Structured Multi-Head Attention Stock Index Prediction Method Based Adaptive Public Opinion Sentiment Vector

The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.

Prediction of atomization characteristics of pressure swirl nozzle with different structures

The structure of the pressure swirl nozzle is an important factor affecting its spray performance.This work aims to study pressure swirl nozzles with different structures by experiment and simulation.In the experiment,10 nozzles with different structures are designed to comprehensively cover various geometric factors.In terms of simulation,steady-state simulation with less computational complexity is used to study the flow inside the nozzle.The results show that the diameter of the inlet and outlet,the direction of the inlet,the diameter of the swirl chamber,and the height of the swirl chamber all affect the atomization performance,and the diameter of the inlet and outlet has a greater impact.It is found that under the same flow rate and pressure,the geometric differences do have a significant impact on the atomization characteristics,such as spray angle and SMD(Sauter mean diameter).Specific nozzle structures can be customized according to the actual needs.Data analysis shows that the spray angle is related to the swirl number,and the SMD is related to turbulent kinetic energy.Through data fitting,the equations for predicting the spray angle and the SMD are obtained.The error range of the fitting equation for the prediction of spray angle and SMD is within 15% and 10% respectively.The prediction is expected to be used in engineering to estimate the spray performance at the beginning of a real project.

Grammar Model Based on Lexical Substring Extraction for RNA Secondary Structure Prediction

[Objective] To examine the grammar model based on lexical substring exac- tion for RNA secondary structure prediction. [Method] By introducing cloud model into stochastic grammar model, a machine learning algorithm suitable for the lexicalized stochastic grammar model was proposed. The word grid mode was used to extract and divide RNA sequence to acquire lexical substring, and the cloud classifier was used to search the maximum probability of each lemma which was marked as a certain sec- ondary structure type. Then, the lemma information was introduced into the training stochastic grammar process as prior information, realizing the prediction on the sec- ondary structure of RNA, and the method was tested by experiment. [Result] The experimental results showed that the prediction accuracy and searching speed of stochastic grammar cloud model were significantly improved from the prediction with simple stochastic grammar. [Conclusion] This study laid the foundation for the wide application of stochastic grammar model for RNA secondary structure prediction.

Prediction of Protein OmpH in Structure of C47-8 Pasteurella multocida

[Objective] This study aimed to predict the structure of protein OmpH from Pasteurella multocida C47-8 （PmC47-8） strain of yak. [Method] Online BLAST, signal peptide prediction, secondary structure prediction and protein characteristics of sequencing result of gene OmpH from PmC47-8 strain were analyzed. [Result] The similarities of gene OmpH from PmC47-8 with the published 81 OmpH genes were between 84% and 99%; a signal peptide was found with the cleavage sites between 20 and 21 in the polypeptide; secondary structure prediction showed that folding structure accounted for 49.8% and loop structure for 50.2%; it predicted that there were 7 O-glycosylation sites in OmpH protein with the amino acid residual sites of 2, 45, 48, 330, 716, 721, 723, respectively, and 2 N-glycosylation sites with the amino acid residual sites of 15 and 35. [Conclusion] This study lays the foundation for the study on the immunity of OmpH gene from yak.

NeurstrucEnergy:A bi-directional GNN model for energy prediction of neural networks in IoT

A significant demand rises for energy-efficient deep neural networks to support power-limited embedding devices with successful deep learning applications in IoT and edge computing fields.An accurate energy prediction approach is critical to provide measurement and lead optimization direction.However,the current energy prediction approaches lack accuracy and generalization ability due to the lack of research on the neural network structure and the excessive reliance on customized training dataset.This paper presents a novel energy prediction model,NeurstrucEnergy.NeurstrucEnergy treats neural networks as directed graphs and applies a bi-directional graph neural network training on a randomly generated dataset to extract structural features for energy prediction.NeurstrucEnergy has advantages over linear approaches because the bi-directional graph neural network collects structural features from each layer's parents and children.Experimental results show that NeurstrucEnergy establishes state-of-the-art results with mean absolute percentage error of 2.60%.We also evaluate NeurstrucEnergy in a randomly generated dataset,achieving the mean absolute percentage error of 4.83%over 10 typical convolutional neural networks in recent years and 7 efficient convolutional neural networks created by neural architecture search.Our code is available at https://github.com/NEUSoftGreenAI/NeurstrucEnergy.git.

Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury:predictors of patient prognosis

Patients with mild traumatic brain injury have a diverse clinical presentation,and the underlying pathophysiology remains poorly understood.Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neuro biological markers after mild traumatic brain injury.This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury.G raph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function.However,most previous mild traumatic brain injury studies using graph theory have focused on specific populations,with limited exploration of simultaneous abnormalities in structural and functional connectivity.Given that mild traumatic brain injury is the most common type of traumatic brain injury encounte red in clinical practice,further investigation of the patient characteristics and evolution of structural and functional connectivity is critical.In the present study,we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury.In this longitudinal study,we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 wee ks of injury,as well as 36 healthy controls.Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis.In the acute phase,patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network.More than 3 months of followup data revealed signs of recovery in structural and functional connectivity,as well as cognitive function,in 22 out of the 46 patients.Furthermore,better cognitive function was associated with more efficient networks.Finally,our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury.These findings highlight the importance of integrating structural and functional connectivity in unde rstanding the occurrence and evolution of mild traumatic brain injury.Additionally,exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury.

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.

Prediction and Reduction of StructureBorne Noise in Vehicle

The structure and the acoustic medium of a passenger vehicle are modeled using the finite element method(FEM), and the interior noise is studied the help of the modal synthesis method (MSM). Sound pressure level (Lp) of the noise is calculated in several conditions of the models, and has good agreements with its test results. The MSM am be consequently used for predicting the vehicle interior noise in dssign stage so that the structure may be optimized for the Purpose of the most reduction of noise.

Uncertainty quantification of predicting stable structures for high-entropy alloys using Bayesian neural networks

High entropy alloys(HEAs)have excellent application prospects in catalysis because of their rich components and configuration space.In this work,we develop a Bayesian neural network(BNN)based on energies calculated with density functional theory to search the configuration space of the CoNiRhRu HEA system.The BNN model was developed by considering six independent features of Co-Ni,Co-Rh,CoRu,Ni-Rh,Ni-Ru,and Rh-Ru in different shells and energies of structures as the labels.The root mean squared error of the energy predicted by BNN is 1.37 me V/atom.Moreover,the influence of feature periodicity on the energy of HEA in theoretical calculations is discussed.We found that when the neural network is optimized to a certain extent,only using the accuracy indicator of root mean square error to evaluate model performance is no longer accurate in some scenarios.More importantly,we reveal the importance of uncertainty quantification for neural networks to predict new structures of HEAs with proper confidence based on BNN.

Corrosion Fatigue Life Prediction of Aircraft Structure Based on Fuzzy Reliability Approach

Material performance of LY12CZ aluminum is greatly degraded because of corrosion and corrosion fatigue, which severely affect the integrity and safety of aircraft structure, especially those of lbe navy aircraft structure. The corrosion and corrosion fatigue failure process of aircraft structure are directly concerned with many factors, such as load, material characteristics, corrosive environment and so on. The damage mechanism is very complicated, and there are both randomness and fuzziness in the failure process. With consideration of the limitation of those conventional probabilistic approaches for prediction of corrosion fatigue life of aircraft structure at present, and based on the operational load spectrum obtained through investigating service status of the aircraft in naval aviation force, a fuzzy reliability approach is proposed, which is more reasonable and closer to the fact. The effects of the pit aspect ratio, the crack aspect ratio and all fuzzy factors on corrosion fatigue life of aircraft structure are discussed. The results demonstrate that the approach can be applied to predict the corrosion fatigue life of aircraft structure.

The CALYPSO methodology for structure prediction

Structure prediction methods have been widely used as a state-of-the-art tool for structure searches and materials discovery, leading to many theory-driven breakthroughs on discoveries of new materials. These methods generally involve the exploration of the potential energy surfaces of materials through various structure sampling techniques and optimization algorithms in conjunction with quantum mechanical calculations. By taking advantage of the general feature of materials potential energy surface and swarm-intelligence-based global optimization algorithms, we have developed the CALYPSO method for structure prediction, which has been widely used in fields as diverse as computational physics, chemistry, and materials science. In this review, we provide the basic theory of the CALYPSO method, placing particular emphasis on the principles of its various structure dealing methods. We also survey the current challenges faced by structure prediction methods and include an outlook on the future developments of CALYPSO in the conclusions.

The Analysis of Seismic Data Structure and Oil and Gas Prediction

In this paper, a new concept called numerical structure of seismic data is introduced and the difference between numerical structure and numerical value of seismic data is explained. Our study shows that the numerical seismic structure is closely related to oil and gas-bearing reservoir, so it is very useful for a geologist or a geophysicist to precisely interpret the oil-bearing layers from the seismic data. This technology can be applied to any exploration or production stage. The new method has been tested on a series of exploratory or development wells and proved to be reliable in China. Hydrocarbon-detection with this new method for 39 exploration wells on 25 structures indi- cates a success ratio of over 80 percent. The new method of hydrocarbon prediction can be applied for: (1) depositional environment of reservoirs with marine fades, delta, or non-marine fades (including fluvial facies, lacustrine fades); (2) sedimentary rocks of reservoirs that are non-marine clastic rocks and carbonate rock; and (3) burial depths range from 300 m to 7000 m, and the minimum thickness of these reservoirs is over 8 m (main frequency is about 50 Hz).

Mem Brain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments,accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called Mem Brain, whose input is the amino acid sequence. Mem Brain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of a-helical membrane proteins. Mem Brain achieves aprediction accuracy of 97.9% of ATMH, 87.1% of AP,3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. Mem BrainContact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction,respectively. And Mem Brain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of13.593. These prediction results provide valuable hints for revealing the structure and function of membrane proteins.Mem Brain web server is free for academic use and available at www.csbio.sjtu.edu.cn/bioinf/Mem Brain/.

Variable structure control with sliding mode prediction for discrete-time nonlinear systems

A new variable structure control algorithm based on sliding mode prediction for a class of discrete-time nonlinear systems is presented. By employing a special model to predict future sliding mode value, and combining feedback correction and receding horizon optimization methods which are extensively applied on predictive control strategy, a discrete-time variable structure control law is constructed. The closed-loop systems are proved to have robustness to uncertainties with unspecified boundaries. Numerical simulation and pendulum experiment results illustrate that the closed-loop systems possess desired performance, such as strong robustness, fast convergence and chattering elimination.

Cluster structure prediction via CALYPSO method

Cluster science as a bridge linking atomic molecular physics and condensed matter inspired the nanomaterials development in the past decades, ranging from the single-atom catalysis to ligand-protected noble metal clusters. The corresponding studies not only have been restricted to the search for the geometrical structures of clusters, but also have promoted the development of cluster-assembled materials as the building blocks. The CALYPSO cluster prediction method combined with other computational techniques have significantly stimulated the development of the cluster-based nanomaterials. In this review, we will summarize some good cases of cluster structure by CALYPSO method, which have also been successfully identified by the photoelectron spectra experiments. Beginning with the alkali-metal clusters, which serve as benchmarks, a series of studies are performed on the size-dependent elemental clusters which possess relatively high stability and interesting chemical physical properties. Special attentions are paid to the boron-based clusters because of their promising applications. The NbSi12 and BeB16 clusters, for example, are two classic representatives of the silicon-and boron-based clusters, which can be viewed as building blocks of nanotubes and borophene. This review offers a detailed description of the structural evolutions and electronic properties of medium-sized pure and doped clusters, which will advance fundamental knowledge of cluster-based nanomaterials and provide valuable information for further theoretical and experimental studies.

RNA structure prediction:Progress and perspective

Many recent exciting discoveries have revealed the versatility of RNAs and their importance in a variety of cellular functions which are strongly coupled to RNA structures. To understand the functions of RNAs, some structure prediction models have been developed in recent years. In this review, the progress in computational models for RNA structure prediction is introduced and the distinguishing features of many outstanding algorithms are discussed, emphasizing three- dimensional （3D） structure prediction. A promising coarse-grained model for predicting RNA 3D structure, stability and salt effect is also introduced briefly. Finally, we discuss the major challenges in the RNA 3D structure modeling.

Prediction of Compressive and Shear Moduli of X-cor Sandwich Structures for Aeronautic Engineering

The so-called″X-cor sandwich structure″is a highly promising novel material as an alternative to honeycomb used in aircraft.Although much work has been conducted on the performance of the X-cor sandwich structure,the gap is still hardly bridged between experimental results and theoretical analyses.Therefore,a method has been innovated to establish semi-empirical formula for the prediction of compressive and shear moduli of X-cor sandwich structure composites,by combining theoretical analyses and experimental data.In addition,aprediction software was first developed based on the proposed method,of which the accuracy was verified through confirmatory experiments.This software can offer a direct reference or guide for engineers in structural designing.

THE STRUCTURE OF TROPICAL CYCLONE BY TOVS AND ITS APPLICATION IN NUMERICAL WEATHER PREDICTION

The TOVS data are used to study the structure of a number of tropical cyclones for the year 2000. Differences are found to some extent between what is found and classic conceptual models in that (1) the horizontal structure is asymmetric and variable so that the low-value centers at low levels of the geopotential height field (or the high-value centers at high levels) do not necessarily coincide with the high-value centers of the temperature field; (2) the vertical structure is also variable in the allocation of the anomalies of the geopotential height field between low values at low levels and high values at high levels. It is especially noted that the centers of the anomalies are tilting at both high and low levels or the high level is only at the edge of a high-pressure zone. There is not any significant high-value anomalous center in a corresponding location with the tropical cyclone. The structure of tropical cyclone in the TOVS is also used as reference to modify the structure of typhoon BOGUS in the numerical prediction model system of tropical cyclones. It is found that the modified BOGUS performs better in coordinating with the environment and predicting the track of the tropical cyclone. The demonstration is two-fold the structure of the typhoon BOGUS is such that it means much in the track prediction and the use of the TOVS-based tropical cyclone structure really helps in improving it. It provides the foundation for modification and evolution of typhoon BOGUS.

Typhoon Track,Intensity,and Structure:From Theory to Prediction

To improve understanding of essential aspects that influence forecasting of tropical cyclones(TCs),the National Key Research and Development Program,Ministry of Science and Technology of the People's Republic of China conducted a five-year project titled“Key Dynamic and Thermodynamic Processes and Prediction for the Evolution of Typhoon Intensity and Structure”(KPPT).Through this project,new understandings of TC intensification,including outer rainbanddriven secondary eyewall formation and the roles of boundary layer dynamics and vertical wind shear,and improvements to TC data assimilation with integrated algorithms and adaptive localizations are achieved.To promote a breakthrough in TC intensity and structure forecasting,a new paradigm for TC evolution dynamics(i.e.,the correlations,interactions,and error propagation among the triangle of TC track,intensity,and structure)is proposed;and an era of dynamic-constrained,big-data driven,and strongly coupled data assimilation at the subkilometer scale and seamless prediction is expected.

Neural Network Based on GA-BP Algorithm and its Application in the Protein Secondary Structure Prediction

The advantages and disadvantages of genetic algorithm and BP algorithm are introduced. A neural network based on GA-BP algorithm is proposed and applied in the prediction of protein secondary structure, which combines the advantages of BP and GA. The prediction and training on the neural network are made respectively based on 4 structure classifications of protein so as to get higher rate of predication---the highest prediction rate 75.65%,the average prediction rate 65.04%.