Processing Capabilities Research Articles

Evaluation of stereoscopic visual fatigue in virtual reality with exploration of brain dynamics

Virtual Reality (VR) provides a highly immersive experience with depth perception and three-dimensional representation, offering tremendous potential for applications in education, entertainment, healthcare, and other fields. However, stereoscopic visual fatigue (SVF) symptoms pose a limitation to its development. To investigate SVF induced by depth perception in VR environments and analyze its impact on the depth perception process, this study combined an improved Go/NoGo paradigm with repeated visual perception tasks to maintain participants’ task attention and induce SVF. The effectiveness of SVF induction and attention control was verified through subjective SVF ratings and behavioral data. Effective connectivity analysis identified key brain regions, critical electroencephalography (EEG) oscillations, and timing associated with the depth perception process following SVF. The results indicate that crucial brain regions are primarily located in the dorsal visual pathway, consistent with existing research on depth perception and attention control. The results also show that SVF is associated with a decline in depth perception awareness processing capabilities and is modulated by attention allocation and self-awareness. One-way repeated measures ANOVA results suggest that subdividing the entire α and β frequency bands into sub-bands can more accurately characterize SVF. The α2/θ and θ/(α2+β1) metrics provide a more precise representation of SVF in VR displays.

Research on BP neural network model-based data mining technique in KOL identification

Abstract. Accompanied by the development of artificial intelligence industry, to play the data value of web crawler technology has been the focus of research in the field of computer network and data science, and the data mining technology based on artificial neural network intelligent algorithm is widely used. In view of this, this paper takes the BP neural network data mining technology, which has excellent nonlinear mapping capability, parallel processing capability and fault tolerance and is widely used, as the basis, and integrates the methods and ideas of data mining into the mining of data laws in the field of KOL identification of key opinion leaders, with a view to finding valuable intrinsic laws and relationships between the mining of web crawler technology and the identification of KOL features. The research content of this paper mainly includes two aspects of research work, the design of high-performance data mining technology and the actual work in the field of KOL recognition. On the one hand, this paper comprehensively describes the basic theory and methods of data mining, and focuses on the in-depth analysis and elaboration of BP neural network-based data mining technology on the basis of understanding and analyzing a variety of data mining technologies. On the other hand, this paper aims to solve the problem of bias in the prediction of traditional KOL model and design the experimental method of KOL recognition by BP neural network algorithm.

Electrophysiological correlates of divergent projections in the avian superior olivary nucleus.

The physiological diversity of inhibitory neurons provides ample opportunity to influence a wide range of computational roles through their varied activity patterns, especially via feedback loops. In the avian auditory brain stem, inhibition originates primarily from the superior olivary nucleus (SON), and so it is critical to understand the intrinsic physiological properties and processing capabilities of these neurons. Neurons in the SON receive ascending input via the cochlear nuclei: directly from the intensity-coding cochlear nucleus angularis (NA) and indirectly via the interaural timing nucleus laminaris (NL), which itself receives input from cochlear nucleus magnocellularis (NM). Two distinct populations of SON neurons provide inhibitory feedback either to ipsilateral NA, NL, and the timing cochlear nucleus NM or to the contralateral SON. To determine whether these populations correspond to distinct response types, we investigated their electrophysiology in brain stem slices, using patch-clamp electrophysiology. We identified three phenotypes: single-spiking, chattering tonic, and regular tonic neurons. The two tonic phenotypes displayed distinct firing patterns and different membrane properties. Fluctuating "noisy" currents used to probe the capability of SON neurons to encode temporal features showed that each phenotype differed in sensitivity to temporally modulated input. By using cell fills and anatomical reconstructions, we could correlate the firing phenotypes with their axonal projection patterns. We found that SON axons exited via three fiber tracts, with each tract composed of specific phenotypes. These results provide a basis for understanding the role of specific inhibitory cell types in auditory function and elucidate the organization of the SON outputs.NEW & NOTEWORTHY Inhibitory inputs for the avian brain stem originate primarily from the superior olivary nucleus (SON). We describe three intrinsic phenotypes of SON neurons and show how they differ in their temporal processing and projection patterns. We propose that the two types of tonic firing neurons (including one novel type) and the single-spiking neurons in SON comprise separate feedback circuits that may differentially influence the auditory information flowing via the cochlear nuclei and nucleus laminaris.

Reconstruction of physical field characteristics of underwater vehicle wake based on data-driven approach

The characteristics of underwater vehicle wakes are intricately linked to multiple parameters, such as speed, depth, and environmental factors. Obtaining wake characteristic information for various operating conditions solely through numerical simulation methods can result in computational space explosion, rendering the time and computational resource costs prohibitive. This paper harnesses the robust image processing capabilities of convolutional neural networks and incorporates strategies such as attention mechanisms, dilated convolution techniques, and multi-scale feature fusion to design and construct a neural network architecture. Through a data-driven approach, it reconstructs multiple physical wake field characteristics resulting from underwater vehicle, including underwater velocity fields, surface divergence fields, surface kelvin wake, and surface thermal wakes. The study establishes a “black box” mapping between relevant parameters and the physical fields of wakes. The results demonstrate that the constructed network model achieves high accuracy in capturing both the macroscopic structures and pixel-level details of various physical fields. In comparison with the truth-values, the average normalized root mean square errors for the underwater velocity field, surface divergence field, surface kelvin wake, and surface thermal wakes are 6.10%, 3.40%, 8.21%, and 10.96%, respectively. The average structural similarity index values are 0.955, 0.966, 0.923, and 0.904, respectively. The predicted results closely match the truth-values for each physical field characteristic, effectively addressing the challenges of nonlinearity and multi-scale feature extraction in predicting complex flow fields, offering support for the rapid forecasting of multi-dimensional and multi-physical field characteristics of underwater vehicle wakes.

LDH-ViT: Fine-grained visual classification through local concealment and feature selection

Many methods based on Vision Transformer (ViT) have been proposed and applied in fine-grained visual classification (FGVC). It is of critical importance to improve the recognition accuracy and generalization ability of computer vision systems. However, the ViT model faces the challenges of increasing the amount of noise calculation, difficulty dealing with small targets with complex, diverse, and subtle characteristics in the images of the dataset, insufficient extraction of fine-grained information, and the impact of a few information patches on accuracy. To solve these problems an LDH-ViT model, which includes three new components. The LPC module randomly overshadows some of the small blocks separated from the ViT by a preset ratio. Subsequently, the local features of these new small blocks are calculated through training, aiming to reduce the influence of noise on the calculation process and further strengthen the difference between the images. The DFS module innovatively introduces a similarity selection layer, which aims to accurately identify and screen tokens with richer target information, thereby optimizing the information processing capability of the model. At the same time, the HMO module successfully constructs a new loss function based on the above improvements through clever fusion and optimization strategy. Comprehensive experiments on four widely used data sets show that LDH-ViT can achieve competitive performance.

NoSQL Technologies in Big Data Ecosystems: a Comprehensive Review of Architectural Paradigms and Performance Metrics

The exponential growth of data in volume, velocity, and variety has challenged traditional relational database management systems, leading to the emergence and widespread adoption of NoSQL and other innovative database technologies. This article provides a comprehensive review of NoSQL databases and their role in modern Big Data infrastructure stacks. Through a systematic analysis of current literature and industry case studies, we explore the architectural paradigms, performance metrics, and use cases of various NoSQL database types, including document-based, key-value, column-family, and graph databases. Our findings indicate that NoSQL solutions offer significant advantages in scalability, flexibility, and real-time processing capabilities, particularly for unstructured and semi-structured data. However, challenges persist in areas such as data consistency, security, and interoperability with existing systems. We also examine emerging trends, including NewSQL, time-series databases, and the integration of artificial intelligence in database management. This article contributes to the understanding of how organizations can leverage NoSQL technologies to optimize their Big Data infrastructure, highlighting both the opportunities and considerations for implementation. Our conclusions underscore the transformative impact of NoSQL on data management practices and provide directions for future research in this rapidly evolving field.

Improved bidirectional attention flow (BIDAF) model for Arabic machine reading comprehension

Abstract Machine reading comprehension (MRC) refers to the process of instructing machines to comprehend and respond to inquiries based on a provided text. There are two primary methodologies for achieving this: extracting answers directly from the text or predicting them. Extracting answers involves anticipating the specific segment of text containing the answer, pinpointed by its starting and ending indices within the paragraph. Despite the increasing interest in MRC, exploration within the framework of the Arabic language faces limitations due to various challenges. A significant impediment arises from the inadequacy of resources available for Arabic textual content, which impedes the development of effective models. Furthermore, the inherent intricacies of Arabic, manifesting in its diverse linguistic forms including classical, modern standard, and colloquial, present distinctive hurdles for tasks involving language comprehension. This paper proposes an enhanced version of the bidirectional attention flow (BIDAF) model for Arabic MRC, constructed upon the Arabic Span-Extraction-based Reading Comprehension Benchmark (ASER). ASER comprises 10,000 sets of questions, answers, and passages, partitioned into a training set constituting 90% of the data and a testing set making up the remaining 10%. By introducing a new input feature based on parts-of-speech (POS) word embeddings and replacing Bidirectional Long Short-Term Memory (bi-LSTM) with bidirectional gated recurrent unit, significant improvements were observed. Eight different POS word embeddings were generated using both Continuous Bag of Words (CBOW) and Skip-gram methods, with varying dimensionalities. Evaluation metrics, including exact match (EM) and F1-measure, were utilized to assess model performance, with emphasis on the latter for its accuracy. The proposed enhanced BIDAF model achieved a remarkable accuracy of 75.22% on the ASER dataset, demonstrating its efficacy in Arabic MRC tasks. Additionally, rigorous statistical evaluation using a two-tailed paired samples t-test further validated the findings, highlighting the significance of the proposed enhancements in advancing Arabic language processing capabilities.

Research on Gating Fusion Algorithm for Power Grid Survey Data Based on Enhanced Mamba Spatial Neighborhood Relationship.

In power grid surveying, it is often necessary to fuse panchromatic and multispectral imagery for the design of power lines. Despite the abundance of deep learning networks for fusing these images, the results often suffer from spectral information loss or structural blurring. This study introduces a fusion model specifically tailored for power grid surveying that significantly enhances the representation of spatial-spectral features in remote sensing images. The model comprises three main modules: a TransforRS-Mamba module that integrates the sequence processing capabilities of the Mamba model with the attention mechanism of the Transformer to effectively merge spatial and spectral features; an improved spatial proximity-aware attention mechanism (SPPAM) that utilizes a spatial constraint matrix to greatly enhance the recognition of complex object relationships; and an optimized spatial proximity-constrained gated fusion module (SPCGF) that integrates spatial proximity constraints with residual connections to boost the recognition accuracy of key object features. To validate the effectiveness of the proposed method, extensive comparative and ablation experiments were conducted on GF-2 satellite images and the QuickBird (QB) dataset. Both qualitative and quantitative analyses indicate that our method outperforms 11 existing methods in terms of fusion effectiveness, particularly in reducing spectral distortion and spatial detail loss. However, the model's generalization performance across different data sources and environmental conditions has yet to be evaluated. Future research will explore the integration of various satellite datasets and assess the model's performance in diverse environmental contexts.

Ai-Driven Big Data Transformation And Personally Identifiable Information Security In Financial Data: A Systematic Review

This systematic review explores the impact of adopting artificial intelligence (AI) to analyze and transform big data in financial and economic contexts, with a specific focus on the privacy and security of personally identifiable information (PII). By examining 37 articles spanning the latest advancements in AI-driven big data technologies, the review identifies both opportunities and challenges in safeguarding PII during financial data transformation. Key findings reveal that while AI enhances data processing capabilities—enabling faster insights and predictive accuracy in economic trends—PII faces increased risks due to sophisticated data aggregation and correlation techniques. The review categorizes major AI methodologies used, including machine learning algorithms, natural language processing, and predictive analytics, highlighting how each can affect data privacy. The findings suggest that, to maintain trust, organizations must adopt AI responsibly, integrating privacy-by-design principles and adhering to data governance standards. This review contributes to a clearer understanding of the interplay between AI and PII protection, offering practical insights for stakeholders in the financial sector aiming to harness AI while prioritizing ethical data handling.

Integrated convolutional kernel based on two-dimensional photonic crystals.

Optical neural networks (ONNs) exhibit significant potential for accelerating artificial intelligence task processing due to their low latency, high bandwidth, and parallel processing capabilities. Photonic crystals (PhCs) are extensively utilized in integrated optoelectronics because of their unique photonic bandgap properties and precise control of light waves. In this study, we propose an optical reconfigurable convolutional kernel based on PhCs. This kernel can perform convolutional operations on weights by constructing a PhC weight bank. The convolutional kernel demonstrates exceptional performance within the developed optical convolutional neural network framework, successfully realizing various image edge processing tasks. It achieves blind recognition accuracies of 97.81% for the MNIST dataset and 80.31% for the Fashion-MNIST dataset. This study not only demonstrates the feasibility of constructing optical neural networks based on PhCs but to our knowledge, also offers new avenues for the future development of optical computing.

Efficient Metal Corrosion Area Detection Model Combining Convolution and Transformer

In the context of rapid industrialization, efficiently detecting metal corrosion areas has become a critical task in preventing material damage. Unlike conventional semantic segmentation targets, metal corrosion characteristics vary significantly in color, texture, and size. Traditional image segmentation methods need improvement in scenarios involving occlusions, shadows, and defects. This paper proposes a convolution and sequence encoding combined network, MCD-Net, for metal corrosion area segmentation. First, a visual Transformer sequence encoder is introduced into the convolutional encoder–decoder network to enhance global information processing capabilities and establish long-range feature dependencies. A feature fusion method based on an attention module is proposed to enhance the model’s ability to recognize corrosion boundaries, thereby enhancing segmentation accuracy and model robustness. Finally, in the model’s decoding stage, a score-based multi-scale feature enhancement method is employed to emphasize significant features in the corrosion areas. Experimental results indicate that this method attained an F1 score of 84.53% on a public corrosion dataset, demonstrating the model’s deeper understanding and reasoning capabilities for shadow and defect features, as well as excellent noise resistance performance.

Edge Computing vs. Cloud Computing: A Comparative Analysis for Real-Time AI Applications

AbstractReal-time AI has been evidenced to be supported by two models, namely edge computing and cloud computing. This research seeks to compare the two methods: Latency, security features, and data processing capabilities will be among the most critical aspects of comparison. Real-time AI applications are envisioned to be robust through 2020, most of which will be used in applications such as autonomous automobile systems and IoT devices that demand low latency for data processing. As data processing is outsourced in clouds, it is elastic and centralized, but this deal faces latency problems when the distance between the source of data and the processing center is large. On the other hand, edge computing refers to conducting computation closer to the data source, which could reduce latency and enhance everyday real-time performance.This research assesses the above models based on the literature review, technical papers, and case studies from industries that depend more on real-time AI. According to the research, edge computing is typically more effective for latency-sensitive workloads, while cloud computing outperforms throughput-intensive applications. Security concerns, however, present themselves as having dual effects; while advancing the privacy of handling data through edge computing, it elevates new risks. As such, this study concludes that no clear winner depends on the necessary application, therefore recommending a symmetric mode for the requirements, where both latency time and computational needs are required. Further studies should be conducted to establish the coordinated implementation approach of these models.

Multimodal In-Sensor Computing System Using Integrated Silicon Photonic Convolutional Processor.

Photonic integrated circuits offer miniaturized solutions for multimodal spectroscopic sensory systems by leveraging the simultaneous interaction of light with temperature, chemicals, and biomolecules, among others. The multimodal spectroscopic sensory data is complex and has huge data volume with high redundancy, thus requiring high communication bandwidth associated with high communication power consumption to transfer the sensory data. To circumvent this high communication cost, the photonic sensor and processor are brought into intimacy and propose a photonic multimodal in-sensor computing system using an integrated silicon photonic convolutional processor. A microring resonator crossbar array is used as the photonic processor to implement convolutional operation with 5-bit accuracy, validated through image edge detection tasks. Further integrating the processor with a photonic spectroscopic sensor, the in situ processing of multimodal spectroscopic sensory data is demonstrated, achieving the classification of protein species of different types and concentrations at various temperatures. A classification accuracy of 97.58% across 45 different classes is achieved. The multimodal in-sensor computing system demonstrates the feasibility of integrating photonic processors and photonic sensors to enhance the data processing capability of photonic devices at the edge.

Salivary Extracellular Vesicles Separation: Analysis of Ultracentrifugation-Based Protocols.

The clinical potential of extracellular vesicles (EVs) is widely acknowledged, yet the standardization and reproducibility of its separation remain challenging. This study compares three protocols: ultracentrifugation (UC), UC with purification step (UC + PS), and a combined protocol using polymer-based precipitation and UC (PBP + UC). Salivary samples were collected from healthy donors. EVs were separated (UC, UC + PS, and PBP + UC) and characterized using transmission electron microscopy, nanoparticle tracking analysis, EV purity, RNA concentration, and Western blotting. miRNA expression was evaluated by quantitative RT-PCR. Statistical analyses comparing groups were performed using ANOVA. All methods successfully separated CD9+ and CD63+ EVs from saliva. The UC + PS and PBP + UC protocols yielded the highest concentrations of EVs, enriched in < 200 nm vesicles. EV purity and RNA recovery were comparable among all methods. Expression of miR-16, miR-27a, and miR-99a was successfully detected using all methods. The UC + PS and PBP + UC protocols demonstrate comparable efficiency in separating salivary EVs. However, the combined PBP + UC protocol, with its simplified processing capability, offers a significant advantage, particularly in the initial phase of EV separation. This finding suggests its potential application in clinical settings where time-sensitive simple processing is critical. Further validation is needed to confirm its effectiveness for transcriptomic and proteomic analyses.

Export Diversification and Economic Growth of ECOWAS Member States

Research Originality: Using the data of export diversification index which is excluded in previous studies and most the pervious only look at the relationship between ED and GDP, excluding per capita income as basics for development. Again, previous studies results are contradictory on impact of export diversification performance. Additionally, this paper provides robust empirical evidence of a positive effect of export diversification on per capita income growth.Research Objectives: This paper has the objective to examines how export diversification (ED) increase economic growth and per capita income.Research Methods: The paper achieve its objectives using, panel least technique and co-integration test are used on time series data of 1984-2022 for ECOWAS states.Empirical Results: The paper shows that the export diversification index has a significant influence on GDP growth but inverse, however, manufacturing value-added shows weak but a positive influence on the growth per capita income. Again, the paper reveals that high skewness of ECOWAS region to primary products export which could be responsible for the low growth per capita income. The finding of this is not the volume of exported products that matters, but how dynamic is exported products.Implications: The paper therefore recommends ECOWAS countries need to develop processing capability for export that comes from endogenous sufficiency.JEL Classification: C23, F10, O47, O55

Improving stability and performance of spiking neural networks through enhancing temporal consistency

Spiking neural networks have gained significant attention due to their brain-like information processing capabilities. The use of surrogate gradients has made it possible to train spiking neural networks with backpropagation, leading to impressive performance in various tasks. However, spiking neural networks trained with backpropagation typically approximate actual labels using the average output, often necessitating a larger simulation timestep to enhance the network’s performance. This delay constraint poses a challenge to the further advancement of spiking neural networks. Current training algorithms tend to overlook the differences in output distribution at various timesteps. Particularly for neuromorphic datasets, inputs at different timesteps can cause inconsistencies in output distribution, leading to a significant deviation from the optimal direction when combining optimization directions from different moments. To tackle this issue, we have designed a method to enhance the temporal consistency of outputs at different timesteps. We have conducted experiments on static datasets such as CIFAR10, CIFAR100, and ImageNet. The results demonstrate that our algorithm can achieve comparable performance to other optimal SNN algorithms. Notably, our algorithm has achieved state-of-the-art performance on neuromorphic datasets DVS-CIFAR10 and N-Caltech101, and can achieve superior performance in the test phase with timestep T = 1.

Research on the Impact of Big Data and Enterprise Digitalization on the E-commerce Industry and Coping Strategies

Abstract: Big data, enterprise digitalization, and e-commerce all have one thing in common: they were born from the Internet. Since the onset of the 21st century, the global Internet industry has experienced rapid growth, with e-commerce emerging as a key player. Participants in e-commerce engage with both the global online marketplace and the physical market, utilizing modern communication networks and information technology to conduct business activities through e-commerce platforms or service platforms. E-commerce embodies characteristics such as market globalization, rapid transactions, virtualization, and cost-effectiveness, making it highly favored by enterprises. Presently, e-commerce relies heavily on robust databases, as well as the data collection, processing, and analysis capabilities of big data. This reliance compels traditional enterprises to undergo digital transformation. This study delves into the impact of big data and enterprise digitalization on the e-commerce industry, exploring the mechanisms underlying their influence on cross-border e-commerce. By examining these dynamics, the study aims to provide insights into the evolving landscape of e-commerce and the transformative potential of big data and digitalization in shaping its future trajectory.

Emergence of information aggregation to rational expectations equilibria in markets populated by biased heuristic traders

Information aggregation is a key economic function of markets. We report results of a computational experiment with markets populated by simple algorithmic traders who follow two heuristics usually thought of as leading to biased information processing in behavioral economics literature (anchor-and-adjust, and representativeness). Outcomes of these markets either tend to cluster around (or fail to do so) rational expectations equilibria under specific conditions, consistent with markets populated by profit-motivated human traders. Algorithmic trader convergence is slower and noisier than that of human traders. Our results illustrate the emergence of rational expectations equilibria through complex interactions among actions of biased heuristic traders with limited information processing capabilities.

AI for Climate Action: Leveraging Artificial Intelligence to Address Climate Change Challenges

This comprehensive article explores the transformative role of Artificial Intelligence (AI) in addressing the global climate crisis, focusing on its applications in climate modeling, renewable energy optimization, and disaster response. Through an extensive literature review, case studies, and expert interviews, we examine how AI technologies are revolutionizing climate prediction accuracy, enhancing data processing capabilities, and enabling sophisticated climate scenario simulations. The article delves into AI's contributions to renewable energy forecasting, smart grid management, and energy storage optimization, highlighting its potential to accelerate the transition to sustainable energy systems. We also investigate AI-driven approaches to disaster response and resilience, including early warning systems, resource allocation during climate-related disasters, and post-disaster recovery planning. While acknowledging the significant advancements AI brings to climate action, this study also addresses the challenges and limitations, such as data quality issues, ethical considerations, and technical barriers. Looking ahead, we discuss emerging AI technologies, their integration with other solutions, and the policy implications for effective climate change mitigation and adaptation. This research underscores the critical role of AI in combating climate change, while emphasizing the need for responsible development and deployment of these technologies to ensure a sustainable and resilient future.

Blockchain Factors in the Design of Smart-Media for E-Healthcare Management.

According to the current situation of deep aging globally, how to provide low-cost and high-quality medical services has become a problem that the whole society needs to consider. To address these challenges, we propose an e-healthcare management system leveraging the integration of the Internet of Things (IoT) and blockchain technologies. Our system aims to provide comprehensive, reliable, and secure one-stop services for patients. Specifically, we introduce a blockchain-based searchable encryption scheme for decentralized storage and real-time updates of electronic health records (EHRs). This approach ensures secure and efficient data traceability across medical equipment, drug supply chains, patient health monitoring, and medical big data management. By improving information processing capabilities, our system aspires to advance the digital transformation of e-healthcare services.