Search Results (5,238)

Microplastic pollution is pervasive in marine ecosystems and poses a growing threat to marine organisms and human health. This study simultaneously investigates microplastic ingestion and phthalate exposure in Parapenaeus longirostris, a commercially valuable and ecologically relevant Mediterranean crustacean occupying an intermediate trophic position. Specimens were collected from three coastal areas in the central Tyrrhenian Sea (Western Mediterranean): near the Tiber River mouth, one of the most polluted rivers in Italy, and two additional sites to the north and south. The frequency of individuals with ingested microplastics varied among locations: 78% near the Tiber River, 64% at site S, and 38% at site N, reflecting anthropogenic pressure gradients. Analyses confirmed the lower occurrence at site N, indicating higher ingestion near land-based pollution sources. Ingested microplastic polymer types varied among sites, reflecting location-specific contamination. Phthalates were present in shrimp muscle at all sites (5–1122 ng/g w.w.) with the highest average concentration (68.26 ± 55.74 ng/g) at the site with the highest microplastic ingestion. Although no statistical correlation was found, the similar spatial distribution of microplastics and phthalates suggests a potential link influenced by local pollution and individual variability. These findings provide novel evidence of microplastic and phthalate contamination in P. longirostris, highlighting its role as a trophic connector mediating contaminant transfer through the food web. While current levels suggest no potential risk to human health, continued monitoring and further studies on exposure along trophic pathways are recommended. Full article

The detection and extraction of weak signals are crucial in various engineering and scientific fields, yet current acoustic sensing technologies are restricted by fundamental pressure detection methods. This paper proposes gradient-equivalent medium-coupled metamaterials (GEMCMs) utilizing strong wave compression and an equivalent medium mechanism to capture weak signals in complex environments and enhance target acoustic signals. Overcoming shape and impedance mismatch limitations of traditional gradient structures, GEMCMs significantly improve control performance. Experimental and numerical simulations indicate that GEMCMs can effectively enhance specific frequency components in acoustic signals, outperforming traditional gradient structures. This enhancement of specific frequency components relies on the resonance effect of the unit cell structure. By introducing acoustic resonance within a spatially wound acoustic channel, a significant amplification of weak acoustic signals is achieved. This provides a new research direction for acoustic wave manipulation and enhancement, and holds significant importance in fields such as mechanical fault diagnosis and medical diagnostics. Full article

Previous studies on Sound Event Localization and Detection (SELD) have primarily focused on CNN- and Transformer-based designs. While CNNs possess local receptive fields, making it difficult to capture global dependencies over long sequences, Transformers excel at modeling long-range dependencies but have limited sensitivity to local time–frequency features. Recently, the VMamba architecture, built upon the Visual State Space (VSS) model, has shown great promise in handling long sequences, yet it remains limited in modeling local spatial details. To address this issue, we propose a novel state space model with an attention-enhanced feature fusion mechanism, termed FFMamba, which balances both local spatial modeling and long-range dependency capture. At a fine-grained level, we design two key modules: the Multi-Scale Fusion Visual State Space (MSFVSS) module and the Wavelet Transform-Enhanced Downsampling (WTED) module. Specifically, the MSFVSS module integrates a Multi-Scale Fusion (MSF) component into the VSS framework, enhancing its ability to capture both long-range temporal dependencies and detailed local spatial information. Meanwhile, the WTED module employs a dual-branch design to fuse spatial and frequency domain features, improving the richness of feature representations. Comparative experiments were conducted on the DCASE2021 Task 3 and DCASE2022 Task 3 datasets. The results demonstrate that the proposed FFMamba model outperforms recent approaches in capturing long-range temporal dependencies and effectively integrating multi-scale audio features. In addition, ablation studies confirmed the effectiveness of the MSFVSS and WTED modules. Full article

Global Navigation Satellite System (GNSS) spoofing, which manipulates PVT solutions through false measurements, increasingly threatens GNSS reliability and user safety. However, most existing simulator-based spoofers, constrained by their inability to access real-time ionospheric information (e.g., Global Ionosphere Maps, GIMs) from external sources, struggle to replicate authentic total electron content (TEC) along each signal propagation path accurately and in a timely manner. In contrast, widespread dual-frequency (DF) receivers with access to the internet can validate local TEC measurements against external references, establishing a pivotal spoofing detection distinction. Here, we propose an Ionospheric Information-Assisted Spoofing Detection Technique (IIA-SDT), exploiting the inherent consistency between TEC values derived from DF pseudo-range measurements and external references in spoofing-free scenarios. Spoofing probably disrupts this consistency: in simulator-based full-channel spoofing where all channels are spoofed, the inaccuracies of the offline ionospheric model used by the spoofer inevitably cause TEC mismatches; in partial-channel spoofing where the spoofer fails to control all channels, an unintended PVT deviation is induced, which also causes TEC deviations due to the spatial variation of the ionosphere. Basic principles and theoretical analysis of the proposed IIA-SDT are elaborated in the paper. Simulations using ionospheric data collected from 2023 to 2024 at a typical mid-latitude location are conducted to evaluate IIA-SDT performance under various parameter configurations. With a window length of 5 s and satellite number of 8, the annual average detection probability approximates 75% at a false alarm rate of $1\times {10}^{-3}$, with observable temporal variations. Field experiments across multiple scenarios further validate the spoofing detection capability of the proposed method. Full article

This study investigates the spatial heterogeneity and species diversity of grassland vegetation in the agro-pastoral ecotone of China’s Loess Plateau, integrating Taylor’s power law model with the minimum area concept to address scale-dependent ecological patterns. Field surveys were conducted across four vegetation types: small-leaf poplar forest (SP), pine–caragana mixed forest (PC), caragana shrubland (RC), and saline grassland (SG). Nested quadrats (0.25–8 m²) were used to establish species–area relationships (SARs), while binary occurrence frequency data fitted to Taylor’s power law quantified spatial heterogeneity parameters (δ_i, δ_c, CACD) and derived diversity indices (H′, J′, D). the results showed that species composition differed significantly among vegetation types, with RC exhibiting the highest richness (25 species) and SG the lowest (12 species). SAR analysis showed distinct z-values: SP had the lowest z (0.14), indicating minimal area effects and high homogeneity, while SG had the highest area sensitivity. Spatial heterogeneity (δ_c) was highest in RC and lowest in SP. Over 82.5% of herb-layer species exhibited aggregated distributions (δ_i > 0). The dominant species Leymus secalinus (Georgi) Tzvelev shifted from regular (δ_i < 0) under SP/SG to aggregated (δ_i > 0) under PC/RC. Diversity metrics peaked in PC plots (highest H′ and richness, lowest dominance), whereas SP showed high dominance but low diversity. CACD values (critical aggregation diversity) were maximized under SG. The integration of power law modeling and minimum area analysis effectively captures scale-dependent vegetation patterns. Pine–caragana mixed forests (PC) optimize biodiversity and spatial heterogeneity, suggesting moderated canopy structures enhance ecological stability. These findings provide a theoretical basis for sustainable grassland management in ecologically sensitive agro-pastoral zones. Full article

Intangible heritage comprises a wealth of knowledge, crafts, and skills that are passed down through the generations, embodied in our cultural practices. Many of these intertwine with landscape and environment; so, they are sensitive to climate change. While there have been studies of the impact of climate change on intangible heritage, these typically use heritage as a lens to examine climate impacts. There are few assessments of specific climate processes that threaten heritage. A climate-based approach allows researchers to identify mechanisms of change and quantify past impacts and project these into the future to give a sense of management options. We explore the threats to UNESCO domains of intangible heritage using weather and climate data from a range of sources to assess threats demonstrating the importance of data-informed approaches and show that timing of season and frequency of extreme events are important in climate-based assessments. These play out over different spatial and temporal scales that reveal elements of sensitivity to environmental change. The management response to the climate threat seems in need of a rights-based approach to empower those who own, safeguard, or practice the heritage. Research to enhance conservation should be translated into a form that speaks to local values and social structures. Full article

Object detection in soccer scenes serves as a fundamental task for soccer video analysis and target tracking. This paper proposes WCC-YOLO, a symmetry-enhanced object detection framework based on YOLOv11n. Our approach integrates symmetry principles at multiple levels: (1) The novel C3k2-WTConv module synergistically combines conventional convolution with wavelet decomposition, leveraging the orthogonal symmetry of Haar wavelet quadrature mirror filters (QMFs) to achieve balanced frequency-domain decomposition and enhance multi-scale feature representation. (2) The Channel Prior Convolutional Attention (CPCA) mechanism incorporates symmetrical operations—using average-max pooling pairs in channel attention and multi-scale convolutional kernels in spatial attention—to automatically learn to prioritize semantically salient regions through channel-wise feature recalibration, thereby enabling balanced feature representation. Coupled with InnerShape-IoU for refined bounding box regression, WCC-YOLO achieves a 4.5% improvement in mAP@0.5:0.95 and a 5.7% gain in mAP@0.5 compared to the baseline YOLOv11n while simultaneously reducing the number of parameters and maintaining near-identical inference latency (δ < 0.1 ms). This work demonstrates the value of explicit symmetry-aware modeling for sports analytics. Full article

Cracks are vital warning signs to reflect the structural deterioration in concrete constructions and buildings. However, their diverse and complex morphologies make accurate segmentation challenging. Deep learning-based methods effectively alleviate the low accuracy of traditional methods, while they are limited by the receptive field and computational efficiency, resulting in suboptimal performance. To address this challenging problem, we propose a novel framework termed High-frequency Refined Mamba with Snake Perception Attention module (HFR-Mamba) for more accurate crack segmentation. HFR-Mamba effectively refines Mamba’s global dependency modeling by extracting frequency domain features and the attention mechanism. Specifically, HFR-Mamba consists of the High-frequency Refined Mamba encoder, the Snake Perception Attention (SPA) module, and the Multi-scale Feature Fusion decoder. The encoder uses Discrete Wavelet Transform (DWT) to extract high-frequency texture features and utilizes the Refined Visual State Space (RVSS) module to fuse spatial features and high-frequency components, which effectively refines the global modeling process of Mamba. The SPA module integrates snake convolutions with different directions to filter background noise from the encoder and highlight cracks for the decoder. For the decoder, it adopts a multi-scale feature fusion strategy and a strongly supervised approach to enhance decoding performance. Extensive experiments show HFR-Mamba achieves state-of-the-art performance in IoU, DSC, Recall, Accuracy, and Precision indicators with fewer parameters, validating its effectiveness in crack segmentation. Full article

The Denoising Wavelet Transform Convolutional Long Short-Term Memory Network (DWTConvLSTM) is a novel ionospheric total electron content (TEC) spatiotemporal prediction model proposed in 2025 that can simultaneously consider high-frequency and low-frequency features while suppressing noise. However, it also has flaws as it only considers unidirectional temporal features in spatiotemporal prediction. To address this issue, this paper adopts a bidirectional structure and designs a bidirectional DWTConvLSTM model that can simultaneously extract bidirectional spatiotemporal features from TEC maps. Furthermore, we integrate a lightweight attention mechanism called Convolutional Additive Self-Attention (CASA) to enhance important features and attenuate unimportant ones. The final model was named CASA-BiDWTConvLSTM. We validated the effectiveness of each improvement through ablation experiments. Then, a comprehensive comparison was performed on the 11-year Global Ionospheric Maps (GIMs) dataset, involving the proposed CASA-BiDWTConvLSTM model and several other state-of-the-art models such as C1PG, ConvGRU, ConvLSTM, and PredRNN. In this experiment, the dataset was partitioned into 7 years for training, 2 years for validation, and the final 2 years for testing. The experimental results indicate that the RMSE of CASA-BiDWTConvLSTM is lower than those of C1PG, ConvGRU, ConvLSTM, and PredRNN. Specifically, the decreases in RMSE during high solar activity years are 24.84%, 16.57%, 13.50%, and 10.29%, respectively, while the decreases during low solar activity years are 26.11%, 16.83%, 11.68%, and 7.04%, respectively. In addition, this article also verified the effectiveness of CASA-BiDWTConvLSTM from spatial and temporal perspectives, as well as on four geomagnetic storms. Full article

Deeply implanted biomedical devices like leadless pacemakers require an antenna with minimal volume and high radiation efficiency to ensure reliable in-body communication and long operational time within the human body. This paper introduces a novel implantable antenna designed to significantly reduce the spatial requirements within an implantable capsule while maintaining high radiation efficiency in lossy media like heart tissue. The design principles of the proposed antenna are outlined, followed by antenna parameters and an equivalent circuit study that demonstrates how to fine-tune the antenna’s resonant frequency. The radiation characteristics of the antenna are thoroughly investigated, revealing a radiation efficiency of up to 28% at the Medical Implant Communication System (MICS) band and 56% at the 2.4 GHz ISM band. The transmission efficiency between two deeply implanted antennas within heart tissue has been improved by more than 15 dB compared to the current state of the art. The radiation and transmission performance of the proposed antennas has been validated through comprehensive simulations using anatomical human body models, phantom measurements, and in vivo animal experiments, confirming their superior radiation performance. Full article

This paper presents the final implemented design and performance evaluation of the ground-based C-band Cheia radar system, developed to enhance Romania’s contribution to the EU Space Surveillance and Tracking (EU SST) network. All data used for performance analysis are real-time, real-life measurements of true spatial test objects orbiting Earth. The radar is based on two decommissioned 32 m satellite communication antennas already present at the Cheia Satellite Communication Center, that were retrofitted for radar operation in a quasi-monostatic architecture. A Linear Frequency Modulated Continuous Wave (LFMCW) Radar design was implemented, using low transmitted power (2.5 kW) and advanced software-defined signal processing for detection and tracking of Low Earth Orbit (LEO) targets. System validation involved dry-run acceptance tests and calibration campaigns with known reference satellites. The radar demonstrated accurate measurements of range, Doppler velocity, and angular coordinates, with the capability to detect objects with radar cross-sections as low as 0.03 m² at slant ranges up to 1200 km. Tracking of medium and large Radar Cross Section (RCS) targets remained robust under both fair and adverse weather conditions. This work highlights the feasibility of re-purposing legacy satellite infrastructure for SST applications. The Cheia radar provides a cost-effective, EUSST-compliant performance solution using primarily commercial off-the-shelf components. The system strengthens the EU SST network while demonstrating the advantages of LFMCW radar architectures in electromagnetically congested environments. Full article

Neuroblastoma (NB) is an aggressive childhood cancer requiring intensive multimodal therapies in high-risk (HRNB) patients. Currently, invasive surgical biopsies are required to classify NB risk group and assign treatment based on the tumour genetic profile. Circulating tumour DNA (ctDNA) obtained from blood samples can be used to identify tumour biomarkers. Here we applied targeted next-generation sequencing (tNGS) using a panel of 42 genes to analyse 32 NB ctDNA samples for the presence of single-nucleotide variants and copy number changes from 28 patients in all NB risk groups. In two additional ctDNA samples, droplet digital PCR was used to detect hotspot ALK variants. Pathogenic mutations with a variant allele frequency (VAF) > 1% were identified in 13/32 (41%) ctDNA samples. ALK and PTPN11 were the most frequent, each being detected in 4/32 (13%) samples, together with oncogene amplifications. Targeted NGS of ctDNA detected actionable variants, including those absent in the diagnostic primary tumour due to spatial and temporal heterogeneity. Our findings confirm the usefulness of ctDNA in detecting genetic abnormalities in NB. Full article

Based on GPM satellite observations during June to September from 2014 to 2023, deep convective systems (DCSs) over the Tibetan Plateau and Sichuan Basin exhibited distinct spatiotemporal and structural characteristics. Over the Plateau, DCSs were primarily concentrated in the central and eastern regions, with echo-top heights typically ranging from 15 to 17 km and 40 dBZ echo tops mostly found between 6 and 8 km. In contrast, the Basin displayed a more spatially uniform distribution of convection, characterized by lower echo-top heights (12–14 km) and higher 40 dBZ echo tops. Although both regions experienced a seasonal peak in DCS frequency in July, their diurnal variations differed significantly. The Plateau exhibited a pronounced unimodal peak between 13:00 and 16:00, which was driven by strong surface heating. In the Basin, a bimodal pattern was observed, with elevated frequencies during 23:00–02:00 and 08:00–11:00. This pattern was likely influenced by local thermodynamic and topographic conditions. The altitude of maximum corrected radar reflectivity (MaxCRF) was predominantly between 4 and 7 km over the Plateau and confined to 2–4 km over the Basin. Over the Plateau, DCS frequency increased significantly with elevation, consistent with the enhancing role of high terrain, whereas no comparable relationship was found in the Basin. Instead, convective activity in the Basin appeared to be modulated primarily by atmospheric instability and moisture availability, highlighting the contrasting environmental controls between the two regions. Full article

With the development of the Industrial Internet of Things (IIoT) technology, fault diagnosis has emerged as a critical component of its operational reliability, and machine learning algorithms play a crucial role in fault diagnosis. To achieve better fault diagnosis results, it is necessary to have a sufficient number of fault samples participating in the training of the model. In actual industrial scenarios, it is often difficult to obtain fault samples, and there may even be situations where no fault samples exist. For scenarios without fault samples, accurately identifying the unknown faults of equipment is an issue that requires focused attention. This paper presents a method for the normal-sample-based mechanical equipment unknown fault detection. By leveraging the characteristics of the autoencoder network (AE) in deep learning for feature extraction and sample reconstruction, normal samples are used to train the AE network. Whether the input sample is abnormal is determined via the reconstruction error and a threshold value, achieving the goal of anomaly detection without relying on fault samples. In terms of input data, the frequency domain features of normal samples are used to train the AE network, which improves the training stability of the AE network model, reduces the network parameters, and saves the occupied memory space at the same time. Moreover, this paper further improves the network based on the traditional AE network by incorporating a convolutional neural network (CNN) and a long short-term memory network (LSTM). This enhances the ability of the AE network to extract the spatial and temporal features of the input data, further improving the network’s ability to extract and recognize abnormal features. In the simulation part, through public datasets collected in factories, the advantages and practicality of this method compared with other algorithms in the detection of unknown faults are fully verified. Full article

We review and as well obtain some new results on the temperature dependence of spatially nonlocal response functions of graphene and their applications to the calculation of both the equilibrium and nonequilibrium Casimir and Casimir–Polder forces. After a brief summary of the properties of the polarization tensor of graphene obtained within the Dirac model in the framework of quantum field theory, we derive the expressions for the longitudinal and transverse dielectric functions. The behavior of these functions at different temperatures is investigated in the regions below and above the threshold. Special attention is paid to the double pole at zero frequency, which is present in the transverse response function of graphene. An application of the response functions of graphene to the calculation of the equilibrium Casimir force between two graphene sheets and the Casimir–Polder forces between an atom (nanoparticle) and a graphene sheet is considered with due attention to the role of a nonzero energy gap, chemical potential and a material substrate underlying the graphene sheet. The same subject is discussed for out-of-thermal-equilibrium Casimir and Casimir–Polder forces. The role of the obtained and presented results for fundamental science and nanotechnology is outlined. Full article