A study on the numerical prediction method for the vertical thermal structure in the Bohai Sea and the Huanghai Sea-I.One-dimensional numerical prediction model

In this paper, on the basis of the heat conduction equation without consideration of the advection and turbulence effects, one-dimensional model for describing surface sea temperature ( T1), bottom sea temperature ( Tt ) and the thickness of the upper homogeneous layer ( h ) is developed in terms of the dimensionless temperature θT and depth η and self-simulation function θT - f(η) of vertical temperature profile by means of historical temperature data.The results of trial prediction with our one-dimensional model on T, Th, h , the thickness and gradient of thermocline are satisfactory to some extent.

Grey Prediction Analysis on Forestry Industrial Structure of China

The grey relevance analysis is applied to study the 1996-2009 output value structure of China forestry system. Based on GM(1,1) model, information model is established to predict the forestry industrial structure of China in the next 10 years. Result shows that grey correlations between the three forestry industries and the forestry output value are 0.849 1, 0.731 1 and 0.821 3, respectively, with its order being secondary industry<tertiary industry<primary industry. Prediction result shows that forestry industry of China is in the middle stage of industrialization; and both secondary and tertiary industries will develop rapidly and become the leading industries.

Heuristic Quasi-physical Algorithm for Protein Structure Prediction

A three-dimensional off-lattice protein model with two species of monomers, hydrophobic and hydrophilic, is studied. Enligh- tened by the law of reciprocity among things in the physical world, a heuristic quasi-physical algorithm for protein structure prediction problem is put forward. First, by elaborately simulating the movement of the smooth elastic balls in the physical world, the algorithm finds low energy configurations for a given monomer chain. An ＂off-trap＂ strategy is then proposed to get out of local minima. Experimental results show promising performance. For all chains with lengths 13≤n ≤55, the proposed algorithm finds states with lower energy than the putative ground states reported in literatures. Furthermore, for chain lengths n = 21, 34, and 55, the algorithm finds new low energy configurations different from those given in literatures.

Application of ACO algorithm in protein structure prediction

The hydrophobic-polar (HP) lattice model is an important simplified model for studying protein folding. In this paper, we present an improved ACO algorithm for the protein structure prediction. In the algorithm, the "lone"ethod is applied to deal with the infeasible structures, and the "oint mutation and reconstruction"ethod is applied in local search phase. The empirical results show that the presented method is feasible and effective to solve the problem of protein structure prediction, and notable improvements in CPU time are obtained.

Comparative Analysis of ARIMA and LSTM Model-Based Anomaly Detection for Unannotated Structural Health Monitoring Data in an Immersed Tunnel

Structural Health Monitoring(SHM)systems have become a crucial tool for the operational management of long tunnels.For immersed tunnels exposed to both traffic loads and the effects of the marine environment,efficiently identifying abnormal conditions from the extensive unannotated SHM data presents a significant challenge.This study proposed amodel-based approach for anomaly detection and conducted validation and comparative analysis of two distinct temporal predictive models using SHM data from a real immersed tunnel.Firstly,a dynamic predictive model-based anomaly detectionmethod is proposed,which utilizes a rolling time window for modeling to achieve dynamic prediction.Leveraging the assumption of temporal data similarity,an interval prediction value deviation was employed to determine the abnormality of the data.Subsequently,dynamic predictive models were constructed based on the Autoregressive Integrated Moving Average(ARIMA)and Long Short-Term Memory(LSTM)models.The hyperparameters of these models were optimized and selected using monitoring data from the immersed tunnel,yielding viable static and dynamic predictive models.Finally,the models were applied within the same segment of SHM data,to validate the effectiveness of the anomaly detection approach based on dynamic predictive modeling.A detailed comparative analysis discusses the discrepancies in temporal anomaly detection between the ARIMA-and LSTM-based models.The results demonstrated that the dynamic predictive modelbased anomaly detection approach was effective for dealing with unannotated SHM data.In a comparison between ARIMA and LSTM,it was found that ARIMA demonstrated higher modeling efficiency,rendering it suitable for short-term predictions.In contrast,the LSTM model exhibited greater capacity to capture long-term performance trends and enhanced early warning capabilities,thereby resulting in superior overall performance.

Deformation,structure and potential hazard of a landslide based on InSAR in Banbar county,Xizang(Tibet)

The Tibetan Plateau is characterized by complex geological conditions and a relatively fragile ecological environment.In recent years,there has been continuous development and increased human activity in the Tibetan Plateau region,leading to a rising risk of landslides.The landslide in Banbar County,Xizang(Tibet),have been perturbed by ongoing disturbances from human engineering activities,making it susceptible to instability and displaying distinct features.In this study,small baseline subset synthetic aperture radar interferometry(SBAS-InSAR)technology is used to obtain the Line of Sight(LOS)deformation velocity field in the study area,and then the slope-orientation deformation field of the landslide is obtained according to the spatial geometric relationship between the satellite’s LOS direction and the landslide.Subsequently,the landslide thickness is inverted by applying the mass conservation criterion.The results show that the movement area of the landslide is about 6.57×10^(4)m^(2),and the landslide volume is about 1.45×10^(6)m^(3).The maximum estimated thickness and average thickness of the landslide are 39 m and 22 m,respectively.The thickness estimation results align with the findings from on-site investigation,indicating the applicability of this method to large-scale earth slides.The deformation rate of the landslide exhibits a notable correlation with temperature variations,with rainfall playing a supportive role in the deformation process and displaying a certain lag.Human activities exert the most substantial influence on the spatial heterogeneity of landslide deformation,leading to the direct impact of several prominent deformation areas due to human interventions.Simultaneously,utilizing the long short-term memory(LSTM)model to predict landslide displacement,and the forecast results demonstrate the effectiveness of the LSTM model in predicting landslides that are in a continuous development and movement phase.The landslide is still active,and based on the spatial heterogeneity of landslide deformation,new recommendations have been proposed for the future management of the landslide in order to mitigate potential hazards associated with landslide instability.

Computational Fluid Dynamics Approach for Predicting Pipeline Response to Various Blast Scenarios: A Numerical Modeling Study

Recent industrial explosions globally have intensified the focus in mechanical engineering on designing infras-tructure systems and networks capable of withstanding blast loading.Initially centered on high-profile facilities such as embassies and petrochemical plants,this concern now extends to a wider array of infrastructures and facilities.Engineers and scholars increasingly prioritize structural safety against explosions,particularly to prevent disproportionate collapse and damage to nearby structures.Urbanization has further amplified the reliance on oil and gas pipelines,making them vital for urban life and prime targets for terrorist activities.Consequently,there is a growing imperative for computational engineering solutions to tackle blast loading on pipelines and mitigate associated risks to avert disasters.In this study,an empty pipe model was successfully validated under contact blast conditions using Abaqus software,a powerful tool in mechanical engineering for simulating blast effects on buried pipelines.Employing a Eulerian-Lagrangian computational fluid dynamics approach,the investigation extended to above-surface and below-surface blasts at standoff distances of 25 and 50 mm.Material descriptions in the numerical model relied on Abaqus’default mechanical models.Comparative analysis revealed varying pipe performance,with deformation decreasing as explosion-to-pipe distance increased.The explosion’s location relative to the pipe surface notably influenced deformation levels,a key finding highlighted in the study.Moreover,quantitative findings indicated varying ratios of plastic dissipation energy(PDE)for different blast scenarios compared to the contact blast(P0).Specifically,P1(25 mm subsurface blast)and P2(50 mm subsurface blast)showed approximately 24.07%and 14.77%of P0’s PDE,respectively,while P3(25 mm above-surface blast)and P4(50 mm above-surface blast)exhibited lower PDE values,accounting for about 18.08%and 9.67%of P0’s PDE,respectively.Utilising energy-absorbing materials such as thin coatings of ultra-high-strength concrete,metallic foams,carbon fiber-reinforced polymer wraps,and others on the pipeline to effectively mitigate blast damage is recommended.This research contributes to the advancement of mechanical engineering by providing insights and solutions crucial for enhancing the resilience and safety of underground pipelines in the face of blast events.

Investigate Targeted Factors to Achieve Prediction Goal in Stroke Convalescence in Terms of Causal Relationships of Prediction Error

Background and Purpose: To investigate target functional independence measure (FIM) items to achieve the prediction goal in terms of the causal relationships between prognostic prediction error and FIM among stroke patients in the convalescent phase using the structural equation modeling (SEM) analysis. Methods: A total of 2992 stroke patients registered in the Japanese Rehabilitation Database were analyzed retrospectively. The prediction error was calculated based on a prognostic prediction formula proposed in a previous study. An exploratory factor analysis (EFA) then the factor was determined using confirmatory factorial analysis (CFA). Finally, multivariate analyses were performed using SEM analysis. Results: The fitted indices of the hypothesized model estimated based on EFA were confirmed by CFA. The factors estimated by EFA were applied, and interpreted as follows: “Transferring (T-factor),” “Dressing (D-factor),” and “Cognitive function (C-factor).” The fit of the structural model based on the three factors and prediction errors was supported by the SEM analysis. The effects of the D- and C-factors yielded similar causal relationships on prediction error. Meanwhile, the effects between the prediction error and the T-factor were low. Observed FIM items were related to their domains in the structural model, except for the dressing of the upper body and memory (p < 0.01). Conclusions: Transfer, which was not heavily considered in the previous prediction formula, was found in causal relationships with prediction error. It is suggested to intervene to transfer together with positive factors to recovery for achieving the prediction goal.

Data Fusion about Serviceability Reliability Prediction for the Long-Span Bridge Girder Based on MBDLM and Gaussian Copula Technique

This article presented a new data fusion approach for reasonably predicting dynamic serviceability reliability of the long-span bridge girder.Firstly,multivariate Bayesian dynamic linear model(MBDLM)considering dynamic correlation among the multiple variables is provided to predict dynamic extreme deflections;secondly,with the proposed MBDLM,the dynamic correlation coefficients between any two performance functions can be predicted;finally,based on MBDLM and Gaussian copula technique,a new data fusion method is given to predict the serviceability reliability of the long-span bridge girder,and the monitoring extreme deflection data from an actual bridge is provided to illustrated the feasibility and application of the proposed method.

Prediction of Gas Chromatographic Retention Indices of Organophosphates by DFT and VSMP Method

Polychlorinated dibenzothiophenes（PCDTs） are a group of important persistent organic pollutants.In the present study,geometrical optimization and electrostatic potential calculations have been performed for all 135 PCDTs congeners at the B3LYP/6-31G＊ level of theory.By means of the VSMP（variable selection and modeling based on prediction） program,one optimal descriptor（molecular polarizability,α） was selected to develop a QSRR model for the prediction of gas chromatographic retention indices（GC-RI） of PCDTs.The estimated correlation coefficients（r2） and LOO-validated correlation coefficients（q2）,all more than 0.99,were built by multiple linear regression,which shows a good estimation ability and stability of the models.A prediction power for the external samples was validated by the model built from the training set with 17 polychlorinated dibenzothiophenes.

中低温岩浆热液型金矿床找矿预测地质模型

我国金矿资源对外依存度长期居高不下,立足国内,寻找并探明一批大型金矿床是一项迫在眉睫的重大任务。中低温岩浆热液型金矿床是我国金的最主要来源,如何寻找其隐伏矿和深部矿成为当前矿产勘查研究的热点。本文以勘查区找矿预测理论为指导,以中低温岩浆热液型金矿床为研究对象,总结了中低温岩浆热液型金矿床地质特征,构建了以成矿地质体、成矿构造和成矿结构面特征和成矿作用特征标志为核心内容的找矿预测地质模型,为该类型金矿床找矿预测提供了新的思路,指导隐伏矿和深部矿金资源勘查取得突破。中低温岩浆热液型金矿床分为中温岩浆热液型金矿床和低温岩浆热液型金矿床两个亚类,广泛发育于陆块区和造山带中,赋矿围岩多种多样,成矿时代广泛,主要集中在中生代。本文厘定了中低温岩浆热液型金矿床成矿地质体为中酸性侵入体,与矿床(体)呈现出空间上相依(1~5km)、时间上相近(10Myr)、成因上相关的内在成生联系和时空配置关系。成矿构造属褶皱、断裂、岩浆侵入复合构造系统;成矿结构面主要有断裂、硅钙面、岩溶构造及岩体侵入接触带、爆破角砾岩体及水压裂隙等;矿化样式受成矿结构面控制,大致可分为四个类型:层状、脉状、块状及其组合而成的复合型。中温岩浆热液型金矿床成矿作用早阶段温度可达450℃左右,形成强度不等的钾长石化、钠长石化或铁白云石化;主成矿阶段成矿温度250℃左右,蚀变主要为硅化、绢云母化、伊利石化,Au和Ag共伴生,同时伴生少量Pb、Zn、Cu等,主要金属矿物为黄铁矿,次为黄铜矿、磁黄铁矿、方铅矿和闪锌矿;而低温岩浆热液型金矿床成矿作用主阶段温度低于250℃,Au和As、Sb共伴生,Ag含量低,常见毒砂和辉锑矿等矿物,成矿作用早阶段蚀变则主要为硅化,有的为次生石英岩化。金的沉淀富集机制包括流体的沸腾、混合和交代等机制。成矿作用中心位于岩体外接触带2~3km和接触带之内,由浅部到深部形成“上脉下层”的二元结构模式,脉状矿体具有侧伏延深规律,在此基础上构建了找矿预测地质模型。在该模型指导下,我们重新厘定了我国重要成矿区带金矿床类型,在深部发现新的矿体样式,拓宽了深部找矿空间,提升了我国重要成矿区带成矿规律的认识水平,带动了我国重要成矿区带金矿找矿新突破。

Research on Service Life Prediction Model of Concrete Structure of Sea-crossing Bridge

According to the chloride corrosion environment,service life prediction model of concrete structure of sea-crossing bridge was built using modified Fick's second law and the whole probability calculation method,which was suitable for China. Furthermore,a visual service life prediction program of concrete structure was developed by optimized Monte Carlo method. Meanwhile,Life 365 program was compared,indicating reliability of the prediction program. Finally,the validity of prediction model was verified in JinTang Bridge of Zhoushan Island Mainland Linkage Project.

Verification of tropical cyclones(TC)wind structure forecasts from global NWP models and ensemble prediction systems(EPSs)

Forecasting wind structure of tropical cyclone(TC)is vital in assessment of impact due to high winds using Numerical Weather Prediction(NWP)model.The usual verification technique on TC wind structure forecasts are based on grid-to-grid comparisons between forecast field and the actual field.However,precision of traditional verification measures is easily affected by small scale errors and thus cannot well discriminate the accuracy or effectiveness of NWP model forecast.In this study,the Method for Object-Based Diagnostic Evaluation(MODE),which has been widely adopted in verifying precipitation fields,is utilized in TC’s wind field verification for the first time.The TC wind field forecast of deterministic NWP model and Ensemble Prediction System(EPS)of the European Centre for Medium-Range Weather Forecasts(ECMWF)over the western North Pacific and the South China Sea in 2020 were evaluated.A MODE score of 0.5 is used as a threshold value to represent a skillful(or good)forecast.It is found that the R34(radius of 34 knots)wind field structure forecasts within 72 h are good regardless of DET or EPS.The performance of R50 and R64 is slightly worse but the R50 forecasts within 48 h remain good,with MODE exceeded 0.5.The R64forecast within 48 h are worth for reference as well with MODE of around 0.5.This study states that the TC wind field structure forecast by ECMWF is skillful for TCs over the western North Pacific and the South China Sea.

Predicting the evolution of electrochemical trepanning for inner blisks with a chamfer structure at blade tip

Electrochemical trepanning(ECTr)is an effective electrochemical machining(ECM)technique that can be used to manufacture the integral components of aero-engine compressors.This study focused on the dynamic evolution of ECTr for production of inner blisks(bladed disks)with a special chamfer structure at blade tip.Due to the existence of chamfer,the ECTr process of inner blades is in a non-equilibrium state during the early stages,and the physical field changes in the machining gap are complex,making it difficult to predict the forming process.In this paper,a dynamic evolution model(DEM)of inner blade ECTr with a special chamfer at blade tip structure is proposed,and an ECTr multi-physical fields simulation study was carried out.The evolution of the chamfer at blade tip was analyzed and data related to chamfer were predicted based on the dependence of anode boundary properties with machining time and feed rate.In addition,the dis-tributions of current density,electrolyte flow rate,bubble volume fraction,temperature rise,and electrolyte conductivity in the machining area at different times were obtained by combining them with the multi-physical fields simulation results.Subsequently,a series of ECTr experiments were conducted,in which,as the feed rate increased,the surface quality and machining accuracy of the inner blades were improved.Compared with the simulation results,the error in machining accu-racy of the chamfer profile is controlled within±2%,and the machining accuracy of the blade full profile was controlled within±0.2 mm,indicating that the model proposed in this study was effec-tive in predicting the evolution of inner blades ECTr with chamfer structures at blade tip.

Protein Structure Prediction:Challenges,Advances,and the Shift of Research Paradigms

Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields,including biochemistry,medicine,physics,mathematics,and computer science.These researchers adopt various research paradigms to attack the same structure prediction problem:biochemists and physicists attempt to reveal the principles governing protein folding;mathematicians,especially statisticians,usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure,while computer scientists formulate protein structure prediction as an optimization problem-finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native structure.These research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman,namely,data modeling and algorithmic modeling.Recently,we have also witnessed the great success of deep learning in protein structure prediction.In this review,we present a survey of the efforts for protein structure prediction.We compare the research paradigms adopted by researchers from different fields,with an emphasis on the shift of research paradigms in the era of deep learning.In short,the algorithmic modeling techniques,especially deep neural networks,have considerably improved the accuracy of protein structure prediction;however,theories interpreting the neural networks and knowledge on protein folding are still highly desired.

Brain networks modeling for studying the mechanism underlying the development of Alzheimer’s disease

Alzheimer’s disease is a primary age-related neurodegenerative disorder that can result in impaired cognitive and memory functions.Although connections between changes in brain networks of Alzheimer’s disease patients have been established,the mechanisms that drive these alterations remain incompletely understood.This study,which was conducted in 2018 at Northeastern University in China,included data from 97 participants of the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset covering genetics,imaging,and clinical data.All participants were divided into two groups:normal control(n=52;20 males and 32 females;mean age 73.90±4.72 years)and Alzheimer’s disease(n=45,23 males and 22 females;mean age 74.85±5.66).To uncover the wiring mechanisms that shaped changes in the topology of human brain networks of Alzheimer’s disease patients,we proposed a local naive Bayes brain network model based on graph theory.Our results showed that the proposed model provided an excellent fit to observe networks in all properties examined,including clustering coefficient,modularity,characteristic path length,network efficiency,betweenness,and degree distribution compared with empirical methods.This proposed model simulated the wiring changes in human brain networks between controls and Alzheimer’s disease patients.Our results demonstrate its utility in understanding relationships between brain tissue structure and cognitive or behavioral functions.The ADNI was performed in accordance with the Good Clinical Practice guidelines,US 21 CFR Part 50-Protection of Human Subjects,and Part 56-Institutional Review Boards(IRBs)/Research Good Clinical Practice guidelines Institutional Review Boards(IRBs)/Research Ethics Boards(REBs).

A seqlet-based maximum entropy Markov approach for protein secondary structure prediction

A novel method for predicting the secondary structures of proteins from amino acid sequence has been presented. The protein secondary structure seqlets that are analogous to the words in natural language have been extracted. These seqlets will capture the relationship be-tween amino acid sequence and the secondary structures of proteins and further form the protein secondary structure dictionary. To be elaborate, the dictionary is organism-specific. Protein sec-ondary structure prediction is formulated as an integrated word segmentation and part of speech tagging problem. The word-lattice is used to represent the results of the word segmentation and the maximum entropy model is used to calculate the probability of a seqlet tagged as a certain secondary structure type. The method is markovian in the seqlets, permitting efficient exact cal-culation of the posterior probability distribution over all possible word segmentations and their tags by viterbi algorithm. The optimal segmentations and their tags are computed as the results of protein secondary structure prediction. The method is applied to predict the secondary struc-tures of proteins of four organisms respectively and compared with the PHD method. The results show that the performance of this method is higher than that of PHD by about 3.9% Q3 accuracy and 4.6% SOV accuracy. Combining with the local similarity protein sequences that are obtained by BLAST can give better prediction. The method is also tested on the 50 CASP5 target proteins with Q3 accuracy 78.9% and SOV accuracy 77.1%. A web server for protein secondary structure prediction has been constructed which is available at http://www.insun.hit.edu.cn:81/demos/bi-ology/index.html.

Link Prediction Based on the Relational Path Inference of Triangular Structures

Link prediction is used to complete the knowledge graph.Convolu-tional neural network models are commonly used for link prediction tasks,but they only consider the direct relations between entity pairs,ignoring the semantic information contained in the relation paths.In addition,the embedding dimension of the relation is generally larger than that of the entity in the ConvR model,which blocks the progress of downstream tasks.If we reduce the embedding dimension of the relation,the performance will be greatly degraded.This paper proposes a convolutional model PITri-R-ConvR based on triangular structure relational infer-ence.The model uses relational path inference to capture semantic information,while using a triangular structure to ensure the reliability and computational effi-ciency of relational inference.In addition,the decoder R-ConvR improves the initial embedding of the ConvR model,which solves the problems of the ConvR model and significantly improves the prediction performance.Finally,this paper conducts sufficient experiments in multiple datasets to verify the superiority of the model and the rationality of each module.

A Prediction Framework for Turning Period Structures in COVID-19 Epidemic and Its Application to Practical Emergency Risk Management

The aim of this paper is first to establish a general prediction framework for turning(period)term structures in COVID-19 epidemic related to the implementation of emergency risk management in the practice,which allows us to conduct the reliable estimation for the peak period based on the new concept of“Turning Period”(instead of the traditional one with the focus on“Turning Point”)for infectious disease spreading such as the COVID-19 epidemic appeared early in year 2020.By a fact that emergency risk management is necessarily to implement emergency plans quickly,the identification of the Turning Period is a key element to emergency planning as it needs to provide a time line for effective actions and solutions to combat a pandemic by reducing as much unexpected risk as soon as possible.As applications,the paper also discusses how this“Turning Term(Period)Structure”is used to predict the peak phase for COVID-19 epidemic in Wuhan from January/2020 to early March/2020.Our study shows that the predication framework established in this paper is capable to provide the trajectory of COVID-19 cases dynamics for a few weeks starting from Feb.10/2020 to early March/2020,from which we successfully predicted that the turning period of COVID-19 epidemic in Wuhan would arrive within one week after Feb.14/2020,as verified by the true observation in the practice.The method established in this paper for the prediction of“Turning Term(Period)Structures”by applying COVID-19 epidemic in China happened early 2020 seems timely and accurate,providing adequate time for the government,hospitals,essential industry sectors and services to meet peak demands and to prepare aftermath planning,and associated criteria for the Turning Term Structure of COVID-19 epidemic is expected to be a useful and powerful tool to implement the so-called“dynamic zero-COVID-19 policy”ongoing basis in the practice.

复杂载荷、极端环境下焊接结构疲劳寿命预测研究综述

焊接接头易出现缺陷和应力集中,在疲劳载荷作用下将成为疲劳裂纹萌生和扩展的薄弱区域。与均质材料相比,接头各区域微观组织及应力局部化使得焊接结构疲劳问题进一步复杂化。区别于理想实验条件,实际焊接结构服役环境复杂,疲劳寿命预测须考虑环境因素与焊接结构耦合特性。为此,对影响焊接结构的内在因素进行总结分析,从复杂载荷和极端服役环境两方面对现有焊接结构寿命预测模型进行综述,结合最新研究进展对改进焊接结构疲劳寿命评估方法提出建议。