高温下HMX热分解反应分子动力学模拟

本文采用ReaxFF-lg反应分子动力学方法研究了奥克托金(HMX)在六种不同温度下的初始反应热分解过程,验证了ReaxFF-lg势函数对HMX体系的适用性,计算了不同温度下HMX体系势能、总物种演化趋势、初始反应产物以及指前因子和活化能.结果表明,HMX热分解过程主要有三种初始分解机理:N-NO_(2)键的断裂,HONO的解离和主环上C-N键的断裂,计算得到的初始分解阶段活化能Ea和指前因子ln(A),与实验值相吻合.

面向重大突发事件的高校智库动态知识服务策略

面向社会重大突发事件的智库应对需要高效的动态知识服务体系做支撑。根据动态能力理论,知识协同、知识生产和知识流构成了动态知识服务体系三要素,动态能力通过知识协同要素、知识生产环节和知识流转效率来表现动态知识服务能力。动态知识服务体系中的知识协同要素、知识生产环节和知识流转效率与智库服务重大突发事件的知识需求还不相适应。为了提高应对突发事件的智库动态知识服务水平,要在知识协同要素的有序化、知识生产环节匹配的精准化和知识流治理的高效化等方面提升服务策略。

秦岭山脉南北麓暴雨触发条件对比分析

采用实况气象观测资料、FY-2G卫星云图资料、多普勒雷达资料和ERA50.25°×0.25°逐小时再分析资料,对2021年8月21-22日同时发生在秦岭南北麓的强等级暴雨过程进行对比分析,探讨秦岭南北麓暴雨触发条件的影响机制。结果表明:秦岭北麓流场上,西风带系统在秦岭特殊地形作用下,对流层低层形成中尺度气旋性环流,通过热力作用触发暴雨,对流性降水持续时间短,强度小;湿斜压性增强是秦岭北麓暴雨开始的一个信号,当湿斜压性减弱以及中层比湿减小时,降水结束。对秦岭南麓而言,地形作用下对流层低层流场形成中尺度辐合线触发暴雨;降水释放的凝结潜热加热低层大气,与中低层入侵的冷空气共同构建对流不稳定结构,上升运动增强,降水增强和持续;对流云团在高温高湿的环境下迅速组织化合并发展,形成中尺度对流复合体(MCC),对流强度大,对流层结深厚,小时雨强大;中低层冷空气的入侵和扩散到地面的时间分别与秦岭南麓强降水的开始和结束时间对应。

三七治疗骨关节炎机制:基于UHPLC-QE-MS、网络药理学及分子动力学模拟

背景:课题组前期研究发现三七能够修复骨细胞的形态结构,对于治疗骨关节炎具有良好的应用前景,但目前对于三七的具体作用机制尚不清楚。目的:采用超高效液相色谱-四极杆-静电场轨道阱串联质谱(ultra-high performance liquid chromatography-Q exactive-mass spectrometry,UHPLC-QE-MS)技术鉴定三七的主要成分,并结合网络药理学、分子对接和分子动力学模拟探究三七治疗骨关节炎的作用机制。方法:利用UHPLC-QE-MS技术鉴定三七的主要成分后,运用TCMSP数据库筛选活性成分,通过TCMSP和Uniprot数据库查找活性成分靶点,通过疾病数据库筛选骨关节炎靶点。在药物靶点与疾病靶点取交集后,导入STRING数据库和Cytoscape软件构建蛋白互作网络筛选关键靶点,通过“活性成分-作用靶点”网络筛选关键活性成分。再对关键靶点进行富集分析,并对关键活性成分和关键靶点进行分子对接验证,最后选取结合能最低的结果进行分子动力学模拟。结果与结论:①在三七溶液中共鉴定出57种活性成分,成分靶点与疾病靶点交集50个,关键活性成分5个(槲皮素、熊脱氧胆酸、山奈酚、柚皮素和红藻氨酸),关键靶点5个(白细胞介素6、基质金属蛋白酶9、白细胞介素1β、白蛋白和趋化因子配体2);②基因本体功能富集642个条目,其中620个条目代表生物过程,21个条目代表分子功能,1个条目代表细胞成分;京都基因与基因组百科全书通路分析63条通路,主要涉及雌激素信号通路、白细胞介素17信号通路和高糖基化终末产物-高糖基化终末产物受体信号通路;③分子对接显示关键活性成分和关键靶点结合活性良好,分子动力学模拟提示槲皮素和基质金属蛋白酶9间的相互作用稳定;④对三七成分进行了较全面研究,初步阐明了其药效物质基础,预测三七可通过多组分、多靶点、多途径和多通路发挥抗炎、软骨保护和免疫调节作用来治疗骨关节炎。

矿山采空区边坡动态稳定性评价方法

矿山边坡因采空区沉降引发的斜面岩土体崩塌破坏,是一种破坏力极强的地质灾害.由于传统的稳定性评价方法未考虑采空区沉降造成的应力场变异,难以实现矿山边坡的动态稳定性评价,因而在矿山地质灾害预警预防方面存在诸多限制.基于此,本研究针对矿山采空区滑坡的成因机制,建立了一套适用于矿山边坡的动态稳定性评价方法.首先通过构建采空区沉降分析模型,计算采空区上方岩体的沉降范围及其产生的冲击作用力,分析沉降对坡体应力场的影响,并在此基础上对传统不平衡推力法进行修正.案例分析结果显示,传统方法未考虑采空区沉降作用和锁固段的破坏情况,导致稳定性系数计算偏大,为1.355.而改进方法通过考虑采空区沉降信息,计算得出锁固段稳定性系数为0.667,整体稳定性系数为0.979,与矿山采空区边坡实际破坏情况一致.当沉降位移比(SHDR)大于0.73时,采空区边坡稳定性会发生明显变异,因而会在相对安全的工况下发生失稳破坏.改进方法通过考虑采空区沉降作用和锁固段的破坏情况,可以更好地实现矿山边坡稳定性的评价,为矿区更好地应对类似地质灾害提供有效参考.

中国儿童道德情感发展的动态演变及生态系统模型建构——基于三轮大样本实证调查

道德情感是品德现实化的动力因素,是考察儿童知行统一性的关键点。基于2016、2019、2022年中国儿童道德发展的大样本整体动态数据(77953,77367,79914),采用回归分析、夏普里值分解发现中国儿童道德情感的发展:(1)类型不均衡,同情心表现需要关注;(2)水平随年龄的增高呈现先增高后降低的发展趋势,10岁为儿童道德情感发展的关键点;(3)存在性别和城乡区域的结构差异;(4)按因素影响强度呈现独特的生态系统逻辑,从高到低排列为“个体-学校-社会支持-家庭”;生活满意度、了解儿童的人、学校和家庭心理环境是高影响因素;不同因素对不同性别和学段儿童的道德情感发展影响存在差异。由上发现提出,中国儿童道德情感培育要重视道德情感类型的发展失衡、群体发展的差异和关键影响因素,优化儿童道德情感发展的生态系统,抓住发展的关键期,进一步明确教育的侧重点,提升针对性和时效性。

欧洲规范铁路桥梁动力分析竖向荷载取值研究

为有助于推动海外工程顺利实施,解决海外工程结构设计中的部分问题,开展国外标准规范系统研究是十分必要的,进而吸收与借鉴国际标准的先进经验,同时增强国际竞争力。针对铁路桥梁动力分析中欧洲规范竖向荷载的相关规定进行了详细研究,探讨分析了其规定差异性,提出了对国际工程设计咨询具有指导价值的结论。有助于解决涉外工程结构分析中荷载计算问题,有利于我国结构设计行业对外交流,为进一步推动海外工程的顺利实施奠定基础。

A Novel Model for Describing Rail Weld Irregularities and Predicting Wheel-Rail Forces Using a Machine Learning Approach

Rail weld irregularities are one of the primary excitation sources for vehicle-track interaction dynamics in modern high-speed railways.They can cause significant wheel-rail dynamic interactions,leading to wheel-rail noise,component damage,and deterioration.Few researchers have employed the vehicle-track interaction dynamic model to study the dynamic interactions between wheel and rail induced by rail weld geometry irregularities.However,the cosine wave model used to simulate rail weld irregularities mainly focuses on the maximum value and neglects the geometric shape.In this study,novel theoretical models were developed for three categories of rail weld irregularities,based on measurements of the high-speed railway from Beijing to Shanghai.The vertical dynamic forces in the time and frequency domains were compared under different running speeds.These forces generated by the rail weld irregularities that were measured and modeled,respectively,were compared to validate the accuracy of the proposed model.Finally,based on the numerical study,the impact force due to rail weld irrregularity is modeled using an Artificial Neural Network(ANN),and the optimum combination of parameters for this model is found.The results showed that the proposed model provided a more accurate wheel/rail dynamic evaluation caused by rail weld irregularities than that established in the literature.The ANN model used in this paper can effectively predict the impact force due to rail weld irrregularity while reducing the computation time.

A Rapid Adaptation Approach for Dynamic Air‑Writing Recognition Using Wearable Wristbands with Self‑Supervised Contrastive Learning

Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios.It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance output.Through wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist movements.Remarkably,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various tasks.The system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and letters.The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training.Its utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.

Efficient and Stable Perovskite Solar Cells and Modules Enabled by Tailoring Additive Distribution According to the Film Growth Dynamics

Gas quenching and vacuum quenching process are widely applied to accelerate solvent volatilization to induce nucleation of perovskites in blade-coating method.In this work,we found these two pre-crystallization processes lead to different order of crystallization dynamics within the perovskite thin film,resulting in the differences of additive distribution.We then tailor-designed an additive molecule named 1,3-bis(4-methoxyphenyl)thiourea to obtain films with fewer defects and holes at the buried interface,and prepared perovskite solar cells with a certified efficiency of 23.75%.Furthermore,this work also demonstrates an efficiency of 20.18%for the large-area perovskite solar module(PSM)with an aperture area of 60.84 cm^(2).The PSM possesses remarkable continuous operation stability for maximum power point tracking of T_(90)>1000 h in ambient air.

Apples to oranges:environmentally derived,dynamic regulation of serotonin neuron subpopulations in adulthood?

Traumatic brain injury(TBI)is a public health problem with an undue economic burden that impacts nearly every age,ethnic,and gender group across the globe(Capizzi et al.,2020).TBIs are often sustained during a dynamic range of exposures to energetic environmental forces and as such outcomes are typically heterogeneous regarding severity and pathology(Capizzi et al.,2020).

Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok:An Application of a Continuous Convolutional Neural Network

The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.

Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

Prediction of stability in SG(Smart Grid)is essential in maintaining consistency and reliability of power supply in grid infrastructure.Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid.It also possesses a better impact on averting overloading and permitting effective energy storage.Even though many traditional techniques have predicted the consumption rate for preserving stability,enhancement is required in prediction measures with minimized loss.To overcome the complications in existing studies,this paper intends to predict stability from the smart grid stability prediction dataset using machine learning algorithms.To accomplish this,pre-processing is performed initially to handle missing values since it develops biased models when missing values are mishandled and performs feature scaling to normalize independent data features.Then,the pre-processed data are taken for training and testing.Following that,the regression process is performed using Modified PSO(Particle Swarm Optimization)optimized XGBoost Technique with dynamic inertia weight update,which analyses variables like gamma(G),reaction time(tau1–tau4),and power balance(p1–p4)for providing effective future stability in SG.Since PSO attains optimal solution by adjusting position through dynamic inertial weights,it is integrated with XGBoost due to its scalability and faster computational speed characteristics.The hyperparameters of XGBoost are fine-tuned in the training process for achieving promising outcomes on prediction.Regression results are measured through evaluation metrics such as MSE(Mean Square Error)of 0.011312781,MAE(Mean Absolute Error)of 0.008596322,and RMSE(Root Mean Square Error)of 0.010636156 and MAPE(Mean Absolute Percentage Error)value of 0.0052 which determine the efficacy of the system.

Emerging structures and dynamic mechanisms ofγ-secretase for Alzheimer’s disease

γ-Secretase,called“the proteasome of the membrane,”is a membrane-embedded protease complex that cleaves 150+peptide substrates with central roles in biology and medicine,including amyloid precursor protein and the Notch family of cell-surface receptors.Mutations inγ-secretase and amyloid precursor protein lead to early-onset familial Alzheimer’s disease.γ-Secretase has thus served as a critical drug target for treating familial Alzheimer’s disease and the more common late-onset Alzheimer’s disease as well.However,critical gaps remain in understanding the mechanisms of processive proteolysis of substrates,the effects of familial Alzheimer’s disease mutations,and allosteric modulation of substrate cleavage byγ-secretase.In this review,we focus on recent studies of structural dynamic mechanisms ofγ-secretase.Different mechanisms,including the“Fit-Stay-Trim,”“Sliding-Unwinding,”and“Tilting-Unwinding,”have been proposed for substrate proteolysis of amyloid precursor protein byγ-secretase based on all-atom molecular dynamics simulations.While an incorrect registry of the Notch1 substrate was identified in the cryo-electron microscopy structure of Notch1-boundγ-secretase,molecular dynamics simulations on a resolved model of Notch1-boundγ-secretase that was reconstructed using the amyloid precursor protein-boundγ-secretase as a template successfully capturedγ-secretase activation for proper cleavages of both wildtype and mutant Notch,being consistent with biochemical experimental findings.The approach could be potentially applied to decipher the processing mechanisms of various substrates byγ-secretase.In addition,controversy over the effects of familial Alzheimer’s disease mutations,particularly the issue of whether they stabilize or destabilizeγ-secretase-substrate complexes,is discussed.Finally,an outlook is provided for future studies ofγ-secretase,including pathways of substrate binding and product release,effects of modulators on familial Alzheimer’s disease mutations of theγ-secretase-substrate complexes.Comprehensive understanding of the functional mechanisms ofγ-secretase will greatly facilitate the rational design of effective drug molecules for treating familial Alzheimer’s disease and perhaps Alzheimer’s disease in general.

Molecular simulation study of the microstructures and properties of pyridinium ionic liquid[HPy][BF_(4)]mixed with acetonitrile

The microstructures and thermodynamic properties of mixed systems comprising pyridinium ionic liquid[HPy][BF_(4)]and acetonitrile at different mole fractions were studied using molecular dynamics simulation in this work.The following properties were determined:density,self-diffusion coefficient,excess molar volume,and radial distribution function.The results show that with an increase in the mole fraction of[HPy][BF_(4)],the self-diffusion coefficient decreases.Additionally,the excess molar volume initially decreases,reaches a minimum,and then increases.The rules of radial distribution functions(RDFs)of characteristic atoms are different.With increasing the mole fraction of[HPy][BF_(4)],the first peak of the RDFs of HA1-F decreases,while that of CT6-CT6 rises at first and then decreases.This indicates that the solvent molecules affect the polar and non-polar regions of[HPy][BF_(4)]differently.

Dynamic Interaction Analysis of Coupled Axial-Torsional-Lateral Mechanical Vibrations in Rotary Drilling Systems

Maintaining the integrity and longevity of structures is essential in many industries,such as aerospace,nuclear,and petroleum.To achieve the cost-effectiveness of large-scale systems in petroleum drilling,a strong emphasis on structural durability and monitoring is required.This study focuses on the mechanical vibrations that occur in rotary drilling systems,which have a substantial impact on the structural integrity of drilling equipment.The study specifically investigates axial,torsional,and lateral vibrations,which might lead to negative consequences such as bit-bounce,chaotic whirling,and high-frequency stick-slip.These events not only hinder the efficiency of drilling but also lead to exhaustion and harm to the system’s components since they are difficult to be detected and controlled in real time.The study investigates the dynamic interactions of these vibrations,specifically in their high-frequency modes,usingfield data obtained from measurement while drilling.Thefindings have demonstrated the effect of strong coupling between the high-frequency modes of these vibrations on drilling sys-tem performance.The obtained results highlight the importance of considering the interconnected impacts of these vibrations when designing and implementing robust control systems.Therefore,integrating these compo-nents can increase the durability of drill bits and drill strings,as well as improve the ability to monitor and detect damage.Moreover,by exploiting thesefindings,the assessment of structural resilience in rotary drilling systems can be enhanced.Furthermore,the study demonstrates the capacity of structural health monitoring to improve the quality,dependability,and efficiency of rotary drilling systems in the petroleum industry.

Nomogram for overall survival in ampullary adenocarcinoma using the surveillance,epidemiology,and end results database and external validation

BACKGROUND Ampullary adenocarcinoma is a rare malignant tumor of the gastrointestinal tract.Currently,only a few cases have been reported,resulting in limited information on survival.AIM To develop a dynamic nomogram using internal and external validation to predict survival in patients with ampullary adenocarcinoma.METHODS Data were sourced from the surveillance,epidemiology,and end results stat database.The patients in the database were randomized in a 7:3 ratio into training and validation groups.Using Cox regression univariate and multivariate analyses in the training group,we identified independent risk factors for overall survival and cancer-specific survival to develop the nomogram.The nomogram was validated with a cohort of patients from the First Affiliated Hospital of the Army Medical University.RESULTS For overall and cancer-specific survival,12(sex,age,race,lymph node ratio,tumor size,chemotherapy,surgical modality,T stage,tumor differentiation,brain metastasis,lung metastasis,and extension)and 6(age;surveillance,epidemiology,and end results stage;lymph node ratio;chemotherapy;surgical modality;and tumor differentiation)independent risk factors,respectively,were incorporated into the nomogram.The area under the curve values at 1,3,and 5 years,respectively,were 0.807,0.842,and 0.826 for overall survival and 0.816,0.835,and 0.841 for cancer-specific survival.The internal and external validation cohorts indicated good consistency of the nomogram.CONCLUSION The dynamic nomogram offers robust predictive efficacy for the overall and cancer-specific survival of ampullary adenocarcinoma.

纳米尺度下水液滴撞击固体表面润湿性行为的分子动力学模拟

本文采用分子动力学方法研究纳米尺度下水液滴碰撞铜壁面的润湿过程,水滴温度和固液作用强度对润湿性行为的影响以及直径为4.67 nm球形液滴在273 K-353 K温区、固液作用强度在1-3范围内,水滴的润湿行为和平衡接触角与温度、固液作用强度之间的变化关系.研究结果显示:液滴在不同润湿性壁面上会产生明显不同的润湿演化特性.模拟结果表明对于亲水材料,由于壁面附近水分子所受的势能束缚随温度升高而增强,平衡接触角则相应减小;对于疏水材料,由于随温度的升高分子之间增加的势能大于固液之间增加的势能而表现出平衡接触角随着温度升高而增大的趋势.因此,针对其材料亲水性能,可以通过改变温度影响其润湿行为,从而提高传热、传质和自清洁性能.

基于图卷积神经网络的WSN零动态攻击检测方法

零动态攻击与一般攻击方式相比,隐蔽性更强,因此更不容易被发现。以往常规的检测方法在检测这种攻击方式时,漏检率和误检率较高。针对上述问题,研究一种基于图卷积神经网络的WSN零动态攻击检测方法。基于零动态攻击原理,以信道状态信息作为采集源,利用CSI-Tools工具实现CSI数据包采集。从CSI数据包中分离出幅值数据和相位数据,针对前者实施去噪处理,针对后者实施校准处理。从幅值数据和相位数据中提取4个特征,以特征为输入,构建图结构,利用图卷积神经网络实现无线传感网络零动态攻击检测。结果表明:基于图卷积神经网络的攻击检测方法的漏检率和误检率相对更低,由此说明该方法对零动态攻击检测更为有效,能够实现更为准确的检测。

多障碍环境下巡检机器人路径规划优化研究

针对大规模、密集的障碍物分布,高效地搜索最佳路径是一个挑战,为规划出更短的巡检路线,并实现多障碍环境下的灵活避障,文中提出一种多障碍环境下巡检机器人路径规划优化方法。使用二维矩阵构建巡检环境模型,应用D*算法在巡检环境模型中进行巡检机器人路径规划,并将传统D*算法中的扩展步长方式改变为自适应扩展步长,使机器人在面积较大的巡检场地能够更快地完成巡检;将代价函数由欧氏距离替换为切比雪夫诺距离和曼哈顿距离融合的代价函数,并引入了平滑度函数优化线路规划结果,使规划的路径更为平滑,在遇到由于多种原因产生的新障碍物时可以重新规划路径。通过实验结果可知,无论是静态地图还是动态地图,该方法均可以快速准确地规划出一条最佳路线,并且在多种环境中应用该方法能够高效获取路径规划结果。