Modeling Analysis of Factors Influencing Wind-Borne Seed Dispersal: A Case Study on Dandelion

A weed is a plant that thrives in areas of human disturbance, such as gardens, fields, pastures, waysides, and waste places where it is not intentionally cultivated. Dispersal affects community dynamics and vegetation response to global change. The process of seed disposal is influenced by wind, which plays a crucial role in determining the distance and probability of seed dispersal. Existing models of seed dispersal consider wind direction but fail to incorporate wind intensity. In this paper, a novel seed disposal model was proposed in this paper, incorporating wind intensity based on relevant references. According to various climatic conditions, including temperate, arid, and tropical regions, three specific regions were selected to establish a wind dispersal model that accurately reflects the density function distribution of dispersal distance. Additionally, dandelions growth is influenced by a multitude of factors, encompassing temperature, humidity, climate, and various environmental variables that necessitate meticulous consideration. Based on Factor Analysis model, which completely considers temperature, precipitation, solar radiation, wind, and land carrying capacity, a conclusion is presented, indicating that the growth of seeds is primarily influenced by plant attributes and climate conditions, with the former exerting a relatively stronger impact. Subsequently, the remaining two plants were chosen based on seed weight, yielding consistent conclusion.

Modeling the Synergetic Effect of Various Factors on Chloride Transport in Nonsaturated Concrete

Diffusion has been systematically described as the main mechanism of chloride transport in reinforced concrete（RC） structure, especially when the concrete is in a saturated state. However, the single mechanism of diffusion is not able to describe the actual chloride ingress in the nonsaturated concrete. Instead, it is dominated by the interaction of diffusion and convection. With the synergetic effects of various factors taken into account, this study aimed to modify and develop an analytical convection- diffusion coupling model for chloride transport in nonsaturated concrete. The model was verified by simulation of laboratory tests and field measurement. The results of comparison study demonstrate that the analytical model developed in this study is efficient and accurate in predicting the chloride profiles in the nonsaturated concrete.

Navigating breast cancer brain metastasis:Risk factors,prognostic indicators,and treatment perspectives

In this editorial,we comment on the article by Chen et al.We specifically focus on the risk factors,prognostic factors,and management of brain metastasis(BM)in breast cancer(BC).BC is the second most common cancer to have BM after lung cancer.Independent risk factors for BM in BC are:HER-2 positive BC,triplenegative BC,and germline BRCA mutation.Other factors associated with BM are lung metastasis,age less than 40 years,and African and American ancestry.Even though risk factors associated with BM in BC are elucidated,there is a lack of data on predictive models for BM in BC.Few studies have been made to formulate predictive models or nomograms to address this issue,where age,grade of tumor,HER-2 receptor status,and number of metastatic sites(1 vs>1)were predictive of BM in metastatic BC.However,none have been used in clinical practice.National Comprehensive Cancer Network recommends screening of BM in advanced BC only when the patient is symptomatic or suspicious of central nervous system symptoms;routine screening for BM in BC is not recommended in the guidelines.BM decreases the quality of life and will have a significant psychological impact.Further studies are required for designing validated nomograms or predictive models for BM in BC;these models can be used in the future to develop treatment approaches to prevent BM,which improves the quality of life and overall survival.

Analytical modeling of sandwich beam for piezoelectric bender elements

Piezoelectric bender elements are widely used as electromechanical sensors and actuators, An analytical sandwich beam model for piezoelectric bender elements was developed based on the first-order shear deformation theory （FSDT）, which assumes a single rotation angle for the whole cross-section and a quadratic distribution function for coupled electric potential in piezoelectric layers, and corrects the effect of transverse shear strain on the electric displacement integration. Free vibration analysis of simplysupported bender elements was carried out and the numerical results showed that, solutions of the present model for various thickness-to-length ratios are compared well with the exact two-dimensional solutions, which presents an efficient and accurate model for analyzing dynamic electromechanical responses of bender elements.

Equilibrium and kinetic Si isotope fractionation factors and their implications for Si isotope distributions in the Earth's surface environments

Several important equilibrium Si isotope fractionation factors among minerals,organic molecules and the H_4SiO_4 solution are complemented to facilitate the explanation of the distributions of Si isotopes in Earth's surface environments.The results reveal that,in comparison to aqueous H_4SiO_4,heavy Si isotopes will be significantly enriched in secondary silicate minerals.On the contrary,quadra-coordinated organosilicon complexes are enriched in light silicon isotope relative to the solution.The extent of ^(28)Si-enrichment in hyper-coordinated organosilicon complexes was found to be the largest.In addition,the large kinetic isotope effect associated with the polymerization of monosilicic acid and dimer was calculated,and the results support the previous statement that highly ^(28)Sienrichment in the formation of amorphous quartz precursor contributes to the discrepancy between theoretical calculations and field observations.With the equilibrium Si isotope fractionation factors provided here,Si isotope distributions in many of Earth's surface systems can be explained.For example,the change of bulk soil δ^(30)Si can be predicted as a concave pattern with respect to the weathering degree,with the minimum value where allophane completely dissolves and the total amount of sesquioxides and poorly crystalline minerals reaches their maximum.When,under equilibrium conditions,the well-crystallized clays start to precipitate from the pore solutions,the bulk soil δ^(30)Si will increase again and reach a constant value.Similarly,the precipitation of crystalline smectite and the dissolution of poorly crystalline kaolinite may explain the δ^(30)Si variations in the ground water profile.The equilibrium Si isotope fractionations among the quadracoordinated organosilicon complexes and the H_4SiO_4solution may also shed light on the Si isotope distributions in the Si-accumulating plants.

A Collaboration Network Model with Multiple Evolving Factors

To describe the empirical data of collaboration networks, several evolving mechanisms have been proposed, which usually introduce different dynamics factors controlling the network growth. These models can reasonably reproduce the empirical degree distributions for a number of we11-studied real-world collaboration networks. On the basis of the previous studies, in this work we propose a collaboration network model in which the network growth is simultaneously controlled by three factors, including partial preferential attachment, partial random attachment and network growth speed. By using a rate equation method, we obtain an analytical formula for the act degree distribution. We discuss the dependence of the act degree distribution on these different dynamics factors. By fitting to the empirical data of two typical collaboration networks, we can extract the respective contributions of these dynamics factors to the evolution of each networks.

Factors Acquisition and Content Estimation of Farmland Soil Organic Carbon Based upon Internet of Things

Aiming at the shortage of sufficient continuous parameters for using models to estimate farmland soil organic carbon(SOC) content, an acquisition method of factors influencing farmland SOC and an estimation method of farmland SOC content with Internet of Things(IOT) are proposed in this paper. The IOT sensing device and transmission network were established in a wheat demonstration base in Yanzhou Distict of Jining City, Shandong Province, China to acquire data in real time. Using real-time data and statistics data, the dynamic changes of SOC content between October 2012 and June 2015 was simulated in the experimental area with SOC dynamic simulation model. In order to verify the estimation results, potassium dichromate external heating method was applied for measuring the SOC content. The results show that: 1) The estimated value matches the measured value in the lab very well. So the method is feasible in this paper. 2) There is a clear dynamic variation in the SOC content at 0.2 m soil depth in different growing periods of wheat. The content reached the highest level during the sowing period, and is lowest in the flowering period. 3) The SOC content at 0.2 m soil depth varies in accordance with the amount of returned straw. The larger the amount of returned straw is, the higher the SOC content.

Calibrating partial factors for Danish railway embankments using probabilistic analyses

High costs are connected with upgrading railway embankments throughout Denmark using the partial factors for geotechnical design calibrated for general application. One way to reduce the costs is reliability-based calibration of the partial factors to a reasonable safety level taking into account the specific design situations and uncertainties relevant to railway embankments. A reliability-based design has been investigated, resulting in an optimal partial factor for the considered subsoil. With a stochastic soil model to simulate the undrained shear strength of soft soil deposits, the partial factor is calibrated using asymptotic sampling for the reliability assessment. The calibration shows that the partial factor can be reduced significantly compared to the value specified in the Danish National Annex to DS/EN 1997-1 （2007）, Eurocode 7.

Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications

Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor(RLF) model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly.Experimental results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for industrial applications demanding highly efficient models.

Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data

High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurately represent them is of great significance.A latent factor(LF)model is one of the most popular and successful ways to address this issue.Current LF models mostly adopt L2-norm-oriented Loss to represent an HiDS matrix,i.e.,they sum the errors between observed data and predicted ones with L2-norm.Yet L2-norm is sensitive to outlier data.Unfortunately,outlier data usually exist in such matrices.For example,an HiDS matrix from RSs commonly contains many outlier ratings due to some heedless/malicious users.To address this issue,this work proposes a smooth L1-norm-oriented latent factor(SL-LF)model.Its main idea is to adopt smooth L1-norm rather than L2-norm to form its Loss,making it have both strong robustness and high accuracy in predicting the missing data of an HiDS matrix.Experimental results on eight HiDS matrices generated by industrial applications verify that the proposed SL-LF model not only is robust to the outlier data but also has significantly higher prediction accuracy than state-of-the-art models when they are used to predict the missing data of HiDS matrices.

Nomograms and risk score models for predicting survival in rectal cancer patients with neoadjuvant therapy

BACKGROUND Colorectal cancer is a common digestive cancer worldwide.As a comprehensive treatment for locally advanced rectal cancer(LARC),neoadjuvant therapy(NT)has been increasingly used as the standard treatment for clinical stage II/III rectal cancer.However,few patients achieve a complete pathological response,and most patients require surgical resection and adjuvant therapy.Therefore,identifying risk factors and developing accurate models to predict the prognosis of LARC patients are of great clinical significance.AIM To establish effective prognostic nomograms and risk score prediction models to predict overall survival(OS)and disease-free survival(DFS)for LARC treated with NT.METHODS Nomograms and risk factor score prediction models were based on patients who received NT at the Cancer Hospital from 2015 to 2017.The least absolute shrinkage and selection operator regression model were utilized to screen for prognostic risk factors,which were validated by the Cox regression method.Assessment of the performance of the two prediction models was conducted using receiver operating characteristic curves,and that of the two nomograms was conducted by calculating the concordance index(C-index)and calibration curves.The results were validated in a cohort of 65 patients from 2015 to 2017.RESULTS Seven features were significantly associated with OS and were included in the OS prediction nomogram and prediction model:Vascular_tumors_bolt,cancer nodules,yN,body mass index,matchmouth distance from the edge,nerve aggression and postoperative carcinoembryonic antigen.The nomogram showed good predictive value for OS,with a C-index of 0.91(95%CI:0.85,0.97)and good calibration.In the validation cohort,the C-index was 0.69(95%CI:0.53,0.84).The risk factor prediction model showed good predictive value.The areas under the curve for 3-and 5-year survival were 0.811 and 0.782.The nomogram for predicting DFS included ypTNM and nerve aggression and showed good calibration and a C-index of 0.77(95%CI:0.69,0.85).In the validation cohort,the C-index was 0.71(95%CI:0.61,0.81).The prediction model for DFS also had good predictive value,with an AUC for 3-year survival of 0.784 and an AUC for 5-year survival of 0.754.CONCLUSION We established accurate nomograms and prediction models for predicting OS and DFS in patients with LARC after undergoing NT.

An Acceptance Model of Using Mobile-Government Services(AMGS)

In recent years,the telecommunications sector is no longer limited to traditional communications,but has become the backbone for the use of data,content and digital applications by individuals,governments and companies to ensure the continuation of economic and social activity in light of social distancing and total closure inmost countries in the world.Therefore,electronic government(e-Government)andmobile government(m-Government)are the results of technological evolution and innovation.Hence,it is important to investigate the factors that influence the intention to use m-Government services among Jordan’s society.This paper proposed a new m-Government acceptance model in Jordan(AMGS);this model combines the Information System(IS)Success Factor Model and Hofstede Cultural Dimensions Theory.The study was conducted by surveying different groups of the Jordanian community.Astructured questionnaire was used to collect data from203 respondents.Multiple regression analysis has been conducted to analyze the data.The results indicate that the significant predictors of citizen intention to use m-Government services in Jordan are Information Quality,Service Quality,Uncertainty Avoidance,and Indulgence vs.restraint.While,the results also suggest that Power Distance is not a significant predictor of citizen intention to use m-Government services.

Regional Clustering and Synchronization of Provincial Business Fluctuations in China

In this article, we propose a novel, multilevel, dynamic factor model, to determine endogenously clustered regions for the investigation of regional clustering and synchronization of provincial business fluctuations in China. The parameter identification and model estimation was conducted using the Markov Chain Monte Carlo method. We then conducted an empirical study of the provincial business fluctuations in China(31 Chinese provinces are considered except Hong Kong, Macao, and Taiwan due to the data unavailability), which were sampled from January 2000 to December 2015. Our results indicated that these provinces could be clustered into four regions: leading, coincident, lagging, and overshooting. In comparison with traditional geographical divisions, this novel clustering into four regions enabled the regional business cycle synchronization to be more accurately captured. Within the four regional clusters it was possible to identify substantial heterogeneities among regional business cycle fluctuations, especially during the periods of the 2008 financial crisis and the ‘four-trillion economic stimulus plan'.

A multiscale model for analyzing the synergy of CS and WSS on the endothelium in straight arteries

A multiscale model was proposed to calculate the circumferential stress （CS） and wall shear stress （WSS） and analyze the effects of global and local factors on the CS, WSS and their synergy on the arterial endothelium in large straight arteries. A parameter pair [Zs, SPA] （defined as the ratio of CS amplitude to WSS amplitude and the phase angle between CS and WSS for different harmonic components, respectively） was proposed to characterize the synergy of CS and WSS. The results demonstrated that the CS or WSS in the large straight arteries is determined by the global factors, i.e. the preloads and the afterloads, and the local factors, i.e. the local mechanical properties and the zero-stress states of arterial walls, whereas the Zs and SPA are primarily determined by the local factors and the afterloads. Because the arterial input impedance has been shown to reflect the physiological and pathological states of whole downstream arterial beds, the stress amplitude ratio Zs and the stress phase difference SPA might be appropriate indices to reflect the influences of the states of whole downstream arterial beds on the local blood flow-dependent phenomena such as angiogenesis, vascular remodeling and atherosgenesis.

Detection of tyrosine,trace metals and nutrients in cow dung:the environmental significance in soil and water environments

This study examined the dissolved organic matter(DOM) components of cow dung using a combination of fluorescence(excitation–emission matrix,EEM)spectroscopy and parallel factor(PARAFAC) modelling along with eleven trace metals using ICP-MS and nutrients(NH_4^+ and NO_3^-) using an AA3 auto analyser. EEM–PARAFAC analysis demonstrated that cow dung predominantly contained only one fluorescent DOM component with two fluorescence peaks(Ex/Em=275/311 nm and Ex/Em=220/311 nm),which could be denoted as tyrosine by comparison with its standard. Occurrence of tyrosine can be further confirmed by the FTIR spectra. Trace metals analysis revealed that Na,K and Mg were significantly higher than Ca,Fe,Mn,Zn Sr,Cu,Ni and Co. The NH_4^+ concentrations were substantially higher than NO_3^-.These results thus indicate that the dissolved components of the cow dung could be useful for better understanding its future uses in various important purposes.

A stock assessment for Illex argentinus in Southwest Atlantic using an environmentally dependent surplus production model

The southern Patagonian stock（SPS） of Argentinian shortfin squid, Illex argentinus, is an economically important squid fishery in the Southwest Atlantic. Environmental conditions in the region play an important role in regulating the population dynamics of the I. argentinus population. This study develops an environmentally dependent surplus production（EDSP） model to evaluate the stock abundance of I. argentines during the period of 2000 to 2010. The environmental factors（favorable spawning habitat areas with sea surface temperature of 16–18°C） were assumed to be closely associated with carrying capacity（K） in the EDSP model. Deviance Information Criterion（DIC） values suggest that the estimated EDSP model with environmental factors fits the data better than a Schaefer surplus model without environmental factors under uniform and normal scenarios.The EDSP model estimated a maximum sustainable yield（MSY） from 351 600 t to 685 100 t and a biomass from 1 322 400 t to1 803 000 t. The fishing mortality coefficient of I. argentinus from 2000 to 2010 was smaller than the values of F（0.1） and F（MSY）. Furthermore, the time series biomass plot of I. argentinus from 2000 to 2010 shows that the biomass of I.argentinus and this fishery were in a good state and not presently experiencing overfishing. This study suggests that the environmental conditions of the habitat should be considered within squid stock assessment and management.

Comprehensive Assessment and Promotion Policy of Producer Services' Competitiveness in Beijing City:A Comparative Analysis among Beijing,Tianjin,Shanghai,Guangzhou and Shenzhen

The five central cities-Beijing,Tianjin,Shanghai,Guangzhou and Shenzhen-of the three most important strategic regions in China,namely Beijing-Tianjin-Hebei metropolitan region,the Yangtze River Delta and the Pearl River Delta,are taken as a comparative analysis on urban producer services' competitiveness,especially focusing on the comparative advantages and disadvantages of producer services in Beijing.Firstly,based on an integrated indicator system including one objective hierarchy,four standard hierarchies and 35 indicator hierarchies,the study applies factor analysis model and analytic hierarchy process model reformed by entropy technology to measure the comprehensive competitiveness of producer services in the above five cities.Secondly,Beijing has comparative advantages of capital resource,industrial base,scientific research resource and market scale,since it takes the first place of the five cities in the comprehensive competitiveness of producer services,the competitiveness of industrial development,business environment and living environment,only inferior to Shanghai in the competitiveness of talent capital.Thirdly,Beijing has comparative disadvantages in the level of producer services development,urban innovation capability and living environment.Finally,five proposals are put forth to enhance the competitiveness of producer services in Beijing,namely perfecting the system of laws and statutes,supporting the development of industry association,implementing scientific development planning,introducing preferential fiscal and taxation policies and strengthening human capital reserve.

Erlotinib inhibits progression to dysplasia in a colitis-associated colon cancer model

AIM:To investigate the role of epidermal growth factor receptor(EGFR) in colitis-associated dysplasia using the EGFR tyrosine kinase inhibitor erlotinib.METHODS:Sprague-Dawley rats received trinitrobenzene sulfonic acid(TNBS;30 mg in 50% ethanol,ic),followed 6 wk later by reactivation with TNBS(5 mg/kg,iv) for 3 d.To induce colitis-associated dysplasia,rats then received TNBS(iv) twice a week for 10 wk.One group received erlotinib(10 mg/kg,ip) for 1 wk before the start of the reactivation of the colitis and 2 wk after(21 d);the rest received the vehicle.After rats were euthanized,the colons were removed and analyzed for damage and expression of the EGFR downstream effectors Erk1/2 and c-Myc.RESULTS:Ninety percent of the vehicle-treated animals had dysplasia in any region of the colon.Erlotinib-treated animals had a significant decrease in the incidence of dysplasia compared to vehicle-treated animals in all regions of the colon(50.00% ± 11.47% vs 90.00% ± 10.00% in proximal,P < 0.05;15.00% ± 8.19% vs 50.00% ± 16.67% in mid,P < 0.05;and 20.00% ± 9.17% vs 70.00% ± 15.28% in distal,P < 0.01).Erlotinib-treated animals also had reduced cell proliferation,reduced active Erk1/2,and reduced c-Myc in colon epithelium compared with the vehicle-treated animals.In vitro,erlotinib treatment was shown to markedly decrease c-Myc and pErk1/2 levels in rat epithelial cells.Proliferation of rat epithelial cells was stimulated by epidermal growth factor and inhibited by erlotinib(P < 0.05).CONCLUSION:Erlotinib can decrease the development of colitis-associated dysplasia,suggesting a potential therapeutic use for erlotinib in patients with long-standing colitis.

A New model to forecast fishing ground of Scomber japonicus in the Yellow Sea and East China Sea

The pelagic species is closely related to the marine environmental factors, and establishment of forecasting model of fishing ground with high accuracy is an important content for pelagic fishery. The chub mackerel（Scomber japonicus） in the Yellow Sea and East China Sea is an important fishing target for Chinese lighting purse seine fishery. Based on the fishery data from China＇s mainland large-type lighting purse seine fishery for chub mackerel during the period of 2003 to 2010 and the environmental data including sea surface temperature（SST）, gradient of the sea surface temperature（GSST）, sea surface height（SSH） and geostrophic velocity（GV）, we attempt to establish one new forecasting model of fishing ground based on boosted regression trees. In this study, the fishing areas with fishing effort is considered as one fishing ground, and the areas with no fishing ground are randomly selected from a background field, in which the fishing areas have no records in the logbooks. The performance of the forecasting model of fishing ground is evaluated with the testing data from the actual fishing data in 2011. The results show that the forecasting model of fishing ground has a high prediction performance, and the area under receiver operating curve（AUC） attains 0.897. The predicted fishing grounds are coincided with the actual fishing locations in 2011, and the movement route is also the same as the shift of fishing vessels, which indicates that this forecasting model based on the boosted regression trees can be used to effectively forecast the fishing ground of chub mackerel in the Yellow Sea and East China Sea.

Development of monitoring and assessment of forest biomass and carbon storage in China

Addressing climate change has become a common issue around the world in the 21st century and equally an important mission in Chinese forestry.Understanding the development of monitoring and assessment of forest biomass and carbon storage in China is important for promoting the evaluation of forest carbon sequestration capacity of China.The author conducts a systematic analysis of domestic publications addressing＂monitoring and assessment of forest biomass and carbon storage＂in order to understand the development trends,describes the brief history through three stages,and gives the situation of new development.Towards the end of the 20th century,a large number of papers on biomass and productivity of the major forest types in China had been published,covering the exploration and efforts of more than 20 years,while investigations into assessment of forest carbon storage had barely begun.Based on the data of the 7th and 8th National Forest Inventories,forest biomass and carbon storage of the entire country were assessed using individual tree biomass models and carbon conversion factors of major tree species,both previously published and newly developed.Accompanying the implementation of the 8th National Forest Inventory,a program of individual tree biomass modeling for major tree species in China was carried out simultaneously.By means of thematic research on classification of modeling populations,as well as procedures for collecting samples and methodology for biomass modeling,two technical regulations on sample collection and model construction were published as ministerial standards for application.Requests for approval of individual tree biomass models and carbon accounting parameters of major tree species have been issued for approval as ministerial standards.With the improvement of biomass models and carbon accounting parameters,thematic assessment of forest biomass and carbon storage will be gradually changed into a general monitoring of forest biomass and carbon storage,in order to realize their dynamic monitoring in national forest inventories.Strengthening the analysis and assessment of spatial distribution patterns of forest biomass and carbon storage through application of remote sensing techniques and geostatistical approaches will also be one of the major directions of development in the near future.