A Space State Forecast Model for Dam Construction Equipments Based on Analysis Prediction Theory

Anti-collision equipments system is developed to solve the collision problems of dam construction equipments, and in the system the determination of equipments＇ space state is important. A uniform moving equation of equipments is established based on the analysis prediction theory and the movements states of equipments. Method of least square was employed to deal with discrete data of equipments＇ space position. Fitting equation matched with the movement equation was presented to do data fitting, and a relevant algorithm was given. Applying the fitting equation, current and future space state of equipments can be accurately predicted. Finally, a case is given and results show that numerical values of data were steady and their precision was high. In LongTan dam construction of the equipments antiollision system, applying this method to forecast the equipments＇ space states and practical running of the system indicate that this method can improve the precision of position, obtain the better forecasting effect and increase the robustness of the system.

A j,υ model for the analysis and prediction of tides

In this paper, the j, υ corrected formulae of the amplitudes and the phases of 58 astronomical constituents are given, and the models for the analysis and prediction of 169 constituents are presented. The new Cartwright's calculated results of the tidal potential are used, and the quadratic analysis is made. It has been proved by a number of trials that the harmonic constants of constituents are more stable and the accuracy of the predicted result reliable.

Prediction and Analysis of O3 Based on the ARIMA Model

Despite of the small amount in the atmosphere,ozone is one of the most critical atmospheric component as it protects human beings and any other life on the earth from the sun's high frequency ultraviolet radiation. In recent decades,the global ozone depletion caused by human activities is w ell know n and produces an " ozone hole",the most direct consequence of w hich is the increase in ultraviolet radiation,w hich w ill affect human survival,climatic environment,ecological environment and other important adverse impacts. Due to the implementation of the M ontreal protocol and other agreement,the total amount of ozone depleting substance in the atmosphere has been prominent reduced,w hich w ill lead to a new round of regional climate change.Therefore,predicting the changes of the total ozone in the future w ill have an important guiding significance for predicting the future climate change and making reasonable measures to deal w ith the climate change. In this paper,based on the ozone data of 1979 to 2016 in the southern hemisphere and ARIM A model algorithm,using time series analysis,w e obtain prediction effect of ARIM A model is good by Ljung-Box Q-test and R^2,and the model can be used to predict the future ozone change. With the help of SPSS softw are,the future trend of the total ozone can be predicted in the future 50 years. Based on the above experiment results,the global ozone change in the future 50 years can be forecasted,namely the atmospheric ozone layer w ill return to its 1980's standard by the middle of this century at the global scale.

Cause Analysis on the Missing Report of First Thunderstorm Weather in Shenyang City in 2010

The first thunderstorm weather appeared in southern Shenyang on May 2,2010 and did not bring about severe lightning disaster for Shenyang region,but forecast service had poor effect without forecasting thunderstorm weather accurately.In our paper,the reasons for missing report of this thunderstorm weather were analyzed,and analysis on thunderstorm potential was carried out by means of mesoscale analysis technique,providing technical index and vantage point for the prediction of thunderstorm potential.The results showed that the reasons for missing report of this weather process were as follows:surface temperature at prophase was constantly lower going against the development of convective weather;the interpreting and analyzing ability of numerical forecast product should be improved;the forecast result of T639 model was better than that of Japanese numerical forecast;the study and application of mesoscale analysis technique should be strengthened,and this service was formally developed after thunderstorm weather on June 1,2010.

Asymmetric outcomes in bilateral maxillary impacted tooth extractions:A case report

BACKGROUND The extraction of maxillary impacted teeth is a common procedure in oral surgery,frequently complicated by oroantral communications.For less-experienced clinicians,accurately assessing the difficulty and associated risks of maxillary third molar extractions remain a significant challenge.CASE SUMMARY We present a case involving disparate outcomes following bilateral extraction of maxillary third molars.Using cone-beam computed tomography and three-dimensional software,we conducted a digital assessment of the factors contributing to extraction difficulty and risk,controlling for potential confounders.Key variables analyzed included alveolar bone volume,bone quality,crown-root angulation,and maxillary sinus mucosal thickness.Additionally,we introduce the novel concept of"tegmen bone"to quantitatively evaluate the bone mass between the teeth and the maxillary sinus.This unique case,with differing outcomes on opposite sides of the same patient,provided an opportunity to minimize extraneous variables and focus on the local anatomical factors influencing the procedures,thereby improving the precision of our analysis.CONCLUSION This case highlights the potential utility of predictive analysis in guiding the management of complex tooth extractions.

An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM

New coronavirus disease(COVID-19)has constituted a global pandemic and has spread to most countries and regions in the world.Through understanding the development trend of confirmed cases in a region,the government can control the pandemic by using the corresponding policies.However,the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction,and even have large estimation errors.To address this issue,we propose an improved method for predicting confirmed cases based on LSTM(Long-Short Term Memory)neural network.This work compares the deviation between the experimental results of the improved LSTM prediction model and the digital prediction models(such as Logistic and Hill equations)with the real data as reference.Furthermore,this work uses the goodness of fitting to evaluate the fitting effect of the improvement.Experiments show that the proposed approach has a smaller prediction deviation and a better fitting effect.Compared with the previous forecasting methods,the contributions of our proposed improvement methods are mainly in the following aspects:1)we have fully considered the spatiotemporal characteristics of the data,rather than single standardized data.2)the improved parameter settings and evaluation indicators are more accurate for fitting and forecasting.3)we consider the impact of the epidemic stage and conduct reasonable data processing for different stage.

Prediction for asphalt pavement water film thickness based on artificial neural network

In order to study the variation o f the asphalt pavement water film thickness influenced by multi-factors,anew method for predicting water film thickness was developed by the combination o f the artificial neural network(ANN)a d two-dimensional shallow water equations based on hydrodynamic theory.Multi-factors included the rainfall intensity,pavement width,cross slope,longitudinal slope a d pavement roughness coefficient.The two-dimensional hydrodynamic method was validated by a natural rainfall event.Based on the design scheme o f Shen-Sha expressway engineering project,the limited training data obtained by the two-dimensional hydrodynamic simulation model was used to predict water film thickness.Furthermore,the distribution of the water film thickness influenced by multi-factors on the pavement was analyzed.The accuracy o f the ANN model was verified by the18sets o f data with a precision o f0.991.The simulation results indicate that the water film thickness increases from the median strip to the edge o f the pavement.The water film thickness variation is obviously influenced by rainfall intensity.Under the condition that the pavement width is20m and t e rainfall intensity is3m m/h,t e water film thickness is below10mm in the fast lane and20mm in t e lateral lane.Athough there is fluctuation due to the amount oftraining data,compared with the calculation on the basis o f the existing criterion and theory,t e ANN model exhibits a better performance for depicting the macroscopic distribution of the asphalt pavement water film.

Simulation and Analysis of Back Siltation in a Navigation Channel Using MIKE 21

The channel back-siltation problem has been restricting the development of channels,and its monitoring is limited by funds and natural conditions.Moreover,predicting the channel back-siltation situation in a timely and accurate manner is difficult.Hence,a numerical simulation of the back-siltation problem in the sea area near the channel is of great significance to the maintenance of a channel.In this study,the back siltation of a deep-water channel in the Lanshan Port area of the Port of Rizhao after dredging is predicted.This paper relies on the MIKE 21 software to establish the wave,tidal current,and sediment numerical models and uses measured data from two observation stations in the study area for verification.On this basis,taking one month as an example,the entire project channel was divided into five sections,and three observation points were set on each section.The results show that the area with offshore siltation is located in the northerly direction of the artificial anti-wave building.Siltation occurred on the northern seabed in the sea a little farther from the shore.Siltation occurred on the seabed surface far away from the shoreline,and with the increase in the distance from the shoreline,the amount of siltation in the south,center,and north became gradually closed,and the results can be used to guide actual engineering practices.This study will play a positive role in promoting the dredging project of Rizhao Lanshan Port.

Identification and predictive analysis for participants at ultra-high risk of psychosis:A comparison of three psychometric diagnostic interviews

BACKGROUND An accurate identification of individuals at ultra-high risk(UHR)based on psychometric tools to prospectively identify psychosis as early as possible is required for indicated preventive intervention.The diagnostic comparability of several psychometric tools,including the comprehensive assessment of at risk mental state(CAARMS),the structured interview for psychosis-risk syndrome(SIPS)and the bonn scale for the assessment of basic symptoms(BSABS),is unknown.AIM To address the psychometric comparability of CAARMS,SIPS and BSABS for subjects who are close relatives of patients with schizophrenia.METHODS In total,189 participants aged 18-58 years who were lineal relative by blood and collateral relatives by blood up to the third degree of kinship of patients with schizophrenia were interviewed in the period of May 2017 to January 2019.Relatives of the participants diagnosed schizophrenia were excluded.All the participants were assessed for a UHR state by three psychometric tools(CAARMS,SIPS and BSABS).The psychometric diagnosis results included at risk of psychosis(UHR+),not at risk of psychosis(UHR-)and psychosis.Demographic and clinical characteristics were also measured.The inter-rater agreement was assessed for evaluation of the coherence of the three scales.Transition rates for UHR+subjects to psychosis within 2 years were also recorded.RESULTS The overall agreement percentages were 93.12%,92.06%and 93.65%of CAARMS and SIPS,SIPS and BSABS and CAARMS and BSABS,respectively.The overall agreement percentage of the relative functional impairment of the three groups(UHR+,not at risk of psychosis and psychosis)were 89.24%,86.36%and 88.12%,respectively.The inter-rater reliability of the CAARMS,SIPS and BSABS total score was 0.90,0.89 and 0.85.The inter-rater reliability was very good to excellent for all the subscales of these three instruments.For CAARMS,SIPS and BSABS,the kappa coefficient about UHR criteria agreement was 0.87,0.84 and 0.82,respectively(P<0.001).The transition rates of UHR+to psychosis within 2 years were 16.7%(CAARMS),10.0%(SIPS)and 17.7%(BSABS).CONCLUSION There is good diagnostic agreement between the CAARMS,SIPS and BSABS towards identification of UHR participants who are close relatives of patients with schizophrenia.

Brittleness index predictions from Lower Barnett Shale well-log data applying an optimized data matching algorithm at various sampling densities

The capability of accurately predicting mineralogical brittleness index (BI) from basic suites of well logs is desirable as it provides a useful indicator of the fracability of tight formations.Measuring mineralogical components in rocks is expensive and time consuming.However,the basic well log curves are not well correlated with BI so correlation-based,machine-learning methods are not able to derive highly accurate BI predictions using such data.A correlation-free,optimized data-matching algorithm is configured to predict BI on a supervised basis from well log and core data available from two published wells in the Lower Barnett Shale Formation (Texas).This transparent open box (TOB) algorithm matches data records by calculating the sum of squared errors between their variables and selecting the best matches as those with the minimum squared errors.It then applies optimizers to adjust weights applied to individual variable errors to minimize the root mean square error (RMSE)between calculated and predicted (BI).The prediction accuracy achieved by TOB using just five well logs (Gr,ρb,Ns,Rs,Dt) to predict BI is dependent on the density of data records sampled.At a sampling density of about one sample per 0.5 ft BI is predicted with RMSE~0.056 and R^(2)~0.790.At a sampling density of about one sample per0.1 ft BI is predicted with RMSE~0.008 and R^(2)~0.995.Adding a stratigraphic height index as an additional (sixth)input variable method improves BI prediction accuracy to RMSE~0.003 and R^(2)~0.999 for the two wells with only 1 record in 10,000 yielding a BI prediction error of>±0.1.The model has the potential to be applied in an unsupervised basis to predict BI from basic well log data in surrounding wells lacking mineralogical measurements but with similar lithofacies and burial histories.The method could also be extended to predict elastic rock properties in and seismic attributes from wells and seismic data to improve the precision of brittleness index and fracability mapping spatially.

Analysis of China’s Crude Oil Supply and Demand in the Year 2010 and the Year 2020

Along with the speedy development of the economic growth in China, the shortage of oil and gas becomes more and more serious. Based on summarizing some related research results, the prediction of China＇s oil demand and supply in the year 2010 and the year 2020 has been given in the paper. The oil supply and demand situation is discussed on three different levels. Accordingly, suggestions about the oil supply safety and the national economy safety strategies have been given.

ANALYSIS OF CRITERIA PREDICTING THE TENDENCY OF PILOT INDUCED OSCILLATION FOR LINEAR NON-LINEAR SYSTEM

Some problems encountered in applying Smith's technique to predict the PIO tendency for non-linear pilot-vehicle loop, are thoroughly analyzed. Subsequently, modified PIO predictable criteria are developed, in addition, to make also a certain improvement on Smith's PIO definition and PIO types. These modified criteria are applied to predict PIO tendency of various different configurations on the variable stability aircraft NT-33 in case of supposed non-linearity, and predicted results are compared with the flight tests and analytical results in the case of linear hypothesis given in Ref. (4)

Online Observability-Constrained Motion Suggestion via Efficient Motion Primitive-Based Observability Analysis

An active perception methodology is proposed to locally predict the observability condition in a reasonable horizon and suggest an observability-constrained motion direction for the next step to ensure an accurate and consistent state estimation performance of vision-based navigation systems. The methodology leverages an efficient EOG-based observability analysis and a motion primitive-based path sampling technique to realize the local observability prediction with a real-time performance. The observability conditions of potential motion trajectories are evaluated,and an informed motion direction is selected to ensure the observability efficiency for the state estimation system. The proposed approach is specialized to a representative optimizationbased monocular vision-based state estimation formulation and demonstrated through simulation and experiments to evaluate the ability of estimation degradation prediction and efficacy of motion direction suggestion.

Predictive analysis of stress regime and possible squeezing deformation for super-long water conveyance tunnels in Pakistan

The prediction of the stress field of deep-buried tunnels is a fundamental problem for scientists and engineers. In this study, the authors put forward a systematic solution for this problem. Databases from the World Stress Map and the Crustal Stress of China, and previous research findings can offer prediction of stress orientations in an engineering area. At the same time, the Andersonian theory can be used to analyze the possible stress orientation of a region. With limited in-situ stress measurements, the Hock-Brown Criterion can be used to estimate the strength of rock mass in an area of interest by utilizing the geotechnical investigation data, and the modified Sheorey＇s model can subsequently be employed to predict the areas＇ stress profile, without stress data, by taking the existing in-situ stress measurements as input parameters. In this paper, a case study was used to demonstrate the application of this systematic solution. The planned Kohala hydropower plant is located on the western edge of Qinghai-Tibet Plateau. Three hydro-fracturing stress measurement campaigns indicated that the stress state of the area is SH - Sh 〉 Sv or SH 〉Sv 〉 Sh. The measured orientation of Sn is NEE （N70.3°-89°E）, and the regional orientation of SH from WSM is NE, which implies that the stress orientation of shallow crust may be affected by landforms. The modified Sheorey model was utilized to predict the stress profile along the water sewage tunnel for the plant. Prediction results show that the maximum and minimum horizontal principal stres- ses of the points with the greatest burial depth were up to 56.70 and 40.14 MPa, respectively, and the stresses of areas with a burial depth of greater than 500 m were higher. Based on the predicted stress data, large deformations of the rock mass surrounding water conveyance tunnels were analyzed. Results showed that the large deformations will occur when the burial depth exceeds 300 m. When the burial depth is beyond 800 m, serious squeezing deformations will occur in the surrounding rock masses, thus requiring more attention in the design and construction. Based on the application efficiency in this case study, this prediction method proposed in this paper functions accurately.

Research on Carbon Peak and Carbon Neutrality Technological Pathways in the Chinese Papermaking Industry

Against the backdrop of the dual carbon goals,the papermaking industry in China faces significant pressure to reduce emissions and lower carbon intensity.Based on historical data of energy consumption in the pulp and paper industry in China from 2000 to 2020,this study analyzed the current status of paper production and energy consumption in China.Two methods were employed to predict the growth trend of paper production in China,and three carbon dioxide emission accounting methods were compared.The study used an accounting method based on the industry’s overall energy consumption and predicted the carbon dioxide(CO_(2))emissions of the Chinese papermaking industry from 2021 to 2060 under three scenarios.The study identified the timing for achieving carbon peak and proposed the measures for carbon neutrality.The results indicated that:(1)the CO_(2)emissions of the Chinese papermaking industry in 2020 were 111.98 million tons.(2)Under low-demand,high-demand,and baseline scenarios,the papermaking industry is expected to achieve carbon peak during the“14th Five-Year Plan”period.(3)In 2060,under the three scenarios,CO_(2)emissions from the papermaking industry will decrease by 11%-31%compared to the baseline year.However,there will still be emissions of 72-93 million tons,requiring reductions in fossil energy consumption at the source,increasing forestry carbon sequestration and utilization of Carbon Capture,Utilization and Storage(CCUS)technology,and taking measures such as carbon trading to achieve carbon neutrality.

The Symbiotic Relationship Unraveling the Interplay between Technology and Artificial Intelligence(An Intelligent Dynamic Relationship)

This article investigates the dynamic relationship between technology and AI(artificial intelligence)and the role that societal requirements play in pushing AI research and adoption.Technology has advanced dramatically throughout the years,providing the groundwork for the rise of AI.AI systems have achieved incredible feats in various disciplines thanks to advancements in computer power,data availability,and complex algorithms.On the other hand,society’s needs for efficiency,enhanced healthcare,environmental sustainability,and personalized experiences have worked as powerful accelerators for AI’s progress.This article digs into how technology empowers AI and how societal needs dictate its progress,emphasizing their symbiotic relationship.The findings underline the significance of responsible AI research,which considers both technological prowess and ethical issues,to ensure that AI continues to serve the greater good.

System Architecture and Key Technologies of Network Security Situation Awareness System YHSAS

Network Security Situation Awareness System YHSAS acquires,understands and displays the security factors which cause changes of network situation,and predicts the future development trend of these security factors.YHSAS is developed for national backbone network,large network operators,large enterprises and other large-scale network.This paper describes its architecture and key technologies:Network Security Oriented Total Factor Information Collection and High-Dimensional Vector Space Analysis,Knowledge Representation and Management of Super Large-Scale Network Security,Multi-Level,Multi-Granularity and Multi-Dimensional Network Security Index Construction Method,Multi-Mode and Multi-Granularity Network Security Situation Prediction Technology,and so on.The performance tests show that YHSAS has high real-time performance and accuracy in security situation analysis and trend prediction.The system meets the demands of analysis and prediction for large-scale network security situation.

Predictive Vegetation Mapping Approach Based on Spectral Data, DEM and Generalized Additive Models

This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.

Oil and Gas Supply and Demand in China and Its Development Strategy

The paper makes a comprehensive prediction of China＇s future demand for oil and gas with two methods, i.e. the prediction method based on the demand for individual oil and gas products and the method for predicting the total demand. According to the demand prediction of, and the historical data on, the oil and gas consumption, we conduct an analysis of the oil and gas consumption trends, which can be described as six different development periods. On the other hand, the paper makes a comprehensive analysis of the domestic oil and gas resources and, on this basis, makes a basic prediction of the domestic output of oil and gas. The supply and demand situation is also analyzed. By using the SWOT analysis method, the paper puts forward the development strategies for China＇s oil and gas industry and gives some related strategic measures.