Enhancing Traffic Prediction with Learnable Filter Module

Modeling future traffic conditions often relies heavily on complex spatial-temporal neural networks to capture spatial and temporal correlations, which can overlook the inherent noise in the data. This noise, often manifesting as unexpected short-term peaks or drops in traffic observation, is typically caused by traffic accidents or inherent sensor vibration. In practice, such noise can be challenging to model due to its stochastic nature and can lead to overfitting risks if a neural network is designed to learn this behavior. To address this issue, we propose a learnable filter module to filter out noise in traffic data adaptively. This module leverages the Fourier transform to convert the data to the frequency domain, where noise is filtered based on its pattern. The denoised data is then recovered to the time domain using the inverse Fourier transform. Our approach focuses on enhancing the quality of the input data for traffic prediction models, which is a critical yet often overlooked aspect in the field. We demonstrate that the proposed module is lightweight, easy to integrate with existing models, and can significantly improve traffic prediction performance. Furthermore, we validate our approach with extensive experimental results on real-world datasets, showing that it effectively mitigates noise and enhances prediction accuracy

NF-Y Recruits Both Transcription Activator and Repressor to Modulate Tissue- and Developmental Stage-Specific Expression of Human γ-Globin Gene

The human embryonic, fetal and adult β-like globin genes provide a paradigm for tissue- and developmental stage-specific gene regulation. The fetal γ-globin gene is expressed in fetal erythroid cells but is repressed in adult erythroid cells. The molecular mechanism underlying this transcriptional switch during erythroid development is not completely understood. Here, we used a combination of in vitro and in vivo assays to dissect the molecular assemblies of the active and the repressed proximal γ-globin promoter complexes in K562 human erythroleukemia cell line and primary human fetal and adult erythroid cells. We found that the proximal γ-globin promoter complex is assembled by a developmentally regulated, general transcription activator NF-Y bound strongly at the tandem CCAAT motifs near the TATA box. NF-Y recruits to neighboring DNA motifs the developmentally regulated, erythroid transcription activator GATA-2 and general repressor BCL11A, which in turn recruit erythroid repressor GATA-1 and general repressor COUP-TFII to form respectively the NF-Y/GATA-2 transcription activator hub and the BCL11A/COUP-TFII/GATA-1 transcription repressor hub. Both the activator and the repressor hubs are present in both the active and the repressed γ-globin promoter complexes in fetal and adult erythroid cells. Through changes in their levels and respective interactions with the co-activators and co-repressors during erythroid development, the activator and the repressor hubs modulate erythroid- and developmental stage-specific transcription of γ-globin gene.</p

DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model

Pervasive integration of GPS-enabled devices and data acquisition technologies has led to an exponential increase in GPS trajectory data, fostering advancements in spatial-temporal data mining research. Nonetheless, GPS trajectories contain personal geolocation information, rendering serious privacy concerns when working with raw data. A promising approach to address this issue is trajectory generation, which involves replacing original data with generated, privacy-free alternatives. Despite the potential of trajectory generation, the complex nature of human behavior and its inherent stochastic characteristics pose challenges in generating high-quality trajectories. In this work, we propose a spatial-temporal diffusion probabilistic model for trajectory generation (DiffTraj). This model effectively combines the generative abilities of diffusion models with the spatial-temporal features derived from real trajectories. The core idea is to reconstruct and synthesize geographic trajectories from white noise through a reverse trajectory denoising process. Furthermore, we propose a Trajectory UNet (Traj-UNet) deep neural network to embed conditional information and accurately estimate noise levels during the reverse process. Experiments on two real-world datasets show that DiffTraj can be intuitively applied to generate high-fidelity trajectories while retaining the original distributions. Moreover, the generated results can support downstream trajectory analysis tasks and significantly outperform other methods in terms of geo-distribution evaluations

Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China)

The successful development of an optimal canopy vegetation index dynamic model for obtaining higher yield can offer a technical approach for real-time and nondestructive diagnosis of rice (Oryza sativa L) growth and nitrogen (N) nutrition status. In this study, multiple rice cultivars and N treatments of experimental plots were carried out to obtain: normalized difference vegetation index (NDVI), leaf area index (LAI), above-ground dry matter (DM), and grain yield (GY) data. The quantitative relationships between NDVI and these growth indices (e.g., LAI, DM and GY) were analyzed, showing positive correlations. Using the normalized modeling method, an appropriate NDVI simulation model of rice was established based on the normalized NDVI (RNDVI) and relative accumulative growing degree days (RAGDD). The NDVI dynamic model for high-yield production in rice can be expressed by a double logistic model: RNDVI = (1 + e-15.2829x(RAGDDi-0.1944))-1 - (1 + e-11.6517x(RAGDDi-1.0267))-1 (R2 = 0.8577**), which can be used to accurately predict canopy NDVI dynamic changes during the entire growth period. Considering variation among rice cultivars, we constructed two relative NDVI (RNDVI) dynamic models for Japonica and Indica rice types, with R2 reaching 0.8764** and 0.8874**, respectively. Furthermore, independent experimental data were used to validate the RNDVI dynamic models. The results showed that during the entire growth period, the accuracy (k), precision (R2), and standard deviation of RNDVI dynamic models for the Japonica and Indica cultivars were 0.9991, 1.0170; 0.9084**, 0.8030**; and 0.0232, 0.0170, respectively. These results indicated that RNDVI dynamic models could accurately reflect crop growth and predict dynamic changes in high-yield crop populations, providing a rapid approach for monitoring rice growth status

Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat

Using remote sensing to rapidly acquire large-area crop growth information (e.g., shoot biomass, nitrogen status) is an urgent demand for modern crop production; unmanned aerial vehicle (UAV) acts as an effective monitoring platform. In order to improve the practicability and efficiency of UAV based monitoring technique, four field experiments involving different nitrogen (N) rates (0–360 kg N ha−1 ) and seven winter wheat (Triticum aestivum L.) varieties were conducted at different eco-sites (Sihong, Rugao, and Xinghua) during 2015–2019. A multispectral active canopy sensor (RapidSCAN CS-45; Holland Scientific Inc., Lincoln, NE, USA) mounted on a multirotor UAV platform was used to collect the canopy spectral reflectance data of winter wheat at key growth stages, three growth parameters (leaf area index (LAI), leaf dry matter (LDM), plant dry matter (PDM)) and three N indicators (leaf N accumulation (LNA), plant N accumulation (PNA) and N nutrition index (NNI)) were measured synchronously. The quantitative linear relationships between spectral data and six growth indices were systematically analyzed. For monitoring growth and N nutrition status at Feekes stages 6.0–10.0, 10.3–11.1 or entire growth stages, red edge ratio vegetation index (RERVI), red edge chlorophyll index (CIRE) and difference vegetation index (DVI) performed the best among the red edge band-based and red-based vegetation indices, respectively. Across all growth stages, DVI was highly correlated with LAI (R2 = 0.78), LDM (R2 = 0.61), PDM (R2 = 0.63), LNA (R2 = 0.65) and PNA (R2 = 0.73), whereas the relationships between RERVI (R2 = 0.62), CIRE (R2 = 0.62) and NNI had high coefficients of determination. The developed models performed better in monitoring growth indices and N status at Feekes stages 10.3–11.1 than Feekes stages 6.0–10.0. To sum it up, the UAV-mounted active sensor system is able to rapidly monitor the growth and N nutrition status of winter wheat and can be deployed for UAV-based remote-sensing of crops

Dual effects of biochar and hyperaccumulator Solanum nigrum L. on the remediation of Cd-contaminated soil

Biochar was widely developed for the soil amendment and remediation of heavy metal contaminated soil. The Cd hyperaccumulator, Solanum nigrum L., has been paid much more attention with the wide application of phytoremediation. The effects of biochar on the growth and accumulation capacity of Solanum nigrum L. in Cd contaminated soil have not been explored so far. The objectives of this study were to explore the dual effects of biochar addition on available Cd in the soil and hyperaccumulation of Cd in Solanum nigrum L. under different Cd contaminated levels. The correlations of soil physicochemical and biochemical properties and Cd absorption of Solanum nigrum L. were analyzed after a 60-day pot experiment under three biochar doses (0%, 1% and 5%) and four Cd concentrations (0, 25, 50 and 100 mg kg−1). The availability of Cd obtained by DTPA extraction significantly decreased after biochar application (P = 0.003, P = 0.0001, P = 0.0001 under 1% biochar addition for 25, 50, and 100 mg kg−1 Cd concentrations, P = 0.0001, P = 0.0001, P = 0.0001 under 5% biochar addition for 25, 50, and 100 mg kg−1 Cd concentrations, n ≥ 3). The 1% biochar dose significantly increased leaf dry weight (P = 0.039, P = 0.002 for the Cd concentrations of 50 and 100 mg kg−1, n ≥ 3) compared with the control in higher Cd concentrations (50 and 100 100 mg kg−1). In the presence of biochar, the bioconcentration factor (BCF) increased under the Cd concentrations of 50 and 100 mg kg−1. The translocation factors (TF) decreased with the biochar doses under the Cd concentration of 100 mg kg−1. The dose of 5% biochar significantly increased the urease activity by 41.18% compared to the 1% biochar addition in the Cd contaminated soil of 50 mg kg−1 concentration. The activities of acid phosphatase were inhibited by 1% biochar dose in all the Cd contaminated soils. The dry weight of the root of Solanum nigrum L. was significantly negatively correlated with acid phosphatase activity and BCF, respectively, indicating acid phosphatase in the rhizosphere soil of Solanum nigrum L. were repressed by Cd toxicity despite of biochar amendment. Biochar had no negative effect on Cd accumulation ability of Solanum nigrum L. Two-way ANOVA analysis showed that both biochar and Cd significantly affected the height of Solanum nigrum L. and the dry weight of leaf and stem. This study implied that biochar addition does not limit the absorption of hyperaccumulator Solanum nigrum L. in the remediation of Cd-contaminated soil. This study implied that the simultaneous application of biochar and hyperccumulator Solanum nigrum L. is promising during the remediation of Cd-contaminated soil

A Hybrid Multiresonances Suppression Method for Nonsynchronous LCL-Type Grid-Connected Inverter Clusters under Weak Grid

Multiresonance phenomena occur in inverter clusters under weak grid, with more complex resonance characteristics. Traditional suppression methods are difficult to determine control parameters when considering dynamic resonance under the variation of grid impedance. To this end, this article proposes a hybrid multiresonances suppression method based on the improved dual-division-summation (D-D-Σ) method and virtual admittance strategy. Multiresonances are divided into dynamic resonance and static resonance, in which dynamic resonance frequency shifts with the variation of grid impedance, and static resonance frequency remains fixed. D-D-Σ method with strong adaptive ability and paralleled virtual admittance strategy reshaped the inverter output impedance are used to suppress dynamic resonance and static resonance, respectively. The proposed hybrid multiresonance suppression method can adapt to parameter changes and simplify the center frequency design process of traditional paralleled virtual admittance. The experimental results of a parallel system with three inverters verify the feasibility of the proposed method in this article