Search Results (4,974)

The fluvial acoustic tomography (FAT) system relies on the arrival time of the system signal to calculate the parameters of the region. The traditional method uses the matching filter method to calculate the peak position of the received acoustic signal after cross-correlation calculation within a certain time as the signal arrival time point, but this method is difficult to be effectively applied to the complex underwater environment, especially in the case of extremely low SNR. To solve this problem, a two-channel deep learning model (DCA-Net) is proposed to detect the arrival time of acoustic chromatographic signals. Firstly, an interactive module is designed to transmit the auxiliary information from the cross-correlation subnetwork to the original signal subnet to improve the feature information extraction capability of the network. In addition, an attention module is designed to enable the network to selectively focus on the important features of the received acoustic signals. Under the background of white Gaussian noise and real river environment noise, we use the received signals of the acoustic tomography system collected in the field to synthesize low SNR data of −10, −15, and −20 different decibels as datasets. The experimental results show that the proposed network model is superior to the traditional matching filtering method and some other deep neural networks in three low SNR datasets. Full article

To cope with common local optimum traps and balance exploration and development in complex multi-peak optimisation problems, this paper puts forth a Dual-Performance Multi-subpopulation Adaptive Restart Differential Evolutionary Algorithm (DPR-MGDE) as a potential solution. The algorithm employs a novel approach by utilising the fitness and historical update frequency as dual-performance metrics to categorise the population into three distinct sub-populations: PM (the promising individual set), MM (the medium individual set) and UM (the un-promising individual set). The multi-subpopulation division mechanism enables the algorithm to achieve a balance between global exploration, local exploitation and diversity maintenance, thereby enhancing its overall optimisation capability. Furthermore, the DPR-MGDE incorporates an adaptive cross-variation strategy, which enables the dynamic adjustment of the variation factor and crossover probability in accordance with the performance of the individuals. This enhances the flexibility of the algorithm, allowing for the prioritisation of local exploitation among the more excellent individuals and the exploration of new search space among the less excellent individuals. Furthermore, the algorithm employs a collision-based Gaussian wandering restart strategy, wherein the collision frequency serves as the criterion for triggering a restart. Upon detecting population stagnation, the updated population is subjected to optimal solution-guided Gaussian wandering, effectively preventing the descent into local optima. Through experiments on the CEC2017 benchmark functions, we verified that DPR-MGDE has higher solution accuracy compared to newer differential evolution algorithms, and proved its significant advantages in complex optimisation tasks with the Wilcoxon test. In addition to this, we also conducted experiments on real engineering problems to demonstrate the effectiveness and superiority of DPR-MGDE in dealing with real engineering problems. Full article

Spoofing detection is critical for GNSS security. To address the issues of low detection rates and insufficient coverage in traditional methods, this study proposes an eye diagram detection method based on the multiscale Canny algorithm with minimum misjudgment probability (EDDM-MSC-MMP). Unlike conventional correlation peak distortion detection techniques, the proposed method uses the MSC-MMP algorithm to perform multiscale edge extraction from the eye diagram generated from the receiver's correlation values. It then calculates the image threshold using minimum misjudgment probability to ensure the accuracy of the eye diagram's edges. This enables the accurate detection of subtle changes in the eye diagram, leading to the better identification of spoofing signals. The results show that the MSC-MMP outperforms traditional edge extraction algorithms by over 0.072 in terms of the optimal dataset scale F score (ODS-F). Compared to signal quality monitoring (SQM) and Carrier-to-Noise Ratio methods, the EDDM-MSC-MMP method increases spoofing detection coverage by over 60%, achieving the highest detection rate in the TEXBAT dataset. Overall, the EDDM-MSC-MMP method improves the reliability and coverage of spoofing detection, providing an effective solution for GNSS spoofing detection. Full article

The significant increase in demand for camel milk has led to a rapid increase in the number of Bactrian camels. However, the widespread occurrence of mastitis significantly impacts the development of the Bactrian camel milk industry and poses a public health risk. Despite this, there is a lack of research on the transcriptional response, immune response pathways, and changes in core genes of Bactrian camels with subclinical mastitis. This study aimed to reveal the changes in immune-related response pathways and gene transcription levels in Bactrian camels with subclinical mastitis by analyzing the blood transcriptional response after the occurrence of subclinical mastitis in natural conditions. This study focused on 7-year-old Bactrian camels and collected 2 mL of blood from the camels that tested positive with a 4-peak California Mastitis Test (CMT) and those that tested negative with a 3-peak CMT. RNA sequencing (RNA-Seq) technology was used to analyze gene expression in the blood samples. Gene expression was verified using quantitative reverse transcription polymerase chain reaction (RT-qPCR). Overall, 1722 differentially expressed genes were sequenced in the blood samples of CMT-positive and CMT-negative Bactrian camels, including 1061 upregulated and 661 downregulated genes. After conducting gene ontology functional enrichment, 453 differentially expressed genes were identified. We also discovered pathways such as immune response, the G-protein-coupled receptor signaling pathway, and internal signal transmission. Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment detected 668 differentially expressed genes annotated onto 309 metabolic pathways, with significantly enriched immune pathways including cytokine–cytokine receptor interaction, complex and coalescence cascades, natural killer cell-mediated cytotoxicity, and T helper type 17 cell differentiation, among others. Through a STRING protein interaction database and cytoscape analysis, it was found that core differentially expressed genes related to immunity included IL10, CCL5, IL1B, OSM, TNFRSF1B, IL7, and CCR3, among others. The RT-qPCR results for six randomly selected core differentially expressed genes showed that the RT-qPCR expression pattern was consistent with the RNA Seq results. The immune-related genes in Bactrian camels affected by subclinical mastitis are primarily concentrated in the immune response and the cytokine–cytokine receptor interaction pathway. Given the importance of these pathways and the connections among related genes, the immune genes within these pathways may play a crucial role in the pathogenesis of subclinical mastitis in Bactrian camels. This study provides a valuable reference for investigating the immune regulatory mechanisms of subclinical mastitis in Bactrian camels. Full article

This study examines at the prevalence and spread of Hepatitis A Virus (HAV) and norovirus GI/GII in local and imported food products in Greece over a five-year period (2019–2024). A total of two hundred sixty-six food samples were evaluated using obligatory inspections and virus detection procedures, including 202 for Hepatitis A and 64 for Norovirus. High-risk categories analyzed were vegetables [138 (HAV), 17 (NoV)], fruits [16 (HAV), 7 (NoV)], soft fruits/berries [37 (HAV), 31 (NoV)], processed meals [4 (HAV), 4 (NoV)], and animal-based products [1 (HAV), 5 (NoV)]. Viral RNA was isolated using QIAamp Viral RNA Mini Kit and detected using established RT-qPCR procedures that met ISO requirements for high sensitivity and reproducibility. The results demonstrated HAV contamination mostly in vegetables (4.35% positive rate), with sporadic findings in other categories. Norovirus GI/GII was detected primarily in soft fruits/berries, with a category-specific positive rate of 6.45%. A temporal study revealed that HAV peaks in 2020, while Norovirus contaminations were detected in 2021 and 2024. The findings highlight the important need to incorporate viral testing into routine food safety procedures, especially for high-risk product categories. This study establishes a basic framework for public health initiatives that address gaps in foodborne virus surveillance in Greece. The study’s ramifications extend to global efforts to monitor and reduce foodborne virus contamination, pushing for higher regulatory requirements and targeted preventative actions. Full article

Subgenomic RNAs (sgRNAs) are potential markers of active SARS-CoV-2 replication, serving as templates for the synthesis of structural and accessory proteins in infectious viral particles. This study aimed to use RT-qPCR to quantify sgRNA and negative RNA intermediates, assessing viral replication in virus samples inactivated by β-propiolactone (βPL). Inactivated viruses subjected to five blind serial passages (BSs) were amplified by RT-qPCR using primers to target the envelope (ENV) and nucleoproteins (N1 and N2) of genomic genes, subgenomic envelope RNA (sgENV), and intermediate envelope RNA (ENV-). All positive controls showed consistent viral titers across passages (10 log10 copies/mL in N1/N2 and 11 log10 copies/mL in ENV) during BSs. Inactivated viral samples for ENV and ENV- targets ranged from 11.34 log10 copies/mL in BS1 to 11.20 log10 copies/mL in BS5. The sgENV was no longer detected in the inactivated SARS-CoV-2 samples after the second passage, suggesting successful inactivation. Replication kinetics showed consistent profiles for N1/N2, ENV, and ENV- targets in the first three post-infection hours (pih) and maintained approximately 5 log10 copies/mL at 1 pih, 2 pih, and 3 pih. A sharp exponential increase in the viral titer was observed from 24 pih onwards, peaking at 11.64 log10 copies/mL at 48 pih. Transmission electron microscopy confirmed viral particles only in cells infected with active SARS-CoV-2. These results support the use of sgRNA as a reliable marker for SARS-CoV-2 replication, especially in distinguishing between active replication and non-viable particles and in the development of diagnostic and therapeutic strategies. Full article

Musculoskeletal injuries induced by high-intensity and repetitive physical activities represent one of the primary health concerns in the fields of public fitness and sports. Musculoskeletal injuries, often resulting from unscientific training practices, are particularly prevalent, with the tibia being especially vulnerable to fatigue-related damage. Current tibial load monitoring methods rely mainly on laboratory equipment and wearable devices, but datasets combining both sources are limited due to experimental complexities and signal synchronization challenges. Moreover, wearable-based algorithms often fail to capture deep signal features, hindering early detection and prevention of tibial fatigue injuries. In this study, we simultaneously collected data from laboratory equipment and wearable insole sensors during in-place running by volunteers, creating a dataset named WearLab-Leg. Based on this dataset, we developed a machine learning model integrating Temporal Convolutional Network (TCN) and Transformer modules to estimate vertical ground reaction force (vGRF) and tibia bone force (TBF) using insole pressure signals. Our model’s architecture effectively combines the advantages of local deep feature extraction and global modeling, and further introduces the Weight-MSELoss function to improve peak prediction performance. As a result, the model achieved a normalized root mean square error (NRMSE) of 7.33% for vGRF prediction and 10.64% for TBF prediction. Our dataset and proposed model offer a convenient solution for biomechanical monitoring in athletes and patients, providing reliable data and technical support for early warnings of fatigue-induced injuries. Full article

This study evaluates the effects of fermented whey (FW) and pumpkin (P) on the excretion of aflatoxin B1 (AFB1) and ochratoxin A (OTA) in rats using immunoaffinity column cleanup and high-performance liquid chromatography–fluorescence detection (IAC-LC-FLD). The method achieved detection limits of 0.1 µg/kg for AFB1 and 0.3 µg/kg for OTA, with recovery rates ranging from 72–92% for AFB1 and 88–98% for OTA. A fecal analysis of 100 rats showed peak AFB1 concentrations of 418 µg/kg and OTA of 1729 µg/kg. In the toxin-exposed groups, OTA levels were higher than AFB1, with males in the OTA-only group showing significantly higher OTA (1729 ± 712 µg/kg) than females (933 ± 512 µg/kg). In the AFB1-only group, the fecal levels were 52 ± 61 µg/kg in males and 91 ± 77 µg/kg in females. The AFB1 + FW group showed notable AFB1 concentrations (211 ± 51 µg/kg in males, 230 ± 36 µg/kg in females). The FW + P combination further influenced excretion, with higher AFB1 and OTA levels. These findings suggest that FW and P modulate mycotoxin excretion and may play a role in mycotoxin detoxification, providing insight into dietary strategies to reduce mycotoxin exposure and its harmful effects. Full article

DNA G-quadruplexes (G4) are non-canonical DNA structures that play key roles in various biological processes. Antibody-dependent sequencing is an important tool for identifying intracellularly formed DNA G4s, and peak calling is a crucial step in processing the sequencing data. As the applicability of existing peak calling algorithms to intracellular G4 data has not been previously assessed, we systematically compared and evaluated these algorithms to determine those best suited for G4 detection. We selected seven representative candidates from 43 published peak calling algorithms for detailed evaluation. The performance of each candidate on six published intracellular G4 sequencing datasets (GSE107690, GSE145090, GSE133379, GSE178668ChIP-seq, GSE178668CUT&Tag, GSE221437) were assessed by precision and recall against customized benchmarks integrating results from multiple algorithms, as well as consistency with known G4 information (pG4 predicted by pqsfinder, oG4 from GSE63874, and multi-cell-line conserved G4s) and epigenetic signals. We identified MACS2, PeakRanger, and GoPeaks as the most effective algorithms for analyzing intracellular G4 sequencing data, and attributed their superior performance partially to the distribution model of sequencing reads/fragments used in the hypothesis testing step of the peak calling procedures. These findings provide guidance and rationale for selecting peak callers appropriate for intracellular G4 data. Full article

Detecting multiple tumor markers is of great importance. It helps in early cancer detection, accurate diagnosis, and monitoring treatment. In this work, gold nanoparticles–toluidine blue–graphene oxide (AuNPs-TB–GO) and gold nanoparticles–carboxyl ferrocene–tungsten disulfide (AuNPs–FMC–WS₂) nanocomposites were prepared for labeling Carcinoembryonic antigen (CEA) antibody and Carbohydrate antigen 72–4 (CA72-4) antibody, respectively, and used as two kinds of probes with different electrochemical signals. With the excellent magnetic performance of biotin immune magnetic beads (IMBs), the biofunctional IMBs were firmly deposited on the magnetic glassy carbon electrode (MGCE) surface by applying a constant magnetic field, and then the CEA and CA72-4 antibody were immobilized on the IMBs by the avidin–biotin conjugation. The assay was based on the change in the detection peak current. Under the optimum experimental conditions, the linear range of detection of CEA is of the two-component immunosensor is from 0.01 to 120 ng/mL, with a low detection limit of 0.003 ng/mL, and the linear range of detection of CA72-4 is from 0.05 to 35 U/mL, with a detection limit of 0.016 U/mL. The results showed that the proposed immunosensor enabled simultaneous monitoring of CEA and CA72-4 and exhibited good reproducibility, excellent high selectivity, and sensitivity. In particular, the proposed multiplexed immunoassay approach does not require sophisticated fabrication and is well-suited for high-throughput biosensing and application to other areas. Full article

Research has found kernel anomaly regions in the power spectrum images of ionospheric electric fields in space, which are widely distributed. To effectively detect these kernel abnormal regions, this paper proposes a new kernel abnormal region detection method, KANs-Unet, based on KANs and U-net networks. The model embeds the KAN-Conv convolutional module based on KANs in the encoder section, introduces the feature pyramid attention module (FPA) at the junction of the encoder and decoder, and introduces the CBAM attention mechanism module in the decoder section. The experimental results show that the improved KANs-Unet model has a mIoU improvement of about 10% compared to the PSPNet algorithm and an improvement of about 7.8% compared to the PAN algorithm. It has better detection performance than the currently popular semantic segmentation algorithms. A higher evaluation index represents that the detected abnormal area is closer to the label value (i.e., the detected abnormal area is more complete), indicating better detection performance. To further investigate the characteristics of kernel anomaly areas and the differences in features during magnetic storms, the author studied the characteristics of kernel anomaly areas during two different intensities of magnetic storms: from November 2021 to October 2022 and from 1 May 2024 to 13 May 2024 (large magnetic storm), and from 11 October 2023 to 23 October 2023 (moderate magnetic storm). During a major geomagnetic storm, the overall distribution of kernel anomaly areas shows a parallel trend with a band-like distribution. The spatial distribution of magnetic latitudes is relatively scattered, especially in the southern hemisphere, where the magnetic latitudes are wider. Additionally, the number of orbits with kernel anomaly areas during ascending increases, especially during peak periods of major geomagnetic storms. The overall spatial distribution of moderate geomagnetic storms does not change significantly, but the global magnetic latitude distribution is relatively concentrated. Full article

Urban heat islands (UHIs) increase urban warming and reduce outdoor thermal comfort due to changing surface characteristics and climate change. This study investigates the role of green walls (GWs) in mitigating UHI, improving outdoor thermal comfort, and reducing carbon emissions under current and future (2050) scenarios. Focusing on Via della Consolata, Turin, Italy, the study combines remote sensing for UHI detection and numerical simulations for thermal analysis during seasonal extremes. The results show that GWs slightly reduce air temperatures, with a maximum decrease of 1.6 °C in winter (2050), and have cooling effects on mean radiant temperature (up to 2.27 °C) during peak summer solar radiation. GWs also improve outdoor comfort, reducing the Universal Thermal Climate Index by 0.55 °C in the summer of 2050. The energy analysis shows that summer carbon emission intensity is reduced by 31%, despite winter heating demand increasing emissions by 45%. The study highlights the potential of GWs in urban climate adaptation, particularly in dense urban environments with low sky view factors. Seasonal optimization is crucial to balance cooling and heating energy demand. As cities face rising temperatures and heat waves, the integration of GWs offers a sustainable strategy to improve microclimate, reduce carbon emissions, and mitigate the effects of UHI. Full article

To produce plug seedlings with uniform growth and which are suitable for high-speed transplanting operations, it is essential to sow seeds precisely at the center of each plug-tray hole. For accurately determining the position of the seed covered by the substrate within individual plug-tray holes, a novel method for detecting the growth points of plug seedlings has been proposed. It employs an adaptive grayscale processing algorithm based on the differential evolution extra-green algorithm to extract the contour features of seedlings during the early stages of cotyledon emergence. The pixel overlay curve peak points within the binary image of the plug-tray’s background are utilized to delineate the boundaries of the plug-tray holes. Each plug-tray hole containing a single seedling is identified by analyzing the area and perimeter of the seedling’s contour connectivity domains. The midpoint of the shortest line between these domains is designated as the growth point of the individual seedling. For laboratory-grown plug seedlings of tomato, pepper, and Chinese kale, the highest detection accuracy was achieved on the third-, fourth-, and second-days’ post-cotyledon emergence, respectively. The identification rate of missing seedlings and single seedlings exceeded 97.57% and 99.25%, respectively, with a growth-point detection error of less than 0.98 mm. For tomato and broccoli plug seedlings cultivated in a nursery greenhouse three days after cotyledon emergence, the detection accuracy for missing seedlings and single seedlings was greater than 95.78%, with a growth-point detection error of less than 2.06 mm. These results validated the high detection accuracy and broad applicability of the proposed method for various seedling types at the appropriate growth stages. Full article

Ultrasonic flowmeters are essential sensor devices widely used in remote metering systems, smart grids, and monitoring systems. In these environments, a low-power design is critical to maximize energy efficiency. Real-time data collection and remote consumption monitoring through remote metering significantly enhance network flexibility and efficiency. This paper proposes a low-complexity structure that ensures an accurate time-of-flight (ToF) estimation within an acceptable error range while reducing computational complexity. The proposed system utilizes Hilbert envelope detection and a differentiator-based parallel peak detector. It transmits and collects data through ultrasonic transmitter and receiver transducers and is designed for seamless integration as a node into wireless sensor networks (WSNs). The system can be involved in various IoT and industrial applications through high energy efficiency and real-time data transmission capabilities. The proposed structure was validated using the MATLAB software, with an LPG gas flowmeter as the medium. The results demonstrated a mean relative deviation of 5.07% across a flow velocity range of 0.1–1.7 m/s while reducing hardware complexity by 78.9% compared to the conventional FFT-based cross-correlation methods. This study presents a novel design integrating energy-efficient ultrasonic flowmeters into remote metering systems, smart grids, and industrial monitoring applications. Full article

The generalized Radon–Fourier transform (GRFT) is a well-established coherent accumulation technique for high-speed and high-mobility target detection. However, this method tends to suffer from the difficulty of identifying the main lobe from multiple blind speed sidelobes (BSSLs) and the computational complexity is generally high. To address these challenges, we propose a new method, namely the BSSL Traction Particle Swarm Optimization (BTPSO), to robustly and accurately extract the main lobe. In the method, the relationship between the main lobe and the BSSLs is used to attract particles to potential positions of the main lobe in the group when trapped in local optimal, and a new termination criterion in which multiple particles should converge to the same optimal value is proposed to avoid local convergence. Simulation examples show that the proposed method can improve the probability of converging to the main lobe peak while reducing cost time, and its good adaptability to low signal-to-noise ratio (SNR) cases is well verified. Full article