Processing Ability Research Articles

Effects of phonetic training and cognitive aptitude on the perception and production of non-native speech contrasts

Abstract Research on second language (L2) speech learning suggests that incidental perception training can lead to the establishment of non-native phonological categories. The present study contributes to this line of enquiry by investigating how this training is mediated by individual differences in working memory capacity and domain-general auditory processing abilities. In our study, 130 native British English speakers without prior knowledge of Portuguese were randomly assigned to trained or untrained conditions. All participants completed a visual digit span task and an auditory processing test battery. We observed improvements from pretest to post-test in production only, but since both groups improved, these gains cannot be attributed to the incidental perception training. The analysis of the ID measures further confirms the important role played by auditory processing abilities in L2 speech learning. However, more research is needed to better understand the role of incidental perception training and the mediating role of cognitive aptitudes.

Sex and hand differences in haptic processing: implications for mental rotation ability.

It has been proposed that the sensorimotor system provides a foundation for the development of cognitive abilities and their hemispheric specialization. In this study, we investigated the potential relationship between haptic processing and mental rotation ability, both of which are typically lateralized to the right hemisphere. Previous research has also indicated that males tend to outperform females in both functions. The current study investigates how the sensorimotor-haptic system relates to mental rotation ability, specifically to examine the influence of hand performance (as a proxy for hemispheric specialization) and biological sex on this relationship. Seventy-five participants (n = 41 females) completed a haptic task, and the well-known mental rotation test (MRT) developed by Shepard and Metzler (Science 171:701-3, 1971). Results confirmed a positive correlation between performance on the haptic and MRT tasks. Further, males outperformed females in both tasks. However, when sex and hand performance were considered, males were better in the haptic task, but only when using their left-hand. Moreover, left-hand haptic performance was the sole predictor of MRT performance. These findings suggest that sex differences in haptic processing may contribute to the observed sex differences in mental rotation ability, supporting the view that sensorimotor processes shape cognitive function and its hemispheric lateralization.

Central auditory processing in epileptic children

BackgroundEpilepsy is characterized by the transient occurrence of signs or symptoms due to hypersynchronous or excessive neuronal activity. Seizures caused by epilepsy during brain development can hinder the normal growth and functioning of the brain, which may negatively impact the acquisition of cognitive skills that are crucial for the learning process. The purpose of the study was to evaluate central auditory processing and cognitive functions in different types of epileptic children.ResultsThe results of central auditory processing tests for epileptic children indicated notable differences when compared to the control group, specifically in the gap in noise, speech in noise, dichotic digit, and time-compressed speech tests. These differences were statistically significant at a level of P < 0.005. Epileptic children showed prolonged latencies and smaller amplitudes of the p300 wave when compared to the control (P < 0.005).ConclusionChildren with focal epilepsy had more deficits in central auditory processing abilities and abnormalities in auditory event-related potentials (p300) than generalized epilepsy.

A novel multi-stage precision reliability assessment method for mechanical system by Bayesian fusion

Precision reliability is crucial for evaluating mechanical system performance. However, limited data makes reliability assessment challenging due to difficult data collection and small sample sizes. Currently, little research has focused on using initial theoretical models as prior information for reliability assessment. This paper proposes a multi-stage precision reliability assessment method for a mechanical system by Bayesian fusion, which can effectively integrate design phase models with usage phase data under limited data conditions to carry out reliability assessment. First, the mechanical system is divided into meta-action units for precision modeling during the design phase. Then, an initial theoretical precision model is developed by incorporating operational error sources. Next, initial theoretical precision model is used to fit the Wiener process-driven model as Bayesian prior information, and reliability assessment is evaluated under different distribution assumptions for both the prior information and experimental data. This approach combines the prior advantages of the theoretical model with the data processing ability of the data-driven model under no prior data and small sample sizes, improving assessment precision and interpretability. Finally, a case study on a machine tool rotary table system validates the effectiveness of this method.

Functional connectivity of the scene processing network at rest does not reliably predict human behaviour on scene processing tasks.

The perception of scenes is associated with processing in a network of scene-selective regions in the human brain. Prior research has identified a posterior-anterior bias within this network. Posterior scene regions exhibit preferential connectivity with early visual and posterior parietal regions, indicating a role in representing egocentric visual features. In contrast, anterior scene regions demonstrate stronger connectivity with frontoparietal control and default mode networks, suggesting a role in mnemonic processing of locations. Despite these findings, evidence linking connectivity in these regions to cognitive scene processing remains limited. In this preregistered study, we obtained cognitive behavioural measures alongside resting-state fMRI data from a large-scale public dataset to investigate interindividual variation in scene processing abilities relative to the functional connectivity of the scene network. Our results revealed substantial individual differences in scene recognition, spatial memory, and navigational abilities. Resting-state functional connectivity reproduced the posterior-anterior bias within the scene network. However, contrary to our preregistered hypothesis, we did not observe any consistent associations between interindividual variation in this connectivity and behavioural performance. These findings highlight the need for further research to clarify the role of these connections in scene processing, potentially through assessments of functional connectivity during scene-relevant tasks or in naturalistic conditions.Significance Statement Our ability to process scenes is crucial for interacting with our environment as it allows us to extract spatial, contextual, and navigational information. However, the mechanisms by which the scene network in the human brain supports these abilities remain poorly understood. To investigate this, we compared behavioural measures of scene processing with resting-state functional connectivity within the scene network. Extensive individual variability was evident in scene recognition, spatial memory, and navigational abilities. However, contrary to our preregistered hypothesis, we did not observe any consistent associations between task performance and the resting-state functional connectivity of the scene network. These results suggest that future research employing task-related or naturalistic designs may be necessary for elucidating the neural basis of scene perception.

Reliable, efficient, and scalable photonic inverse design empowered by physics-inspired deep learning

Abstract On-chip computing metasystems composed of multilayer metamaterials have the potential to become the next-generation computing hardware endowed with light-speed processing ability and low power consumption but are hindered by current design paradigms. To date, neither numerical nor analytical methods can balance efficiency and accuracy of the design process. To address the issue, a physics-inspired deep learning architecture termed electromagnetic neural network (EMNN) is proposed to enable an efficient, reliable, and flexible paradigm of inverse design. EMNN consists of two parts: EMNN Netlet serves as a local electromagnetic field solver; Huygens–Fresnel Stitch is used for concatenating local predictions. It can make direct, rapid, and accurate predictions of full-wave field based on input fields of arbitrary variations and structures of nonfixed size. With the aid of EMNN, we design computing metasystems that can perform handwritten digit recognition and speech command recognition. EMNN increases the design speed by 17,000 times than that of the analytical model and reduces the modeling error by two orders of magnitude compared to the numerical model. By integrating deep learning techniques with fundamental physical principle, EMNN manifests great interpretability and generalization ability beyond conventional networks. Additionally, it innovates a design paradigm that guarantees both high efficiency and high fidelity. Furthermore, the flexible paradigm can be applicable to the unprecedentedly challenging design of large-scale, high-degree-of-freedom, and functionally complex devices embodied by on-chip optical diffractive networks, so as to further promote the development of computing metasystems.

A machine learning model the prediction of athlete engagement based on cohesion, passion and mental toughness

Athlete engagement is influenced by several factors, including cohesion, passion and mental toughness. Machine learning methods are frequently employed to construct predictive models as a result of their high efficiency. In order to comprehend the effects of cohesion, passion and mental toughness on athlete engagement, this study utilizes the relevant methods of machine learning to construct a prediction model, so as to find the intrinsic connection between them. The construction and comparison methods of predictive models by machine learning algorithms are investigated to evaluate the level of predictive models in order to determine the optimal predictive model. The results show that the PSO-SVR model performs best in predicting athlete engagement, with a prediction accuracy of 0.9262, along with low RMSE (0.1227), MSE (0.0146) and MAE (0.0656). The prediction accuracy of the PSO-SVR model exhibits an obvious advantage. This advantage is mainly attributed to its strong generalization ability, nonlinear processing ability, and the ability to optimize and adapt to the feature space. Particularly noteworthy is that the PSO-SVR model reduces the RMSE (7.54%), MSE (17.05%), and MAE (3.53%) significantly, while improves the R2 (1.69%), when compared to advanced algorithms such as SWO. These results indicate that the PSO-SVR model not only improves the accuracy of prediction, but also enhances the reliability of the model, making it a powerful tool for predicting athlete engagement. In summary, this study not only provides a new perspective for understanding athlete engagement, but also provides important practical guidance for improving athlete engagement and overall performance. By adopting the PSO-SVR model, we can more accurately identify and optimise the key factors affecting athlete engagement, thus bringing far-reaching implications for research and practice in sport science and related fields.

Intelligent Surveillance System for Real-Time Detection of Anomalous Activities in Video Streams

The increasing complexity of surveillance structures necessitates advanced techniques for monitoring big volumes of video statistics. This record opinions the utility of convolutional neural networks (CNN) and deep getting to know techniques for detecting suspicious pastime in video streams. The technique involves preliminary video statistics processing to extract key features, observed by training a CNN version to distinguish between normal and abnormal behaviours through recognizing spatial and temporal patterns within the video frames. techniques consisting of transfer gaining knowledge of and statistics augmentation are employed to enhance the model's generality and robustness. The effectiveness of this approach was validated via various checks, including experiments on datasets like u.s.a. Pedestrian and road, where the CNN-primarily based technique established high accuracy and go back quotes in figuring out suspicious activities. The scalability and actual-time processing abilities of the version make it adaptable to various tracking environments. These findings are great for the advancement of the surveillance era, offering a dependable technique for real-time detection of suspicious sports in video streams. The proposed CNN-based approach is promising for bolstering safety in public spaces, transportation structures, and important infrastructure, thus contributing to better safety features.

Baseline liver fibrosis-4 score correlates to the progression of anxiety and cognitive impairment in patients with Parkinson’s disease

BackgroundNon-alcoholic fatty liver disease (NAFLD) or liver fibrosis may share similar pathophysiological features with Parkinson’s disease (PD), yet their correlation was unclear. This study aimed to explore their correlation between PD and liver fibrosis using the fibrosis-4 score (FIB-4) as a surrogate marker.MethodsWe analyzed Parkinson’s Progression Markers Initiative (PPMI) data and enrolled PD patients with comprehensive baseline and 5-year follow-up time-point clinical data. Participants were categorized based on FIB-4 levels to assess the association between FIB-4 scores and various clinical scales, controlling for potential confounders. Differences in the progression of clinical scales over five years were compared using generalized linear mixed models (GLMM).ResultsBaseline FIB-4 levels positively correlated to scores of baseline section III of the Unified-Parkinson Disease Rating Scale (UPDRS III) (r = 0.145, p = 0.017), Epworth Sleepiness Scale (EPSS) (r = 0.140, P = 0.022), Hopkins Verbal Learning Test (HVLT)-delayed recall (r = 0.128, P = 0.036) and HVLT-retention (r = 0.128, p = 0.036). GLMM analysis revealed an independent correlation between FIB-4 subgroup*time and several clinical scales including the State-trait Anxiety Inventory (STAI), Symbol Digit Modalities Test (SDMT), Semantic Fluency Test (SF), HVLT-total recall, and HVLT-delayed recall, with the high FIB-4 subgroup exhibiting a greater decline in these scores compared to the low FIB-4 subgroup (all p&amp;lt;0.05).ConclusionElevated baseline FIB-4 correlated to more severe baseline daytime sleepiness, motor symptoms, and memory function in PD patients, along with a more rapid decline in cognitive functions such as executive function, information processing ability, and memory. Additionally, a high FIB-4 might confer a protective effect against anxiety.

Health Insurance Premium Prediction Based on Multiple Machine Learning Algorithms

The purpose of this paper is to predict the health insurance premium through a variety of machine learning algorithms, and compare and analyze the prediction effect of different algorithms. An open source dataset was selected for the study, and the experiments involved three machine learning models: linear regression, decision trees, and random forests. By testing these models, we obtain their performance in health insurance premium forecasts. The results show that the prediction performance of the random forest regression model is better than other models, and its score reaches 0.8564, which is the best algorithm among the three models. Second, the linear regression model has a score of 0.7584, and although its performance is not as good as that of random forest, it still shows some predictive power. Finally, the prediction effect of decision tree model is relatively poor, and the score is only 0.7097. To sum up, the experiments in this paper prove that the random forest model is undoubtedly the best choice in the prediction of health insurance premiums, which not only has good prediction accuracy, but also shows strong data processing ability. In the context of the growing importance of health insurance premium collection and analysis, the use of advanced machine learning algorithms such as Random Forest for forecasting will, to some extent, help insurance companies better price and assess risk. Therefore, it can be concluded that random forest regression model has the best performance for health insurance premium prediction and is an effective tool to achieve accurate prediction.

LLM Agents for Smart City Management: Enhancing Decision Support Through Multi-Agent AI Systems

This study investigates the implementation of LLM agents in smart city management, leveraging both the inherent language processing abilities of LLMs and the distributed problem solving capabilities of multi-agent systems for the improvement of urban decision making processes. A multi-agent system architecture combines LLMs with existing urban information systems to process complex queries and generate contextually relevant responses for urban planning and management. The research is focused on three main hypotheses testing: (1) LLM agents’ capability for effective routing and processing diverse urban queries, (2) the effectiveness of Retrieval-Augmented Generation (RAG) technology in improving response accuracy when working with local knowledge and regulations, and (3) the impact of integrating LLM agents with existing urban information systems. Our experimental results, based on a comprehensive validation dataset of 150 question–answer pairs, demonstrate significant improvements in decision support capabilities. The multi-agent system achieved pipeline selection accuracy of 94–99% across different models, while the integration of RAG technology improved response accuracy by 17% for strategic development queries and 55% for service accessibility questions. The combined use of document databases and service APIs resulted in the highest performance metrics (G-Eval scores of 0.68–0.74) compared to standalone LLM responses (0.30–0.38). Using St. Petersburg’s Digital Urban Platform as a testbed, we demonstrate the practical applicability of this approach to create integrated city management systems with support complex urban decision making processes. This research contributes to the growing field of AI-enhanced urban management by providing empirical evidence of LLM agents’ effectiveness in processing heterogeneous urban data and supporting strategic planning decisions. Our findings suggest that LLM-based multi-agent systems can significantly enhance the efficiency and accuracy of urban decision making while maintaining high relevance in responses.

Association of serum neurofilament light chain with cognitive impairment: findings from the National Health and Nutrition Examination Survey.

Serum Neurofilament Light chain (NfL) is a promising biomarker of neuronal damage, used to assess the extent of neuronal injury and neurodegeneration, and it is widely applied in the diagnosis of neurodegenerative disease and monitoring disease progression. This article aims to determine whether serum NfL associated with cognitive level. Using NHANES data, we conducted an analysis of cognitive test results for 450 adults aged 60 years and older and examined their correlation with serum NfL levels. When exploring the association between cognitive test scores and serum NfL levels, regression models and restricted cubic spline (RCS) regression models were employed to adjust for potential confounding factors. The least absolute shrinkage and selection operator (LASSO) regression was applied for identifying key cognitive impairment factors, which was then included in the establishment of a risk prediction nomogram model, with the receiver operating characteristic (ROC) curve being built to evaluate its discriminatory power for cognitive impairment. It was found that there is a strong positive correlation between serum NfL levels and both low total cognitive function (total-CF) OR: 1.028 (95%CI = 1.015-1.041 p < 0.001) and low Digit Symbol Substitution Test (DSST) OR: 1.026 (95%CI = 1.003-1.050, p = 0.027). Furthermore, using the RCS model, we observed a linear trend in the relationship between NfL and low total-CF. The nomogram model based on NfL identified by LASSO regression displayed a considerable predicative value for low total-CF, with an area under the curve [AUC = 85.6% (81.6-89.3%)]. There is a strong correlation between serum NfL levels and cognitive function, especially DSST, which reflects attention and information processing abilities, as well as overall cognitive function, but not memory and language fluency. Thus, NfL may serve as a serum biomarker for dementia monitoring.

Voice processing ability predicts second-language phoneme learning in early bilingual adults

Voice processing ability predicts second-language phoneme learning in early bilingual adults

Application of simulated annealing algorithm in multi-objective cooperative scheduling of load and storage of source network for load side of new power system

To improve the adaptability of grid load collaborative scheduling, a multi-objective collaborative scheduling method based on a simulated annealing algorithm for the load storage of grid loads on the load side of a new power system is proposed. Local bus transmission technology is adopted to collect the dynamic parameters of energy network load energy storage on the load side of the new power system. The collected load dynamic parameters are fused with energy distribution state parameters to extract the state characteristics of energy network load storage. The simulated annealing algorithm is adopted to realize the load characteristics fusion and adaptive scheduling processing of energy network on the load side of the power system, and the spectral characteristics of the load dynamic parameters are extracted. The dynamic scheduling method of simulated annealing is used to realize the multi-objective optimization of dynamic load of energy network. Based on the co-optimization results of simulated annealing, the optimization application of the simulated annealing algorithm in the multi-objective co-scheduling of loads and energy storage in a new power system is realized. The experimental results show that after 400 iterations, the control convergence accuracy of the proposed method reaches 0.980, which is significantly better than that of the comparison method, and performs well in terms of scheduling efficiency improvement, load scheduling stability, scheduling time and energy waste ratio, proving that the method has good multi-objective integration and strong optimization ability in the scheduling process, and improves the load balanced scheduling and adaptive control ability of the power system.

Inference of Behavioral Decision-Making Using AI and Statistical Data Analysis in the Big Data Environment

Big data are of essence to enhance the accuracy of making decisions and in improving the ability to forecast. However, working with large and complicated datasets, the traditional methods of analyzing data tend to be grossly inadequate. It is at this point, amongst others, that artificial intelligence has now presented a feasible solution to such limitations, especially with the model called machine learning. Based on that, this research will look into the aspect of integration between artificial intelligence and statistical methods of analysis in inferring behavioral decisions from big data. This work considers a number of datasets related to marketing, health care, and finance, comparing the efficiency of the application of a range of artificial intelligence (AI) models, especially random forests, Long Short Term Memory (LSTM), and convolutional neural network (CNN) algorithms, against the classical statistical ones. Standard performance evaluation indicators apply: accuracy, precision, recall rate, F1 score are applied in the model. Results have shown that, though model interpretability and overfitting are challenges, the predictive accuracy of artificial intelligence models is somewhat better in comparison with conventional statistical methods. The present study underlines the transformative potential of AI in changing decision-making across many industries but also highlights key areas for further improvement on behalf of real-time processing abilities and ethical deployment consideration.

Parameter Design in Production by Means of Robust Fuzzed PMOO in Case of Desirable Target

A proper parameter design in the production process is critical to guarantee the quality of products and their improvement. The rationality of the previous traditional approaches for robust assessment including the Taguchi method and dual response method is questionable. In this article, the combination of probabilistic multi-objective optimization (PMOO) with membership approach in fuzzy theory is developed to conduct parameter design of production in case of desirable target with robustness deeply, which is furthermore applied to two examples of both parametric design of gas metal arc (GMA) welding process and printing machine's ability. In the new approach, the mean value of "complement" of membership value of a set of test data belonging to its desired target of an objective response is taken as one sub-objective response, which is an unbeneficial type of index in the assessment to contribute the first part of the partial preferable probability of the objective. In contrast, the dispersion of a set of test data in terms of membership with respect to the desired target value is taken as the other sub-objective response to contribute the second part of the partial preferable probability of the objective simultaneously, which is an unbeneficial index. Thus, the fuzzed PMOO approach is regulated comprehensively. Besides, the consequences of application examples reflect the reasonability of the approach as an auxiliary measure for PMOO consistently to perform optimal robust design.

Influence of auditory-based cognitive training on auditory resolution, executive function, and working memory skills in individuals with mild cognitive impairment – a pilot randomized controlled study

Background Age-related central auditory processing disorder and mild cognitive impairment (MCI) can be concomitant in older adults, making it difficult to communicate, especially in challenging listening conditions. This preliminary study investigated the efficacy of auditory-based cognitive training on the auditory processing abilities and cognitive functions of older adults with MCI. Methods In this randomized controlled trial twenty-two older adults with mild cognitive impairment (MCI) were randomly assigned to either an experimental (n=11) or a control group (n=11). The experimental group received 15 cognitive training sessions through tasks involving the auditory domain. The outcome measures of this study included auditory resolution (Temporal gap detection, frequency discrimination, and modulation detection) and cognitive measures (Trail making tests and digit recall), which were administered at three-time points (before training, post-training, and follow-up). The linear mixed model computed the effects of training on the outcome measures. Results A significant improvement was observed in the modulation detection threshold between baseline and follow-up and between post-training and follow-up sessions. However, GDT and FD thresholds did not reveal any statistically significant difference. In the trail making test, Part B showed consistent significance across the time points, whereas Part A and the delayed recall task showed no significant difference. Conclusion Auditory-based cognitive training may improve auditory processing and executive function in older adults with mild cognitive impairment (MCI). Trial registration CTRI/2019/01/017073, registered on 14.01.2019

Simultaneous Removal of Antibiotic-resistant Bacteria, Genes, and Inhibition of Horizontal Transfer using Vis-rGO-CNCF-enhanced Peroxymonosulfate Activation Process

As emerging contaminants, antibiotic-resistant bacteria （ARBs） and antibiotic-resistant genes （ARGs） pose a serious threat to human health and ecological security. Here, a reduced graphene oxide and g-C3N4 co-doped copper ferrite （rGO-CNCF） were synthesized. The composite material was characterized using XRD, FTIR, XPS, SEM-EDS, TEM, and DRS analysis methods, and a visible-light-assisted rGO-CNCF-activated PMS system was constructed for the removal of ARB and ARGs in water. The results showed that the complete inactivation of 8.01 log SA-ARB could be achieved within 30 min when the catalyst dosage was 0.2 g·L-1, The PMS dosage was 0.3 g·L-1, and the initial pH value of the solution was 7.0. The Vis-rGO-CNCF/PMS system was able to effectively reduce the horizontal transfer of SA-ARGs, and this system had a good destructive ability for intracellular and extracellular SA-ARGs. The destruction ability of the advanced oxidation process for the two pollutants together, SMT and SA-ARB, was maintained at a high level. This system could destroy the cell membrane structure of resistant bacteria, causing cell fragmentation, and quenching experiments showed that singlet oxygen （1O2） played a major role in the system. This study can provide a promising method for controlling ARB and ARG pollution in water and controlling the horizontal transfer of ARGs.

Effect of femtosecond laser processing parameters on microstructures and wetting properties of copper surface

In this work, the ablation characteristic, surface structure, and wetting property of the femtosecond laser-processed copper (Cu) surface are systematically studied. With the increase of laser pulse number, the area of the ablation crater increases and then tends to be stable, the period (0.62λ ∼ λ) of the generated ripple structures decreases, and the area of the periodic ripple structures in the Cu ablation crater first increases and then decreases. With the increase in laser fluence, the area of the ablation crater slightly increases, the period of the generated ripple structures varies oscillatively, and the area of the periodic ripple structures in the Cu ablation craters increases. Different columnar structures on the Cu surface are obtained through femtosecond laser cross-scanning processing. The height of columnar structures basically increases with the increase in laser power, laser scanning times, and laser scanning spacing. The wetting properties of the Cu columnar structure surfaces are also investigated. It reveals that the droplet contact angle obviously decreases with the increase in laser power and laser scanning times, and increases with the increase in laser scanning spacing. The variation of droplet contact angle on the laser-processed Cu surface is attributed to the different columnar structures and their different heights. It also reveals that the Cu surface changes from original hydrophobic to superhydrophilic with a contact angle of 8.9°. This work indicates the ability of femtosecond laser processing in regulating micro/nanostructure and wetting property of the Cu surface, which can be applicable to the surface treatment and performance control of other metallic materials.

Microscopic structural origin of slow dynamics in glass-forming liquids.

Supercooled liquids display sluggish dynamics, often attributed to their structural characteristics, yet the underlying mechanism remains elusive. Here we conduct numerical investigations into the structure-dynamics relationship in model glass-forming liquids, with a specific focus on an elementary particle rearrangement mode known as the 'T1 process'. We discover that the ability of a T1 process to preserve glassy structural order before and after is pivotal towards determining a liquid's fragility-whether it exhibits super-Arrhenius-like or Arrhenius-like behaviour. If a T1 process disrupts local structural order, it must occur independently without cooperativity, resulting in Arrhenius-like behaviour. By contrast, if it can maintain order, it sequentially propagates from disordered peripheries to the middle of high-structural-order regions, leading to cooperativity and super-Arrhenius-like behaviour. Our study establishes a microscopic link between liquid-structure ordering, dynamic cooperativity and super-Arrhenius-like dynamics, extending the understanding of the structure-dynamics relationships in supercooled liquids.