Processing Ability Research Articles

Development of ionic-liquid-impregnated activated carbon for sorptive removal of PFAS in drinking water treatment

Adsorption of per- and poly-fluoroalkyl substances (PFAS) on activated carbon (AC) is considerably hindered by the surface water constituents, degrading the ability of the AC adsorption process to remove PFAS in drinking water treatment. Herein, we developed ionic-liquid-impregnated AC (IL/AC) as an alternative to AC for PFAS sorption and demonstrated its performance with real surface water for the first time. Ionic liquids (ILs) of 1-ethyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (IL(C2)) and 1-hexyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide (IL(C6)) were selected from among 272 different ILs using the conductor-like screening model for realistic solvents (COSMO-RS) simulation. Impregnation of the ILs in AC was verified using various analytical techniques. Although the synthesized IL/ACs were less effective than pristine AC in treating PFAS in deionized water, their performances were less impacted by the surface water constituents, resulting in comparable or sometimes better performances than pristine AC for treating PFAS in surface water. The removal efficiencies of 10 wt% IL(C6)/AC for six PFAS were 1.40–1.96 times higher than those of pristine AC in a surface water sample containing 2.6 mg/L dissolved organic carbon and millimolar-level divalent cation concentration. PFAS partitioning from the surface water to ILs was not hindered by dissolved organic matter and was enhanced by the divalent cations, indicating the advantages of IL/ACs for treating significant amounts of PFAS in water. The synthesized IL/ACs were effective at treating coexisting pharmaceutical and personal-care products in surface water, showcasing their versatility for treating a broad range of water micropollutants.

Impact of Otitis Media With Effusion in Early Age on Auditory Processing Abilities in Children: A Systematic Review and Meta-Analysis.

Objective: Early-onset otitis media with effusion (OME) can affect the development of the auditory nervous system and thus lead to auditory processing abnormalities. This study aims to review the effect of childhood OME on auditory processing abilities in children. Methods: A systematic review of the literature, restricted to the English language from 1990 to 2022 was conducted using search engines like PubMed, Embase, and Google Scholar. After selecting the articles following predefined inclusion and exclusion criteria, the data were extracted and meta-analysis was performed. Results: A total of 10 articles met the inclusion criteria. Children with a history of OME had poorer performance in most behavioral and electrophysiological tests. Pooled analysis of various tests such as the gap in noise test, frequency pattern test (verbal and nonverbal), and latencies of auditory brainstem response-I, V, I to III, and I to V showed a difference between the 2 groups. Conclusion: Childhood OME can significantly affect auditory processing abilities in children.

A Study of Major Depressive Disorder Based on Resting-State Multilayer EEG Function Network

Depression is a complex mental disease with its pathological mechanism unclear. To depict the complete picture of the abnormal information interaction in a depressed brain, this study is the first to apply fully connected multilayer brain functional (FCMBF) network framework and proposed composite FCMBF (CFCMBF) network framework, combined with graph theory to analyze the within-frequency coupling (WFC) and cross-frequency coupling (CFC) of sensor-layer and source-layer electroencephalography (EEG) signals in relevant subjects. Results showed that in the sensor-layer FCMBF network, depressive patients showed significantly reduced functional connectivity, as well as abnormal global and local information processing abilities of the network, and these network properties were significantly correlated with depressive symptoms. In addition, from the perspective of depression recognition, we found that the sensor-layer CFCMBF network could achieve better classification accuracy, especially when using the overlapping degree of node under the right center region, its accuracy could reach <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$86.88\% \pm 9.25 \%$</tex-math> </inline-formula> . More importantly, the construction of the CFCMBF network has higher time efficiency and less information loss, since it not only measures the WFC and CFC between brain region representative signals (BRRSs) extracted from different brain regions, but also measures these two couplings between all nodes within each brain region. Although the FCMBF network contains more complete information by calculating WFC and CFC between all nodes distributed in each region, it will result in an enormous computational cost. In summary, this study proved the utility of multilayer brain network in revealing the abnormal brain interaction patterns of depression, and our proposed method might provide methodological support for efficient depression recognition research based on multilayer brain networks.

YOLOv5s fabric defect detection model with mixed attention mechanism

In the field of industrial quality inspection, fabric defect detection is regarded as an important research topic. The traditional inspection method mainly relies on manual visual inspection, which is time-consuming and easy to be affected by human factors, and has the problems of low accuracy, slow speed and high cost. Therefore, it is very important to use digital image processing technology and target detection technology to develop high precision, high efficiency and low-cost fabric defect detection model suitable for application deployment. Driven by deep learning-based neural network technology, many models with excellent performance have emerged in the field of object detection. This paper will improve on the basis of YOLOv5s model. ACmix module is introduced in Backbone layer of YOLOv5s model. This module overcomes the problem that convolution operation may cause local information loss after feature extraction, improves the model's limitation on feature extraction of complex background images, and enhances the model's processing ability in local feature fusion. Compared with the original YOLOv5s algorithm, mAP has improved by 0.6%. In the complex background, the detection accuracy of fabric defects targets has been significantly optimized and improved.

Cytosolic retention of HtrA2 during mitochondrial protein import stress triggers the DELE1-HRI pathway

Mitochondrial stress inducers such as carbonyl cyanide m-chlorophenyl hydrazone (CCCP) and oligomycin trigger the DELE1-HRI branch of the integrated stress response (ISR) pathway. Previous studies performed using epitope-tagged DELE1 showed that these stresses induced the cleavage of DELE1 to DELE1-S, which stimulates HRI. Here, we report that mitochondrial protein import stress (MPIS) is an overarching stress that triggers the DELE1-HRI pathway, and that endogenous DELE1 could be cleaved into two forms, DELE1-S and DELE1-VS, the latter accumulating only upon non-depolarizing MPIS. Surprisingly, while the mitochondrial protease OMA1 was crucial for DELE1 cleavage in HeLa cells, it was dispensable in HEK293T cells, suggesting that multiple proteases may be involved in DELE1 cleavage. In support, we identified a role for the mitochondrial protease, HtrA2, in mediating DELE1 cleavage into DELE1-VS, and showed that a Parkinson’s disease (PD)-associated HtrA2 mutant displayed reduced DELE1 processing ability, suggesting a novel mechanism linking PD pathogenesis to mitochondrial stress. Our data further suggest that DELE1 is likely cleaved into DELE1-S in the cytosol, while the DELE1-VS form might be generated during halted translocation into mitochondria. Together, this study identifies MPIS as the overarching stress detected by DELE1 and identifies a novel role for HtrA2 in DELE1 processing.

Effects of Bilingualism on Cognitive Processing

Purpose: The general purpose of the study was to examine the effects of bilingualism on cognitive processing.&#x0D; Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library.&#x0D; Findings: The findings reveal that there exists a contextual and methodological gap relating to the effects of bilingualism on cognitive processing. Preliminary empirical review revealed that bilingualism is associated with cognitive advantages, such as enhanced attentional control, executive function, and working memory capacity. Through a review of existing literature, it was found that bilingual individuals often outperform monolinguals in cognitive tasks, supporting the Bilingual Advantage Theory. Additionally, evidence suggested that bilingualism may contribute to a slower rate of cognitive decline in older age. These findings have implications for educators, policymakers, and healthcare professionals, highlighting the potential benefits of promoting bilingual education and language preservation efforts.&#x0D; Unique Contribution to Theory, Practice and Policy: The Bilingual Advantage theory, Neuroplasticity and Cognitive Reserve theory may be used to anchor future studies on bilingualism and cognitive processing. The study made significant contributions to theory, practice, and policy. It supported the Bilingual Advantage Theory by demonstrating enhanced cognitive processing abilities in bilingual individuals. Recommendations included further exploration of underlying mechanisms, tailored interventions in educational and clinical settings, and policies supporting bilingual education and language diversity. These findings and recommendations aimed to advance understanding of bilingualism's cognitive benefits, inform practice in education and healthcare, and influence policy development for promoting linguistic diversity and cognitive development.

Research on Data Fusion and Analysis Based on Artificial Intelligence Algorithm in Sensor Networks

This paper discusses the data fusion and analysis technology based on artificial intelligence algorithm in sensor networks. Firstly, the basic concept, composition and structure of sensor network are summarized, and its characteristics and challenges are analyzed. Then, the basic principle of data fusion technology and its application in sensor networks are introduced, and the basic concept, classification and application of artificial intelligence algorithm in data fusion are described in detail. On this basis, this paper constructs a data fusion framework based on artificial intelligence, and proposes a new data fusion method combining deep learning and machine learning algorithm. This method can effectively improve the accuracy and reliability of sensor data and reduce data redundancy and noise interference. In addition, the thesis also conducts in-depth research on the application scenarios of data fusion and analysis in sensor networks, including Environmental monitoring, intelligent transportation, medical health and other fields. The effectiveness and practicability of the proposed method are verified by experiments and case studies. Experimental results show that the data fusion technology based on artificial intelligence algorithm can significantly improve the data processing ability and analysis accuracy of sensor networks, and provide strong support for related applications. Finally, this paper summarizes the main conclusions of the study, and points out the shortcomings of current research and future prospects. In the future, the data fusion method based on artificial intelligence will be further optimized and improved, and its application in more fields will be expanded, which will provide more powerful support for the development and application of sensor network technology.

Seasonal tourism's impact on wastewater composition: Evaluating the potential of alternating activated adsorption in primary treatment

Primary treatment processes have gained attention in recent research and development due to their potential for redirecting carbon towards anaerobic digestion, which can subsequently be used for the production of biomethane. The alternating activated adsorption (AAA) process is implemented on full-scale at several wastewater treatment plants across Europe. However, there is a lack of full-scale studies of advanced carbon capture technology implementations in literature. This study demonstrates the ability of a full-scale AAA process to remove and redirect carbon in a region heavily influenced by tourism. Periods in high and off-season were compared to study the impact of tourism on the composition of the wastewater and the AAA-process. The wastewater characteristics of the high season differed significantly from the low season. During the high season, the PE increased by 37 %, total suspended solids went up by 75 % and chemical oxygen demand increased by 58 %, compared to the low season. Additionally, 80 % of the low volatile lipophilic substances (LVLS) measured were attributed to the impact of tourism. A mass-balance of primary treatment for chemical oxygen demand (COD) and LVLS was conducted for both trial periods. The primary treatment was able to eliminate 56 % of the COD and 62 % of the LVLS in the non-tourist season and 53 % of the COD and 54 % of the LVLS in the tourist season. The increased wastewater load was effectively managed in the AAA-process. Key process parameters like sludge settling characteristics, hydraulic retention time and total suspended solids removal rates remained stable during the high season in winter.

Fast Inter-frame Motion Prediction for Compressed Dynamic Point Cloud Attribute Enhancement

Recent years have witnessed the success of deep learning methods in quality enhancement of compressed point cloud. However, existing methods focus on geometry and attribute enhancement of single-frame point cloud. This paper proposes a novel compressed quality enhancement method for dynamic point cloud (DAE-MP). Specifically, we propose a fast inter-frame motion prediction module (IFMP) to explicitly estimate motion displacement and achieve inter-frame feature alignment. To maintain motion continuity between consecutive frames, we propose a motion consistency loss for supervised learning. Furthermore, a frequency component separation and fusion module is designed to extract rich frequency features adaptively. To the best of our knowledge, the proposed method is the first deep learning-based work to enhance the quality for compressed dynamic point cloud. Experimental results show that the proposed method can greatly improve the quality of compressed dynamic point cloud and provide a fast and efficient motion prediction plug-in for large-scale point cloud. For dynamic point cloud attribute with severely compressed artifact, our proposed DAE-MP method achieves up to 0.52dB (PSNR) performance gain. Moreover, the proposed IFMP module has a certain real-time processing ability for calculating the motion offset between dynamic point cloud frame.

Harnessing the Power of AI: A Comprehensive Review of Left Ventricular Ejection Fraction Assessment With Echocardiography.

The quantification of left ventricular ejection fraction (LVEF) has important clinical utility in the assessment of cardiac function and is vital for the diagnosis of cardiovascular diseases. A transthoracic echocardiogram serves as the most commonly used tool for LVEF assessment for several reasons, including, its noninvasive nature, great safety profile, real-time image processing ability, portability, and cost-effectiveness. However, transthoracic echocardiogram is highly dependent on the clinical skill of the sonographer and interpreting physician. Moreover, even amongst well-trained clinicians, significant interobserver variability exists in the quantification of LVEF. In search of possible solutions, the usage of artificial intelligence (AI) has been increasingly tested in the clinical setting. While AI-derived ejection fraction is in the preliminary stages of development, it has shown promise in its ability to rapidly quantify LVEF, decrease variability, increase accuracy, and utilize higher-order processing capabilities. This review will delineate the latest advancements of AI in evaluating LVEF through echocardiography and explore the challenges and future trajectory of this emerging domain.

Sensory processing abilities in relation to motor capabilities in children with different types of cerebral palsy

Background and purpose. children with cerebral palsy have sensory processing issues as well as motor problems, which lead to severe developmental disability. In this study, we aimed to assess correlation between sensory processing abilities and motor capabilities in children with cerebral palsy and assess the effect of cerebral palsy types on sensory processing abilities and motor capabilities. Materials and methods. One hundred cerebral palsy children of both sex were included in the study; their age ranged from 4-10 years old were selected from pediatric rehabilitation out clinic of faculty of Physical Therapy Cairo University, fulfilling inclusion criteria. All children were assessed by Short sensory profile: to measure sensory processing ability, Gross Motor Function Classification System: to measure the gross motor skills and Manual Ability Classification System: to measure the fine motor skills. Result. there was a significant correlation between GMFCS, MACS and the total score of SSP as well as Low energy weak, tactile sensitivity score, taste smell sensitivity, Movement sensitivity, and Under-responsive seeks sensation (P ≤ 0.05*). There was no significant correlations between both auditory filtering and Visual-auditory sensitivity with GMFCS and MACS (P = 0.676 and 0.266, respectively). There was statistical variation among different CP types regarding SSP and motor assessment (P ≤ 0.05*). Conclusion. the study showed: sensory processing abilities effect on motor capabilities in children with cerebral palsy.

Analysis of Import and Export Trade Status in Guangdong Province

In 2023, the total volume of foreign trade in Guangdong Province is outstanding, and ranks first in the country for many consecutive years. As the largest province of foreign trade in the country, Guangdong should continue to play a good role in the ballast of foreign trade in the country. Based on the analysis of the current situation of foreign trade in Guangdong Province, this paper finds that the overall development of foreign trade is good, but there are factors such as weak growth, lack of independent innovation ability and external environment in the development process. Based on this, this paper puts forward suggestions from the aspects of market development, industrial upgrading and trade facilitation reform, in order to provide reference for the development of foreign trade in Guangdong Province.

8-Year Follow-up of a Maternal Education Trial in a Low-Resource Setting.

Nutrition and stimulation interventions promote early childhood development, but little is known about their long-term benefits in low- and middle-income countries. We conducted a follow-up study of a cluster-randomized maternal education trial performed in children aged 6 to 8 months to assess the sustainability of developmental benefits after 8 years. The education intervention lasted 6 months and consisted of nutrition, hygiene, sanitation, and child stimulation aspects. We assessed child processing and cognitive abilities using the Kaufman Assessment Battery for Children Second Edition (KABC-II) and attention and inhibitory control using the Test of Variables of Attention after 8 years. The original trial included 511 mother-child pairs (intervention, n = 263; control, n = 248), whereas in the current study, 361 (71%; intervention, n = 185; control, n = 176) pairs were available for analyses. The intervention group scored higher than the controls (all P < .001) on all 5 KABC-II subscales and on the KABC-II global score (mean difference: 14; 95% confidence interval, 12-16; P < .001). For all 5 Test of Variables of Attention variables, the intervention group scored higher than the controls on both the visual and auditory tasks (all P < .05). Because the intervention was delivered as a package, a limitation is that we cannot pinpoint the individual contribution of each component (nutrition, hygiene, and stimulation) to the developmental benefits. The intervention group consistently scored markedly higher on both neuropsychological tests. Thus, even 8 years after the original maternal education intervention, the developmental benefits that we observed at child age of 1, 2, and 3 years, were sustained.

Auditory Processing and Speech-Sound Disorders.

Speech-sound disorders (SSD) have been linked to auditory processing difficulties, and auditory processing disorders (APD) have been related to phonological awareness and literacy development. To this date, there has not been a systematic literature review investigating the results of psychophysiology and language assessments related to SSD and APD in children. The literature search was conducted in PubMed, Medline EBSCO, and Scopus to identify studies with children diagnosed/suspected of having APDs and SSDs. The quality of methodology in the selected articles was evaluated with the Newcastle Ottawa Scale. Seven out of 378 relevant studies met the selection criteria. The findings were summarized for children with SSD and APD based on (a) metalinguistic and literacy skills, (b) cognitive abilities, and (c) temporal processing abilities. Three articles indicated that children with APD and SSD exhibit lower temporal task accuracy and reaction time. In two studies, children with SSD exhibited lower scores in discrimination, sequencing, and recall of brief stimuli in rapid succession. This review revealed associations between SSD severity and APD that may underline low performance in metalinguistic skills. Diagnostic assessments have been proposed based on the review to adequately identify children with SSD and APD and provide useful information for more suitable intervention.

Hydrogel-Gated FETs in Neuromorphic Computing to Mimic Biological Signal: A Review.

Hydrogel-gated synaptic transistors offer unique advantages, including biocompatibility, tunable electrical properties, being biodegradable, and having an ability to mimic biological synaptic plasticity. For processing massive data with ultralow power consumption due to high parallelism and human brain-like processing abilities, synaptic transistors have been widely considered for replacing von Neumann architecture-based traditional computers due to the parting of memory and control units. The crucial components mimic the complex biological signal, synaptic, and sensing systems. Hydrogel, as a gate dielectric, is the key factor for ionotropic devices owing to the excellent stability, ultra-high linearity, and extremely low operating voltage of the biodegradable and biocompatible polymers. Moreover, hydrogel exhibits ionotronic functions through a hybrid circuit of mobile ions and mobile electrons that can easily interface between machines and humans. To determine the high-efficiency neuromorphic chips, the development of synaptic devices based on organic field effect transistors (OFETs) with ultra-low power dissipation and very large-scale integration, including bio-friendly devices, is needed. This review highlights the latest advancements in neuromorphic computing by exploring synaptic transistor developments. Here, we focus on hydrogel-based ionic-gated three-terminal (3T) synaptic devices, their essential components, and their working principle, and summarize the essential neurodegenerative applications published recently. In addition, because hydrogel-gated FETs are the crucial members of neuromorphic devices in terms of cutting-edge synaptic progress and performances, the review will also summarize the biodegradable and biocompatible polymers with which such devices can be implemented. It is expected that neuromorphic devices might provide potential solutions for the future generation of interactive sensation, memory, and computation to facilitate the development of multimodal, large-scale, ultralow-power intelligent systems.

Clinical reasoning skills of German and Dutch manual therapists: a cross-sectional study

ABSTRACT Introduction Reflective clinical reasoning (CR) is believed to play a crucial role in achieving an efficient therapy process, supported by evidence-based approaches that improve patient outcomes. The aim of this study was to evaluate the CR process of German manual therapists (MTs) using the CR skills of Dutch MTs as a reference. Methods This cross-sectional study evaluated the CR process and diagnostic ability of German MTs. Dutch MTs served as the reference standard due to their experience with direct access. The CR skills were assessed using the Diagnostic Thinking Inventory (DTI), a questionnaire measuring diagnostic competence. Descriptive and inferential statistics were conducted to compare the groups. Subgroup analyses were performed to analyze the influence of work experience (in years) and the level of education on CR. Results 396 manual therapists (229 German and 167 Dutch) completed the DTI. Dutch MTs revealed higher DTI sum-scores compared to German MTs (p < .001; Cohen´s d = .41). Subgroup analyses regarding professional education revealed no significant differences in DTI sum-scores within German MTs and between both groups. In the subgroup analyses regarding professional experience, differences of moderate effect size were reached between German and Dutch novice MTs (in favor of Dutch novice MTs; p = .001; Cohen´s d = .62), and between German novice MTs and experienced German MTs (in favor of experienced German MTs; p < .001; Cohen´s d = .6). Conclusion The results suggest that academically educated German and Dutch MTs as well as experienced German and Dutch MTs are similar in terms of their hypothetical-deductive CR skills. In turn, German novice MTs seem to use hypothetical-deductive processing to a lesser extent compared to Dutch novice MTs and experienced German MTs, which in turn may support the hypothesis that the level of professional experience and education has a significant impact on the development of hypothetical-deductive CR skills.

Anti-attack algorithm of cloud storage attribute base based on dynamic authorized access

Cloud storage attribute libraries usually store a large amount of sensitive data such as personal information and trade secrets. Attackers adopt diverse and complex attack methods to target the cloud storage attribute database, which makes the defense work more challenging. In order to realize the secure storage of information, an attribute based cloud storage anti-attack algorithm based on dynamic authorization access is proposed. According to the characteristic variables of the sample, the data correlation matrix is calculated, and the principal component analysis method is adopted to reduce the dimension of the data, build the anti-attack code model, simulate the dynamic authorization access rights, and calculate the packet loss rate according to the anti-attack flow. Design the initialization stage, cluster stage and cluster center update stage to realize the attack prevention of cloud storage attribute database. The experimental results show that the proposed algorithm can accurately classify the anti-attack code, has good packet processing ability, relatively short page request time, and anti-attack success rate is higher than 90%, which can effectively ensure the stability of the algorithm.

Online learning in supporting students' procedural abilities viewed from a constructivist approach

Purpose the study to explain the perspective online learning in supporting students procedural abilities from constructivist point of view.This study used literature review methodology to elaborate topics online learning procedural skills and constructivist theory.Scopus article search engines such as springer Nature,SAGE, Taylor &amp; Francis and google scholar provide collected literature.There are 350 articles covering search topic. There were 78 additional articles that met variables study. Selected articles were evaluated along with 47 references. Online learning has fundamental principle of constructivism. Constructivism encourages learning process that places students as the source process through concrete procedural empirical and systematic activities. Online learning provides new model how learning process can be carried out without face to face can increase student ability especially in terms practicum that requires ability in every learning process.

Rotational photonic spin Hall effect on twisted bilayer metasurfaces

Optical differential operations possess the remarkable advantages of high efficiency and real-time imaging, which can improve the ability of image processing to enhance image edge detection. The photonic spin Hall effect has been demonstrated to realize optical differential operations. However, the direction of optical differential operation generated by the conventional photonic spin Hall effect is single and fixed. Here, we propose a scheme for the rotational photonic spin Hall effect on twisted bilayer metasurfaces to obtain the optical differential operations in any desirable directions and phase reconstruction. The rotation angle of the rotational photonic spin Hall effect is defined as the tangent of the in-plane spin-dependent shift to the transverse spin-dependent shift. The twist angle of the bilayer metasurfaces influence the Fresnel reflection coefficients which affect the transverse spin-dependent shift and the in-plane spin-dependent shift. Therefore, the direction of rotational photonic spin Hall effect can be controlled by the twist angle of the bilayer metasurfaces. Moreover, we show applications of the rotational photonic spin Hall effect including image edge detection and phase reconstruction. These findings open up new horizons for future applications of the rotational photonic spin Hall effect in optics.

Cognitive psychology and the classroom: what teachers need to know

This paper examines the dynamics of learning and how they relate to the optimal teaching strategies for learners. The authors aim to enhance learners' information processing abilities by highlighting the importance of considering different learning theories in the classroom. These considerations are based on the qualitative behaviour of learners' conscious awareness, cognitive and metacognitive engagement. A multi-stage cluster sampling procedure was employed to select a sample of 840 Grade 11 learners from 20 schools in the education district. A questionnaire was used to collect data, and statistical tests were conducted to validate and analyse the data. The study's results suggest that it is crucial for teachers to use a variety of learning theories and to consider variables that facilitate effective information processing by learners. Understanding diverse learning theories and their interplay positively influence the cognitive development of learners. The study's findings, which include SEM and HLM analyses and implications from teachers' application of juxtaposed theories, suggest that inefficiencies may exist in certain teaching approaches and in individual learners' information processing. Teachers who understand and apply the simultaneous impact of juxtaposed theories of learning can enable learners to assimilate or accommodate incoming information into their cognitive schema, leading to deeper and more meaningful understanding.