Developer’s willingness to construct green dwellings in China: factors and stimulating policies

Green dwelling (GD) is a way to mitigate impacts of the building stock on environment and provide a better living condition for residences. However, the number of GDs is relatively small. Especially in China, GDs only account for less than 0.4% of the total buildings. It is directly due to the shortage of supply, which is influenced by the lack of willingness to develop. To stimulate the GD diffusion, the study applies structural equation model to analyse the factors in developers’ willingness to construct GDs, considering the inter-relationships and relative influences among factors. Further, it calculates the influence of each policy and policy subseries on developers’ willingness. It is showed that developers’ willingness is mostly influenced by their developing ability, followed by market development. Then comes government policy and corporate responsibility. In terms of stimulating policies on developers’ willingness, floor-to-area density award is of the biggest impact, followed by the green building requirement and interest rare. As to the policy subseries, mandatory requirements has larger effects on developers then voluntary incentives. Besides, in China, developers are more sensitive to financial incentives than non-financial ones. Accordingly, suggestions for policy making are proposed to stimulate developers to construct GDs

Marketization allocation, land price, and local government land speculation, China

Land value appreciation in the urbanization process has triggered market speculation. The Land Bank System strengthens local governments’ ability to control land supply and distribution rights. Local governments are considered close stakeholders. Under the pressure of guaranteeing economic growth and promotion, local governments have increased their dependence on land finances. It is important for investors to understand the local governments’ behaviors, and draw up business strategies. This study aims to examine the influencing factors and formation mechanism of local government land hoarding. The research hypothesis was tested by collating provincial-level panel data of China from 2004 to 2015 and using dynamic panel data estimated by the Generalized Method of Moments (GMM). A significant positive correlation was found between residential land price and land hoarding area by local governments. Land speculation in the eastern region is also more pronounced than that in central and western regions. In addition, empirical studies have found a correlation between the degree of government intervention and local government land hoarding behavior. The higher the degree of government intervention, the less land sold through bid invitation, auction, and listing, which are linked to the corresponding hoarding land area

Silicon nanoparticles in sustainable agriculture: synthesis, absorption, and plant stress alleviation

Silicon (Si) is a widely recognized beneficial element in plants. With the emergence of nanotechnology in agriculture, silicon nanoparticles (SiNPs) demonstrate promising applicability in sustainable agriculture. Particularly, the application of SiNPs has proven to be a high-efficiency and cost-effective strategy for protecting plant against various biotic and abiotic stresses such as insect pests, pathogen diseases, metal stress, drought stress, and salt stress. To date, rapid progress has been made in unveiling the multiple functions and related mechanisms of SiNPs in promoting the sustainability of agricultural production in the recent decade, while a comprehensive summary is still lacking. Here, the review provides an up-to-date overview of the synthesis, uptake and translocation, and application of SiNPs in alleviating stresses aiming for the reasonable usage of SiNPs in nano-enabled agriculture. The major points are listed as following: (1) SiNPs can be synthesized by using physical, chemical, and biological (green synthesis) approaches, while green synthesis using agricultural wastes as raw materials is more suitable for large-scale production and recycling agriculture. (2) The uptake and translocation of SiNPs in plants differs significantly from that of Si, which is determined by plant factors and the properties of SiNPs. (3) Under stressful conditions, SiNPs can regulate plant stress acclimation at morphological, physiological, and molecular levels as growth stimulator; as well as deliver pesticides and plant growth regulating chemicals as nanocarrier, thereby enhancing plant growth and yield. (4) Several key issues deserve further investigation including effective approaches of SiNPs synthesis and modification, molecular basis of SiNPs-induced plant stress resistance, and systematic effects of SiNPs on agricultural ecosystem

A Vision Transformer Network With Wavelet-Based Features for Breast Ultrasound Classification

Breast cancer is a prominent contributor to mortality associated with cancer in the female population on a global scale. The timely identification and precise categorization of breast cancer are of utmost importance in enhancing patient prognosis. Nevertheless, the task of precisely categorizing breast cancer based on ultrasound imaging continues to present difficulties, primarily due to the presence of dense breast tissues and their inherent heterogeneity. This study presents a unique approach for breast cancer categorization utilizing the wavelet based vision transformer network. To enhance the neural network’s receptive fields, we have incorporated the discrete wavelet transform (DWT) into the network input. This technique enables the capture of significant features in the frequency domain. The proposed model exhibits the capability to effectively capture intricate characteristics of breast tissue, hence enabling correct classification of breast cancer with a notable degree of precision and efficiency. We utilized two breast tumor ultrasound datasets, including 780 cases from Baheya hospital in Egypt and 267 patients from the UDIAT Diagnostic Centre of Sabadell in Spain. The findings of our study indicate that the proposed transformer network achieves exceptional performance in breast cancer classification. With an AUC rate of 0.984 and 0.968 on both datasets, our approach surpasses conventional deep learning techniques, establishing itself as the leading method in this domain. This study signifies a noteworthy advancement in the diagnosis and categorization of breast cancer, showcasing the potential of the proposed transformer networks to enhance the efficacy of medical imaging analysis

OPTIMIZATION DESIGN OF INTERNAL GEAR PUMP BASED ON IMPROVED PARTICLE SWARM OPTIMIZATION

In order to reduce the flow pulsation of the inner meshing gear pump,taking PGH type involute internal gear pump as the research object,the mathematical model of flow pulsation rate and tooth profile parameters was established,and the instantaneous flow rate of gear pump outer gear ring and internal gear in different meshing relations was analyzed. Based on these,a mathematical model of gear pump optimization was established. The improved particle swarm optimization was obtained by introducing the genetic operation and mutation operation of the genetic algorithm into the particle swarm optimization algorithm,through this algorithm、the parameters of gear indexing circle pressure angle、modification coefficient、addendum coefficient and tooth number were optimized,and the gear parameters of gear pump with minimum flow pulsation rate were obtained. The simulation verification was carried out by MATLAB,and the results showed that the flow pulsation rate and the tooth shape parameters of the gear pump are nonlinear; Under the condition of satisfying the structural requirements and reasonable transmission,the flow pulsation rate of the gear pump is reduced by 7. 27%,and the flow pulsation of the internal gear pump is reduced

Research on neural network model reference adaptive disturbance rejection control of digital hydraulic cylinder

The nonlinear factors in the digital hydraulic cylinder will reduce the accuracy of the control system. In order to improve the control accuracy of the control system, in this paper, a model reference adaptive disturbance rejection control method based on neural network is proposed. Firstly, the dead zone model is used to replace the nonlinear link in the feedback mechanism. A detailed mathematical model of digital hydraulic cylinder is established and the nonlinear hydraulic spring force is also considered, and a complete nonlinear state space model of digital hydraulic cylinder is derived based on LuGre friction model. Then the reference model is designed. By introducing ESO (extended state observer), the uncertainties and external disturbances of the controlled object are all equivalent to a total disturbance. The RBF (Radial Basis Function) network is used to approximate the unknown function FZ, the neural model reference adaptive disturbance rejection composite controller is designed by using Lyapunov direct method and Barbalat lemma. Finally, the simulation verification is carried out by using MATLAB. The simulation results show that the control strategy can effectively improve the response characteristics of the system, reduce the steady-state error of the system, and improve the robustness of the system

Effects of impregnation of softwood with sulfuric acid and sulfur dioxide on chemical and physical characteristics, enzymatic digestibility, and fermentability

Hydrothermal pretreatment improves bioconversion of lignocellulose, but the effects of different acid catalysts are poorly understood. The effects of sulfuric acid (SA) and sulfur dioxide (SD) in continuous steam pretreatment of wood of Norway spruce were compared in the temperature range 195 degrees C-215 degrees C. The inhibitory effects of the pretreatment liquid on cellulolytic enzymes and Saccharomyces cerevisiae yeast were higher for SD-than for SApretreated material, and the inhibitory effects increased with increasing pretreatment temperature. However, the susceptibility to cellulolytic enzymes of wood pretreated with SD was 2.0-2.9 times higher than that of wood pretreated with SA at the same temperature. Data conclusively show that the superior convertibility of SDpretreated material was not due to inhibition phenomena but rather to the greater capability of the SD pretreatment to reduce the particle size through partial delignification and cellulose degradation. Particle size was shown to be correlated with enzymatic digestibility (R-2 0.97-0.98).Bio4Energ

Automated low-cost terrestrial laser scanner for measuring diameters at breast height and heights of plantation trees.

A terrestrial laser scanner is a fast, high-precision data acquisition device, which has been applied more and more to the research area of forest inventory. In this study, a type of automated low-cost terrestrial laser scanner was designed and implemented based on a SICK LMS-511 two-dimensional laser scanning sensor and a stepper motor. The new scanner was named BEE(developed by the department of Electronic Engineering, Beijing Forestry University), which can scan the forest trees in three dimensions. The BEE scanner and its supporting software are specifically designed for forest inventory. The specific software was developed to smoothly control the BEE scanner and to acquire the data, including the angular data, range data, and intensity data, and the data acquired by the BEE scanner could be processed into point cloud data, a range map, and an intensity map. Based on the point cloud data, the trees were detected by a single slice of the single scan in a plot, and the local ground plane was fitted for each detected tree. Then the diameter at breast height (DBH), tree height, and tree position could be estimated automatically by using the specific software. The experiments have been performed by using the BEE scanner in an artificial ginkgo forest which was located in the Haidian District of Beijing. Four 10 m × 10 m square plots were selected for the experiments. The BEE scanner scanned in the four plots and acquired the single-scan data, respectively. The DBH, tree height, and tree position of the trees in the four plots were estimated and analyzed. For comparison, manually-measured data was also collected in the four plots. The trunk detection rate for all four plots was 92.75%; the root mean square error of the DBH estimation was 1.27 cm; the root mean square error of the tree height estimation was 0.24 m; and the tree position estimation was in line with the actual position. The scanner also was tested in more natural forest in the JiuFeng Forest Park. Two plots with a radius of 5 meters were scanned. Eleven trees in the plot with a flat ground were detected and DBH were estimated. But tree detection was failed in the other plot because of the undulating ground. Experimental results show that the BEE scanner can efficiently estimate the structure parameters of plantation trees and has good potential in practical applications of forest inventory