NetWork
A Hybrid Random Access Scheme Assisted by Access Class Barring
CHEN Mengyan;QIAN Wenyan;HUANG Yuan;YANG Jing;In ultra-massive machine-type communication scenarios for sixth-generation mobile communication systems， burst concurrent access from massive devices leads to network congestion， access collisions， reduced access success rates， and increased energy consumption. To address these issues， an access class barring (ACB)-assisted hybrid random access scheme is proposed.In this scheme， whether to perform an ACB check is dynamically determined based on the real-time number of devices requesting access. When the number of requesting devices does not exceed the maximum number of preambles， all devices initiate random access directly; otherwise， devices must pass an ACB check. The devices that pass the check are classified into delay-sensitive and delay-tolerant types according to their service characteristics， and they adopt the two-step and four-step random access procedures， respectively， while sharing the same preamble pool. Under the constraint of device access delay， the ACB factor， the number of preambles， and the optimal probability of a device being classified as delay-sensitive are dynamically adjusted to maximize the random access efficiency. The simulation results show that when the number of requested access devices exceeds the maximum number of preamble codes， the average random access efficiency of the proposed scheme is significantly higher than that of the traditional random access scheme， the scheme combining two-step - four-step random access， and the joint access control and resource allocation scheme. The average access delay is reduced by approximately 50% compared to the traditional random access scheme， and is lower than that of the scheme combining two-step to four-step random access and the joint access control and resource allocation scheme. The average energy consumption is the lowest among the four schemes， and the growth rate with the increase in the number of devices is the most moderate. Through dynamic access control， differentiated random access procedures， and shared preamble mechanism， the proposed scheme effectively alleviates access congestion and collision problems under high-load conditions， thereby improving system access capacity and resource utilization efficiency.Furthermore， by introducing a comparative analysis of average random access efficiency under different maximum retransmission times， it was verified that the proposed scheme can still maintain stable performance advantages under different retransmission constraints.
Study on environmental influence of heavy metals in downstream farmland soil of a gold-copper mine tailings pond that has been out of service
SUN Fengrui;YU Zhengmao;ZHANG Wenguo;XIAO E;JU Weiwei;Elevated heavy metal concentrations exist in decommissioned gold-copper tailings ponds and may migrate， posing a potential risk to the downstream farmland soil environment. This study focuses on the heavy metal contamination extent and degree in farmland downstream of a decommissioned gold-copper tailings pond. Strata lithological distribution and groundwater flow direction in the study area are determined via drilling exploration. Heavy metal concentrations in surface soil samples， collected from the tailings pond and downstream farmland within a 2 km radius， are analyzed. Results show that several heavy metal concentrations in tailings pond soil significantly exceed the agricultural land soil environmental pollution risk screening values， with a Nemerow composite pollution index of 5.10， and the site is classified as severely polluted. In contrast， both single-factor and comprehensive multi-factor pollution indices of nine targeted heavy metals in surface soils from downstream farmland within 1 km are categorized as "clean." No discernible spatial trends or correlations of heavy metal concentrations are observed along the valley of the study area. Mild pollution caused by Cu is detected in soils 2 km downstream of the tailings pond. Based on the assessment of upstream topography， groundwater conditions and heavy metal distribution patterns， the potential of Cu migration from upstream soil or groundwater to this exceedance site is considered low. Strengthened environmental management and further identification of pollution sources are required in this local contaminated area. This study suggests that with proper closure management after decommissioning—including leachate collection systems and improved drainage systems—the risk of heavy metal contamination to downstream farmland soils from this gold-copper tailings pond is relatively low.
Degradation methods for fungicide residues in fruits
CAO Jiajia;GU Qiuyu;WANG Suyan;SHI Xinchi;Pedro LABORDA;Fungal diseases represent a major threat to the sustainable development of the fruit industry. Although chemical fungicides can effectively control plant diseases， their extensive application has led to a series of problems， including excessive pesticide residues， enhanced resistance of pathogenic bacteria， and environmental pollution. Current degradation methods for fungicides in fruits can achieve considerable degradation efficiency; however， information on their degradation products and potential toxicity remains relatively insufficient. Therefore， the development of efficient and green pesticide residue degradation technologies is of great importance for ensuring fruit quality and safety and promoting the green transformation of the fruit industry. This paper systematically reviews the degradation mechanisms and technical pathways of fungicide residues in fruits， focusing on the principles， degradation efficacy and application characteristics of physical， chemical and biological approaches.
A population-regulated attention network for assisted diagnosis of autism spectrum disorder
ZHANG Xiongtao;MA Yuan;WANG Feng;YAN Qiqi;SHEN Qing;Graph Neural Networks (GNNs) based on multimodal Magnetic Resonance Imaging (MRI) have garnered significant attention in medical imaging due to their ability to effectively characterize the non-Euclidean structure of brain networks. However， most existing studies overlook significant individual heterogeneity in patient symptoms and merely introduce demographic data via simple concatenation， thereby limiting classification performance. Furthermore， the heterogeneity of multi-site data often results in suboptimal model performance during cross-site external validation. To address these challenges， this study proposes a demographic-regulated multimodal graph attention network framework (DM-GAT). First， the model incorporates a demographic-aware dynamic graph structure learning (D-GSL) module， which utilizes demographic information to reconstruct the latent topology of brain networks， thereby mitigating noise and individual variability issues inherent in static functional connectivity. Second， a multi-view demographic interaction attention (M-DIA) mechanism is introduced. This mechanism employs demographic features as gating signals to dynamically regulate the attention weights of nodes and edges， enhancing the model's capability for individualized representation. Experimental results on the ABIDE and ADHD-200 cross-site datasets demonstrate that the proposed model achieves a classification accuracy of 74.51% on the ABIDE dataset (17 sites， 943 subjects)， outperforming the state-of-the-art MHNet model (73.27%). Ablation studies further validate the effectiveness of the D-GSL module (improving accuracy by approximately 4%) and the M-DIA module.
Laplacian spectra and coherence of a class of multilayer weighted edge corona networks
LIU Jiabao;LI Qichang;To reveal the coupling mechanism between structure and function in complex systems， a class of multilayer weighted edge corona network models is constructed， and their Laplacian spectral properties as well as first-order and second-order coherence behaviors are systematically analyzed， aiming to provide a theoretical basis for the robust design of multi-agent systems. Firstly， based on graph theory and matrix analysis， the Laplacian spectrum of the multilayer weighted edge corona network is derived using Kronecker product operations and block matrix eigenvalue decomposition. Then， exact expressions for first-order and second-order coherence are obtained from the Laplacian spectrum， and the influences of the number of layers， weights， and factor network topologies on coherence behavior and robustness are examined. As an application， the number of spanning trees and the Laplacian energy of the network are further computed. The results show that second-order coherence is more sensitive to topological changes than first-order coherence， and increasing the number of layers and weights can significantly enhance network robustness. For the same G2， when the factor network G1 possesses higher network coherence， the multilayer weighted edge corona network ■ generally exhibits higher first-order coherence and lower second-order coherence. For the same G1， when the factor network G2 possesses higher network coherence， the multilayer weighted edge corona network ■ also generally exhibits higher network coherence. These findings clarify the intrinsic coupling law between topology and coherence in multilayer weighted edge corona networks， providing a theoretical foundation for flexibly regulating the cooperative ability and anti-interference performance of multi-agent systems by adjusting the number of layers and weights.
Measurement of China's green low-carbon development level and research on its spatial correlation network characteristics based on complex network theory
LIU Jiabao;GUO Jialin;Against the background of China’s “dual carbon” goals， scientifically measuring the level of green and low-carbon development and revealing its spatial association characteristics are of great significance for promoting coordinated regional green transformation. Taking 30 provinces in China from 2011 to 2023 as the research sample， this study constructs a comprehensive evaluation index system for green and low-carbon development from the economic， social， and ecological dimensions. The entropy-weighted TOPSIS method is employed to measure the green and low-carbon development level of each province， while kernel density estimation is used to depict its dynamic distribution characteristics. On this basis， an interprovincial spatial association network is constructed using an improved gravity model， and social network analysis is applied to systematically examine the structural characteristics of the spatial network of green and low-carbon development from both overall and individual perspectives. The results show that: (1) During the study period， China’s overall level of green and low-carbon development exhibits a steady upward trend; (2) The kernel density curves shift rightward and become increasingly concentrated， indicating continuous improvement in green and low-carbon development and a mitigation of interprovincial differentiation; (3) The spatial association network structure of green and low-carbon development remains relatively stable， featuring a low network density yet high connectivity efficiency， and has gradually exhibited small-world characteristics; (4) Economically developed eastern provinces occupy core positions in the network over the long term， exerting strong spillover and intermediary effects， and the overall network displays a pronounced “core–periphery” structure. These findings provide empirical evidence and policy implications for optimizing regional collaborative pathways toward green and low-carbon development and for advancing the achievement of China’s “dual carbon” goals.
Characteristics and source analysis of the xenolithic debris in the pyroclastic rocks of the Huangjian Formation in the Daqiaowu area，Northwest Zhejiang Province
ZHANG Zhixing;GU Luning;GUO Fangju;WANG Hongzuo;SHU Xujie;QIAN Peng;Large volumes of rhyolitic pyroclastic rocks were exposed along the southeastern coastal region during the Cretaceous， these rocks show complex provenance and contain abundant exotic clasts， which lowers the potentials of whole-rock geochemical compositions to explore the genesis and evolution of these volcanic rocks. Researches on the sources of these exotic detrital materials have rarely been carried out previously. In addition， these pyroclastic rocks are important host rocks for hydrothermal uranium deposits in China. Therefore， provenance analysis of these rocks is of great significance for advancing studies on the sources of uranium. Here we present a case study of the pyroclastic rocks in the Daqiaowu area. Field investigations and petrographic studies suggest that xenolithic debris entrained within the pyroclastic rocks includes fragments of ortho- and parametamorphic rocks， as well as granite clasts. After removing the xenolithic debris， major and trace element analyses were carried out on both debris-removed and untreated samples. The analytical results indicate， to some extent， variations in several major and trace elements between the two groups， such as Ce， Gd， and Tb. For example， Gd contents in the debris-removed samples range from 9.4×10-6 to 13.6×10-6， which is higher than those of the untreated samples (4.9×10-6–11.1×10-6). A similar pattern is observed for Tb， with concentrations of 1.3×10-6–2.2×10-6 in the debris-removed samples compared to 0.8×10-6–1.8×10-6 in the untreated samples. This suggests that pyroclastic rocks were indeed contaminated by xenolithic debris， to some extent， changing original geochemistry for volcanic rocks. Element co-variation diagrams reflect a source of the Chencai Group parametamorphic rock， rather than granite and quartzite， for xenolithic debris. Furthermore， previous zircon dating of pyroclastic rocks revealed some xenocryst zircons of 720～850 Ma simultaneous with the Chencai Group， this also demonstrate a contribution of basement parametamorphic rock debris. Thus， during the eruption of pyroclastic rocks in the Daqiaowu area， some basement metamorphic rocks were broken to debris as explosion and entrapped in the magma， xenolithic debris was dominately sourced from the Chencai Group parametamorphic rocks.
Advances in the Application of Fluoropolymers for Triboelectric Energy Harvesting
DUAN Yiyang;WANG Ping;ZHANG Yan;With the growing global demand for energy and the urgent need for sustainable development， triboelectric nanogenerators (TENGs) have attracted significant attention due to their low cost， simple structure， and high efficiency in harvesting low-frequency mechanical energy. Among the various candidate materials， fluoropolymers have emerged as key components for fabricating high-performance triboelectric layers， owing to their strong electronegativity， low surface energy， chemical inertness， and stable dielectric properties. This review provides a comprehensive summary of recent progress in fluoropolymer-based TENGs. First， various fabrication methods for triboelectric layers， including electrospinning， melt injection molding， surface spraying， and 3D printing， are introduced and comparatively analyzed in terms of their advantages and limitations. Next， strategies for enhancing output performance—such as surface treatment， structural modification， functional filler incorporation， and material hybridization—are highlighted， demonstrating the unique role of fluoropolymers in improving charge density， energy conversion efficiency， and long-term stability. Furthermore， the current challenges in this field， including difficulties in large-scale fabrication， insufficient environmental adaptability， and issues of durability and device integration， are discussed. Finally， the future development directions are outlined， focusing on the design of novel fluorinated materials， multifunctional integrated structures， and wearable energy systems. This review aims to provide insights into the optimization and application of fluoropolymer-based TENGs， contributing to the advancement of green and sustainable energy technologies.
Modeling and Parameter Extraction Methods of InP HEMT for Next-Generation Wireless Communications
FENG Changle;LUO Kun;ZHANG Ao;GAO Jianjun;Due to its high signal-to-noise ratio， easy integration and high electron mobility， high electron mobility transistors (HEMTs) have become a popular choice of semiconductor devices in next-generation wireless communication systems. Highly accurate models are critical to precisely predict the device performance.The linear model and parameter extraction method of high electron mobility transistor are studied in this paper， and the distributed capacitance effect is considered. Based on the small-signal equivalent circuit model， a direct parameter extraction procedure for the intrinsic elements of InP HEMT is established. The 25 × 2 μm (unit gate width×number of gate fingers) InP HEMT device was characterized and validated under multiple bias conditions over the frequency range from 500 MHz to 40 GHz.The results show that the modeled and measured data are in good agreement， which verifies the accuracy of the model and the extraction method.
Alzheimer's Disease EEG Classification Based on Phase-Aware and Fourier Decomposition
GE Jiachang;JIANG Yizhang;HUANG Lijun;XIA Kaijian;To address the challenges of background rhythm interference and attention head mode collapse in Alzheimer’s disease electroencephalography classification， we propose a phase-aware cyclical Transformer framework， termed PACformer. The model first employs an adaptive phase projector， composed of a large-kernel 1D convolution and a multilayer perceptron， to estimate the starting phase of each EEG epoch and generate phase-aware gating weights for refining token embeddings. It then introduces a Fourier residual decomposition encoder that learns periodic background trends with a trainable Fourier series and follows a subtract-attend-add strategy to separate stationary rhythms from pathology-related residuals. In addition， a multi-head spectral diversity loss is designed to encourage different attention heads to cover complementary frequency bands. Experiments on the APAVA dataset show that PACformer achieves 77.75% accuracy， 86.56% AUROC， and 87.02% AUPRC， outperforming 11 competitive baselines. These results indicate that explicit phase alignment and frequency-domain decomposition improve the modeling of non-stationary physiological signals and support more robust EEG-based AD screening.