demands increased. Moreover, differences in the REA were observed in older listeners, suggesting increased engagement of the hemisphere responsible for acoustic analysis in processing challenging dichotic stimuli. Experiment 2 examined how older individuals’ acoustic processing abilities contributed to their dichotic listening performance. It was shown that older adults with acoustic processing abilities comparable to those of younger individuals demonstrated correct rates and REAs similar to those of younger cohorts. These results revealed the nonnegligible role of acoustic processing in the dichotic listening paradigm and the significance of considering listeners’ auditory perceptual abilities in investigating language lateralisation using the dichotic listening paradigm.

Analysis of Big Data Network Security Defense Mechanism Application of Artificial Intelligence

In the context of the close integration of computer network technology and daily life, big data network security faces many challenges. With its powerful information processing ability, this AI can effectively identify and handle potential threats in the network, thereby improving network security. This paper discusses the application of artificial intelligence (AI) in enhancing big data network security defense, especially in the threat detection link. This paper proposes a detection algorithm that combines support vector machine (SVM) and K nearest neighbor (KNN), namely SVM-KNN algorithm, for network attack detection and defense. When the hazard data is 60 under the test sample, the detection accuracy of KNN, SVM, and SVM-KNN is 86.88%, 89.51%, and 96.75% respectively; when the hazard data is 60 under the experimental sample, the detection accuracy of KNN, SVM, and SVM-KNN is 82.70%, 83.88%, and 89.26% respectively.response.

Systems engineering barriers to legacy system evolution: Legacy system assessment

AbstractThis study evaluates the effectiveness of common engineering processes in conducting system assessments, with a focus on legacy system assessments, which are currently a barrier to legacy system evolution. These processes include reverse systems engineering (RSE), which remains in its infancy, and more established processes such as software and hardware reverse engineering. The study assesses these methods to better understand their ability to define systems of interest and meet the criteria set by the four horsemen of traditional systems engineering, as defined by Rebovich and White, and the International Council of Systems Engineering. Additionally, RE techniques are reviewed to ensure that a formal, systems‐level process—with detailed steps and diagrams—is documented and accessible to systems or reverse engineers. Lastly, this investigation elucidates the ability of each reverse engineering process to discover, recover, or reproduce information pertaining to the systems of interest that is missing, incomplete, or outdated in documentation relevant to the development or operations of that system. This information can be used to better understand the areas of RSE that still need to be developed and aid in the maturation of a repeatable process that is applicable to legacy systems. The results of this study are expected to contribute to the further development of the Enterprise Lifecycle Model proposed by Terry and Chandrasekar.

Introducing CHiDO-A No Code Genomic Prediction software implementation for the characterization and integration of driven omics.

Climate change represents a significant challenge to global food security by altering environmental conditions critical to crop growth. Plant breeders can play a key role in mitigating these challenges by developing more resilient crop varieties; however, these efforts require significant investments in resources and time. In response, it is imperative to use current technologies that assimilate large biological and environmental datasets into predictive models to accelerate the research, development, and release of new improved varieties that can be more resilient to the increasingly variable climatic conditions. Leveraging large and diverse datasets can improve the characterization of phenotypic responses due to environmental stimuli and genomic pulses. A better characterization of these signals holds the potential to enhance our ability to predict trait performance under changes in weather and/or soil conditions with high precision. This paper introduces characterization and integration of driven omics (CHiDO), an easy-to-use, no-code platform designed to integrate diverse omics datasets and effectively model their interactions. With its flexibility to integrate and process datasets, CHiDO's intuitive interface allows users to explore historical data, formulate hypotheses, and optimize data collection strategies for future scenarios. The platform's mission emphasizes global accessibility, democratizing statistical solutions for situations where professional ability in data processing and data analysis is not available.

Automatic Semantic Annotation of Indonesian Language Phrase Using N-Gram Language Model

Building semantic data populations in unstructured data or text is challenging. In this type of data, several problems can be raised, some of which are difficult to analyze. Some groups of words or expressions cannot be defined according to their meaning and can be a source of ambiguity. It can have a different meaning depending on the context of its use. This work aims to automatically annotate Indonesian Language text, especially phrases, with the existing knowledge base. The result is text with semantic markup. Machines can automatically process this type of text because it describes its meaning. This work applies an n-gram language model to identify meaningful phrases and defines them as a unit so that every existing word or phrase is automatically semantically tagged. This work uses the DBpedia and schema.org knowledge base. The percentage of successfully labeled data in this job was 78% with 84.95% accuracy using DBpedia and 5.9% with 97.46% accuracy using schema .org. Some factors affect the accuracy score, including the availability of the required data with the data contained in the knowledge base, the system's ability in the POS tagging process, and many new terminology and local cultures that have not yet been contained in the knowledge bases, especially schema.org that is utilized as a standard for all search engines. This work will help the machine understand the semantics of text data. All pages obtained will be semantically tagged and, therefore, will be understood by machines. This ability will support the following processes.

Ferroelectric Polarization Enhanced Optoelectronic Synaptic Response of a CuInP2S6 Transistor Structure.

Neuromorphic computing can simulate brain function and is a pivotal element in next-generation computing, providing a potential solution to the limitations brought by the von Neumann bottleneck. Optoelectronic synaptic devices are highly promising tools for simulating biomimetic nervous systems. In this study, we developed an optoelectronic neuromorphic device with a transistor structure constructed using ferroelectric CuInP2S6. Essential synaptic behaviors in this device are observed in response to light and electrical stimuli. The optoferroelectric coupling is revealed, and the highly tunable gate modulation of the charge carrier is realized in a single device. On this basis, the light adaptation of the biological eyes and smarter Pavlovian dogs was implemented successfully and enhanced by ferroelectric polarization. The gate voltage application promotes the migration of additional Cu+ ions in the in-plane direction, thus enhancing the synaptic performance of electrical stimulation. Meanwhile, the processing ability of convolutional kernel noise images in ferroelectric devices has been achieved. Our results offer the important observation and application of ferroelectric polarization-enhanced synaptic properties of a transistor structure and have great potential in promoting the development of two-dimensional van der Waals materials and devices.

THE INFLUENCE OF GENERIC SKILLS ON GENDER DIFFERENCES IN SCIENTIFIC PROCESSING AND CRITICAL THINKING IN PHYSICS MOTION DYNAMICS

This study investigates the gender differences in generic skills, scientific process abilities, and critical thinking among high school students during physics learning, specifically within motion dynamics. The research aims to uncover the impact of generic skills on students' scientific processing and critical thinking abilities while examining how gender influences these cognitive domains. Employing a comparative quantitative research design, data were gathered from 180 students (90 male and 90 female) in Jambi City using observation sheets, test questions, and questionnaires. The sample was selected through simple random sampling, and statistical analyses were performed to examine the differences in learning outcomes between genders. Results revealed notable gender disparities in generic skills, scientific processing, and critical thinking, with male and female students demonstrating distinct proficiencies in physics learning. This underscores the necessity of adopting gender-sensitive educational approaches to foster equitable learning experiences. The uniqueness of this research lies in its detailed exploration of the interconnectedness between generic skills, scientific processing, and critical thinking within the framework of gender. Unlike prior studies, which often focus on isolated skills, this study integrates these core competencies to provide a more holistic understanding of how gender influences physics learning. The findings emphasize the need for inclusive curricula and gender-tailored instructional strategies that promote equal opportunities for all students in science education, particularly in physics, thus contributing to a more balanced and equitable academic environment.

Grey Bayesian network model for electromagnetic pulse vulnerability assessment of engine system

Firstly, in view of the uncertainty of electromagnetic pulse vulnerability assessment of engine system caused by limited experimental data and incomplete information, a grey Bayesian network model is proposed by introducing interval grey numbers in grey system theory to characterize the uncertainty of sensitivity threshold and fault logic relationship of engine system components, so as to improve the processing ability of Bayesian network for uncertain information. Secondly, taking broadband high-power microwave as an example, the interval grey number failure probability of engine system and the posterior failure probability of sensor interval grey number are calculated. The former represents the survivability of the engine system as a whole under strong electromagnetic pulse, and the latter reflects the vulnerability order of each sensor under the condition of engine system failure. The evaluation conclusion can provide a reference for the design of vehicle electromagnetic protection.

Stock Prediction using Deep Learning: A Comparison

Stock market prediction (SMP) is one of the most important areas in finance that greatly helps investors and financial institutions to anticipate market fluctuations and make wise decisions. This becomes very important in view of the potential challenge of obtaining optimum revenues and effective portfolio management strategies in view of the volatile market environments typically governed by underlying economic and socio-political influences. This paper proceeds to categorize stock prediction works into three principal categories: Convolutional Neural Network (CNN)-based models, Recurrent Neural Network (RNN)-based models, and hybrid models using more than one forecasting technique. We reviewed nine research papers and compared them in view of different datasets to show efficacy distinctions among these methodologies. Our experiments stated that the hybrid models that marry the spatial data processing ability of CNNs to the temporal sensitivity of RNNs outperform other approaches in accuracy and strength of predictions. This synthesis approach captures complex patterns more efficiently as well as relevant to the dynamic nature of financial markets. Results underscore the potential contribution of hybrid frameworks being a leading methodology for future developments in SMP. Further improving these models with the advancements of machine learning and data analytics may likely push the boundaries of what is feasible in financial forecasting.

Central Auditory Processing Abilities in Children with Non-Syndromic Cleft Lip and Palate: An Electrophysiological Study.

The present study compared the central auditory processing abilities using electrophysiological tests in children with non-syndromic cleft lip and palate (NSCLP) and their age-matched control group. Thirty children aged 7 to 15 years were recruited for the study. Participants were divided into 2 groups. The clinical group (children with NSCLP) comprised 15 children, while the control group (craniofacially typical peers) comprised 15 children with normal hearing sensitivity and auditory processing skills. Electrophysiological tests, including auditory brainstem responses (ABR), binaural interaction component (BIC) of ABR, auditory late latency responses (ALLR), and P300 were assessed. The results showed deviant responses in ABR, BIC, and ALLR in children with NSCLP compared to craniofacially typical counterparts. However, no significant difference was observed in P300 between the two groups. Children with NSCLP may be at a higher risk of central auditory processing disorder due to their abnormal neural transmission in the auditory nervous system. Also, assessing auditory processing abilities in children with NSCLP should include electrophysiological tests in the test battery for additional information regarding neural transmission.

Towards Understanding AI Delegation: The Role of Self-Efficacy and Visual Processing Ability

Recent work has proposed artificial intelligence (AI) models that can learn to decide whether to make a prediction for a task instance or to delegate it to a human by considering both parties’ capabilities. In simulations with synthetically generated or context-independent human predictions, delegation can help improve the performance of human-AI teams—compared to humans or the AI model completing the task alone. However, so far, it remains unclear how humans perform and how they perceive the task when individual instances of a task are delegated to them by an AI model. In an experimental study with 196 participants, we show that task performance and task satisfaction improve for the instances delegated by the AI model, regardless of whether humans are aware of the delegation. Additionally, we identify humans’ increased levels of self-efficacy as the underlying mechanism for these improvements in performance and satisfaction, and one dimension of cognitive ability as a moderator to this effect. In particular, AI delegation can buffer potential negative effects on task performance and task satisfaction for humans with low visual processing ability. Our findings provide initial evidence that allowing AI models to take over more management responsibilities can be an effective form of human-AI collaboration in workplaces. 1

Security primitives for memoryless IoT devices based on Physical Unclonable Functions and True Random Number Generators

The article describes various security primitives for significantly resource-constrained devices, such as sensors or sensor networks, IoT devices, wearables, etc. — i.e., devices without programmable memory. It is dedicated to parts which cannot handle complex algorithms of modern secure cryptography, cannot be equipped with programmable memories, or their circuits or data in permanent memories can be easily reverse-engineered. Instead, all security techniques (e.g., identification, authentication, and encryption) are based on modern hardware cryptography, mainly: physical unclonable functions (PUFs) and true random number generators (TRNGs). The paper addresses numerous issues from untraceable identification to mutual authentication to one-time pad encryption. The communication security is considered to be a trade-off between the device’s resources (processing ability, energy consumption, implementation size, response time), preparation complicity (initialization time, size of a server data storage) and the security capabilities and protection levels. Primitives can be included into the communication protocol based on particular needs and available hardware resources.

Visual Attention and Cardiac Response to Faces in Relation to Emotion Recognition

Abstract: Rapid and accurate emotion recognition is a crucial skill for social interactions, and visual attention to informative social cues in the environment can facilitate emotion recognition. Studies have found that emotion recognition is also associated with underlying cardiac autonomic responses. The current study examined (1) visual attention and cardiac response (change in heart rate) to emotionally-expressive faces and houses (as a nonsocial control image), and (2) associations between these responses and emotion recognition on the Reading the Mind in the Eyes Test (RMET) in college students. Results showed increased overall attention, but shorter first fixation durations, to faces as compared to houses. Across faces, attention was greater to eyes than mouth, but the magnitude of this difference was dependent on emotion. Analysis of cardiac responses revealed greater heart rate deceleration to happy and fearful faces as compared to neutral faces and houses. Better emotion recognition accuracy on the RMET was related to greater attention to faces, and eyes specifically, as well as greater heart rate deceleration to faces relative to houses. No relations between visual attention and cardiac response were found. The current work points to variations in visual attention and cardiac responses while viewing emotional faces that can be markers of emotion processing ability.

SF6 Gas Humidity Prediction Model Based on Deep Learning

SF6 gas is widely used in Gas Insulated Switchgear(GIS) as an insulating and arc extinguishing medium in electric power industry. However, SF6 gas humidity has a significant impact on the performance and reliability of GIS equipment. The accurate prediction of humidity level is one of the keys to ensure the long-term stable operation of GIS equipment. Traditional humidity prediction methods are often limited by model complexity and data processing ability, which is difficult to meet the actual demand. In this paper, deep learning, as a powerful data-driven approach, shows great potential in gas humidity prediction. An efficient SF6 gas humidity prediction model for GIS equipment is constructed based on deep learning algorithm. The deep learning model can learn complex feature representations from a large number of historical data, and then accurately predict SF6 gas humidity.

Speckle-free holography with a diffraction-aware global perceptual model

Computer-generated holography (CGH) based on neural networks has been actively investigated in recent years, and convolutional neural networks (CNNs) are frequently adopted. A convolutional kernel captures local dependencies between neighboring pixels. However, in CGH, each pixel on the hologram influences all the image pixels on the observation plane, thus requiring a network capable of learning long-distance dependencies. To tackle this problem, we propose a CGH model called Holomer. Its single-layer perceptual field is 43 times larger than that of a widely used 3×3 convolutional kernel, thanks to the embedding-based feature dimensionality reduction and multi-head sliding-window self-attention mechanisms. In addition, we propose a metric to measure the networks’ learning ability of the inverse diffraction process. In the simulation, our method demonstrated noteworthy performance on the DIV2K dataset at a resolution of 1920×1024, achieving a PSNR and an SSIM of 35.59 dB and 0.93, respectively. The optical experiments reveal that our results have excellent image details and no observable background speckle noise. This work paves the path of high-quality hologram generation.

Research on Ship Data Analysis Platform Development and Its Application in Teaching

This paper delves into the significant importance of developing a ship data analysis platform, which aims to enhance the operational efficiency and safety of the shipping industry by integrating big data processing technologies. The development of the platform centers on crucial aspects such as data collection, preprocessing, analysis, and visualization, enabling real-time monitoring and intelligent prediction of ship operational status. This platform not only assists in optimizing route planning and reducing energy consumption costs but also provides robust support for navigational safety. In project-based teaching, this platform serves as a bridge between theory and practice, cultivating students' abilities in big data processing and analysis through case studies, practical exercises, and other forms, thereby promoting the cultivation of high-quality talents and technological innovation in the field of shipping.