A Bayesian Approach to Block Structure Inference in AV1-based Multi-rate Video Encoding

Due to differences in frame structure, existing multi-rate video encoding algorithms cannot be directly adapted to encoders utilizing special reference frames such as AV1 without introducing substantial rate-distortion loss. To tackle this problem, we propose a novel bayesian block structure inference model inspired by a modification to an HEVC-based algorithm. It estimates the posterior probabilistic distributions of block partitioning, and adapts early terminations in the RDO procedure accordingly. Experimental results show that the proposed method provides flexibility for controlling the tradeoff between speed and coding efficiency, and can achieve an average time saving of 36.1% (up to 50.6%) with negligible bitrate cost.Comment: published in IEEE Data Compression Conference, 201

Development, validation, and visualization of a web-based nomogram to predict the effect of tubular microdiscectomy for lumbar disc herniation

ObjectiveThe purpose of this study was to retrospectively collect the relevant clinical data of lumbar disc herniation (LDH) patients treated with the tubular microdiscectomy (TMD) technique, and to develop and validate a prediction model for predicting the treatment improvement rate of TMD in LDH patients at 1 year after surgery.MethodsRelevant clinical data of LDH patients treated with the TMD technology were retrospectively collected. The follow-up period was 1 year after surgery. A total of 43 possible predictors were included, and the treatment improvement rate of the Japanese Orthopedic Association (JOA) score of the lumbar spine at 1 year after TMD was used as an outcome measure. The least absolute shrinkage and selection operator (LASSO) method was used to screen out the most important predictors affecting the outcome indicators. In addition, logistic regression was used to construct the model, and a nomogram of the prediction model was drawn.ResultsA total of 273 patients with LDH were included in this study. Age, occupational factors, osteoporosis, Pfirrmann classification of intervertebral disc degeneration, and preoperative Oswestry Disability Index (ODI) were screened out from the 43 possible predictors based on LASSO regression. A total of 5 predictors were included while drawing a nomogram of the model. The area under the ROC curve (AUC) value of the model was 0.795.ConclusionsIn this study, we successfully developed a good clinical prediction model that can predict the effect of TMD for LDH. A web calculator was designed on the basis of the model (https://fabinlin.shinyapps.io/DynNomapp/)

Influences of nitrogen input forms and levels on phosphorus availability in karst grassland soils

The availability of soil phosphorus (P), a crucial nutrient influencing plant productivity and ecosystem function, is impacted by continuously increasing nitrogen (N) enrichment, which changes the soil P cycle. The effect of varying forms of N input on soil P dynamics in P-limited karst grassland ecosystems remains unclear. To address this knowledge gap, we conducted a greenhouse experiment to explore the effects of various forms of N addition [Ca(NO3)2, NH4Cl, NH4NO3, Urea] on soil P fractions in these ecosystems, applying two levels (N1: 50 mg N kg−1soil, N2: 100 mg N kg−1soil) of N input in two soils (yellow soil, limestone soil). Results indicated that P fractions in both soil types were significantly affected by N additions, with yellow soil demonstrating a higher sensitivity to these additions, and this effect was strongly modulated by the form and level of N added. High N addition, rather than low N, significantly affect the P fractions in both soil types. Specially, except for Ca(NO3)2, high N addition significantly increased the available P in both soils, following the order: Urea and NH4NO3 > NH4Cl > Ca(NO3)2, and decreased NaHCO3-Pi in both soils. High N addition also significantly reduced NaOH-Po and C.HCl-Po fractions in yellow soil. Additionally, the response of root biomass and alkaline phosphatase activity in both soils to N input paralleled the trends observed in the available P fractions. Notably, changes in soil available P were strongly correlated with plant root biomass and soil alkaline phosphatase activity. Our study highlights that the N addition form significantly influences soil P availability, which is closely tied to plant root biomass and alkaline phosphatase activity. This finding underscores the importance of considering N input form to boost soil fertility and promote sustainable agriculture

Broadband microwave metamaterial absorber with lumped resistor loading

Narrow absorption bandwidth has been a fundamental drawback hindering many metamaterial absorbers for practical applications. In this paper, by loading lumped resistors, we have successfully designed a microwave metamaterial absorber with multioctave wide absorption bandwidth covering the entire X- and Ku-bands, while keeping the thickness of the absorber less than 1/10 of the working wavelength. The polarization-insensitive absorber shows a good angular stability for both transverse electric (TE) and transverse magnetic (TM) incidences. Prototype has been fabricated and measured to validate the design principle and the simulated results, and good agreements are observed between simulated and measured results. The proposed metamaterial absorber offers an efficient way to realize broadband microwave absorption with stable angular performance, which may find potential uses in many applications, for example, electromagnetic compatibility

Urban waterlogging prediction and risk analysis based on rainfall time series features: A case study of Shenzhen

In recent years, the frequency of extreme weather has increased, and urban waterlogging caused by sudden rainfall has occurred from time to time. With the development of urbanization, a large amount of land has been developed and the proportion of impervious area has increased, intensifying the risk of urban waterlogging. How to use the available meteorological data for accurate prediction and early warning of waterlogging hazards has become a key issue in the field of disaster prevention and risk assessment. In this paper, based on historical meteorological data, we combine domain knowledge and model parameters to experimentally extract rainfall time series related features for future waterlogging depth prediction. A novel waterlogging depth prediction model that applies only rainfall data as input is proposed by machine learning algorithms. By analyzing a large amount of historical flooding monitoring data, a “rainfall-waterlogging amplification factor” based on the geographical features of monitoring stations is constructed to quantify the mapping relationship between rainfall and waterlogging depths at different locations. After the model is trained and corrected by the measured data, the prediction error for short-time rainfall basically reaches within 2 cm. This method improves prediction performance by a factor of 2.5–3 over featureless time series methods. It effectively overcomes the limitations of small coverage of monitoring stations and insufficient historical waterlogging data, and can achieve more accurate short-term waterlogging prediction. At the same time, it can provide reference suggestions for the government to conduct waterlogging risk analysis and add new sensor stations by counting the amplification factor of other locations

Cumulative live birth rates and birth outcomes after IVF/ICSI treatment cycles in young POSEIDON patients: A real-world study

ObjectiveThe aim of this study was to describe the cumulative live birth rates (CLBRs) of young women with or without low prognosis according to the POSEIDON criteria after IVF/ICSI cycles and to investigate whether the diagnosis of low prognosis increases the risk of abnormal birth outcomes.DesignRetrospective study.SettingA single reproductive medicine center.PopulationFrom January 2016 to October 2020, there were 17,893 patients (<35 years) involved. After screening, 4,105 women were included in POSEIDON group 1, 1,375 women were included in POSEIDON group 3, and 11,876 women were defined as non-POSEIDON.Intervention(s)Baseline serum AMH level was measured on the D2–D3 of menstrual cycle before IVF/ICSI treatment.Main outcome measure(s)Cumulative live birth rate (CLBR), birth outcomes.Result(s)After four stimulation cycles, the CLBRs in POSEIDON group 1, POSEIDON group 3, and non-POSEIDON group reached 67.9% (95% CI, 66.5%–69.3%), 51.9% (95% CI, 49.2%–54.5%), and 79.6% (95% CI, 78.9%–80.3%), respectively. There was no difference in gestational age, preterm delivery, cesarean delivery, and low birth weight infants between the three groups, but macrosomia was significantly higher in non-POSEIDON group, after adjusting for maternal age and BMI.Conclusion(s)The POSEIDON group shows lower CLBRs than the non-POSEIDON group in young women, while the risk of abnormal birth outcomes in the POSEIDON group will not increase