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Autonomous vehicles with augmented reality internet of things and edge intelligence system for industry 5.0 based on 6G
In an era of rapidly evolving technology, traditional cloud computing struggles to meet the demands of resource-intensive smart devices. This necessitates a shift towards Edge Computing (EC), which brings computation and data storage closer to the network’s edge, enhancing efficiency and reducing latency. This is particularly crucial for the Internet of Things (IoT), where supporting mobility, location awareness, and real-time processing are paramount. However, the scalability of EC applications is significantly influenced by network parameters and the capabilities of the computing system. This paper proposes a novel system architecture for Industry 5.0 that leverages the synergy between 6G networks, autonomous vehicles, Augmented Reality (AR), IoT, and edge intelligence to revolutionize transportation systems. Our approach integrates AR for enhanced user interfaces, utilizes IoT for data acquisition and control, and employs edge computing for real-time decision-making. Our experimental results demonstrate a strong correlation between processing speed and network bandwidth. While increasing either parameter individually enhances overall system performance. The two-tier architecture, combined with the Entity Objects (EO) model, demonstrates superior scalability compared to traditional approaches. By distributing processing tasks and leveraging the resources of other edge servers, the system can handle increasing numbers of AVs and data loads without compromising performance.
Predicting the strength of microsilica lime stabilized sulfate sand using hybrid machine learning models optimized with sparrow search algorithm
Retraction: Correlations between parameters of glycaemic variability and foetal growth, neonatal hypoglycaemia and hyperbilirubinemia in women with gestational diabetes
An 11-qubit atom processor in silicon
Abstract Phosphorus atoms in silicon represent a promising platform for quantum computing, as their nuclear spins exhibit coherence times over seconds 1,2 with high-fidelity readout and single-qubit control 3 . By placing several phosphorus atoms within a radius of a few nanometres, they couple by means of the hyperfine interaction to a single, shared electron. Such a nuclear spin register enables high-fidelity multi-qubit control 4 and the execution of small-scale quantum algorithms 5 . An important requirement for scaling up is the ability to extend high-fidelity entanglement non-locally across several spin registers. Here we address this challenge with an 11-qubit atom processor composed of two multi-nuclear spin registers that are linked by means of electron exchange interaction. Through the advancement of calibration and control protocols, we achieve single-qubit and multi-qubit gates with all fidelities ranging from 99.10% to 99.99%. By entangling all combinations of local and non-local nuclear-spin pairs, we map out the performance of the processor and achieve state-of-the-art Bell-state fidelities of up to 99.5%. We then generate Greenberger–Horne–Zeilinger (GHZ) states with an increasing number of qubits and show entanglement of up to eight nuclear spins. By establishing high-fidelity operation across interconnected nuclear spin registers, we realize a key milestone towards fault-tolerant quantum computation with atom processors.
Investigation of the effects of aerators in reducing cavitation damage on spillways using two-phase numerical modeling
Retraction: Dye diffusion during laparoscopic tubal patency tests may suggest a lymphatic contribution to dissemination in endometriosis: A prospective, observational study
Titan’s strong tidal dissipation precludes a subsurface ocean
Research on equivalent simplified modeling and simulation of digital hydraulic cylinder
Editorial Note: Marked reduction in fertility among African women with urogenital infections: A prospective cohort study
TomatoRipen-MMT: transformer-based RGB and NIR spectral fusion for tomato maturity grading
Retraction: Expression of microRNAs and their target genes in melanomas originating from gynecologic sites
Evaluation of sulphate contamination in surface and groundwater with health hazards in the largest opencast lignite mine region of Asia
Retraction: Optimization of house price evaluation model based on multi-source geographic big data and deep neural network
Biomechanical analysis of femoral component malalignment in medial unicompartmental knee arthroplasty
EdgeCaseDNet: An enhanced detection architecture for edge case perception in autonomous driving
Autonomous driving perception systems still encounter significant challenges in edge scenarios involving multi-scale target changes and adverse weather, which seriously compromise detection reliability. To address this issue, we introduce a novel edge case dataset that extends existing benchmarks by capturing extreme road conditions (fog, rain, snow, nighttime et al.) with precise annotations, and develop EdgeCaseDNet as an optimized object-detection framework. EdgeCaseDNet’s architecture extends YOLOv8 through four synergistic innovations: (1) a Haar_HGNetv2 backbone that enables hierarchical feature extraction with enhanced long-range dependencies, (2) an asymptotic feature pyramid network for context-aware multi-scale fusion, (3) a hybrid partial depth-wise separable convolution module, and (4) Wise-IoU loss optimization for accelerated convergence. Comprehensive evaluations demonstrated the superiority of EdgeCaseDNet over YOLOv8, achieving improvements of +10.6% in mAP@50, and +8.4% in mAP@[.5:.95]. All the relevant codes are available at https://github.com/yutianku/EdgeCaseDNet .
Doughnut-like wrinkled fibrous nano silica architecture as a novel and effective reusable green heterogeneous nanocatalyst for the Hantzsch reaction
Directed-edge-based mining of regular routes for enhanced traffic pattern recognition from travel trajectories
Trajectory analysis serves as a critical technique for uncovering patterns in users’ transportation behaviors. This paper introduces a direction-aware regular route mining algorithm that systematically processes GPS (Global Positioning System, GPS) trajectory data by preprocessing, detecting stay regions, segmenting abnormal trajectories, and extracting frequent directed edges and supporting paths through route clustering, ultimately constructing users’ regular travel routes. By integrating stop-rate features, the algorithm effectively distinguishes between transportation modes, such as public transit and private vehicles. Experimental results based on the Geolife dataset, which includes trajectory data from 208 users covering a total distance of 1.35 million kilometers, indicate that the proposed method reduces the Mean Absolute Percentage Error (MAPE) by 56%, 49%, and 32% compared to the Rules-based method, CNN (Convolutional Neural Network, CNN), and DBSCAN (Density-Based Spatial Clustering of Applications with Noise, DBSCAN) algorithms, respectively, in both regular route extraction and transportation mode recognition. This improvement highlights the algorithm’s enhanced accuracy in identifying travel patterns. The proposed approach offers valuable support for applications in dynamic traffic prediction and personalized route recommendation systems.
Seasonal and cultural effects on calendar day variations in trauma incidence in Japan
Abstract Trauma incidence patterns are influenced by societal and cultural factors. However, research has mainly focused on short-term periods. This study examined daily trauma trends throughout the year to identify fluctuations aligned with lifestyle patterns under a relatively homogeneous ethnocultural context, using long-term nationwide trauma data. Data from patients with trauma recorded in the Japan Trauma Data Bank (2004–2021; n = 383,473) were retrospectively analyzed. Participants were grouped by transport date into 365 daily cohorts. Daily patient volumes, injury severity, suicide attempts, and mortality were assessed. Outliers were identified using negative binomial regression with periodic functions, logistic regression adjusted for trauma severity, and the Generalized Extreme Studentized Deviate test. Human behavioral patterns appeared to significantly influence the trauma incidence. Cases increased during Golden Week (late April–early May), Sports Day (October 10), Culture Day (November 3), and the end of the year while declining during the Obon holiday (mid-August) and in early January. Suicide attempts peaked in May–June and September, diverging from overall trauma trends. Mortality rates remained consistent, with no significant seasonal variation or outliers. Long-term data suggest that trauma volumes vary in relation to seasonal and cultural events, offering valuable insights for optimizing trauma resource allocation and preventive strategies.
Contrasting effects of DNA demethylation on cancer-germline gene expression in breast cancer and leukemia cells
Human germline gene expression is normally constrained to the germ cells, responsible for the production of sperm and oocytes. Cancer-germline (CG) genes, a subset of germline genes involved in testis development, are frequently aberrantly activated in cancer cells. The present study investigates the broader hypothesis that epigenetic modifications, specifically DNA methylation, can modulate the expression profiles of several CG genes in cancer and germ cells. Breast cancer (BC), normal breast (NB), and chronic myelogenous leukemia (CML) cell lines were treated with the DNA methyltransferase inhibitor (DNMTi) 5-aza-2’-deoxycytidine for three days. The effects of this treatment on the transcriptional activation of CG genes ( SYCP1 , ADAD1 , SYCE1 , PRSS54 , DMRTC2 , and TEX101 ) were then evaluated. We comprehensively analyzed differential methylation, survival analysis (Kaplan-Meier), correlation (Spearman’s), and pathway enrichment analysis (GO/KEGG) of CG genes (SYCP1, ADAD1, SYCE1, PRSS54, DMRTC2, and TEX101) in BC and leukemia. Treatment with 5-aza-2’-deoxycytidine upregulated CG genes in BC cells but downregulated them in leukemia cells, highlighting tissue-specific epigenetic responses. Differential methylation analysis revealed cancer-specific patterns: ADAD1 was hypermethylated in both malignancies, while PRSS54 was hypomethylated in leukemia. Survival analysis linked SYCE1 and PRSS54 to prolonged survival in BC, whereas TEX101 and SYCP1 correlated with poorer outcomes. Functional enrichment identified ADAD1 and SYCP1 as key players in BC and leukemia pathways, respectively. Meta-analysis validated SYCP1 as a robust biomarker with consistent effect sizes across datasets. Methylation-expression correlations were stronger in tumors, with SYCE1 and DMRTC2 showing inverse relationships in leukemia. These findings demonstrate that the expression of a subset of CG genes is responsive to modulation by hypomethylating drugs in a tissue-specific manner, highlighting their promise as candidates for future investigation in cancer immunotherapy.