Effects of Initial and Boundary Conditions on Heavy Rainfall Simulation over the Yellow Sea and the Korean Peninsula:Comparison of ECMWF and NCEP Analysis Data Effects and Verification with Dropsonde Observation

This study evaluated the simulation performance of mesoscale convective system(MCS)-induced precipitation,focusing on three selected cases that originated from the Yellow Sea and propagated toward the Korean Peninsula.The evaluation was conducted for the European Centre for Medium-Range Weather Forecasts(ECMWF)and National Centers for Environmental Prediction(NCEP)analysis data,as well as the simulation result using them as initial and lateral boundary conditions for the Weather Research and Forecasting model.Particularly,temperature and humidity profiles from 3D dropsonde observations from the National Center for Meteorological Science of the Korea Meteorological Administration served as validation data.Results showed that the ECMWF analysis consistently had smaller errors compared to the NCEP analysis,which exhibited a cold and dry bias in the lower levels below 850 hPa.The model,in terms of the precipitation simulations,particularly for high-intensity precipitation over the Yellow Sea,demonstrated higher accuracy when applying ECMWF analysis data as the initial condition.This advantage also positively influenced the simulation of rainfall events on the Korean Peninsula by reasonably inducing convective-favorable thermodynamic features(i.e.,warm and humid lower-level atmosphere)over the Yellow Sea.In conclusion,this study provides specific information about two global analysis datasets and their impacts on MCS-induced heavy rainfall simulation by employing dropsonde observation data.Furthermore,it suggests the need to enhance the initial field for MCS-induced heavy rainfall simulation and the applicability of assimilating dropsonde data for this purpose in the future.

Effect of boundary conditions on shakedown analysis of heterogeneous materials

The determination of the ultimate load-bearing capacity of structures made of elastoplastic heterogeneous materials under varying loads is of great importance for engineering analysis and design. Therefore, it is necessary to accurately predict the shakedown domains of these materials. The static shakedown theorem, also known as Melan's theorem, is a fundamental method used to predict the shakedown domains of structures and materials. Within this method, a key aspect lies in the construction and application of an appropriate self-equilibrium stress field(SSF). In the structural shakedown analysis, the SSF is typically constructed by governing equations that satisfy no external force(NEF) boundary conditions. However, we discover that directly applying these governing equations is not suitable for the shakedown analysis of heterogeneous materials. Researchers must consider the requirements imposed by the Hill-Mandel condition for boundary conditions and the physical significance of representative volume elements(RVEs). This paper addresses this issue and demonstrates that the sizes of SSFs vary under different boundary conditions, such as uniform displacement boundary conditions(DBCs), uniform traction boundary conditions(TBCs), and periodic boundary conditions(PBCs). As a result, significant discrepancies arise in the predicted shakedown domain sizes of heterogeneous materials. Built on the demonstrated relationship between SSFs under different boundary conditions, this study explores the conservative relationships among different shakedown domains, and provides proof of the relationship between the elastic limit(EL) factors and the shakedown loading factors under the loading domain of two load vertices. By utilizing numerical examples, we highlight the conservatism present in certain results reported in the existing literature. Among the investigated boundary conditions, the obtained shakedown domain is the most conservative under TBCs.Conversely, utilizing PBCs to construct an SSF for the shakedown analysis leads to less conservative lower bounds, indicating that PBCs should be employed as the preferred boundary conditions for the shakedown analysis of heterogeneous materials.

Classification research of TCM pulse conditions based on multi-label voice analysis

Objective:To explore the feasibility of remotely obtaining complex information on traditional Chinese medicine(TCM)pulse conditions through voice signals.Methods: We used multi-label pulse conditions as the entry point and modeled and analyzed TCM pulse diagnosis by combining voice analysis and machine learning.Audio features were extracted from voice recordings in the TCM pulse condition dataset.The obtained features were combined with information from tongue and facial diagnoses.A multi-label pulse condition voice classification DNN model was built using 10-fold cross-validation,and the modeling methods were validated using publicly available datasets.Results: The analysis showed that the proposed method achieved an accuracy of 92.59%on the public dataset.The accuracies of the three single-label pulse manifestation models in the test set were 94.27%,96.35%,and 95.39%.The absolute accuracy of the multi-label model was 92.74%.Conclusion: Voice data analysis may serve as a remote adjunct to the TCM diagnostic method for pulse condition assessment.

Dynamic characterization of tailing dam using fully coupled dynamic analysis with different boundary conditions-a case study

The seismic response analysis of a tailing dam is studied using a fully coupled effective stress approach in conjunction with an advanced multi yield surface plastic constitutive model for tailing material.Strain controlled static and cyclic triaxial tests were carried out to obtain the constitutive model for the tailing material.The tailing materials were collected from the Rampura Agucha tailing dam(Rajasthan State,India).A 2D nonlinear finite element(FE)model was then developed using different boundary conditions from the tailing embankment constructed using the downstream and upstream method of rising using OpenSees software.In first case,the model boundary was fixed in both the X and Y directions,and in the second case,viscous dashpots were introduced for both side and horizontal boundaries.The model was validated with experimental results on tailing material.Analyses were carried out considering five different earthquake motions,which were applied at the base.Comparisons of the different boundary conditions in terms of displacement flow vectors,pore pressure and stress-strain curves during shaking are presented.From the analysis,it was observed that the viscous boundary condition replicates the actual field conditions more accurately than the fixed boundary condition.In addition,it was found that the tailing embankment constructed by the downstream and upstream method of rising is not susceptible to liquefaction and lateral spreading for earthquake motions,even for a magnitude>5.5.

Free Vibration Analysis of Rectangular Plate with Cutouts under Elastic Boundary Conditions in Independent Coordinate Coupling Method

Based on Kirchhoff plate theory and the Rayleigh-Ritz method,the model for free vibration of rectangular plate with rectangular cutouts under arbitrary elastic boundary conditions is established by using the improved Fourier series in combination with the independent coordinate coupling method(ICCM).The effect of the cutout is taken into account by subtracting the energies of the cutouts from the total energies of the whole plate.The vibration displacement function of the hole domain is based on the coordinate system of the hole domain in this method.From the continuity condition of the vibration displacement function at the cutout,the transition matrix between the two coordinate systems is constructed,and the mass and stiffness matrices are completely obtained.As a result,the calculation is simplified and the computational efficiency of the solution is improved.In this paper,numerical examples and modal experiments are presented to validate the effectiveness of the modeling methods,and parameters related to influencing factors of the rectangular plate are analyzed to study the vibration characteristics.

Survival Analysis Using Cox Proportional Hazards Regression for Pile Bridge Piles Under Wet Service Conditions

This paper studies the deterioration of bridge substructures utilizing the Long-Term Bridge Performance(LTBP)Program InfoBridge^(TM)and develops a survival model using Cox proportional hazards regression.The survival anal­ysis is based on the National Bridge Inventory(NBI)dataset.The study calculates the survival rate of reinforced and prestressed concrete piles on bridges under marine conditions over a 29-year span(from 1992 to 2020).The state of Maryland is the primary focus of this study,with data from three neighboring regions,the District of Columbia,Vir­ginia,and Delaware to expand the sample size.The data obtained from the National Bridge Inventory are condensed and filtered to acquire the most relevant information for model development.The Cox proportional hazards regres­sion is applied to the condensed NBI data with six parameters:Age,ADT,ADTT,number of spans,span length,and structural length.Two survival models are generated for the bridge substructures:Reinforced and prestressed concrete piles in Maryland and reinforced and prestressed concrete piles in wet service conditions in the District of Columbia,Maryland,Delaware,and Virginia.Results from the Cox proportional hazards regression are used to construct Markov chains to demonstrate the sequence of the deterioration of bridge substructures.The Markov chains can be used as a tool to assist in the prediction and decision-making for repair,rehabilitation,and replacement of bridge piles.Based on the numerical model,the Pile Assessment Matrix Program(PAM)is developed to facilitate the assessment and main­tenance of current bridge structures.The program integrates the NBI database with the inspection and research reports from various states’department of transportation,to serve as a tool for condition state simulation based on mainte­nance or rehabilitation strategies.

Analysis of the Conditional Correlations from Different Genetic Systems Between the Protein Content and the Appearance Quality Traits of Indica Rice

A factorial mating design in two environments was conducted using 7 cytoplasmic male sterile lines （A） and 5 restorer lines （R） along with their F1 （A × R） and F2 populations. The unconditional and conditional analyses of genetic models and the corresponding statistic methods, including endospermic, cytoplasmic, and maternal plant genetic systems, were used to analyze the genetic relationships between protein content （PC） and the appearance quality traits of indica rice （Oryza sativa L.）. The results from unconditional analysis indicated that PC was significantly correlated with the appearance quality traits of rice, except for the brown rice thickness （BRT）. Only the genetic covariance between PC and the brown rice width （BRW） was positively correlative, whereas all the other pairwise traits were negatively correlative. The results from conditional analysis revealed that the weight of brown rice （WBR） or the amylose content （AC） could significantly affect the relationships between PC and the appearance quality traits of indica rice. The conditional analysis showed that WBR might negatively affect the relationships between PC and the brown rice length （BRL）, BRW, or BRT through the geuotype x environmental （GE） interaction effects, but positively affected the relationships between PC and the ratio of brown rice length to width （RLW） or the ratio of brown rice length to thickness （RLT）. The amylase content could positively affect the relationships between PC and BRL, RLW, RLT through the cytoplasmic effects and maternal additive effects, but negatively affected the relationships between PC and BRW.

Analysis and Modeling of the Central Air-Conditioning System in Intelligent Buildings

The central air conditioning system in an intelligent building (IB) was analyzed and modeled in order to perform the optimization scheduling strategy of the central air conditioning system. A set of models proposed and a type of periodically autoregressive model (PAR) based on the improved genetic algorithms (IGA) were used to perform the optimum energy saving scheduling. The example of the Liangmahe Plaza was taken to show the effectiveness of the methods.

Evaluation of slope stability through rock mass classification and kinematic analysis of some major slopes along NH-1A from Ramban to Banihal, North Western Himalayas

The network of Himalayan roadways and highways connects some remote regions of valleys or hill slopes,which is vital for India’s socio-economic growth.Due to natural and artificial factors,frequency of slope instabilities along the networks has been increasing over last few decades.Assessment of stability of natural and artificial slopes due to construction of these connecting road networks is significant in safely executing these roads throughout the year.Several rock mass classification methods are generally used to assess the strength and deformability of rock mass.This study assesses slope stability along the NH-1A of Ramban district of North Western Himalayas.Various structurally and non-structurally controlled rock mass classification systems have been applied to assess the stability conditions of 14 slopes.For evaluating the stability of these slopes,kinematic analysis was performed along with geological strength index(GSI),rock mass rating(RMR),continuous slope mass rating(CoSMR),slope mass rating(SMR),and Q-slope in the present study.The SMR gives three slopes as completely unstable while CoSMR suggests four slopes as completely unstable.The stability of all slopes was also analyzed using a design chart under dynamic and static conditions by slope stability rating(SSR)for the factor of safety(FoS)of 1.2 and 1 respectively.Q-slope with probability of failure(PoF)1%gives two slopes as stable slopes.Stable slope angle has been determined based on the Q-slope safe angle equation and SSR design chart based on the FoS.The value ranges given by different empirical classifications were RMR(37-74),GSI(27.3-58.5),SMR(11-59),and CoSMR(3.39-74.56).Good relationship was found among RMR&SSR and RMR&GSI with correlation coefficient(R 2)value of 0.815 and 0.6866,respectively.Lastly,a comparative stability of all these slopes based on the above classification has been performed to identify the most critical slope along this road.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Practice and analysis of recycling non-drinking water from air-condition and reverse-osmosis system into rainwater collection system

This paper is based on the rainwater collection project in the retrofit of the Dongyi teaching block in Zhejiang University Xixi Campus.The analysis incorporates the local meteorological data, recycling water utilization, and precipitation adjustment.The rainwater collection system in this program also adds the condensation water from the heating, ventilation and air conditioning （ HVAC） system and the concentration from the reverse-osmosis system used for watering greens and supplying waterscapes.By calculating, the quantity of the HVAC condensation water in summer is 3.48 m3/d, and the quantity of the reverse-osmosis concentrated water is 198 to 396 L/d.This method solves the water shortage caused by high evaporation in summer and low precipitation in winter.Supported by empirical monitoring data, the proposed method significantly increases the economic efficiency of the system during the summer period.

Analysis on Agricultural Meteorological Condition of Osmanthus Blooming

Osmanthus, an ornamental plants, is favored by people, and forecast of Osmanthus florescence is of vital significance for enjoying the sight. Based on 15 years of flowering observation data, analysis was made on agricultural meteorologi- cal conditions of Osmanthus in Guilin and the results showed that ground tempera- ture, humidity, precipitation, sunshine have significant effects on Osmanthus flores- cence. In forecasting florescence, therefore, it is necessary to take comprehensive consideration of weather conditions and changes in the future, to release Osman- thus flowering forecast as per method of combination of long-term forecasting and short-term forecast.

Improving autoencoder-based unsupervised damage detection in uncontrolled structural health monitoring under noisy conditions

Structural health monitoring is widely utilized in outdoor environments,especially under harsh conditions,which can introduce noise into the monitoring system.Therefore,designing an effective denoising strategy to enhance the performance of guided wave damage detection in noisy environments is crucial.This paper introduces a local temporal principal component analysis(PCA)reconstruction approach for denoising guided waves prior to implementing unsupervised damage detection,achieved through novel autoencoder-based reconstruction.Experimental results demonstrate that the proposed denoising method significantly enhances damage detection performance when guided waves are contaminated by noise,with SNR values ranging from 10 to-5 dB.Following the implementation of the proposed denoising approach,the AUC score can elevate from 0.65 to 0.96 when dealing with guided waves corrputed by noise at a level of-5 dB.Additionally,the paper provides guidance on selecting the appropriate number of components used in the denoising PCA reconstruction,aiding in the optimization of the damage detection in noisy conditions.

Ore-forming Conditions and Prospecting in the West Kunlun Area,Xinjiang, China

The West Kunlun ore-forming belt is located between the northwestern Qinghai-Tibet Plateau and southwestern Tarim Basin. It situated between the Paleo-Asian Tectonic Domain and Tethyan Tectonic Domain. It is an important component of the giant tectonic belt in central China (the Kunlun-Qilian-Qinling Tectonic Belt or the Central Orogenic Belt). Many known ore-forming belts such as the Kunlun-Qilian Qinling ore-forming zone, Sanjiang (or Three river) ore-forming zone, Central Asian ore-forming zone, etc. pass through the West Kunlun area. Three ore-forming zones and seven ore-forming subzones were classified, and eighteen mineralization areas were marked. It is indicated that the West Kunlun area is one of the most favorable region for finding out large and superlarge ore deposits.

Tool Health Condition Recognition Method for High Speed Milling of Titanium Alloy Based on Principal Component Analysis (PCA) and Long Short Term Memory (LSTM)

The healthy condition of the milling tool has a very high impact on the machining quality of the titanium components.Therefore,it is important to recognize the healthy condition of the tool and replace the damaged cutter at the right time.In order to recognize the health condition of the milling cutter,a method based on the long short term memory(LSTM)was proposed to recognize tool health state in this paper.The various signals collected in the tool wear experiments were analyzed by time-domain statistics,and then the extracted data were generated by principal component analysis(PCA)method.The preprocessed data extracted by PCA is transmitted to the LSTM model for recognition.Compared with back propagation neural network(BPNN)and support vector machine(SVM),the proposed method can effectively utilize the time-domain regulation in the data to achieve higher recognition speed and accuracy.

Sensitivity analysis of key input parameters in conditional cell transmission model for oversaturated arterials

A novel conditional cell transmission model （CCTM） is a potential simulation tool because it accommodates all traffic conditions from light condition to oversaturated condition. To test the performance of the CCTM, a series of experiments for sensitivity analysis were designed and performed for a multilane, two-way, three-signal sample network. Experiment 1 shows that the model is performed in a logical and expected manner with variations in traffic demand with time and direction. Experiment 2 shows when the possibility of the occurrence of a useful gap increases to 60% and 100%, the delays in left rams decrease by 5% and 15%, respectively. In Experiment 3, comparing the possibility of a conditional cell of 0 with 100%, delay of left turn and delay of the entire network were underestimated by 58% and 11%, respectively. Hence, sensitivity analysis demonstrates that by reflecting local drivers＇ behaviors properly, the CCTM provides an accurate representation of traffic flow in simulating oversaturated traffic conditions.

Analysis of Meteorological Conditions during the Blossom Period of Apple and Peach in Haiyang City

Through observing the phenology of two kinds of fruit trees,apple and peach trees,during their flowering periods in the past seven years,the meteorological conditions in the flowering stages were analyzed and summarized in this paper.The late frost weather situation occurred in late April in Haiyang City also was elaborated in the paper.According to the data analysis,the terrain effect had induced a large temperature differences between north and south in April in Haiyang.Early flowering of fruit trees is as early as 5 to 8 days in the northern region than that in the southern region;accumulated temperature which was greater than or equal to 0 ℃ and the date of the temperature stably through a boundary,were the important meteorological indicators of the fruit trees' early flowering.The late frost in mid-late April is meteorological disasters of the fruit trees flowering period.The weather background of the occurred late frost,the disaster reasons and the measures for the prevention of late frost were proposed.

Massive Power Device Condition Monitoring Data Feature Extraction and Clustering Analysis using MapReduce and Graph Model

Effective storage,processing and analyzing of power device condition monitoring data faces enormous challenges.A framework is proposed that can support both MapReduce and Graph for massive monitoring data analysis at the same time based on Aliyun DTplus platform.First,power device condition monitoring data storage based on MaxCompute table and parallel permutation entropy feature extraction based on MaxCompute MapReduce are designed and implemented on DTplus platform.Then,Graph based k-means algorithm is implemented and used for massive condition monitoring data clustering analysis.Finally,performance tests are performed to compare the execution time between serial program and parallel program.Performance is analyzed from CPU cores consumption,memory utilization and parallel granularity.Experimental results show that the designed framework and parallel algorithms can efficiently process massive power device condition monitoring data.

Axisymmetric Powell-Eyring fluid flow with convective boundary condition: optimal analysis

The effects of axisymmetric flow of a Powell-Eyring fluid over an impermeable radially stretching surface are presented. Characteristics of the heat transfer process are analyzed with a more realistic condition named the convective boundary condition. Governing equations for the flow problem are derived by the boundary layer approximations. The modeled highly coupled partial differential system is converted into a system of ordinary differential equations with acceptable similarity transformations. The convergent series solutions for the resulting system are constructed and analyzed. Optimal values are obtained and presented in a numerical form using an optimal homotopy analysis method （OHAM）. The rheological characteristics of different parameters of the velocity and temperature profiles are presented graphically. Tabular variations of the skin friction coefficient and the Nusselt number are also calculated. It is observed that the temperature distribution shows opposite behavior for Prandtl and Biot numbers. Furthermore, the rate of heating/cooling is higher for both the Prandtl and Biot numbers.

ANALYSIS OF METEOROLOGICAL CONDITION AND RESEARCH ON FORECAST OF LANDSLIDES AND DEBRIS FLOWS IN ZHEJIANG PROVINCE

1 INTRODUCTION Meteorological factors, especially precipitation, have close links with geological calamities. According to the statistics, more than 70% of the geological calamities in China occur in rainy seasons. Many researchers are thus motivated to study extensively to determine their relationship in the prediction of geological calamltles . They either rely on single measurements of rainfall to seek basis for widespread occurrence of geological calamities or treat antecedent diurnal rainfall with equal importance, though with account of the accumulated effect of preceding rainfall. Furthermore, it is common for quite a number of models to use the rainfall recorded at hydrological or meteorological rain gauges as the one for the interested day, reducing the time validity of the prediction. In our analysis, it is found that the landslides and debris flows in Zhejiang province are related with the antecedent precipitation （but not by a simple accumulation）. Critical amounts of accumulated and effective rainfall are used in this work to tell whether there will be geological calamities. Moreover, MM5 is used to forecast rainfall, taking account in equations of the predictand for landslides and debris flows, in attempts to predict the appearance of meteorological condition for geological calamities and improve the rationality of forecasting procedures and time validity of forecasts.