Search Results (331)

Accurate short-term weather forecasting plays a vital role in disaster response, agriculture, and energy management, where timely and reliable predictions are essential for decision-making. Graph neural networks (GNNs), known for their ability to model complex spatial structures and relational data, have achieved remarkable success in meteorological forecasting by effectively capturing spatial dependencies among distributed weather stations. However, most existing GNN-based approaches rely on pairwise station connections, limiting their capacity to represent higher-order spatial interactions. Moreover, their dependence on supervised learning makes them vulnerable to spatial heterogeneity and temporal non-stationarity. This paper introduces a novel spatial–temporal pretraining framework, Hypergraph-enhanced Meteorological Pretraining (HyMePre), which combines hypergraph neural networks with self-supervised learning to model high-order spatial dependencies and improve generalization across diverse climate regimes. HyMePre employs a two-stage masking strategy, applying spatial and temporal masking separately, to learn disentangled representations from unlabeled meteorological time series. During forecasting, dynamic hypergraphs group stations based on meteorological similarity, explicitly capturing high-order dependencies. Extensive experiments on large-scale reanalysis datasets show that HyMePre outperforms conventional GNN models in predicting temperature, humidity, and wind speed. The integration of pretraining and hypergraph modeling enhances robustness to noisy data and improves generalization to unseen climate patterns, offering a scalable and effective solution for operational weather forecasting. Full article

Cattle–yak, a hybrid of yak and cattle, exhibits significant heterosis but male infertility, hindering heterosis fixation. Although extensive research has been conducted on transcriptional mechanisms in the testes of cattle–yak, the understanding of their translational landscape remains limited. In this study, we characterized the translational landscape of yak and cattle–yak based on Ribo-seq technology integrated with RNA-seq data. The results revealed that gene expression was not fully concordant between transcriptional and translational levels, whereas cattle–yak testes exhibited a stronger correlation across these two regulatory layers. Notably, genes that were differentially expressed at the translational level only (MEIOB, MEI1, and SMC1B) were mainly involved in meiosis. A total of 4,236 genes with different translation efficiencies (TEs) were identified, and the TEs of most of the genes gradually decreased as the mRNA expression level increased. Further research revealed that genes with higher TE had a shorter coding sequence (CDS) length, lower GC content, and higher normalized minimum free energy in the testes of yaks, but this characteristic was not found in cattle–yaks. We also identified upstream open reading frames (uORFs) in yak and cattle–yak testes, and the sequence characteristics of translated uORFs and untranslated uORFs were markedly different. In addition, we identified several short polypeptides that may play potential roles in spermatogenesis. In summary, our study uncovers distinct translational dysregulations in cattle–yak testes, particularly affecting meiosis, which provides novel insights into the mechanisms of spermatogenesis and male infertility in hybrids. Full article

The ultimate goal of research on seismic mitigation technologies is engineering application. However, current studies primarily focus on the application of dampers in planar structures, while actual engineering structures are three-dimensional (3D) in nature. A type of damper, making up tuned mass dampers (TMDs) and inerters, has excellent vibration mitigation performance and needs brackets to connect to structures. In this work, a coupled dynamic model of an energy dissipation system (EDS) comprising a TMD, an inerter, a bracket, and a 3D building structure is presented, along with analytical solutions for stochastic seismic responses. The main work is as follows. Firstly, based on D’Alembert’s dynamics principle, the seismic dynamic equations of an EDS considering a realistic damper and a 3D structure are formulated. The general dynamic equations governing the bidirectional horizontal motion of the EDS are further derived using the dynamic finite element technique. Secondly, analytical expressions for spectral moments and variances of seismic responses are obtained. Finally, four numerical examples are presented to investigate the following: (1) verification of the proposed response solutions, showing that the calculation time of the proposed method is approximately 1/500 of that of the traditional method; (2) examination of spatial effects in 3D structures under unidirectional excitation, revealing that structural seismic responses in the direction along the earthquake ground motion is approximately 10⁴ times that in the direction perpendicular to the ground motion; (3) investigation of the spatial dynamic characteristics of a 3D structure subjected to unidirectional seismic excitation, showing that the bracket parameters significantly affect the damping effects on an EDS; and (4) application of the optimization method for the damper’s parameters that considers system dynamic reliability and different weights of the damper’s parameters as constraints, indicating that the most economical damping parameters can achieve a reduction in displacement spectral moments by 30–50%. The proposed response solutions and parameter optimization technique provide an effective approach for evaluating stochastic seismic responses and optimizing damper parameters in large-scale and complex structures. Full article

Yellow leaf disease (YLD), caused by the areca palm yellow leaf phytoplasma (APYL), poses a significant threat to the sustainability of the areca palm industry. Timely and accurate detection is essential for effectively diagnosing and managing this disease. This study developed a novel nested PCR system using primers specifically designed from conserved regions of the phytoplasma 16S rDNA sequence to overcome limitations such as false positives often associated with universal nested PCR primers. The resulting primer pairs HNP-1F/HNP-1R (outer) and HNP-2F/HNP-2R (inner) consistently amplified a distinct 429 bp fragment from APYL strains belonging to the 16SrI and 16SrII groups. The detection sensitivity reached 7.5 × 10⁻⁷ ng/μL for 16SrI and 4 × 10⁻⁷ ng/μL for 16SrII. Field validation using leaf samples from symptomatic areca palms confirmed the high specificity and reliability of the new primers in detecting APYL. Compared to conventional universal primers (P1/P7 and R16mF2/R16mR1), this newly developed nested PCR system demonstrated higher specificity, sensitivity, and speed, making it a valuable tool for the early diagnosis and management of YLD in areca palms. Full article

The widespread environmental dissemination of imidazole compounds necessitates robust analytical monitoring tools. This study developed a novel isotope-labeled surrogate-based high-performance liquid chromatography coupled with tandem mass spectrometry (HPLC-MS/MS) method for the simultaneous determination of 21 imidazoles in water, sediment, and soil. The optimized SPE protocol using Oasis HLB cartridges achieved high recoveries, with chromatographic separation completed in 25 min. Six isotope-labeled standards effectively corrected matrix effects (−57% to 8%), yielding MQLs < 1.0 ng·L⁻¹ (water) and <1.0 μg·kg⁻¹ (sediment/soil). Validation confirmed linearity (R² > 0.995), accuracy (60–120% recovery for 20/21 analytes), and precision (relative standard deviation, RSD < 15%). Its application in Jiulong River revealed significant contamination, detecting eight imidazoles in both water (up to 49.29 ng·L⁻¹) and sediment (up to 24.01 μg·kg⁻¹). This standardized tool enables routine monitoring of pharmaceutical residues across environmental compartments, supporting regulatory frameworks. Full article

Based on the sewage pipe network system in the service area of Qianshan-Gongbei Plant in Zhuhai City, the characteristics of water quality and quantity were analyzed, and the common problems were diagnosed. Through the establishment of a hydraulic-water quality model, the flow state of sewage in the pipe network is simulated, and the actual data is checked. It is found that there are significant differences in the quantity and quality of sewage pipe network systems in different seasons and land use types, and there is an obvious seawater backflow phenomenon in coastal areas. To solve these problems, this paper puts forward a series of optimization suggestions to improve the operation efficiency of sewage treatment plants and the reliability of urban drainage systems. Full article

Accurately detecting lane lines is a hot topic in computer vision. How to effectively utilize the relationship between lane features for detection is still an open question. In this paper, we propose a novel lane detection model based on convolutional neural network (CNN), namely, the BSCNNLaneNet (Bidirectional Spatial CNN Lane Detection Network). The proposed model is based on the spatial CNN method and incorporates a bidirectional recurrent neural network (BRNN) block to learn the spatial relationships between slice features. Additionally, a convolutional block attention mechanism is introduced to gain global features, which enhance the global connection between slice features in different directions. We conduct extensive experiments on the TuSimple dataset. The results demonstrate that the proposed method surpasses the original spatial CNN method, achieving an increase in accuracy from 96.53% to 96.86%. Full article

Detecting oriented electrical equipment plays a fundamental role in enabling intelligent defect diagnosis in power systems. However, existing oriented object detection methods each have their own limitations, making it challenging to achieve robust and accurate detection under varying conditions. This work proposes a model ensemble approach that leverages the complementary strengths of two representative detectors—Oriented R-CNN and S²A-Net—to enhance detection performance. Recognizing that discrepancies in confidence score distributions may negatively impact ensemble results, this work first designs a calibration method to align the confidence levels of predictions from each model. Following calibration, a soft non-maximum suppression (Soft-NMS) strategy is employed to fuse the outputs, effectively refining the final detections by jointly considering spatial overlap and the calibrated confidence scores. The proposed method is evaluated on an infrared image dataset for electric power equipment detection. Experimental results demonstrate that our approach not only improves the performance of each individual model by 1.95 mean Average Precision (mAP) but also outperforms other state-of-the-art methods. Full article

Bacterial infections, particularly those caused by multidrug-resistant strains, remain a significant global public health challenge. The growing resistance to traditional antibiotics highlights the urgent need for novel antibacterial strategies. Herein, we successfully synthesized three types of nitrogen-doped carbon dots (tBuCz-CDs, HAH-CDs, and EC-CDs) via hydrothermal method using tert-butyl carbazate, hydroxyacetic acid hydrazide, and ethyl carbazate as precursors. tBuCz-CDs, HAH-CDs, and EC-CDs exhibited potent antibacterial activity against methicillin-resistant Staphylococcus aureus (MRSA), with minimum inhibitory concentrations (MICs) of 100, 100, and 150 µg/mL, respectively. Their antibacterial effect on MRSA was comparable to that of the widely used antibiotic vancomycin hydrochloride, as shown by the zone of inhibition assay. Furthermore, the carbon dots exhibited low cytotoxicity and hemolytic activity showing their excellent biocompatibility both in vitro and in vivo. They also significantly promoted wound healing compared to untreated controls. Notably, the serial passaging of MRSA exposed to these carbon dots did not result in the bacterial resistance. Mechanistic studies revealed that the carbon dots exerted antibacterial effects through multiple mechanisms, including the disruption of bacterial membranes, inhibition and eradication of biofilm formation, generation of reactive oxygen species, and DNA damage. This work highlights the potential of nitrogen-doped CDs as a promising material for combating drug-resistant bacterial infections and underscores their potential for further biomedical development. Full article

High demand for 6G wireless has made photonics-aided D-band (110–170 GHz) communication a research priority. Photonics-aided technology integrates optical and wireless communications to boost spectral efficiency and transmission distance. This study presents a Radio-over-Fiber (RoF) communication system utilizing photonics-aided technology for 4600 m long-distance D-band transmission. We successfully show the transmission of a 50 Gbps (25 Gbaud) QPSK signal utilizing a 128.75 GHz carrier frequency. Notwithstanding these encouraging outcomes, RoF systems encounter considerable obstacles, including pronounced nonlinear distortions and phase noise related to laser linewidth. Numerous factors can induce nonlinear impairments, including high-power amplifiers (PAs) in wireless channels, the operational mechanisms of optoelectronic devices (such as electrical amplifiers, modulators, and photodiodes), and elevated optical power levels during fiber transmission. Phase noise (PN) is generated by laser linewidth. Despite the notable advantages of classical Volterra series and deep neural network (DNN) methods in alleviating nonlinear distortion, they display considerable performance limitations in adjusting for phase noise. To address these problems, we propose a novel post-processing approach utilizing a two-dimensional convolutional neural network (2D-CNN). This methodology allows for the extraction of intricate features from data preprocessed using traditional Digital Signal Processing (DSP) techniques, enabling concurrent compensation for phase noise and nonlinear distortions. The 4600 m long-distance D-band transmission experiment demonstrated that the proposed 2D-CNN post-processing method achieved a Bit Error Rate (BER) of 5.3 × 10⁻³ at 8 dBm optical power, satisfying the soft-decision forward error correction (SD-FEC) criterion of 1.56 × 10⁻² with a 15% overhead. The 2D-CNN outperformed Volterra series and deep neural network approaches in long-haul D-band RoF systems by compensating for phase noise and nonlinear distortions via spatiotemporal feature integration, hierarchical feature extraction, and nonlinear modelling. Full article

In order to solve the problems of identity solidification, key duration, and lack of anonymity in communications between unmanned aerial vehicles (UAVs) and ground control stations (GCSs), a mutual secure communication scheme named Dynamic Identity and Hybrid Encryption is proposed in this paper. By constructing an identity update mechanism and a lightweight hybrid encryption system, the anonymity and untraceability of the communicating parties can be realized within a resource-limited environment, and threats such as man-in-the-middle (MITM) attacks, identity forgery, and message tampering can be effectively resisted. Dynamic Identity and Hybrid Encryption (DIHE) uses a flexible encryption strategy to balance security and computing cost and satisfies security attributes such as mutual authentication and forward security through formal verification. Our experimental comparison shows that, compared with the traditional scheme, the calculation and communication costs of DIHE are lower, making it especially suitable for the communication environment between UAVs and GCSs with limited computing power, thus providing a feasible solution for secure low-altitude Internet of Things (IoT) communication. Full article

Bimetallic catalysts can effectively enhance the catalytic degradation efficiency of peroxymonosulfate (PMS), which is usually attributed to the enhancement of electron transfer, but currently, there is no clear explanation of the mechanism of how the electron transfer is enhanced. A nitrogen-doped Fe/Mn composite biochar (FeMn-NBC) was co-constructed by hydrothermal synthesis and high-temperature calcination. The FeMn-NBC activated PMS more efficiently than the monometallic one due to the enhanced electron transfer between Fe and Mn. The FeMn-NBC/PMS system activated PMS with Mn as the active center, and the high oxidation state of Mn⁴⁺ promoted the acceleration of the PMS adsorption of the generation of Mn²⁺/Mn³⁺. This gaining effect accelerated the electron cycling between Fe²⁺/Fe³⁺ and Mn²⁺/Mn³⁺/Mn⁴⁺, which enhanced the PMS catalysis to generate free radicals (•OH, SO₄^•− and •O₂⁻) and non-radicals (¹O₂) for the efficient degradation of diisobutyl phthalate (DIBP). Benefiting from this gaining effect, the degradation rate of DIBP by the FeMn-NBC/PMS system was increased by 2.43 and 3.38 times compared to Fe-NBC and Mn-NBC. The bimetallic-enhanced electron transfer mechanism proposed in this study facilitated the development of efficient catalysts for more efficient and selective removal of organic pollutants. Full article

The internal growth patterns and surface micromorphology of diamonds provide a record of their multi-stage evolution, from initial formation within the mantle to their eventual ascent to the Earth’s surface via deeply derived kimberlite magmas. In this study, gemological microscopic examination, Diamond View^TM, Raman spectroscopy, and electron probe analysis were employed to analyze the surface features, internal patterns, and inclusions of the Tancheng alluvial diamonds in Shandong Province, China. The results show that surface features of octahedra with triangular and sharp edges, thick steps with irregular contours or rounded edges, and thin triangular or serrated layers are developed on diamonds during deep-mantle storage, as well as during the growth process of diamonds, when they are not subjected to intense dissolution. The rounding of octahedral and cubic diamond edges and their transformation into tetrahedral (THH) shapes are attributed to resorption in kimberlitic magma. These characteristics indicate that the Tancheng diamonds were commonly resorbed by carbonate–silicate melts during mantle storage. Abnormal birefringence phenomena, including irregular extinction patterns, petaloid and radial extinction patterns, and banded birefringence, were formed during the diamond growth stage. In contrast, fine grid extinction patterns and composite superimposed extinction patterns are related to later plastic deformation. The studied diamonds mainly contain P-type inclusions of olivine and graphite, with a minority of E-type inclusions, including coesite and omphacite. The pressure of entrapment of olivine inclusions within the Tancheng diamonds ranges from 4.3 to 5.9 GPa, which is consistent with that of coesite inclusions, which yield pressure ranging from 5.2 to 5.5 GPa, and a temperature range of 1083–1264 °C. Overall, the evidence suggests that Tancheng diamonds probably originated from hybrid mantle sources metasomatized by the subduction of ancient oceanic lithosphere. Full article

Background: Health departments disseminate health education related to influenza to the public through various media in China. We examined knowledge, attitudes, and practices regarding influenza and the influenza vaccine (KAP-flu) over time among pregnant women (PW) compared to non-PW. Methods: A cross-sectional survey was conducted in Suzhou, China in 2013 and 2023. We included and interviewed PW seeking prenatal care and excluded PW there for non-routine visits. The comparison group was drawn from non-PW seeking physical examinations at the same facilities. Stratified cluster sampling was used to enroll participants from the various levels of prenatal-care facilities. Results: In 2013, we surveyed 1673 PW and 401 non-PW, and in 2023, we surveyed 2195 PW and 1171 non-PW. The proportion of PW who had ever heard of the influenza vaccine showed no significant change, at 56% in 2013 and 57% in 2023; by contrast, there was a significant increase among non-PW (55% to 78%). The proportion of pregnant participants who knew when to get vaccinated dropped from 14% to 12%, in contrast to the increase among non-PW (6% to 20%). The proportion of PW who believed that the influenza vaccine is effective dropped from 91% in 2013 to 76% in 2023, in contrast to the stable value among non-PW (84% to 82%). In 2023, pregnant participants exhibited lower levels of knowledge about both influenza disease and the influenza vaccine, along with less positive attitudes toward the effectiveness and safety of the vaccine. They also showed lower willingness to vaccinate and lower vaccination rates compared to non-pregnant participants. Concerning KAP-flu among PW, less than half recognized that influenza is different from a common cold; fewer than one in five understood the timing and frequency of vaccination or the policy prioritizing PW for influenza vaccination; vaccination coverage remained below 2% over time. Conclusions: PW had concerning gaps in knowledge and attitudes regarding influenza and the influenza vaccine compared to non-PW in Suzhou, China. Specific actions targeting PW, such as initiatives leveraging the maternal and child healthcare system, are warranted to reduce the gaps. Full article

Global food security is facing numerous severe challenges. Population growth, climate change, and irrational agricultural inputs have led to a reduction in available arable land, a decline in soil fertility, and difficulties in increasing crop yields. As a result, the supply of food and agricultural products is under serious threat. Against this backdrop, the development of new technologies to increase the production of food and agricultural products and ensure their supply is extremely urgent. Agricultural nanotechnology, as an emerging technology, mainly utilizes the characteristics of nanomaterials such as small size, large specific surface area, and surface effects. It plays a role in gene delivery, regulating crop growth, adsorbing environmental pollutants, detecting the quality of agricultural products, and preserving fruits and vegetables, providing important technical support for ensuring the global supply of food and agricultural products. Currently, the research focus of agricultural nanotechnology is concentrated on the design and preparation of nanomaterials, the regulation of their properties, and the optimization of their application effects in the agricultural field. In terms of the research status, certain progress has been made in the research of nano-fertilizers, nano-pesticides, nano-sensors, nano-preservation materials, and nano-gene delivery vectors. However, it also faces problems such as complex processes and incomplete safety evaluations. This review focuses on the horticultural industry, comprehensively expounding the research status and application progress of agricultural nanotechnology in aspects such as the growth regulation of horticultural crops and the quality detection and preservation of horticultural products. It also deeply analyzes the opportunities and challenges faced by the application of nanomaterials in the horticultural field. The aim is to provide a reference for the further development of agricultural nanotechnology in the horticultural industry, promote its broader and more efficient application, contribute to solving the global food security problem, and achieve sustainable agricultural development. Full article