A dissolved oxygen prediction model based on GRU–N-Beats

Dissolved oxygen is one of the most important water quality parameters in aquaculture, and the level determines whether fish can grow healthily. Since there is a delay in equipment control in the aquaculture environment, dissolved oxygen prediction is needed to reduce the loss due to low dissolved oxygen. To solve the problem of insufficient accuracy and poor interpretability of traditional methods in predicting dissolved oxygen from multivariate water quality parameters, this paper proposes an improved N-Beats-based prediction network. First, the maximum expectation algorithm [expectation–maximization (EM)] was used to fill in the original data by fitting the missing values. Second, the discrete wavelet transform (DWT) was used to reduce the overall noise of the sample, then the gated recurrent unit (GRU) feature extraction network was employed to extract the water quality information from the temporal dimension, the N-Beats was utilized to predict the preprocessed data, and the residual operation through Stack was performed to obtain the prediction results. The improved algorithm overcomes the challenge of insufficient prediction accuracy of the traditional algorithm. The GRU–N-Beats network proposed in this paper can extract features from multivariate time dimensions for prediction. The values of root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and R2 for the proposed algorithm were 0.171, 0.120, 0.015, and 0.97, respectively. In particular, they were 28.5%, 32.1%, 51.6%, 24.3%, 14.9%, 36.4%, and 19.3% higher than those of long short-term memory (LSTM), GRU, temporal convolutional network (TCN), LSTM–TCN, PatchTST, back-propagation neural network (BPNN), and N-Beats on RMSE, respectively

Nonreciprocal ultrastrong magnon-photon coupling in the bandgap of photonic crystals

We observe a nonreciprocal ultrastrong magnon-photon coupling in the bandgap of photonic crystals by introducing a single crystal YIG cylinder into copper photonic crystals cavity as a point defect. The coupling strength reaches up to 1.18 GHz, which constitutes about 10.9% of the photon energy compared to the photon frequency around 10.8 GHz. It is fascinating that the coupling achieves unidirectional signal transmission in the whole bandgap. This study demonstrates the possibility of controlling nonreciprocal magnon-photon coupling by manipulating the structure of photonic crystals, providing new methods to investigate the influence of magnetic point defects on microwave signal transmission.Comment: 6 pages, 5 figure

VaBUS: Edge-Cloud Real-Time Video Analytics via Background Understanding and Subtraction

Edge-cloud collaborative video analytics is transforming the way data is being handled, processed, and transmitted from the ever-growing number of surveillance cameras around the world. To avoid wasting limited bandwidth on unrelated content transmission, existing video analytics solutions usually perform temporal or spatial filtering to realize aggressive compression of irrelevant pixels. However, most of them work in a context-agnostic way while being oblivious to the circumstances where the video content is happening and the context-dependent characteristics under the hood. In this work, we propose VaBUS, a real-time video analytics system that leverages the rich contextual information of surveillance cameras to reduce bandwidth consumption for semantic compression. As a task-oriented communication system, VaBUS dynamically maintains the background image of the video on the edge with minimal system overhead and sends only highly confident Region of Interests (RoIs) to the cloud through adaptive weighting and encoding. With a lightweight experience-driven learning module, VaBUS is able to achieve high offline inference accuracy even when network congestion occurs. Experimental results show that VaBUS reduces bandwidth consumption by 25.0%-76.9% while achieving 90.7% accuracy for both the object detection and human keypoint detection tasks

Landscape factors influencing bird nest site selection in urban green spaces

IntroductionUnderstanding the birds’ breeding strategies in urban habitats is vital for ensuring their continued existence. Therefore, more research must be conducted on bird breeding and urban adaptation strategies in urban green spaces. This study aimed to address this gap by investigating the influence of landscape factors on the selection of bird nest sites. MethodsData on the presence and absence of magpie (Pica pica) and gray magpie (Cyanopica cyana) nests were collected through field surveys conducted in the campus of Nanjing Forestry University during the 2023 breeding season. Generalized additive models (GAMs) incorporating landscape variables were employed to assess the effects of these predictors on nest occurrence. The model with the lowest Akaike’s information criterion value was selected among the candidate GAMs.ResultsBelow is a summary of the main results. Nest tree height (TH), distance from the central lawn (D), and tree coverage (TC) within the sampled area were identified as the primary landscape factors influencing nest site choice. Conversely, factors such as the shortest distance to the water source, herb coverage, shrub coverage, percentage of buildings, and percentage of hard pavement did not significantly impact on nest site selection. Furthermore, the nesting potential of magpies and grey magpies initially increased with tree height, reaching a maximum at ca. TH=25 meters after which it began to decline. The nesting occurrence rate showed an initial decrease tendency with increasing distance from the central lawn, reaching a minimum at D=400 meters, and then increased with further distance. Additionally, nesting potential decreased initially with increasing of TC in the range of 0–20%, fluctuated evenly between 20–60% TC, and decreased rapidly when TC exceeded 60%.DiscussionThis study provides valuable insights into the selection of nest sites by birds in urban habitats, specifically with respect to landscape factors. The understanding of the impact of urban green spaces on urban birds and the underlying mechanisms of their behavior contributes to the conservation of wild birds and promotes the harmonious development of urban areas

Characterization of the complete chloroplast genome of Eutrema deltoideum (Brassicaceae)

AbstractEutrema deltoideum (Hook. f. et Thoms.) has been recognized as a potentially important vegetable and medicinal resource. In this study, we present the complete chloroplast genome of E. deltoideum and conduct a phylogenetic analysis. The chloroplast genome is 154,051 bp long and consists of a large single-copy (LSC) region of 84,149 bp, two inverted repeat (IR) regions of 26,065 bp each, and a small single-copy (SSC) region of 17,772 bp. It contains 132 complete genes, including 87 protein-coding genes, 8 ribosomal RNA genes, and 37 tRNA genes. Additionally, we identified 78 simple sequence repeats (SSRs). The phylogenetic tree reveals that E. deltoideum is closely related to E. heterophyllum, and the Eutrema genus is monophyletic. This study provides valuable information about E. deltoideum and enhances our understanding of its taxonomic classification

Characterization of the complete chloroplast genome of <i>Eutrema deltoideum</i> (Brassicaceae)

Eutrema deltoideum (Hook. f. et Thoms.) has been recognized as a potentially important vegetable and medicinal resource. In this study, we present the complete chloroplast genome of E. deltoideum and conduct a phylogenetic analysis. The chloroplast genome is 154,051 bp long and consists of a large single-copy (LSC) region of 84,149 bp, two inverted repeat (IR) regions of 26,065 bp each, and a small single-copy (SSC) region of 17,772 bp. It contains 132 complete genes, including 87 protein-coding genes, 8 ribosomal RNA genes, and 37 tRNA genes. Additionally, we identified 78 simple sequence repeats (SSRs). The phylogenetic tree reveals that E. deltoideum is closely related to E. heterophyllum, and the Eutrema genus is monophyletic. This study provides valuable information about E. deltoideum and enhances our understanding of its taxonomic classification.</p

Millimeter-Wave InSAR Image Reconstruction Approach by Total Variation Regularized Matrix Completion

Millimeter-wave interferometric synthetic aperture radiometer (InSAR) can provide high-resolution observations for many applications by using small antennas to achieve very large synthetic aperture. However, reconstruction of a millimeter-wave InSAR image has been proven to be an ill-posed inverse problem that degrades the performance of InSAR imaging. In this paper, a novel millimeter-wave InSAR image reconstruction approach, referred to as InSAR-TVMC, by total variation (TV) regularized matrix completion (MC) in two-dimensional data space, is proposed. Based on the a priori knowledge that natural millimeter-wave images statistically hold the low-rank property, the proposed approach represents the object images as low-rank matrices and formulates the data acquisition of InSAR in two-dimensional data space directly to undersample visibility function samples. Subsequently, using the undersampled visibility function samples, the optimal solution of the InSAR image reconstruction problem is obtained by simultaneously adopting MC techniques and TV regularization. Experimental results on simulated and real millimeter-wave InSAR image data demonstrate the effectiveness and the significant improvement of the reconstruction performance of the proposed InSAR-TVMC approach over conventional and one-dimensional sparse InSAR image reconstruction approaches

The electrostatic field networking in three isolated thunderstorms

A method for networking atmospheric electrostatic field by a quasi-normal charge distribution model based on radar and sounding data in isolated storm cells has been proposed. The charge distribution parameters of thundercloud are first estimated and inversed, and then the network of atmospheric electrostatic field can be calculated with the obtained parameters. The method was used to analyze three isolated thunderstorms that passed through the experiment site in 2009. It was shown that the electrostatic field networking and the charge distribution were concordant with the location of lightning and radar echo. It is revealed that the model and obtained parameters are reasonable to some extent and the method for networking electrostatic field using radar and sounding data is feasible. DOI: http://dx.doi.org/10.11591/telkomnika.v11i1.187

Soil microbial community structure dynamics shape the rhizosphere priming effect patterns in the paddy soil

Microbial community structure plays a crucial part in soil organic carbon (SOC) decomposition and variation of rhizo-sphere priming effects (RPEs) during plant growth. However, it is still uncertain how bacterial community structure regulates RPEs in soil and how RPE patterns respond to plant growth. Therefore, we conducted an experiment to ex-amine the RPE response to plant growth and nitrogen (N) addition (0 (N0), 150 (N150), and 300 (N300) kg N ha-1) using the 13C natural abundance method in a C3 soil (paddy soil) -C4 plant (maize, Zea mays L.) system; we then explored the underlying biotic mechanisms using 16S rRNA sequencing techniques. Networks were constructed to identify keystone taxa and to analyze the correlations between network functional modules of bacterial community and C decomposition. The results indicated that negative and positive RPEs occurred on Day 30 and Day 75 after maize planting, respectively. Bacterial community structure significantly changed and tended to shift from r-strategists to-ward K-strategists with changing labile C: N stoichiometry and soil pH during plant growth stages. The different net -work modules of bacterial community were aggregated in response to RPE pattern variation. Caulobacteraceae, Bacillus, and Chitinophagaceae were keystone taxa on Day 30, while Gemmatimonas, Candidatus Koribacter, and Xanthobacteraceae were keystone taxa on Day 75. Moreover, keystone taxa with different C utilization strategieswere significantly different between the two growth stages and related closely to different RPE patterns. This study provides deeper insights into the network structure of bacterial communities corresponding to RPE patterns and em-phasizes the significance of keystone taxa in RPE variation