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.

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.

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.

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.

Computational prediction of RNA tertiary structures using machine learning methods

RNAs play crucial and versatile roles in biological processes. Computational prediction approaches can help to understand RNA structures and their stabilizing factors, thus providing information on their functions, and facilitating the design of new RNAs. Machine learning (ML) techniques have made tremendous progress in many fields in the past few years. Although their usage in protein-related fields has a long history, the use of ML methods in predicting RNA tertiary structures is new and rare. Here, we review the recent advances of using ML methods on RNA structure predictions and discuss the advantages and limitation, the difficulties and potentials of these approaches when applied in the field.

Geoscience material structures prediction via CALYPSO methodology

Many properties of planets such as their interior structure and thermal evolution depend on the high-pressure properties of their constituent materials. This paper reviews how crystal structure prediction methodology can help shed light on the transformations materials undergo at the extreme conditions inside planets. The discussion focuses on three areas:(i) the propensity of iron to form compounds with volatile elements at planetary core conditions(important to understand the chemical makeup of Earth's inner core),(ii) the chemistry of mixtures of planetary ices(relevant for the mantle regions of giant icy planets), and(iii) examples of mantle minerals. In all cases the abilities and current limitations of crystal structure prediction are discussed across a range of example studies.

FATIGUE DAMAGE AND LIFETIME PREDICTION OF AERONAUTIC WELDED STRUCTURES UNDER HIGH TEMPERATURE

The fatigue damage evolution equations and the relation of fatigue damage parameter with maximum cyclic stress of superalloy GH150 and its welded structures are established. The fatigue damage evolution equations in a multiaxial stress state are also given. By use of cyclic thermal elastoplastic damage constitutive relations, the fatigue damage and lifetime predictions are carried out for the welded combustion chamber of aeroengine.

DFT Predictions on Structures and Stabilities of Eleven-vertex nido- and closo-Heteroboranes

Based on the octadecahedron of eleven-vertex closo-borane, the eleven-vertex closo-heteroborane was suggested with nonmetallic atoms instead of the different nonequivalent boron, and the stabilities were predicted at G96PW91/6-31＋G（3d,2p） level. The small heteroatoms, C, N, O, preferentially occupy vertex 2 with the absolutely lowest relative energy to form the high stabilization closo-heteroboranes. They cap four-membered rings to satisfy the geometrical demand of short B--Z bonds. The electron attractions from the vicinal boron atoms make the frameworks shrink. Differently, Si and Ge preferentially substitute for boron at vertex 1 with six tight B--Z bonds and form stabilized molecules. P, As, S, and Se tend to occupy vertex 4 and the optimized structures belong to the nido configura- tions. In contrast to high electronegative heteroatoms, S and Se transfer less negative charges to framework and the electropositive heteroatoms, Si and Ge transfer more negative charges to framework to form the delocalization structures. The HOMO-LUMO gaps show that most of predicted clusters possess chemical stabilities. The substitutions of heteroatoms for boron atoms in eleven-vertex closo-heteroboranes are consistent with the topological charge stabilization rule proposed by Gimarc.

Ab Initio Theoretical Prediction on Structures of Boron Cationic Cluster B_(17)^+

Four isomers of the three-dimensionally connected bare boron cationic cluster B were investigated by using ab initio molecular orbital theory at the HF/6-31G level. The results show that the D5h symmetric isomer of B is a possible isomer candidate of its stable geometries with closed structure.

Study on the Uncertainty of Earthquake Damage Prediction Based on Damage Indices for RC Structures

Seismic damage indices of structure are widely used to quantificationally analyze structural damage levels under earthquake action. In this paper, a five-storey building model and a seventeen-storey building model are established. According to seven typical indices and different earthquake-inputs, a structural damage prediction is performed, with the results showing serious uncertainty of structural damage prediction due to different indices. Understanding of this phenomenon aids the comprehension and application of the results of earthquake damage prediction.

Multiple-Step Predictive Control for Offshore Structures

Ocean wave propagation is slow, visible and measurable, so a wave theory can be used to approximately predict the imminnent wave force on an offshore structure based on measured, real-time wave elevation near the structure. This predictability suggests the development of a more efficient algorithm, than those that have been developed for structures under wind and seismic loads, for the active vibration control of offshore structures. The present study delveops a mutiple-step predictive optimal control (MPOC) algorithm that accounts for multiple step external loading in the determination of optimal control forces. The control efficiency of the newly developed MPOC algorithm has been Investigated under both regular (single-frequency) and irregular (multiple-frequency) wave loads, and compared with that of two other well-known optimal control algorithms: classical linear optimal control(CLOC) and instantaneous optimal control(IOC).

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.

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.

Sequence-Based Protein Crystallization Propensity Prediction for Structural Genomics: Review and Comparative Analysis

Structural genomics (SG) is an international effort that aims at solving three-dimensional shapes of important biological macro-molecules with primary focus on proteins. One of the main bottlenecks in SG is the ability to produce dif-fraction quality crystals for X-ray crystallogra-phy based protein structure determination. SG pipelines allow for certain flexibility in target selection which motivates development of in- silico methods for sequence-based prediction/ assessment of the protein crystallization pro-pensity. We overview existing SG databanks that are used to derive these predictive models and we discuss analytical results concerning protein sequence properties that were discov-ered to correlate with the ability to form crystals. We also contrast and empirically compare mo- dern sequence-based predictors of crystalliza-tion propensity including OB-Score, ParCrys, XtalPred and CRYSTALP2. Our analysis shows that these methods provide useful and compli-mentary predictions. Although their average ac- curacy is similar at around 70%, we show that application of a simple majority-vote based en-semble improves accuracy to almost 74%. The best improvements are achieved by combining XtalPred with CRYSTALP2 while OB-Score and ParCrys methods overlap to a larger extend, although they still complement the other two predictors. We also demonstrate that 90% of the protein chains can be correctly predicted by at least one of these methods, which suggests that more accurate ensembles could be built in the future. We believe that current protein crystalli-zation propensity predictors could provide useful input for the target selection procedures utilized by the SG centers.

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.

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%.