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.

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.

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.

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.

Analysis of Causes and Seasonal Prediction of the Severe Floods in Yangtze／Huaihe Basins during Summer 1991

The present paper shows that a seasonal prediction for the large scale flooding and waterlogging of the mid-lower Yangtze/ Huaihe River basins in the summer of 1991 made successfully in early April 1991.The seasonal forecasting method and some predictors are also introduced and analyzed herein. Because the extra extent of the abnormally early onset of the plum rain period in 1991 was unexpected,great efforts have been made to find out the causes of this abnormality. These causes are mainly associated with the large scale warming of SST surrounding the south and east part of Asia during the preceding winter,while the ENSO-like pattern of SSTA occurred in the North Pacific.In addition,the possible influence of strong solar proton events is analyzed.In order to improve the seasonal pre4iction the usage of the predicted SOl in following spring/summer is also introduced.The author believes thatthe regional climate anomaly can be correctly predicted for one season ahead only on the basis of physical understanding of the interactions of many preceding factors.

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.

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.

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.

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.

Interannual Variability and Scenarios Projection of Sea Ice in Bohai Sea Part Ⅰ: Variation Characteristics and Interannual Hindcast

The Bohai Sea is one of the southernmost areas for sea ice formation in the northern hemisphere.Sea ice disasters in this body of water severely affect marine activities and the safety of coastal residents.In this study,we analyze the variation characteristics of the sea ice in the Bohai Sea and establish an annual regression model based on predictable mode analysis method.The results show the following:1)From 1970 to 2018,the average ice grade is(2.6±0.8),with a maximum of 4.5 and a minimum of 1.0.Liaodong Bay(LDB)has the heaviest ice conditions in the Bohai Sea,followed by Bohai Bay(BHB)and Laizhou Bay(LZB).Interannual variation is obvious in all three bays,but the linear decreasing trend is significant only in BHB.2)Three modes are obtained from empirical orthogonal function analysis,namely,single polarity mode with the same sign of anomaly in all of the three bays and strong interannual variability(82.0%),the north–south dipole mode with BHB and LZB showing an opposite sign of anomalies to that in LDB and strong decadal variations(14.5%),and a linear trend mode(3.5%).Critical factors are analyzed and regression equations are established for all the principal components,and then an annual hindcast model is established by synthesizing the results of the three modes.This model provides an annual spatial prediction of the sea ice in the Bohai Sea for the first time,and meets the demand of operational sea ice forecasting.

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.

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.

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.

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.

Empirical Study on the Sustainable Development of Domestic Tourism

Taking the number of domestic tourists as the research object, an appropriate model is established to analyze the sustainable development of China’s tourism industry. The number of domestic tourists in China from 1985 to 2015 was taken as a sample, and the ARIMA model was established using Eviews 6.0. Finally, the ARIMA(0, 2, 1) model is established. After testing, the model fits well (MAPE = 7.363) and the prediction accuracy is extremely high (99.56%). The ARIMA model’s short-term prediction of the number of tourists is reasonable.

Identifying the occurrence time of an impending mainshock:a very recent case

The procedure by means of which the occurrence time of an impending mainshock can be identified by analyzing in natural time the seismicity in the candidate area subsequent to the recording of a precursory seismic electric signals（SES） activity is reviewed. Here, we report the application of this procedure to an MW5.4 mainshock that occurred in Greece on 17 November 2014. This mainshock（which is pretty rare since it is the strongest in that area for more than half a century） was preceded by an SES activity recorded on 27 July 2014, and the results of the natural time analysis reveal that the system approached the critical point（mainshock occurrence） early in the morning on 15 November 2014.

Stability-mutation feature identification of Web search keywords based on keyword concentration change ratio

Purpose: The aim of this paper is to discuss how the keyword concentration change ratio(KCCR) is used while identifying the stability-mutation feature of Web search keywords during information analyses and predictions.Design/methodology/approach: By introducing the stability-mutation feature of keywords and its significance, the paper describes the function of the KCCR in identifying keyword stability-mutation features. By using Ginsberg's influenza keywords, the paper shows how the KCCR can be used to identify the keyword stability-mutation feature effectively.Findings: Keyword concentration ratio has close positive correlation with the change rate of research objects retrieved by users, so from the characteristic of the 'stability-mutation' of keywords, we can understand the relationship between these keywords and certain information. In general, keywords representing for mutation fit for the objects changing in short-term, while those representing for stability are suitable for long-term changing objects. Research limitations: It is difficult to acquire the frequency of keywords, so indexes or parameters which are closely related to the true search volume are chosen for this study.Practical implications: The stability-mutation feature identification of Web search keywords can be applied to predict and analyze the information of unknown public events through observing trends of keyword concentration ratio.Originality/value: The stability-mutation feature of Web search could be quantitatively described by the keyword concentration change ratio(KCCR). Through KCCR, the authors took advantage of Ginsberg's influenza epidemic data accordingly and demonstrated how accurate and effective the method proposed in this paper was while it was used in information analyses and predictions.

Effect of Curcumin on Aged Drosophila Melanogaster:A Pathway Prediction Analysis

Objective： To re-analyze the data published in order to explore plausible biological pathways that can be used to explain the anti-aging effect of curcumin. Methods： Microarray data generated from other study aiming to investigate effect of curcumin on extending lifespan of Drosophila melanogaster were further used for pathway prediction analysis. The differentially expressed genes were identified by using GeneSpdng GX with a criterion of 3.0-fold change. Two Cytoscape plugins including BisoGenet and molecular complex detection （MCODE） were used to establish the protein-protein interaction （PPI） network based upon differential genes in order to detect highly connected regions. The function annotation clustering tool of Database for Annotation, Visualization and Integrated Discovery （DAVID） was used for pathway analysis. Results： A total of 87 genes expressed differentially in D. melanogaster treated with curcumin were identified, among which 50 were up-regulated significantly and 37 were remarkably down-regulated in D. melanogaster treated with curcumin. Based upon these differential genes, PPI network was constructed with 1,082 nodes and 2,412 edges. Five highly connected regions in PPI networks were detected by MCODE algorithm, suggesting anti-aging effect of curcumin may be underlined through five different pathways including Notch signaling pathway, basal transcription factors, cell cycle regulation, ribosome, Wnt signaling pathway, and p53 pathway. Conclusion： Genes and their associated pathways in D. rnelanogaster treated with anti-aging agent curcumin were identified using PPI network and MCODE algorithm, suggesting that curnumin may be developed as an alternative therapeutic medicine for treating aging-associated diseases.