Population Quantity Variations of Oriental Fruit Fly (Bactrocera dorsalis Hendel) on the Basis of Stepwise Regression Analysis

[Objective] The research aimed to study the significant influence factors of the population variations of oriental fruit fly. [Method] Using stepwise regression analysis, the population variations law of oriental fruit fly in Jianshui County of Yunnan province and the meteorological factors that caused its occurrence were analyzed. And the regression model was built. Finally, the regression model was tested on the basis of the data in Jianshui County of Yunnan Province during 2004-2006.[Result] The main meteorological factors that influenced the occurrence of oriental fruit fly were relative humidity, the lowest monthly temperature and rainfall. [Conclusion] This study will provide certain reference for the prediction researches on the time, quantity and occurrence peak of oriental fruit fly.

New empirical model to evaluate groundwater flow into circular tunnel using multiple regression analysis

There are various analytical, empirical and numerical methods to calculate groundwater inflow into tun- nels excavated in rocky media. Analytical methods have been widely applied in prediction of groundwa- ter inflow to tunnels due to their simplicity and practical base theory. Investigations show that the real amount of water infiltrating into jointed tunnels is much less than calculated amount using analytical methods and obtained results are very dependent on tunnel＇s geometry and environmental situations. In this study, using multiple regression analysis, a new empirical model for estimation of groundwater seepage into circular tunnels was introduced. Our data was acquired from field surveys and laboratory analysis of core samples. New regression variables were defined after perusing single and two variables relationship between groundwater seepage and other variables. Finally, an appropriate model for estima- tion of leakage was obtained using the stepwise algorithm. Statistics like R, R2, R2e and the histogram of residual values in the model represent a good reputation and fitness for this model to estimate the groundwater seepage into tunnels. The new experimental model was used for the test data and results were satisfactory. Therefore, multiple regression analysis is an effective and efficient way to estimate the groundwater seeoage into tunnels.

Analysis and Evaluation Indicator Selection of Chilling Tolerance of Different Cotton Genotypes

[Objectivc] This study aimed to investigate the chilling tolerance of seedlings of different cotton genotypes and screen appropriate indicators for assess- ing chilling tolerance, to establish reliable mathematical evaluation model for chilling tolerance of cotton, thus providing theoretical basis for breeding and promoting new chilling-tolerant cotton germplasms and large-scale evaluation of chilling tolerance of cotton varieties. [Method] Fifteen cotton varieties （lines） were used as experimental materials. The photosynthetic gas exchange parameters, chlorophyll fluorescence ki- netic parameters, chlorophyll content, relative soluble sugar content, malonaldehyde content, relative proiine content, relative conductivity and other 12 physiological indi- cators of seedling leaves under low temperature treatment （5 ℃, 12 h） and recovery treatment （25 ℃. 24 h） were determined; based on the chilling tolerance coefficient （CTC） of various individual indicators, the comprehensive evaluation of chilling toler- ance was conducled by using principal component analysis, hierarchical cluster anal- ysis and stepwise regression analysis. [Result] The results showed that the 12 indi- vidual physiological indicators could be classified into 7 independent comprehensive components by principal component analysis; 15 cotton varieties （lines） were clus- tered into three categories by using membership function method and hierarchical cluster analysis; the mathematical model for evaluating chilling tolerance of cotton seedlings was established： D =0.275 -0.244Fo1 ＋0.206Fv/Fm1＋0.326g,%-0.056SS ＋ 0.225MDA＋O.O38REC （FF=0.995）, and the evaluation accuracy of the equation was higher than 94.25%,0. Six identification indicators closely related to chilling tolerance were screened, including Fo,, Fv/Fm1, Seedling leaves of cotton varieties （lines） gs2, SS, MDA, and REC. [Conclusion] with high chilling tolerance are less dam- aged under low temperature stress, and are able to maintain relatively high photo- synthetic electron transport capacity and high stomatal conductance after recovery treatment, which is contributed to gas exchange and recovery of photosynthetic ca- pacity. Determination of the six indicators under the same stress condition can be adopted for rapid identification and prediction of the chilling tolerance of other cotton varieties, which provides basis for the breeding, promotion, identification and screen- ing of chilling tolerant germplasms.

Impacts of COVID-19 pandemic on urban park visitation:a global analysis

The COVID-19 pandemic has resulted in over 33 million confirmed cases and over 1 million deaths globally,as of 1 October 2020.During the lockdown and restrictions placed on public activities and gatherings,green spaces have become one of the only sources of resilience amidst the coronavirus pandemic,in part because of their positive effects on psychological,physical and social cohesion and spiritual wellness.This study analyzes the impacts of COVID-19 and government response policies to the pandemic on park visitation at global,regional and national levels and assesses the importance of parks during this global pandemic.The data we collected primarily from Google’s Community Mobility Reports and the Oxford Coronavirus Government Response Tracker.The results for most countries included in the analysis show that park visitation has increased since February 16th,2020 compared to visitor numbers prior to the COVID-19 pandemic.Restrictions on social gathering,movement,and the closure of workplace and indoor recreational places,are correlated with more visits to parks.Stay-at-home restrictions and government stringency index are negatively associated with park visits at a global scale.Demand from residents for parks and outdoor green spaces has increased since the outbreak began,and highlights the important role and benefits provided by parks,especially urban and community parks,under the COVID-19 pandemic.We provide recommendations for park managers and other decision-makers in terms of park management and planning during health crises,as well as for park design and development.In particular,parks could be utilized during pandemics to increase the physical and mental health and social well-being of individuals.

QSAR Studies on 6-(1-Naphthylmethyl) Substituted S-DABO Derivatives as Novel Non-nucleoside HIV-1 Reverse Transcriptase Inhibitors

The AM1 and B3LYP methods were employed to calculate the structural properties of 20 6-（1-naphthylmethyl） substituted S-DABO derivatives with β-carbonyl group on the C（2） side chain as novel potent non-nucleoside HIV-1 reverse transcriptase inhibitors. The correlation analysis （CA） and stepwise multiple regression analysis （SMR） were performed. The QSAR models indicate that the physicochemical parameters of Qc9, MRR1, ELUMO, ∏R2 and μ have significant influence on the activities of these derivatives. The substitution of hydrophobic R2 and bulky aromatic R1 to form a conjugated system with the frame of those S-DABO series compounds should be considered to design new potent compounds for anti-HIV-1.

Effect of Complex Parameters of Porous Media on Desalination Performance in Air-Gap Diffusion Distillation Based on Multiple Regression Analysis

Air-Gap Diffusion Distillation(AGDD) is a new technology aiming at solving the problem of the safety of drinking water for residents in remote areas that uses a super hydrophilic porous medium as the hot channel and evaporation surface. In the experiment, it was found that the parameters of porous media have a significant influence on the desalination(evaporation) efficiency of AGDD. Although porous media are widely used as evaporation components, the factors affecting their evaporation efficiency are not fully understood. The evaporation process in super hydrophilic porous media is rarely discussed. A large number of experiments have been carried out based on AGDD. The introduction of statistical methods solves the problem that experiments cannot distinguish the contribution of complex parameters of porous media to evaporation efficiency. Stepwise regression analysis is used to reduce the dimensionality of the independent variables and construct regression equations(coefficient of determination R~2 reached 81.3%-96.8%). Evaporation flux correlations and dimensionless mass transfer correlations are established based on porous media parameters. We found that the surface evaporation of super hydrophilic porous media can be divided into three stages: diffusion evaporation, capillary evaporation, and thermal evaporation. The evaporation efficiency of these three stages is controlled by the vapor diffusion process resistance, capillary force, and energy supply. At low saturation, evaporation efficiency is limited by the resistance of the vapor diffusion process. The evaporation efficiency of the porous media is affected predominantly by the pore size, the specific surface area, porosity and the characteristic length. At high saturation, the evaporation efficiency becomes influenced primarily by the permeability. A small thickness and a high hydrophilicity also improve the evaporation efficiency.

Experiences of Fatigue at Sea—A Comparative Study in European and Chinese Shipping Industry

Fatigue has negative impacts on the general working population as well as on seafarers. In order to study seafarers’ fatigue, a questionnaire-base survey was conducted to gain information about potential risk factors for fatigue and construct indexes indicating fatigue. The study applies T-test to compare strata of seafarers to analyse work and sleep patterns in global seafaring. Qualitative analysis are also employed to explore the impacts of fatigue on seafarer’s occupational health and safety.

Simplified criteria for diagnosing superficial esophageal squamous neoplasms using Narrow Band Imaging magnifying endoscopy

AIM To simplify the diagnostic criteria for superficial esophageal squamous cell carcinoma(SESCC) on Narrow Band Imaging combined with magnifying endoscopy(NBI-ME).METHODS This study was based on the post-hoc analysis of a randomized controlled trial. We performed NBI-ME for 147 patients with present or a history of squamous cell carcinoma in the head and neck, or esophagus between January 2009 and June 2011. Two expert endoscopistsdetected 89 lesions that were suspicious for SESCC lesions, which had been prospectively evaluated for the following 6 NBI-ME findings in real time: "intervascular background coloration"; "proliferation of intrapapillary capillary loops(IPCL)"; and "dilation", "tortuosity", "change in caliber", and "various shapes(VS)" of IPCLs(i.e., Inoue's tetrad criteria). The histologic examination of specimens was defined as the gold standard for diagnosis. A stepwise logistic regression analysis was used to identify candidates for the simplified criteria from among the 6 NBI-ME findings for diagnosing SESCCs. We evaluated diagnostic performance of the simplified criteria compared with that of Inoue's criteria.RESULTS Fifty-four lesions(65%) were histologically diagnosed as SESCCs and the others as low-grade intraepithelial neoplasia or inflammation. In the univariate analysis, proliferation, tortuosity, change in caliber, and VS were significantly associated with SESCC(P < 0.01). The combination of VS and proliferation was statistically extracted from the 6 NBI-ME findings by using the stepwise logistic regression model. We defined the combination of VS and proliferation as simplified dyad criteria for SESCC. The areas under the curve of the simplified dyad criteria and Inoue's tetrad criteria were 0.70 and 0.73, respectively. No significant difference was shown between them. The sensitivity, specificity, and accuracy of diagnosis for SESCC were 77.8%, 57.1%, 69.7% and 51.9%, 80.0%, 62.9% for the simplified dyad criteria and Inoue's tetrad criteria, respectively.CONCLUSION The combination of proliferation and VS may serve as simplified criteria for the diagnosis of SESCC using NBIME.

Variation characteristics of stable isotopes in atmospheric precipitation in Adelaide,Australia

Background,aim,and scope Stable isotope in water could respond sensitively to the variation of environment and be reserved in different geological archives,although they are scarce in the environment.And the methods derived from the stable isotope composition of water have been widely applied in researches on hydrometeorology,weather diagnosis,and paleoclimate reconstruction,which help well for understanding the water-cycle processes in one region.Here,it is aimed to explore the temporal changes of stable isotopes in precipitation from Adelaide,Australia and determine the influencing factors at different timescales.Materials and methods Based on the isotopic data of daily precipitation over four years collected in Adelaide,Australia,the variation characteristics of dailyδD,δ^(18)O,and dexcess in precipitation and its relationship with meteorological elements were analyzed.Results The results demonstrated the local meteoric water line(LMWL)in Adelaide,wasδD=6.38×δ^(18)O+6.68,with a gradient less than 8.There is a significant negative correlation between dailyδ^(18)O and precipitation amount or relative humidity at daily timescales in both the whole year and wither/summerhalf year(p<0.001),but a significant positive correlation between dailyδ^(18)O and temperature in the whole year and the winter half-year(p<0.001).Discussion The correlation coefficients betweenδ^(18)O and daily mean temperature didn’t show a significant positive correlation,which may be attributed to that the precipitation in Adelaide area in January was mainly influenced by strong convective weather,and the stable isotope values in precipitation were significantly negative.Furthermore,this propose was also evidenced by the results from dexcess of precipitation with larger value in the winter half-year than that in the summer half-year,which may be resulted from the precipitation events in winter are mostly influenced by oceanic water vapor,while the sources of water vapor in summer precipitation events are more complicated and influenced by strong convective weather.On the other hand,the slope and intercept of theδ^(18)O—P regression lines in the summer months(-0.41 and 0.50‰)are larger and smaller than those in the winter months(-0.22 and-2.15‰),respectively,indicating that the precipitation stable isotopes have a relatively stronger rainout effect in the summer months than in the winter months.Besides,the measured values ofδ^(18)O in daily precipitation have a good linear relationship with our simulated values ofδ^(18)O,demonstrating the established regression model could provide a reliable simulation for theδ^(18)O values in daily precipitation in Adelaide area.It’s worth noting that the precipitation events with low precipitation amount,low relative humidity and high temperature,usually had relatively small slope and intercept of MWL,implying that raindrops may be strongly affected by sub-cloud secondary evaporation in the falling process.Conclusions The variation ofδ^(18)O in daily precipitation from Adelaide region was controlled by different factors at different timescales.And the water vapor sources and the meteorological conditions of precipitation events(such as the degree of sub-cloud secondary evaporation)also played an important role on the variation ofδ^(18)O.Recommendations and perspectives Stable isotope in daily precipitation can provide more accurate information about water-cycle and atmosphere circulation,it is therefore necessary to continue to collect and analyze daily-scale precipitation data over a longer time span.The results of this study will provide the basis for the fields of hydrometeorology,meteorological diagnosis and paleoclimate reconstruction in Adelaide,Australia.

Influence of Chemical Composition on Phase Transformation Temperature and Thermal Expansion Coefficient of Hot Work Die Steel

On the basis of the uniform design method, six kinds of martensitic hot work die steels were designed. The phase transformation temperatures including Ac1 , Ac3 , and Ms were measured by DIL805A quenching dilatometer. The influences of the main elements on phase transformation temperatures were analyzed by quadratic stepwise regression analysis, and three corresponding equations were obtained. These equations, in which the interactions of the elements were considered, showed more effectiveness than the traditional ones. In addition, the thermal expansion coefficients of these steels in annealed state and quenched state were also obtained during the tests. The influences of chemical composition and temperature on the thermal expansion coefficient were analyzed; the equations obtained were verified by using several kinds of steels. The predicted values were in accordance with the results of the experiments.

Spectral Features of Soil Organic Matter

The study on soil spectral reflectance features is the physical basis for soil remote sensing. Soil organic matter content influences the soil spectral reflectance dramatically. This paper studied the spectral curves between 400 nm-2500 nm of 174 soil samples which were collected in Hengshan county and Yixing county. Fourteen types of transformations were applied to the soil reflectance R to remove the noise and to linearize the correlation between reflectance （independent vari- able） and soil organic matter （SOM） content （dependent variable）. Then, the methods such as derivative spectrum technology and stepwise regression analysis, were applied to study the relationship between these soil spectral features and soil organic matter content. It shows that order 1 derivative of the logarithm of reflectance （01DLA） is the most sensitive to SOM among the various transform types of reflectance in consideration. The regression model whose coefficient of determination reaches 0.885 is built. It predicted the soil organic matter content with higher effect.

Photodegradation of methylmercury in the water body of the Three Gorges Reservoir

Biogeochemical cycling of mercury in the young Three Gorges Reservoir （TGR）, China, is strongly considered. Although methylmercury （MMHg） photodegradation （PD） is an important process involved in mercury cycling in this zone, little is known about this process. In situ incubation experiments were therefore performed to quantify the effect of different wave- length radiations and environmental factors on the PD process of MMHg in the water bodies of TGR. It was found that the ef- fect of solar radiation on MMHg PD was highly dependent on wavelength and water depth. All PD-rate constants resulting from each wavelength range decreased rapidly with water depth. For surface water, UV-A radiation （320-400 nm） was the key driver, accounting for 49%-62% of MMHg PD. For the entire water column, both photosynthetically active radiation （PAR, 400-700 nm） and UV-A were responsible for MMHg PD. MMHg PD fluxes peaked in summer （7.5-18 ng m-2 d-1）, followed by spring （3.3-8.0 ng m-2 d-1）, autumn （1.0-2.7 ng m-2 d-1）, and winter （0.060-0.15 ng m-2 d-1）. The annual fluxes of MMHg PD were estimated to be 1.1-2.8 μg m-2 at. Filtering the reservoir water and amending it with chemicals （i.e., CV, NO C, and dissolved organic matter （DOM）） showed significant effects on MMHg PD rate constants. Stepwise regression analysis showed that intensity of solar radiation, suspended particulate matter （SPM）, DOM, CI-, and NO3- were involved in the PD process. Path analysis clarified the relationship between MMHg PD rate constants and environmental variables, as well as the comparative strength of direct and indirect relationships among variables. The results are of great importance for understanding MMHg cycling characteristics in TGR and also facilitate the understanding of the underlying process, MMHg PD, in natural waters.

Spatio-temporal pattern changes of land space in Hengduan Mountains during 1990–2015

Hengduan Mountains offer land space for a variety of ecological services. However, the sustainable development and management of land space has been challenged by increased human activities in recent years. This paper performs the spatial pattern analysis of the quantitative and structural changes of various landscapes at different altitudes, and uses the land use data in 1990, 2000, 2010 and 2015 to reveal how various land patterns have changed. The results show that, within the production-living-ecological space schema, the ecological space dominates Hengduan Mountains, while the production and living space was mainly distributed in south region. During 1990-2015, the production-living-ecological spatial changes had been gradually accelerated and the regional differences had become more prominent. The agricultural production space had continuously decreased by 1132.31 km^2, and the industrial and mining production space had rapidly increased by 281.4 km^2 during 1990-2015. The living space had steadily increased, and the ecological space had increased with fluctuations. The land space pattern in Hengduan Mountains was greatly restricted by the terrain, such as altitude and slope. The implementations of China Western Development Strategy and the Returning Farmland to Forest Program had favorably promoted the changes of land spatial pattern in Hengduan Mountains.

Rural Poverty Patterns and Influencing Factors in Yunnan Province,China:Based on County Level Dataset

The identification of poverty at the county level is the precondition for poverty alleviation by formulating accurate strategies that are targeted for a certain area.Yunnan Province has the largest number of poverty counties in China.The vast number of people living under the poverty-line,and the deep degree of poverty across a wide distribution range,pose major challenges.Based on the rural poverty incidence data,this paper describes the rural poverty patterns in Yunnan Province in 2010 and 2015,and then it explores the main factors which influence the incidence and changes in rural poverty at the county level in Yunnan Province using a stepwise regression analysis method.This study found that the rural poverty in counties of Yunnan Province was deeply affected by natural conditions and the geographical environment.In 2010 and 2015,the rural poverty situation in the middle region of Yunnan Province was relatively light,while it was more serious in the northwest,northeast and south regions.The pattern of county poverty is in good agreement with the topography and landforms of Yunnan Province and the poverty-stricken areas.There are strong correlations between the incidence of rural poverty in Yunnan Province with both the annual per capita net income of rural residents and the degree of agricultural mechanization.These factors are related to the living standards and agricultural production necessary for the peasantry to sustain their livelihood.The change in the incidence of rural poverty at the county level in Yunnan Province from 2010 to 2015 is significantly correlated with changes in the value-added of the primary industries and the degree of agricultural mechanization.These correlations indicate that the development of primary industry plays a key role in the process of lifting rural residents in Yunnan Province out of poverty so they can achieve prosperity.Therefore,improving the annual per capita net income of rural residents and the degree of agricultural mechanization for rural areas in Yunnan Province are still the main points for focused efforts.In the current phase of poverty alleviation,Yunnan Province should emphasize increasing rural residents'income and agricultural production and management in order to formulate effective policies and measures for poverty alleviation.

Feature Selection, Deep Neural Network and Trend Prediction

The literature generally agrees that longer-horizon(over a month) predictions make more sense than short-horizon ones. However, it's an especially challenging task due to the lack of data(in unit of long horizon)and economic data have a low S/N ratio. We hypothesize that the stock trend is largely dictated by driving factors which are filtered by psychological factors and work on behavioral factors: representative indicators from these three aspects would be adequate in trend prediction. We then extend the Stepwise Regression Analysis(SRA)algorithm to constrained SRA(c SRA) to carry out a further feature selection and lag optimization. During modeling stage, we introduce the Deep Neural Network(DNN) model in stock prediction under the suspicion that economic interactions are too complex for shallow networks to capture. Our experiments indeed show that deep structures generally perform better than shallow ones. Instead of comparing to a kitchen sink model, where over-fitting can easily happen with a shortage of data, we turn around and use a model ensemble approach which indirectly demonstrates our proposed method is adequate.