Frequency Information Research Articles

Harmonic analysis of phase-sensitive optical time-domain reflectometer

This study innovatively proposes a method of using harmonics to mitigate the influence of fading noise in Phase-Sensitive Optical Time-Domain Reflectometer (Φ-OTDR). Numerical modeling was conducted to investigate the behavior of the Φ-OTDR. A section optical fiber is coiled around a PZT (piezoelectric transducer). By applying a signal to the PZT, it emulates an external vibration. The response of the simulated vibration was extracted using phase demodulation algorithm. In the response spectrum of the vibration, the fundamental frequency may be submerged because of the fading noise, causing the useful information to be obscured. However, the region impacted by fading noise in the fundamental frequency is unaffected in certain harmonic components. Thus, information from the harmonics can be used to compensate for the missing information in the fundamental frequency. By analyzing multiple harmonic components, the accuracy of vibration detection and localization can be improved. Experiment was performed to corroborate the findings, and the results demonstrated good consistency with the simulated data. Both the simulation and the experiment proved that considering harmonics can enhance the measurement accuracy of Φ-OTDR. This discovery provides new insights and methods for the application of Φ-OTDR.

CNCAN: Contrast and normal channel attention network for super-resolution image reconstruction of crops and weeds

Numerous studies have been performed to apply camera vision technologies in robot-based agriculture and smart farms. In particular, to obtain high accuracy, it is essential to procure high-resolution (HR) images, which requires a high-performance camera. However, due to high costs it is difficult to widely apply the camera in agricultural robots. To overcome this limitation, we propose contrast and normal channel attention network (CNCAN) for super-resolution reconstruction (SR), which is the first research for the accurate semantic segmentation of crops and weeds even with low-resolution (LR) images captured by low-cost and LR camera. Attention block and activation function that considers high frequency and contrast information of images are used in CNCAN, and the residual connection method is applied to improve the learning stability.As a result of experimenting with three open datasets, namely, Bonirob, rice seedling and weed, and crop/weed field image (CWFID) datasets, the mean intersection of union (MIOU) results of semantic segmentation for crops and weeds with SR images through CNCAN were 0.7685, 0.6346, and 0.6931 in the Bonirob, rice seedling and weed, and CWFID datasets, respectively, confirming higher accuracy than other state-of-the-art methods for SR.

Effective modeling of open quantum systems by low-rank discretization of structured environments.

The accurate description of the interaction of a quantum system with its environment is a challenging problem ubiquitous across all areas of physics and lies at the foundation of quantum mechanics theory. Here, we pioneer a new strategy to create discrete low-rank models of the system-environment interaction, by exploiting the frequency and time domain information encoded in the fluctuation-dissipation relation connecting the system-bath correlation function and the spectral density. We demonstrate the effectiveness of our methodology by combining it with tensor-network methodologies and simulating the quantum dynamics of complex excitonic systems in a highly structured bosonic environment. The new modeling framework sets the basis for a leap in the analysis of open quantum systems, providing controlled accuracy at significantly reduced computational costs, with benefits in all connected research areas.

UCC: A unified cascade compression framework for vision transformer models

In recent years, Vision Transformer (ViT) and its variants have dominated many computer vision tasks. However, the high computational consumption and training data requirements of ViT make it challenging to be deployed directly on resource-constrained devices and environments. Model compression is an effective approach to accelerate deep learning networks, but existing methods for compressing ViT models are limited in their scopes and struggle to strike a balance between performance and computational cost. In this paper, we propose a novel Unified Cascaded Compression Framework (UCC) to compress ViT in a more precise and efficient manner. Specifically, we first analyze the frequency information within tokens and prune them based on a joint score of their both spatial and spectral characteristics. Subsequently, we propose a similarity-based token aggregation scheme that combines the abundant contextual information contained in all pruned tokens with the host tokens according to their weights. Additionally, we introduce a novel cumulative cascaded pruning strategy that performs bottom-up cascaded pruning of tokens based on cumulative scores, avoiding information loss caused by individual idiosyncrasies of blocks. Finally, we design a novel two-level distillation strategy, incorporating imitation and exploration, to ensure the diversity of knowledge and better performance recovery. Extensive experiments demonstrate that our unified cascaded compression framework outperforms most existing state-of-the-art approaches, compresses the floating-point operations of ViT-Base as well as DeiT-Base models by 22 % and 54.1 %, and improves the recognition accuracy of the models by 3.74 % and 1.89 %, respectively, significantly reducing model computational consumption while enhancing performance, which enables efficient end-to-end training of compact ViT models.

SFDiff: Diffusion model with sufficient spatial‐Fourier frequency information interaction for low‐light image enhancement

AbstractDiffusion models are increasingly applied in low‐light image enhancement tasks due to their exceptional capability to model data distributions, but most current methods focus only on the original pixel space and neglect the potential of Fourier frequency information. In this article, SFDiff is proposed, a novel low‐light image enhancement method that integrates Fourier frequency information into the diffusion process. Specifically, Fourier transforms are applied at both the image and feature levels to separately enhance the amplitude and phase components, which restores global illumination degradation and positional information. Then a Spatial‐Frequency Fusion (SFF) block is used to fully integrate and interact with the information across spatial and frequency domains. Since illumination degradation is primarily manifested in the amplitude component, a loss function based on maximum likelihood learning is employed to constrain the amplitude component at each step of the sampling process, ensuring that the reverse process maintains an optimal trajectory. Owing to the streamlined network design and the fact that the Fourier transform requires no extra parameters, SFDiff achieves a reduction in parameters of over compared to several state‐of‐the‐art (SOTA) diffusion models and delivers high‐quality enhancement results on multiple real‐world datasets. The code is available at https://github.com/MrWan001/SFDiff.

Infrared image super-resolution reconstruction based on visible light image guidance and recursive fusion

Due to the limitations of hardware equipment, infrared images generally have problems such as low resolution, blurred details and poor visual quality during acquisition. Using visible light images to guide the super-resolution reconstruction of infrared images is an effective way to improve the resolution of infrared images. However, the imaging principles of visible light images and infrared images are different, resulting in differences in detail information between the two images, so problems such as blur and ghosting may occur during reconstruction. This paper proposes an infrared image super-resolution network based on visible light image guidance and recursive fusion. In this network, a flow Fourier residual module is designed, and modules of different depths are used to extract information of different frequencies in visible light images and infrared images, so that each module focuses on the appropriate frequency information. At the same time, a hybrid attention module is used to obtain detail information in multimodal images from channel and spatial perspectives, and fuse them in a complementary way, which helps to eliminate the generation of artifacts. On this basis, a global recursive fusion branch is designed to consider the correlation between multi-layer features, adaptively fuse multi-layer features, and generate clearer high-resolution infrared images. Experimental results show that compared with the comparative methods, this method performs better in objective evaluation indicators; in terms of subjective visual comparison, the images reconstructed by this method have clearer textures and fewer artifacts, and better object distinction in complex environments.

A Review of CiteSpace Visualisation and Analysis of High-Temperature Corrosion Inhibitors for Oil and Gas Fields

With the increasing global energy demand, the exploitation depth of oil and gas fields is gradually increasing. At the same time, importing many high-acid crude oil makes it increasingly heavy and inferior, and its acid value is constantly improving. This change has led to the increasingly severe corrosion problem of oil and gas field equipment, which has become an essential factor affecting the national economy and society's sustainable development. As an economical and practical anticorrosion measure, corrosion inhibitors play a crucial role in the anticorrosion of oil and gas field equipment. Through the research method of biorientation, retrieve the Web of Science database in 2008-2024 about oil and gas field high-temperature corrosion inhibitor research literature information, using CiteSpace measurement analysis software visual analysis in the literature keywords, publications, high citation frequency, cooperation and word clustering information change trend, analyze the research situation of oil and gas field in recent years, summarizes the research hotspot of oil field high-temperature corrosion inhibitor, prominent authors and institutions, reference relationship and development trend. The number of documents on high-temperature corrosion inhibitors in oil and gas fields is smaller, and the authors, institutions, and countries are not closely related. There is less research cooperation on high-temperature corrosion inhibitors in oil and gas fields, and more independent studies exist. China University of Petroleum has the first number of publications, and China is the country with the first number of publications. The study of mass spectrometry for corrosion inhibitors in oil and gas fields is prominent, while the literature on high-temperature corrosion inhibitors is rare.

Crossfeat: a transformer-based cross-feature learning model for predicting drug side effect frequency.

Safe drug treatment requires an understanding of the potential side effects. Identifying the frequency of drug side effects can reduce the risks associated with drug use. However, existing computational methods for predicting drug side effect frequencies heavily depend on known drug side effect frequency information. Consequently, these methods face challenges when predicting the side effect frequencies of new drugs. Although a few methods can predict the side effect frequencies of new drugs, they exhibit unreliable performance owing to the exclusion of drug-side effect relationships. This study proposed CrossFeat, a model based on convolutional neural network-transformer architecture with cross-feature learning that can predict the occurrence and frequency of drug side effects for new drugs, even in the absence of information regarding drug-side effect relationships. CrossFeat facilitates the concurrent learning of drugs and side effect information within its transformer architecture. This simultaneous exchange of information enables drugs to learn about their associated side effects, while side effects concurrently acquire information about the respective drugs. Such bidirectional learning allows for the comprehensive integration of drug and side effect knowledge. Our five-fold cross-validation experiments demonstrated that CrossFeat outperforms existing studies in predicting side effect frequencies for new drugs without prior knowledge. Our model offers a promising approach for predicting the drug side effect frequencies, particularly for new drugs where prior information is limited. CrossFeat's superior performance in cross-validation experiments, along with evidence from case studies and ablation experiments, highlights its effectiveness.

Incidence of haploidy and triploidy in trophectoderm biopsies of blastocysts derived from normally and abnormally fertilized oocytes.

We aimed to identify the correlation between morphological pronuclear (PN) status and the genetically determined ploidy configuration in preimplantation embryos. A retrospective observational study was conducted on 1982 embryos displaying normal fertilization and 380 embryos showing an atypical PN pattern, tested for aneuploidies and ploidy status via preimplantation genetic testing (PGT) between May 2019 and May 2024. Ploidy prediction was performed using a validated targeted-NGS approach and a proprietary bioinformatic pipeline analyzing SNPs B-allele frequency information. Ploidy results were obtained in relation to the morphological PN pattern and further stratified by mode of PN observation, maternal age, and embryo quality parameters. Abnormal ploidy results in 2PN-derived embryos were 1% (n = 20/1982): 0.8% showed triploidy and 0.2% haploidy. Ploidy results in relation to PN number in atypical fertilization were as follows: 0PN (n = 150/380) associated with 87.3% of diploidy, 8.7% of haploidy, and 4.0% of triploidy; 1PN-derived blastocysts (n = 73/153) were haploid in 47.7% of cases, 6.5% were triploid, and 45.7% diploid; 2.1PN (n = 23/280) and 3PN patterns (n = 54/280) predicted a triploid result in 34.8% and 74.1% of cases, respectively. PN observation with time-lapse increased ploidy status predictivity from 28.3% to 80.4% (p < 0.01) and reduced expected diploid rates to 19.6% (p < 0.01). Diploidy rate was higher for maternal age ≤ 35years and for morphologically high-grade embryos. Morphological PN check can be improved by incorporating ploidy analysis within the conventional PGT workflow. Euploid 2PN-derived embryos can be further selected removing haploids and triploids, and some atypical PN pattern can be better classified.

Allelic Diversity and Development of Breeder-Friendly Marker Specific to floury2 Gene Regulating the Accumulation of α-Zeins and Essential Amino Acids in Maize Kernel.

Maize zeins lack essential amino acids, such as methionine, lysine, and tryptophan. The floury2 (fl2) mutation reduces zein synthesis and increases methionine and lysine content in kernels. In this study, fl2 gene (1612bp) was sequenced in eight wild-type and two mutant inbreds and detected 218 SNPs and 18 InDels. Transversion of C to T at 343bp position caused the substitution of alanine by valine in the fl2 mutant. A PCR-based marker (FL-SNP-CT) was developed, which distinguished the favorable mutant fl2 allele (T) from the wild-type (C) Fl2 allele. Gene-based diversity analysis using seven gene-based InDel markers grouped 48 inbred lines into three major clusters, with an average genetic dissimilarity coefficient of 0.534. The average major allele frequency, gene diversity, heterozygosity, and polymorphism information content of the InDel markers were 0.701, 0.392, 0.039, and 0.318, respectively. Haplotype analysis revealed 29 haplotypes of fl2 gene among these 48 inbreds. Amino acid substitution (Ala-Val) at the signal peptide cleavage site produced unprocessed 24-kDa mutant protein instead of 22-kDa zein found in normal genotype. Eight paralogues of fl2 detected in the study showed variation in exon lengths (616-1170bp) and translation lengths (135-267 amino acids). Orthologue analysis among 15 accessions of Sorghum bicolor and two accessions of Saccharum spontaneum revealed a single exon in fl2 gene, ranging from 267 to 810bp. The study elucidated the molecular basis of fl2 mutation and reported a breeder-friendly marker for molecular breeding programs. This is the first study to characterize fl2 gene in a set of subtropically adapted inbreds.

Lyapunov-based distributed secondary frequency and voltage control for distributed energy resources in islanded microgrids with expected dynamic performance improvement

Microgrids (MGs) can effectively integrate the high penetration distributed energy resources (DERs) for the energy and environmental crisis. Still, fluctuations in intermittent DERs may lead to severe frequency and voltage deviations or even the instability of islanded MGs. Secondary control has successfully compensated for the frequency and voltage deviations. Research on secondary control mainly focused on steady-state operating objectives, i.e., frequency and voltage recovery and power sharing. Still, improving the dynamic responses of secondary control is crucial for system-stable operation, especially in the presence of synchronous DERs. This is because these units can cause undesired oscillatory modes, leading to system instability. In addition, because of the line impedance effect, accurate reactive power sharing is usually ignored when voltage recovery is considered. This will lead to an overload of MGs. To improve the dynamics of secondary control while trading off voltage regulation and reactive power sharing, we propose a Lyapunov-Function (LF)–based distributed secondary control strategy. In the proposed LF-based control strategy, the frequency, voltage, and power information are introduced into feedback control based on the Lyapunov stability theorem. Therefore, the improved frequency and voltage deviations and accurate power sharing can be guaranteed when the Lyapunov function asymptotically attenuates to zero. The inherent conflict between voltage regulation and reactive power sharing is addressed by converging the average voltage to the rated reference. Besides, the improved dynamic performance of secondary control is achieved by considering global power variations, which are obtained by distribution-level phasor measurement units. Global power variations can establish control actions to dampen system oscillations and accelerate system restoration. Furthermore, the controller design method based on the Lyapunov stability theorem can naturally promise the stability of the proposed secondary frequency and voltage controllers. Numerical simulations on the IEEE 34-bus system validate that the proposed control strategy can ensure the significantly improved dynamic performance of secondary control while achieving steady-state operation objectives.

Application of a novel visual representation method for urban noise source monitoring

Increasing urban mobility has led to a rise in the number of vehicles, resulting in urban noise pollution, which can cause health problems. Conventional methods for noise control can be adopted to reduce noise near the sources. However, this is not cost-effective to reduce overall noise level in the urban area. Therefore, it is preferable to encourage drivers to voluntarily reduce noise by applying methods such as cracking down on road noise sources. To this end, a real-time urban noise source monitoring system is needed, but processing large amounts of data can be difficult. In this study, a novel visual representation method, DoAgram, is applied for urban noise source monitoring. DoAgram can represent the time, frequency, and spatial information of the sound source within a 2D color graph. Therefore, the long-term spatial information of the sound source acquired from the sensor array node can be transmitted as a single image. Also, one can restore spatial information of the source from an image through a decoding process corresponding to the encoding method. One can find that the proposed method can be utilized as a database that can efficiently conduct urban noise source monitoring.

A modified Fuvar fusion algorithm based on adaptive end-member selection for hyperspectral remote sensing images

ABSTRACT A significant strategy for achieving a balance between spatial and spectral resolution is to combine hyperspectral remote sensing images with low spatial resolution and multispectral remote sensing images with high spatial resolution. For the issues of spectrum distortion and lack of the ability of gaining high frequency information when the Fuvar algorithm employs the Vertex Component Analysis (VCA) technique, this thesis presents a modified Fuvar (MFuvar) algorithm that utilizes the Maximum Distance Analysis (MDA) method instead of the VCA approach. The two subsets from the hyperspectral remote sensing image of GF-5 and the multispectral remote sensing image of Sentinel-2A, representing different land cover types were used as test data. The spectral fidelity and the ability of gaining high frequency information were assessed by using visual and statistical analysis. Fused images are compared with eight fusion methods, including SFIMHS, GLPHS, MAPSMM, CNMF, Hysure, SpaFusion, LTTR, and Fuvar, respectively. The results show that the MFuvar algorithm can keep the best balance between spectral fidelity and the ability of gaining high frequency information, and it is generally better than the compared eight algorithms. And it fulfils the automatic selection of end elements without manual intervention and increases the efficiency of algorithm operation.

Extreme Space Weather Impacts on GNSS Timing Signals for Electricity Grid Management

AbstractExtreme space weather events can have serious impacts on critical infrastructure, including Global Navigation Satellite Systems (GNSS). The use of GNSS, particularly as sources of accurate timing signals, is becoming more widespread, with one example being the measurement of electricity grid frequency and phase information to aid grid management and stability. Understanding the likelihood of extreme space weather impacts on GNSS timing signals is therefore becoming vital to maintain national electricity grid resilience. This study determines critical intensity thresholds above which the complete failure of a GNSS based timing system may occur. Solar radio bursts are identified as a simple example to investigate in more detail. The probability of occurrence of an extreme space weather event with an intensity equal to or greater than the critical intensity is estimated. Both a power law and extreme value theory were used to evaluate recurrence probabilities based on historical event frequencies. The probability was estimated to be between 3%–12% per decade to cause the complete failure of any GNSS‐based timing system.

Geospatial Intelligence for Landslide Susceptibility and Risk Analysis: Insights from NH31A and East Sikkim Himalaya Settlements

Slope instability is a serious concern in the Sikkim Himalayas. The town and numerous road segments along National Highway 31A were ravaged by multiple landslides that occurred in the nearby region. A bivariate statistical method known as frequency ratio (FR), information value (IV), and certainty factor (CF) analysis was employed in this work to examine landslide risk assessment (LRA) and landslide susceptibility zonation (LSZ) maps in the Rorachu watershed. This study represents the first comprehensive analysis of landslide risk in the populated areas of East Sikkim and along NH31A, offering a deeper understanding of the risks involved and contributing to the enhancement of local resilience against landslide hazards. A total of 153 different landslide locations were mapped using Google Earth and GIS software; 30% (46) of these locations were used to validate the models, and 70% of these (107) served as training data for the FR, IV, and CF models. The thirteen landslide causative factors (geology, soil, elevations, slope, curvature, drainage density (DD), road density (RD), rainfall, normalized difference vegetation index (NDVI), land use land cover (LULC), topographic position index (TPI), stream power index (SPI), and topographic wetness index (TWI)) were extracted from a spatial database for LSZ mapping. Landslides were most prevalent on slopes (35° to 50°), heights (2,500–4,100 m), and rainfall (2000–2,500 mm and 3,000–3,300 mm). The area under the curves (AUC) for the FR, IV, and CF models are 0.925 (92.50%), 0.846 (84.60%), and 0.868 (86.20%), respectively. The prediction rates are shown by the AUCs for the FR, IV, and CF models, which are 0.828 (82.8%), 0.750 (%), and 0.836 (83.60%), respectively. According to the landslide risk assessment (LRA), the FR (20.75%), IV (40.91%) and CF (18.78%) models showed high risk on Highway 31A, while the FR (9.05%), IV (38.59%) and CF (20.90%) models showed high risk in densely populated areas. These landslide risk and vulnerability maps can be used to develop land use planning strategies that can save lives and are useful for planners and mitigation measures. Special attention should be paid to urbanization, highway construction, and deforestation.

WITHDRAWN: Design of Group Key Agreement Method Based on Elliptic Curve Cryptography in New Energy Extensive Access Security Protection

Aiming at the security problem of power system communication network under the condition of extensive access of new energy, we design the group key agreement method based on elliptic curve cryptography in the security protection of new energy extensive access. Design the architecture of power system security protection network under the condition of extensive access to new energy. The network architecture adopts wireless communication technology to realize the transmission of new energy security monitoring information. For the data confidentiality problem in the wire communication technology, the group key agreement method based on elliptic curve cryptography is proposed, and the group key agreement model is constructed under the set conditions, and the group key is generated through the processes of main parameter selection, group composite public key distribution, group key agreement protocol design, group key update protocol for node joining event, and group key update protocol for members departure event, etc. The group key is utilized for the communication. The experimental results show that this method can generate the key with high randomness and excellent key sensitivity, which can effectively resist the attacks of character frequency statistics and information entropy and guarantee the security of communication information.

Improving fluid identification in well logging using Continuous Wavelet Transform and Vision Transformers: An innovative approach

Well logging fluid prediction is one of the key steps in assessing oil and gas reserves. By analyzing downhole logging data, different types of fluids contained in underground rocks, such as crude oil, natural gas, and water, can be determined. This information is crucial for assessing the abundance and recoverable reserves of oil and gas resources and helps guide oil and gas exploration and development work. We have introduced a novel model called CWT (Continuous Wavelet Transform)-ViT (Vision Transformer). CWT can simultaneously provide frequency information at different scales, enabling the model to analyze downhole logging data more comprehensively and accurately at different scales. Underground rock structures often exhibit features at multiple scales, and CWT can effectively capture these features, aiding in better differentiation of different types of fluids. The ViT model utilizes the Transformer architecture, allowing for global attention over input sequences without being limited by sequence length. This enables the model to comprehensively understand the overall information of downhole logging data and extract richer features. For complex geological structures and fluid distributions in geological exploration, the global attention mechanism helps the model better grasp the overall situation, thereby improving the accuracy of fluid prediction. When we used the CWT-ViT method for well logging fluid prediction, we achieved a high accuracy rate of 97.50% in the first dataset, which further improved to 97.77% in the second dataset. These results demonstrate the significant robustness and efficiency of the CWT-ViT method in lithology prediction using well logging data. We also conducted blind well experiments, and our CWT-ViT model outperformed other models, achieving a blind well prediction accuracy of 97.36%. Therefore, the experiments indicate that the key to improving accuracy in well logging fluid prediction with CWT lies in its multiscale analysis capability, effectively capturing different fluid characteristic frequencies. Additionally, CWT enhances signal features and removes noise, increasing the precision of fluid identification. Finally, the integration with ViT further optimizes fluid prediction performance, making it outstanding in complex geological environments. The advantages of ViT in fluid prediction include its excellent sequence modeling capability, effective handling of long-distance dependencies, and enhanced ability to capture fluid characteristics in complex well logging data.

SNS화장품 광고에서 화장품구매행동, 소비성향, 외모관심도가 추천의도에 미치는 영향에 관한 연구 : 광고모델 매력성의 조절효과를 중심으로

The increasing use of social media is having a significant impact on online and offline purchasing behavior, as consumers share content they are interested in and exchange information. Businesses are responding to this shift by increasing their social media advertising, which has a positive impact on brand purchase behavior. This study aims to empirically investigate the influence of consumers' spending behavior and appearance interest on recommendation intention in cosmetics advertisements on SNS and the moderating effect of advertisement model attractiveness. For this study, an online survey was conducted among 568 consumers who have purchased cosmetics on SNS. SPSS 28.0 was used for statistical analysis, and factor analysis, reliability test, and multiple regression analysis were conducted. The results of this study were as follows First, the experience of purchasing cosmetics on SNS and the frequency of information acquisition were found to have a positive effect on recommendation intention. Second, spending behavioral attitudes such as sympathy and impulsivity were found to have a static effect on recommendation intent. Third, appearance orientation has a static effect on recommendation intention. Fourth, advertisement model attractiveness moderated the relationship between SNS cosmetics purchase frequency and YouTube cosmetics information acquisition frequency and recommendation intention, and advertisement model attractiveness moderated the relationship between impulsivity and appearance orientation and recommendation intention. The study found the propensity of splurge and agreeableness on spending behavioral suggests that consumers use social media to show off their social status and tend to imitate the behavior of others. In addition, the effect of appearance orientation on recommendation intention highlights the importance of appearance as a factor in consumer behavior. The moderating effect of advertisement model attractiveness suggests that it is important to consider the personality of the model to fit the cosmetics brand image when considering the influence of advertisement model attractiveness. This study aims to contribute to consumer behavior theory by deepening our understanding of the effectiveness of social media advertising and proposing an integrated model of recommendation intention formation. Future research should expand to a wider range of products to confirm the persistence of social media advertising effects. The results of this study can be used as a useful basis for companies to establish social media advertising strategies.

Rhenium-platinum group elements reveal seawater incursion induced massive lacustrine organic carbon burial

Organic carbon burial in ancient lacustrine settings is a crucial source of petroleum resources. Unlike the marine environment, the dynamics of organic carbon burial in the terrestrial realm are more complex due to the interplay of global and regional climate-tectonic factors. There appears to be a potential linkage between seawater incursion events (SWIEs) and the generation of lacustrine source rocks. However, reliable proxies to reconstruct the frequency and extent of SWIEs are currently lacking. Here, we explore the potential of rhenium-platinum group elements (Re-PGE) system as a novel proxy for determining SWIEs in the Nenjiang Formation of the lacustrine Songliao Basin in China that is noted for its high-quality source rock. By comparing marine and non-marine intervals, we validate the utility of Re-PGE fractionation patterns and osmium (Os) isotope compositions. Moreover, the Re/Ir ratios demonstrate two main episodes of quantitative seawater-lake water exchange. The comparison of variable indicators shows that the Re-PGE system is more sensitive to the changes in water sources, thus providing detailed information of frequency and exchange amount. The inverse variation between seawater contribution and total organic carbon content further implies that the massive sulfate influx from SWIEs facilitated bacterial sulfate reduction in the sediment pile, which had the effect of recycling nutrients (e.g., phosphorous) back into the water column. The SWIEs-triggered eutrophication induced a positive feedback loop between productivity and hypoxia, creating ideal conditions for the preservation of organic carbon. Our data reveals the detailed mechanism of SWIEs-triggered organic carbon burial and emphasizes the significant role of SWIEs in generating economically important hydrocarbon reservoirs.

Road surface semantic segmentation for autonomous driving

Although semantic segmentation is widely employed in autonomous driving, its performance in segmenting road surfaces falls short in complex traffic environments. This study proposes a frequency-based semantic segmentation with a transformer (FSSFormer) based on the sensitivity of semantic segmentation to frequency information. Specifically, we propose a weight-sharing factorized attention to select important frequency features that can improve the segmentation performance of overlapping targets. Moreover, to address boundary information loss, we used a cross-attention method combining spatial and frequency features to obtain further detailed pixel information. To improve the segmentation accuracy in complex road scenarios, we adopted a parallel-gated feedforward network segmentation method to encode the position information. Extensive experiments demonstrate that the mIoU of FSSFormer increased by 2% compared with existing segmentation methods on the Cityscapes dataset.