Medical condition, population density, and residents’ savings in China’s contiguous destitute areas

This paper uses the 2005–2012 spatial panel data of China’s 11 Contiguous Destitute Areas (CDAs) and different kinds of econometric regression models, we examines the implications of medical condition and population density for the residents’ savings in these 11 CDAs. We find that, the increase in population density not only would reduce residents’ savings through its own, but also has negative effect on residents’ savings through the way of medical condition, while medical condition has positively and significantly effect on the residents’ savings. This means that as a CDAs’ population density increases, the needs of medical condition will increase too, and then it will cause the medical condition to be deteriorating relatively, thereby reducing households’ precautionary savings. In most of the models, especially in the direct effects, indirect effects, and total effects, these results are roughly the same and robust. These findings mean that medical condition and population density not only have influence on residents’ savings on their own, but also will decrease the residents’ savings by their interactio

A Network Celebrity Identification and Evaluation Model Based on Hybrid Trust Relation

Trust-based celebrity user identification is the key to the industry\u27s reputation for electronic word of mouth. However, trust and mistrust are independent and coexistent concepts. In this context, we need to consider the existence of the two kinds of user relations brought about by the impact. This paper analyzes the characteristics of trust and distrust in social networks, and gives formal descriptions of trust networks, untrusted networks, and mixed trust networks. Based on the indicators such as degree distribution, correlation coefficient, and matching coefficient, the structural properties of mixed trust networks are studied. Based on the PageRank algorithm, the HTMM metrics affecting users under the mixed trust network environment are proposed. Finally, the validity of HTMM is verified through a real data set containing trust and distrust. Experimental results show that the results of HTMM\u27s celebrity user identification method still have a low level of trust

An investigation on the best-fit models for sugarcane biomass estimation by Linear Mixed-Effect Modelling on Unmanned Aerial Vehicle-Based Multispectral Images: a case study of Australia

Due to the worldwide population growth and the increasing needs for sugar-based products, accurate estimation of sugarcane biomass is critical to the precise monitoring of sugarcane growth. This research aims to find the imperative predictors correspond to the random and fixed effects to improve the accuracy of wet and dry sugarcane biomass estimations by integrating ground data and multi-temporal images from Unmanned Aerial Vehicles (UAVs). The multispectral images and biomass measurements were obtained at different sugarcane growth stages from 12 plots with three nitrogen fertilizer treatments. Individual spectral bands and different combinations of the plots, growth stages, and nitrogen fertilizer treatments were investigated to address the issue of selecting the correct fixed and random effects for the modelling. A model selection strategy was applied to obtain the optimum fixed effects and their proportional contribution. The results showed that utilizing Green, Blue, and Near Infrared spectral bands on models rather than all bands improved model performance for wet and dry biomass estimates. Additionally, the combination of plots and growth stages outperformed all the candidates of random effects. The proposed model outperformed the Multiple Linear Regression (MLR), Generalized Linear Model (GLM), and Generalized Additive Model (GAM) for wet and dry sugarcane biomass, with coefficients of determination (R2) of 0.93 and 0.97, and Root Mean Square Error (RMSE) of 12.78 and 2.57 t/ha, respectively. This study indicates that the proposed model can accurately estimate sugarcane biomasses without relying on nitrogen fertilizers or the saturation/senescence problem of Vegetation Indices (VIs) in mature growth stages

Two-Stage Hybrid Supervision Framework for Fast, Low-resource, and Accurate Organ and Pan-cancer Segmentation in Abdomen CT

Abdominal organ and tumour segmentation has many important clinical applications, such as organ quantification, surgical planning, and disease diagnosis. However, manual assessment is inherently subjective with considerable inter- and intra-expert variability. In the paper, we propose a hybrid supervised framework, StMt, that integrates self-training and mean teacher for the segmentation of abdominal organs and tumors using partially labeled and unlabeled data. We introduce a two-stage segmentation pipeline and whole-volume-based input strategy to maximize segmentation accuracy while meeting the requirements of inference time and GPU memory usage. Experiments on the validation set of FLARE2023 demonstrate that our method achieves excellent segmentation performance as well as fast and low-resource model inference. Our method achieved an average DSC score of 89.79\% and 45.55 \% for the organs and lesions on the validation set and the average running time and area under GPU memory-time cure are 11.25s and 9627.82MB, respectively

Temporal distribution characteristics of earthquakes in Taiwan, China

The characteristics of seismic temporal distribution represent an important basis for earthquake prediction and seismic hazard analysis. In this paper, based on the seismic catalogs in Taiwan, and using Poisson (exponential distribution), Gamma, Lognormal, Weibull, and Brownian passage time distributions as target models, we adopt the maximum likelihood method for estimating model parameters. The optimal model for describing the temporal distribution of earthquakes in Taiwan is determined according to the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), K-S test, Chi-square test, and coefficient of determination R2 results. The results show that for moderate-strong earthquakes events (MW < 7.0), the Gamma distribution model can well describe the temporal distribution characteristics of earthquakes, while large earthquakes (MW ≥ 7.0) can be described entirely by exponential distribution. In addition, the temporal correlation between earthquakes is also examined through diffusion entropy analysis. The results show that seismic activity features temporal correlation, and earthquakes with relatively small magnitude (MW < 7) are affected by larger events (MW ≥ 7.0), thus suggesting long-term memory in time. In this study, the probability of the occurrence of a major earthquake in Taiwan is also calculated. The results show that the probability of an MW ≥ 7.0 earthquake in Taiwan in the next 10 years reaches 91.3%. The results may be used to inform the selection of seismic time distribution models and the calculation of seismic activity parameters in earthquake prediction and seismic hazard calculation, and hold scientific significance for understanding the mechanism of earthquake genesis

HIV-1gp120 Induces Neuronal Apoptosis through Enhancement of 4-Aminopyridine-Senstive Outward K+ Currents

Human immunodeficiency virus type 1 (HIV-1)-associated dementia (HAD) usually occurs late in the course of HIV-1 infection and the mechanisms underlying HAD pathogenesis are not well understood. Accumulating evidence indicates that neuronal voltage-gated potassium (Kv) channels play an important role in memory processes and acquired neuronal channelopathies in HAD. To examine whether Kv channels are involved in HIV-1-associated neuronal injury, we studied the effects of HIV-1 glycoprotein 120 (gp120) on outward K+ currents in rat cortical neuronal cultures using whole-cell patch techniques. Exposure of cortical neurons to gp120 produced a dose-dependent enhancement of A-type transient outward K+ currents (IA). The gp120-induced increase of IA was attenuated by T140, a specific antagonist for chemokine receptor CXCR4, suggesting gp120 enhancement of neuronal IA via CXCR4. Pretreatment of neuronal cultures with a protein kinase C (PKC) inhibitor, GF109203X, inhibited the gp120-induced increase of IA. Biological significance of gp120 enhancement of IA was demonstrated by experimental results showing that gp120-induced neuronal apoptosis, as detected by terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay and caspase-3 staining, was attenuated by either an IA blocker 4-aminopyridine or a specific CXCR4 antagonist T140. Taken together, these results suggest that gp120 may induce caspase-3 dependent neuronal apoptosis by enhancing IA via CXCR4-PKC signaling

Soil respiration components and their temperature sensitivity under chemical fertilizer and compost application: the role of nitrogen supply and compost substrate quality

Understanding autotrophic (Ra) and heterotrophic (Rh) components of soil respiration (Rs) and their temperature sensitivity (Q10) is critical for predicting soil carbon (C) cycle and its feedback to climate change. In agricultural systems, these processes can be considerably altered by chemical fertilizer and compost application due to changes in nitrogen (N) supply and substrate quality (decomposability). We conducted a field experiment including control, urea and four compost treatments. Ra and Rh were separated using the root exclusion method. Composts were characterized by chemical analyses, 13C solid‐state NMR, and lignin monomers. Annual cumulative Ra, along with root biomass, increased with soil mineral N, while Rh was suppressed by excessive N supply. Thus, Ra was stimulated but Rh was decreased by urea alone application. Annual Rh was increased by application of compost, especially that containing most lignin vanillyl and syringyl units, O‐alkyl C, di‐O‐alkyl C, and manganese. However, during the initial period, Rh was most effectively stimulated by the compost containing most carbohydrates, lignin cinnamyl units, phenolic C and calcium. Ra was mediated by N release from compost decomposition, and thus exhibited similar responses to compost quality as Rh. The Rh Q10 was reduced while Ra Q10 was increased by chemical fertilizer and compost application. Moreover, the Rh Q10 negatively related to soil mineral N supply and compost indicators referring to high substrate quality. Overall, our results suggest that N supply and substrate quality played an important role in regulating soil C flux and its response to climate warming

PHTrans: Parallelly Aggregating Global and Local Representations for Medical Image Segmentation

The success of Transformer in computer vision has attracted increasing attention in the medical imaging community. Especially for medical image segmentation, many excellent hybrid architectures based on convolutional neural networks (CNNs) and Transformer have been presented and achieve impressive performance. However, most of these methods, which embed modular Transformer into CNNs, struggle to reach their full potential. In this paper, we propose a novel hybrid architecture for medical image segmentation called PHTrans, which parallelly hybridizes Transformer and CNN in main building blocks to produce hierarchical representations from global and local features and adaptively aggregate them, aiming to fully exploit their strengths to obtain better segmentation performance. Specifically, PHTrans follows the U-shaped encoder-decoder design and introduces the parallel hybird module in deep stages, where convolution blocks and the modified 3D Swin Transformer learn local features and global dependencies separately, then a sequence-to-volume operation unifies the dimensions of the outputs to achieve feature aggregation. Extensive experimental results on both Multi-Atlas Labeling Beyond the Cranial Vault and Automated Cardiac Diagnosis Challeng datasets corroborate its effectiveness, consistently outperforming state-of-the-art methods. The code is available at: https://github.com/lseventeen/PHTrans.Comment: 10 pages, 3 figure