Search Results (162)

The rapid evolution of 5G and emerging 6G networks has increased system complexity, data volume, and security risks, making anomaly detection vital for ensuring reliability and resilience. However, existing machine learning (ML)-based approaches still face challenges related to poor generalization, weak temporal modeling, and degraded accuracy under heterogeneous and imbalanced real-world conditions. To overcome these limitations, a hybrid time series transformer–deep belief network (HTST-DBN) is introduced, integrating the sequential modeling strength of TST with the hierarchical feature representation of DBN, while an improved orchard algorithm (IOA) performs adaptive hyper-parameter optimization. The framework also embodies the concept of symmetry and asymmetry. The IOA introduces controlled symmetry-breaking between exploration and exploitation, while the TST captures symmetric temporal patterns in network traffic whose asymmetric deviations often indicate anomalies. The proposed method is evaluated across four benchmark datasets (ToN-IoT, 5G-NIDD, CICDDoS2019, and Edge-IoTset) that capture diverse network environments, including 5G core traffic, IoT telemetry, mobile edge computing, and DDoS attacks. Experimental evaluation is conducted by benchmarking HTST-DBN against several state-of-the-art models, including TST, bidirectional encoder representations from transformers (BERT), DBN, deep reinforcement learning (DRL), convolutional neural network (CNN), and random forest (RF) classifiers. The proposed HTST-DBN achieves outstanding performance, with the highest accuracy reaching 99.61%, alongside strong recall and area under the curve (AUC) scores. The HTST-DBN framework presents a scalable and reliable solution for anomaly detection in next-generation mobile networks. Its hybrid architecture, reinforced by hyper-parameter optimization, enables effective learning in complex, dynamic, and heterogeneous environments, making it suitable for real-world deployment in future 5G/6G infrastructures. Full article

Urban agglomerations are key to balancing carbon emissions (CEs) and ecosystem services (ESs), yet structural imbalances exist between LUCE and ESs due to the lack of standardized frameworks and clear governance strategies. This study investigates the relationship between LUCE and ESs in the Chang-Zhu-Tan urban agglomeration using multi-source data from 2010 to 2023. The study aims to address three main research questions: (1) How do LUCE and ES networks evolve over time? (2) What factors drive their heterogeneity? (3) How do urbanization and ecological restoration impact LUCE and ES network dynamics? To answer these, we apply centrality metrics and develop heterogeneity indices to evaluate connectivity, accessibility, and driving factors. The findings show that both LUCE and ES networks exhibit corridor-like structures, with asymmetric node distributions. The LUCE-Network’s degree centrality increased from 0.16 to 0.29, while the ES-Network’s rose from 0.16 to 0.23. Heterogeneity was initially positive but turned negative by 2023, indicating a shift from LUCE dominance to an increased emphasis on ES. This transition was influenced by urbanization, land use changes, and ecological restoration efforts. Notably, the proportion of built-up land (X11) grew from 0.0187 in 2010 to 0.1500 in 2023, intensifying the disparity between LUCE and ESs. Similarly, urbanization (X7) surged to 0.1558 in 2023, increasing CEs and the demand for ESs. A collaborative pathway is proposed to address these challenges, involving controlled urban development, restoration of green spaces, and prioritizing multimodal transport and energy efficiency. This framework offers actionable diagnostics for improving low-carbon and ecological governance in urban agglomerations. Full article

Background: Autism Spectrum Disorder (ASD) is characterized by atypical brain oscillatory dynamics and altered connectivity, impairing sensory integration, socio-communicative responsiveness, and behavioral regulation. Methods: Radio Electric Asymmetric Conveyer (REAC) technology delivers non-invasive neurobiological modulation through standardized, operator-independent protocols. The Brain Wave Optimization Neurodevelopment–Autism (BWO ND-A) protocol was designed to address oscillatory patterns frequently altered in ASD, aiming to promote network coherence and multidomain functional improvement. This retrospective pre–post single-arm study evaluated 39 children with ASD (31 males, 8 females; mean age 7.85 ± 2.90 years). All received one Neuro Postural Optimization (NPO) session to prime central nervous system adaptive capacity, followed by BWO ND-A (18 sessions, ~8 min each), administered 3–4 times daily over ~two weeks. The primary outcome was the Autism Treatment Evaluation Checklist (ATEC) total score; secondary outcomes were its four subscales. Results: Mean total ATEC decreased from 67.76 ± 16.11 to 56.25 ± 23.66 (mean change −11.51 ± 14.48; p < 0.0001; Cohen’s dz = 0.78). Clinically meaningful improvement (≥8-point reduction) occurred in 59% of participants. In 10.3% of cases, caregiver ratings indicated an apparent worsening (≥8-point increase). However, no objective deterioration or adverse effects were observed. This pattern was most likely related to a transient phase of functional re-adaptation, during which emerging changes may initially be perceived by caregivers as worsening before stabilizing into improvement. Conclusions: While these findings suggest promising short-term real-world efficacy and safety, the absence of a control group, lack of objective neurophysiological measures, and no long-term follow-up limit causal inference. Future controlled studies with neurophysiological monitoring are needed to confirm the targeted neuromodulatory action and durability of effects. Full article

Mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer’s disease (AD), comprises three potential trajectories: reversion, stability, or progression. Accurate prediction of these trajectories is crucial for disease modeling and early intervention. We propose a novel analytical framework that integrates a healthy control–AD difference template (HAD) with a large-scale Granger causality algorithm based on long short-term memory networks (LSTM-lsGC) to construct effective connectivity (EC) networks. By applying principal component analysis for dimensionality reduction, modeling dynamic sequences with LSTM, and estimating EC matrices through Granger causality, the framework captures both symmetrical and asymmetrical connectivity, providing a refined characterization of the network alterations underlying MCI progression and reversion. Leveraging graph-theoretical features, our method achieved an MCI subtype classification accuracy of 84.92% (AUC = 0.84) across three subgroups and 90.86% when distinguishing rMCI from pMCI. Moreover, key brain regions, including the precentral gyrus, hippocampus, and cerebellum, were identified as being associated with MCI progression. Overall, by developing a symmetry-aware effective connectivity framework that simultaneously investigates both MCI progression and reversion, this study bridges a critical gap and offers a promising tool for early detection and dynamic disease characterization. Full article

In horizontal well technology, hydraulic fracturing has been established as an essential technique for enhancing hydrocarbon production. However, the complex architecture of fracture networks challenges conventional monitoring methods. Micro-seismic monitoring, recognized for its superior resolution and sensitivity, enables precise fracture morphology characterization. This study advances diagnostic capabilities through integrated field–laboratory investigations and multi-domain signal processing. Hydraulic fracturing experiments under varied geological conditions generated critical micro-seismic datasets, with quantitative analyses revealing asymmetric propagation patterns (total length 312 ± 15 m, east wing 117 m/west wing 194 m) forming a 13.37 × 10⁴ m³ stimulated reservoir volume. Spatial event distribution exhibited density disparities correlating with geophone offsets (west wing 3.8 events/m vs. east 1.2 events/m at 420–794 m distances). Advanced time–frequency analyses and inversion algorithms differentiated signal characteristics demonstrating logarithmic SNR (Signal-to-Noise Ratio)–magnitude relationships (SNR 0.49–4.82, R² = 0.87), with near-field events (<500 m) showing 68% reduced magnitude variance compared to far-field counterparts. Coupled numerical simulations confirmed stress field interactions where fracture trajectories deviated 5–15° from principal stress directions due to prior-stage stress shadows. Branch fracture networks identified in Stages 4/7/9/10 with orthogonal/oblique intersections (45–65° dip angles) enhanced stimulation reservoir volume (SRV) by 37–42% versus planar fractures. These geometric parameters—including height (20 ± 3 m), width (44 ± 5 m), spacing, and complexity—were quantitatively linked to micro-seismic response patterns. The developed diagnostic framework provides operational guidelines for optimizing fracture geometry control, demonstrating how heterogeneity-driven signal variations inform stimulation strategy adjustments to improve reservoir recovery and economic returns. Full article

The escalating heatwave risks over the Tibetan Plateau (TP) highlight unresolved gaps in understanding multitype mechanisms and diurnal urban canopy heat island (UCHI) responses. Using Xining’s high-density observational network (2018–2023) and by employing comparative analysis (urban–rural, heatwave versus non-heatwave days) and composite analysis, we found: During the record-breaking July 2022 heatwave across the TP, Xining reached an extreme UCHI peak (z-score: 3.0). Critically asymmetric UCHI responses as daytime heatwaves amplify mean intensity by 0.35 °C via extreme value shifts, whereas nighttime events suppress it by 0.31 °C. Crucially, heatwaves induce negligible daytime UCHI modulation but drive comparable magnitude nighttime UCHI intensification (during daytime events) and reduction (during nighttime events), demonstrating type-dependent and diurnally asymmetric urban thermal sensitivities. Heatwaves driven by distinct synoptic patterns; daytime events are controlled by an anomaly anticyclone (cloudless, dry conditions), while nighttime events occur under plateau-north anticyclones (cloudy, humid conditions). These patterns fundamentally reshape heatwave–UCHI interactions through divergent mechanisms: Daytime/nighttime heatwaves amplify/suppress nocturnal UCHI through enhanced/reduced urban heat storage and accelerated/inhibited rural radiative cooling. Our case study demonstrates that although heatwaves generally amplify nocturnal UCHI, in dry regions, their synoptic drivers significantly modify this nighttime synergy. The nocturnal UCHI during heatwave is not only driven by humidity effects but also modulated by cloud cover-regulated rural radiative cooling and urban thermal storage. These findings establish a mechanistic framework for heatwaves–UCHI interactions and provide actionable insights for heat-resilient planning in high-altitude arid cities. Full article

Federated Learning (FL) has been proposed as a new machine learning paradigm to ensure data privacy by training the model in a decentralized manner. However, FL is challenged by device heterogeneity, asymmetric data contribution, and imbalanced datasets, which complicate system control and hinder performance due to long waiting times for aggregation. To tackle the FL challenges, we propose Adaptive Time Threshold Client Selection using DRL (ATCS-FL) to adjust the time threshold (α) in each communication round based on computing and resource capacity of each device and the volume of data updates. The Double Deep Q-Network (DDQN) model determines the appropriate α, according to the variations in local training time that achieves performance improvement alongside latency reduction. Based on the α, the server selects a subset of clients with adequate resources that can finish training within the α for participating in the training process. Our approach dynamically adjusts the α and adaptively selects the number of clients, effectively mitigates the impact of heterogeneous training speeds and significantly enhances communication efficiency. Our experiment utilizes CIFAR-10 and MNIST benchmarked datasets for image classification training with convolutional neural networks across non-IID distributed levels in FL. Specifically, ATCS-FL demonstrates performance improvement and latency reduction of 77% and 75%, respectively, compared to FedProx and FLASH-RL. Full article

High impedance grounding faults (HIGFs) are a common yet difficult-to-detect issue in distribution networks. Characterized by low fault currents and prolonged durations, they pose a significant risk of triggering secondary hazards such as wildfires. Existing HIGF prevention and control technologies face challenges in effectively addressing arc ignition, fault current limitation, and wildfire mitigation. To tackle these limitations, this paper proposes a novel asymmetric operational structure incorporating a single-phase isolation transformer and supplementary edge-phase capacitance. Through theoretical modeling and simulation analysis, the interrelations among fault current, phase voltage, zero-sequence voltage, and HIGF characteristics are systematically explored. A coordinated control strategy is developed to optimize three-phase voltage distribution within the distribution network. Simulation results demonstrate that the proposed configuration significantly reduces edge-phase voltages, suppresses fault current levels, prevents arc initiation, extends arc ignition delay times, and consequently mitigates wildfire risk. This study presents a new technical pathway for HIGF prevention and control, offering both practical engineering value and theoretical insight. Full article

This paper examines the impact of symmetry and asymmetry on the dynamic modeling and nonlinear control of a mobile robot with Ackermann steering geometry. A neural network-based residual model is incorporated as a novel control enhancement. This study presents a control-oriented formulation that addresses both idealized symmetric dynamics and real-world asymmetric behaviors caused by actuator imperfections, tire slip, and environmental variability. Using the Euler–Lagrange formalism, the robot’s dynamic equations are derived, and a modular simulation framework is implemented in MATLAB/Simulink R2022a, that incorporates distinct steering and propulsion subsystems. Symmetric elements, such as the structure of the inertia matrix and kinematic constraints, are contrasted with asymmetries introduced through actuator lag, unequal tire stiffness, and nonlinear friction. A residual neural network term is introduced to capture unmodeled dynamics and improve the robustness. The simulation results show that the control strategy, originally developed under symmetric assumptions, remains effective when adapted to systems exhibiting asymmetry, such as actuator delays and tire slip. Explicitly modeling these asymmetries enhances the precision of trajectory tracking and the overall system robustness, particularly in scenarios involving varied terrain and obstacle-rich environments. Full article

Chinese coalbed methane (CBM) reservoirs exhibit characteristically low recovery rates due to adsorbed gas dominance and “three-low” properties (low permeability, low pressure, and low saturation). CO₂ thermal drive (CTD) technology addresses this challenge by leveraging dual mechanisms—thermal desorption and displacement to enhance production; however, its effectiveness necessitates uniform fracture networks for temperature field homogeneity—a requirement unmet by conventional long-fracture fracturing. To bridge this gap, a coupled seepage–heat–stress–fracture model was developed, and the temperature field evolution during CTD in coal under non-uniform fracture networks was determined. Integrating multi-cluster fracture propagation with stress barrier and intra-stage stress differential characteristics, a stress-barrier-responsive diverting fracturing technology meeting CTD requirements was established. Results demonstrate that high in situ stress and significant stress differentials induce asymmetric fracture propagation, generating detrimental CO₂ channeling pathways and localized temperature cold islands that drastically reduce CTD efficiency. Further examination of multi-cluster fracture dynamics identifies stress shadow effects and intra-stage stress differentials as primary controlling factors. To overcome these constraints, an innovative fracture network uniformity control technique is proposed, leveraging synergistic interactions between diverting parameters and stress barriers through precise particle size gradation (16–18 mm targeting toe obstruction versus 19–21 mm sealing heel), optimized pumping displacements modulation (6 m³/min enhancing heel efficiency contrasted with 10 m³/min improving toe coverage), and calibrated diverting concentrations (34.6–46.2% ensuring uniform cluster intake). This methodology incorporates dynamic intra-stage adjustments where large-particle/low-rate combinations suppress toe flow in heel-dominant high-stress zones, small-particle/high-rate approaches control heel migration in toe-dominant high-stress zones, and elevated concentrations (57.7–69.2%) activate mid-cluster fractures in central high-stress zones—collectively establishing a tailored framework that facilitates precise flow regulation, enhances thermal conformance, and achieves dual thermal conduction and adsorption displacement objectives for CTD applications. Full article

Background: Public beliefs about dietary risks, such as excessive salt intake, are often not isolated misconceptions but part of structured cognitive systems. This study aimed to explore how individuals organize their beliefs and misperceptions regarding salt-related health consequences. Material and Methods: Using data from an international online survey, we applied a system of multivariate proportional odds logistic regression (POLR) models to estimate conditional associations among beliefs about salt’s links to various diseases—including cardiovascular, metabolic, renal, neuropsychiatric, and mortality outcomes. In addition, exploratory and confirmatory factor analyses (EFA and CFA) were conducted to identify and validate latent constructs underlying the belief items. Beliefs were modeled as interdependent, controlling for latent constructs, sociodemographics, and self-reported health awareness. Statistically significant associations (p < 0.05) were visualized via a heatmap of beta coefficients. Results: Physicians showed almost universal agreement that salt contributes to hypertension (µ = 0.97), compared to non-medical respondents (µ = 0.85; p < 0.0001). Beliefs about mortality (µ = 1.55 for MDs vs. 0.99 for non-medical; p < 0.0001) emerged as central hubs in the belief network. Strong inter-item associations were observed, such as between hypertension and heart failure (β = −0.39), and between obesity and type 2 diabetes (β = −0.94). Notably, cognitive gaps were found, including a lack of association between atrial fibrillation and stroke, and non-reciprocal links between hypertension and heart failure. Conclusions: Beliefs about the health effects of salt are structured and sometimes asymmetrical, reflecting underlying reasoning patterns rather than isolated ignorance. Understanding these structures provides a systems-level view of health literacy and may inform more effective public health communication and education strategies. Full article

This study focuses on the adaptive tracking control issue for nonlinear multi-agent systems (MASs) under the influence of asymmetric input constraints and external disturbances. Firstly, an auxiliary system is proposed, which can ensure flexible prescribed performance under input saturation conditions. Meanwhile, by introducing a transformation function, the distributed errors are freed from initial constraints. Employing the backstepping method, the adaptive technique, and a neural network approximation technology, a finite-time prescribed performance adaptive tracking control algorithm is designed, enabling the tracking errors to stably converge within the prescribed performance bounds. Secondly, a composite disturbance observer is developed to estimate and mitigate the combined disturbances, which include external perturbations and approximation errors from radial basis function neural networks (RBF NNs). It not only achieves effective disturbance compensation but also further suppresses the approximation errors of RBF NNs. Finally, stability analysis using the Lyapunov function demonstrates that all closed-loop signals remain uniformly ultimately bounded (UUB), with adaptive tracking errors converging to a compact region within a finite time. Simulation results and comparative studies confirm the proposed method’s effectiveness and advantages, providing a basis for its practical use in distributed control applications. Full article

Background: The red nucleus (RN) is a phylogenetically conserved structure within the midbrain that is traditionally associated with general motor coordination; however, its specific role in controlling directional movement remains poorly understood. Methods: This study systematically investigates the function and mechanism of RN neurons in directional movement by combining stereotactic brain injections, fiber photometry recordings, multi-unit in vivo electrophysiological recordings, optogenetic manipulation, and anterograde trans-synaptic tracing. Results: We analyzed mice performing standardized T-maze turning tasks and revealed that anatomically distinct RN neuronal ensembles exhibit direction-selective activity patterns. These neurons demonstrate preferential activation during ipsilateral turning movements, with activity onset consistently occurring after movement initiation. We establish a causal relationship between RN neuronal activity and directional motor control: selective activation of RN glutamatergic neurons facilitates ipsilateral turning, whereas temporally precise inhibition significantly impairs the execution of these movements. Anterograde trans-synaptic tracing using H129 reveals that RN neurons selectively project to spinal interneuron populations responsible for ipsilateral flexion and coordinated limb movements. Conclusions: These findings offer a framework for understanding asymmetric motor control in the brain. This work redefines the RN as a specialized hub within midbrain networks that mediate lateralized movements and offers new avenues for neuromodulatory treatments for neurodegenerative and post-injury motor disorders. Full article

In the era of Industry 5.0, future wireless networks are increasingly shifting from traditional symmetric architectures toward heterogeneous and asymmetric paradigms, driven by the demand for diversified and dynamic services. This architectural evolution gives rise to complex and asymmetric flows, such as the coexistence of periodic and burst flows with varying latency, jitter, and deadline constraints, posing new challenges for deterministic transmission. Traditional time-sensitive networking (TSN) is well-suited for periodic flows but lacks the flexibility to effectively handle dynamic, asymmetric traffi. To address this limitation, we propose a two-stage asymmetric flow scheduling framework with dynamic deadline control, termed A-TSN. In the first stage, we design a Deep Q-Network-based Dynamic Injection Time Slot algorithm (DQN-DITS) to optimize slot allocation for periodic flows under varying network loads. In the second stage, we introduce the Dynamic Deadline Online (DDO) scheduling algorithm, which enables real-time scheduling for asymmetric flows while satisfying flow deadlines and capacity constraints. Simulation results demonstrate that our approach significantly reduces end-to-end latency, improves scheduling efficiency, and enhances adaptability to high-volume asymmetric traffic, offering a scalable solution for future deterministic wireless networks. Full article

In intelligent vehicular networks, the accuracy of semantic segmentation in road scenes is crucial for vehicle-mounted artificial intelligence to achieve environmental perception, decision support, and safety control. Although deep learning methods have made significant progress, two main challenges remain: first, the difficulty in balancing global and local features leads to blurred object boundaries and misclassification; second, conventional convolutions have limited ability to perceive irregular objects, causing information loss and affecting segmentation accuracy. To address these issues, this paper proposes a global–local collaborative attention module and a spider web convolution module. The former enhances feature representation through bidirectional feature interaction and dynamic weight allocation, reducing false positives and missed detections. The latter introduces an asymmetric sampling topology and six-directional receptive field paths to effectively improve the recognition of irregular objects. Experiments on the Cityscapes, CamVid, and BDD100K datasets, collected using vehicle-mounted cameras, demonstrate that the proposed method performs excellently across multiple evaluation metrics, including mIoU, mRecall, mPrecision, and mAccuracy. Comparative experiments with classical segmentation networks, attention mechanisms, and convolution modules validate the effectiveness of the proposed approach. The proposed method demonstrates outstanding performance in sensor-based semantic segmentation tasks and is well-suited for environmental perception systems in autonomous driving. Full article