Impaired implicit emotion regulation in patients with panic disorder:An event-related potential study on affect labeling

BACKGROUND Panic disorder(PD)involves emotion dysregulation,but its underlying mechanisms remain poorly understood.Previous research suggests that implicit emotion regulation may play a central role in PD-related emotion dysregulation and symptom maintenance.However,there is a lack of studies exploring the neural mechanisms of implicit emotion regulation in PD using neurophysiological indicators.AIM To study the neural mechanisms of implicit emotion regulation in PD with eventrelated potentials(ERP).METHODS A total of 25 PD patients and 20 healthy controls(HC)underwent clinical evaluations.The study utilized a case-control design with random sampling,selecting participants for the case group from March to December 2018.Participants performed an affect labeling task,using affect labeling as the experimental condition and gender labeling as the control condition.ERP and behavioral data were recorded to compare the late positive potential(LPP)within and between the groups.RESULTS Both PD and HC groups showed longer reaction times and decreased accuracy under the affect labeling.In the HC group,late LPP amplitudes exhibited a dynamic pattern of initial increase followed by decrease.Importantly,a significant group×condition interaction effect was observed.Simple effect analysis revealed a reduction in the differences of late LPP amplitudes between the affect labeling and gender labeling conditions in the PD group compared to the HC group.Furthermore,among PD patients under the affect labeling,the late LPP was negatively correlated with disease severity,symptom frequency,and intensity.CONCLUSION PD patients demonstrate abnormalities in implicit emotion regulation,hampering their ability to mobilize cognitive resources for downregulating negative emotions.The late LPP amplitude in response to affect labeling may serve as a potentially valuable clinical indicator of PD severity.

Analysis of The Use of Implicit Nursing Knowledge in The Teaching of Cardiovascular Internal Medicine Nursing

Objective: To explore the utilization of implicit nursing knowledge in the teaching of cardiovascular internal medicine nursing and to provide a reference for improving the quality and efficiency of cardiovascular internal medicine nursing work. Methods: Thirty-six trainee nurses working in the cardiovascular internal medicine department of our hospital from September 2022 to September 2023 were selected and randomly divided into a control group and an observation group of 18 trainees each. The control adopted the traditional teaching methods while the observation group adopted the implicit nursing knowledge in their clinical practice work. The assessment scores and teamwork ability of the two groups were analyzed and compared. Results: The performance of the observation group was better than that of the control group, and the difference between the two groups was statistically significant (P < 0.05). The teamwork ability of the observation group was significantly better than that of the control group in teamwork ability (P < 0.05). Conclusion: Implicit nursing knowledge teaching is conducive to the cultivation of high-quality nursing talents and meets the development needs of hospitals. Therefore, the importance of implicit nursing knowledge should be strengthened in the teaching of cardiovascular internal medicine nursing and it should be comprehensively organized to improve the quality of nursing services.

An efficient method for train-track-substructure dynamic interaction analysis by implicit-explicit integration and multi-time-step solution

In this work,a method is put forward to obtain the dynamic solution efficiently and accurately for a large-scale train-track-substructure(TTS)system.It is called implicit-explicit integration and multi-time-step solution method(abbreviated as mI-nE-MTS method).The TTS system is divided into train-track subsystem and substruc-ture subsystem.Considering that the root cause of low effi-ciency of obtaining TTS solution lies in solving the alge-braic equation of the substructures,the high-efficient Zhai method,an explicit integration scheme,can be introduced to avoid matrix inversion process.The train-track system is solved by implicitly Park method.Moreover,it is known that the requirement of time step size differs for different sub-systems,integration methods and structural frequency response characteristics.A multi-time-step solution is pro-posed,in which time step size for the train-track subsystem and the substructure subsystem can be arbitrarily chosen once satisfying stability and precision demand,namely the time spent for m implicit integral steps is equal to n explicit integral steps,i.e.,mI=nE as mentioned above.The numeri-cal examples show the accuracy,efficiency,and engineering practicality of the proposed method.

Implicit Continuous User Authentication for Mobile Devices based on Deep Reinforcement Learning

The predominant method for smart phone accessing is confined to methods directing the authentication by means of Point-of-Entry that heavily depend on physiological biometrics like,fingerprint or face.Implicit continuous authentication initiating to be loftier to conventional authentication mechanisms by continuously confirming users’identities on continuing basis and mark the instant at which an illegitimate hacker grasps dominance of the session.However,divergent issues remain unaddressed.This research aims to investigate the power of Deep Reinforcement Learning technique to implicit continuous authentication for mobile devices using a method called,Gaussian Weighted Cauchy Kriging-based Continuous Czekanowski’s(GWCK-CC).First,a Gaussian Weighted Non-local Mean Filter Preprocessing model is applied for reducing the noise pre-sent in the raw input face images.Cauchy Kriging Regression function is employed to reduce the dimensionality.Finally,Continuous Czekanowski’s Clas-sification is utilized for proficient classification between the genuine user and attacker.By this way,the proposed GWCK-CC method achieves accurate authen-tication with minimum error rate and time.Experimental assessment of the pro-posed GWCK-CC method and existing methods are carried out with different factors by using UMDAA-02 Face Dataset.The results confirm that the proposed GWCK-CC method enhances authentication accuracy,by 9%,reduces the authen-tication time,and error rate by 44%,and 43%as compared to the existing methods.

Speeding-up direct implicit particle-in-cell simulations in bounded plasma by obtaining future electric field through explicitly propulsion of particles

The direct implicit particle-in-cell is a powerful kinetic method for researching plasma characteristics.However,it is time-consuming to obtain the future electromagnetic field in such a method since the field equations contain time-dependent matrix coefficients.In this work,we propose to explicitly push particles and obtain the future electromagnetic field based on the information about the particles in the future.The new method retains the form of implicit particle pusher,but the future field is obtained by solving the traditional explicit equation.Several numerical experiments,including the motion of charged particle in electromagnetic field,plasma sheath,and free diffusion of plasma into vacuum,are implemented to evaluate the performance of the method.The results demonstrate that the proposed method can suppress finite-grid-instability resulting from the coarse spatial resolution in electron Debye length through the strong damping of high-frequency plasma oscillation,while accurately describe low-frequency plasma phenomena,with the price of losing the numerical stability at large time-step.We believe that this work is helpful for people to research the bounded plasma by using particle-in-cell simulations.

Interactive Restoration of Three-Dimensional Implicit Surface with Irregular Parts

Implicit surface generation based on the interpolation of surface points is one of the well-known modeling methods in the area of computer graphics.Several methods for the implicit surface reconstruction from surface points have been proposed on the basis of radial basis functions,a weighted sum of local functions,splines,wavelets,and combinations of them.However,if the surface points contain errors or are sparsely distributed,irregular components,such as curvature-shaped redundant bulges and unexpectedly generated high-frequency components,are commonly seen.This paper presents a framework for restoring irregular components generated on and around surfaces.Users are assumed to specify local masks that cover irregular components and parameters that determine the degree of restoration.The algorithm in this paper removes the defects based on the user-specific masks and parameters.Experiments have shown that the proposed methods can effectively remove redundant protrusions and jaggy noise.

Grey Wolf-Based Method for an Implicit Authentication of Smartphone Users

Smartphones have now become an integral part of our everyday lives.User authentication on smartphones is often accomplished by mechanisms(like face unlock,pattern,or pin password)that authenticate the user’s identity.These technologies are simple,inexpensive,and fast for repeated logins.However,these technologies are still subject to assaults like smudge assaults and shoulder surfing.Users’touch behavior while using their cell phones might be used to authenticate them,which would solve the problem.The performance of the authentication process may be influenced by the attributes chosen(from these behaviors).The purpose of this study is to present an effective authentication technique that implicitly offers a better authentication method for smartphone usage while avoiding the cost of a particular device and considering the constrained capabilities of smartphones.We began by concentrating on feature selection methods utilizing the grey wolf optimization strategy.The random forest classifier is used to evaluate these tactics.The testing findings demonstrated that the grey wolf-based methodology works as a better optimum feature selection for building an implicit authentication mechanism for the smartphone environment when using a public dataset.It achieved a 97.89%accuracy rate while utilizing just 16 of the 53 characteristics like utilizing minimum mobile resources mainly;processing power of the device and memory to validate individuals.Simultaneously,the findings revealed that our approach has a lower equal error rate(EER)of 0.5104,a false acceptance rate(FAR)of 1.00,and a false rejection rate(FRR)of 0.0209 compared to the methods discussed in the literature.These promising results will be used to create a mobile application that enables implicit validation of authorized users yet avoids current identification concerns and requires fewer mobile resources.

A New Analysis of an Implicit Mimetic Scheme for the Heat Equation

A mimetic finite difference scheme for the transient heat equation under Robin’s conditions is presented. The scheme uses second order gradient and divergence mimetic operators, on a staggered grid, to approximate the space derivatives. The temporal derivative is replaced by a first order backward difference approximation to obtain an implicit formulation. The resulting scheme contains nonstandard finite difference stencils. An original convergence analysis by the matrix’s method shows that the proposed scheme is unconditionally stable. A comparative study against standard finite difference schemes, based on central difference or first order one side approximations, reveals the advantages of our scheme without being its implementation more expensive or difficult to achieve.

Solving Arithmetic Word Problems of Entailing Deep Implicit Relations by Qualia Syntax-Semantic Model

Solving arithmetic word problems that entail deep implicit relations is still a challenging problem.However,significant progress has been made in solving Arithmetic Word Problems(AWP)over the past six decades.This paper proposes to discover deep implicit relations by qualia inference to solve Arithmetic Word Problems entailing Deep Implicit Relations(DIR-AWP),such as entailing commonsense or subject-domain knowledge involved in the problem-solving process.This paper proposes to take three steps to solve DIR-AWPs,in which the first three steps are used to conduct the qualia inference process.The first step uses the prepared set of qualia-quantity models to identify qualia scenes from the explicit relations extracted by the Syntax-Semantic(S2)method from the given problem.The second step adds missing entities and deep implicit relations in order using the identified qualia scenes and the qualia-quantity models,respectively.The third step distills the relations for solving the given problem by pruning the spare branches of the qualia dependency graph of all the acquired relations.The research contributes to the field by presenting a comprehensive approach combining explicit and implicit knowledge to enhance reasoning abilities.The experimental results on Math23K demonstrate hat the proposed algorithm is superior to the baseline algorithms in solving AWPs requiring deep implicit relations.

Knowledge-enriched joint-learning model for implicit emotion cause extraction

Emotion cause extraction(ECE)task that aims at extracting potential trigger events of certain emotions has attracted extensive attention recently.However,current work neglects the implicit emotion expressed without any explicit emotional keywords,which appears more frequently in application scenarios.The lack of explicit emotion information makes it extremely hard to extract emotion causes only with the local context.Moreover,an entire event is usually across multiple clauses,while existing work merely extracts cause events at clause level and cannot effectively capture complete cause event information.To address these issues,the events are first redefined at the tuple level and a span-based tuple-level algorithm is proposed to extract events from different clauses.Based on it,a corpus for implicit emotion cause extraction that tries to extract causes of implicit emotions is constructed.The authors propose a knowledge-enriched jointlearning model of implicit emotion recognition and implicit emotion cause extraction tasks(KJ-IECE),which leverages commonsense knowledge from ConceptNet and NRC_VAD to better capture connections between emotion and corresponding cause events.Experiments on both implicit and explicit emotion cause extraction datasets demonstrate the effectiveness of the proposed model.

Quinpi:Integrating Conservation Laws with CWENO Implicit Methods

Many interesting applications of hyperbolic systems of equations are stiff,and require the time step to satisfy restrictive stability conditions.One way to avoid small time steps is to use implicit time integration.Implicit integration is quite straightforward for first-order schemes.High order schemes instead also need to control spurious oscillations,which requires limiting in space and time also in the linear case.We propose a framework to simplify considerably the application of high order non-oscillatory schemes through the introduction of a low order implicit predictor,which is used both to set up the nonlinear weights of a standard high order space reconstruction,and to achieve limiting in time.In this preliminary work,we concentrate on the case of a third-order scheme,based on diagonally implicit Runge Kutta(DIRK)integration in time and central weighted essentially non-oscillatory(CWENO)reconstruction in space.The numerical tests involve linear and nonlinear scalar conservation laws.

Deinterleaving of radar pulse based on implicit feature

In the complex countermeasure environment,the pulse description words(PDWs)of the same type of multi-function radar emitters are similar in multiple dimensions.Therefore,it is difficult for conventional methods to deinterleave such emitters.In order to solve this problem,a pulse deinterleaving method based on implicit features is proposed in this paper.The proposed method introduces long short-term memory(LSTM)neural networks and statistical analysis to mine new features from similar PDW features,that is,the variation law(implicit features)of pulse sequences of different radiation sources over time.The multi-function radar emitter is deinterleaved based on the pulse sequence variation law.Statistical results show that the proposed method not only achieves satisfactory performance,but also has good robustness.

A novel three-step implicit iteration process for threeΦ-hemicontractive mapping in the intermediate sense

In this paper,we introduce a three-step composite implicit iteration process for approximating the common fixed point of three uniformly continuous and asymptotically generalizedΦ-hemicontractive mappings in the intermediate sense.We prove that our proposed iteration process converges to the common fixed point of three finite family of asymptotically generalizedΦ-hemicontractive mappings in the intermediate sense.Our results extends,improves and complements several known results in literature.

内隐情绪信念与大学生心理幸福感:认知重评与心理韧性的链式中介效应

目的:探讨内隐情绪信念与大学生心理幸福感的关系,并考察认知重评和心理韧性在其中的作用机制。方法:采用内隐情绪信念问卷、情绪调节量表、心理韧性量表及心盛量表对1134名大学生进行问卷调查。结果:内隐情绪信念与认知重评、心理韧性及心理幸福感均呈显著正相关(r=0.443,0.502,0.464;P<0.01),认知重评、心理韧性与心理幸福感呈显著正相关(r=0.553,0.757;P<0.01)。认知重评、心理韧性分别在内隐情绪信念与心理幸福感间起部分中介作用;认知重评和心理韧性在内隐情绪信念与心理幸福感间起链式中介作用。结论:通过培养成长型内隐情绪信念,可以帮助个体主动使用认知重评等适应性调节策略来提高心理韧性,进而提升大学生的心理健康和幸福感水平。

融合尺度与信心指数的三维表面重建算法

针对包含细小结构场景的三维表面重建精度和完整度难以提高的问题,提出一种融合三维点云尺度与信心指数的三维表面重建算法。构建了一个表面隐函数,采用高斯函数为其基函数,三阶贝塞尔函数为其权重函数,并根据点云尺度与信心指数大小自适应调整基函数覆盖范围与权重函数取值。采用八叉树存储与处理数据,根据点云尺度大小自适应划分八叉树结构,从而自适应调整表面隐函数三维采样密度。通过无约束等值面提取方法,提取隐函数等值面,从而得到三维表面。与几种典型表面重建算法对比实验,结果表明所提算法重建三维表面细节清晰,整体重建精度与完整度均有提高。

1225例新型冠状病毒感染老年患者证候规律研究

目的探索老年群体新型冠状病毒感染常见证候分类及特征,为临床辨治提供参考。方法采集四川省内6家三甲医院的新型冠状病毒感染的老年患者的四诊信息,构建数据库。运用隐结构法的Lantern 5.0软件,基于双步隐树分析,构建隐结构模型,通过综合聚类提取新型冠状病毒感染常见证候。采用SPSS 23.0软件进行因子分析和系统聚类分析,推断潜在证候。结合不同方法结果和专业知识确定新型冠状病毒感染证候分类。结果共纳入1225例新型冠状病毒感染患者,共收集103个症状。纳入分析频次≥40的59个症状构建隐结构模型,得到15个隐变量,综合聚类得到4种常见证候,分别为邪袭肺卫证、邪热蕴肺证、湿邪困脾证、阴虚夹湿证。得到19个公因子,用公因子进行系统聚类分析,推断出4个常见证候。结合不同方法和专业知识最终确定4种常见证候及特征。结论四川地区老年群体新型冠状病毒感染常见证候为邪袭肺卫证、邪热蕴肺证、湿邪困脾证、阴虚夹湿证。研究结果较客观地反映了四川地区老年患者新型冠状病毒感染的实际情况,可为四川地区老年患者新型冠状病毒感染辨证治疗提供参考。

基于隐式参数化模型的地铁车体轻量化设计

为实现地铁新车型前期快速的概念设计和性能评估,引用“分析驱动设计”理念对重庆6号线地铁的MP车体进行了隐式参数化建模;并录入车体的17个主要钣金件进行设计分析。以板厚为设计变量,筛选出模态和刚度对质量相对灵敏度较大的7个车体钣金件,进行基于响应面法的多目标轻量化优化设计;车体的刚度、模态等多项性能指标达到了良好的轻量化效果。研究结果表明:所引入“分析驱动设计”理念的车体隐式参数化建模及基于响应面法的多目标优化方法能应用于车体结构的轻量化设计,具有较好的工程指导意义。

基于在线同伴互评的内隐行为规律挖掘及其影响机理研究

学习行为规律能够有效预测学业成绩,是开展个性化教育的关键,但大部分研究成果以探究学生的外显学习行为为主,对学生内隐行为的分析与挖掘较少。同伴互评是表征学习内隐行为的重要方式,文章以其产生的评语和反馈为分析对象,采用定量内容分析、滞后序列分析法和社会网络分析法,从情感、认知和元认知角度分析高、低成绩学生的内隐行为,并引入系统动力学方法剖析基于在线同伴互评的内隐行为影响机理。依据同伴互评中内隐行为所表现出的情感、认知和元认知差异,洞悉学习过程数据背后隐藏的学习成长轨迹,促进学生自主学习,支持教师及时、精准、有效地进行在线学业预警。

人工智能视域下大学英语教学中国家意识的隐性教育研究

人工智能视域下大学英语教学中国家意识的隐性教育,对于树立大学生远大理想与坚定信念有着促进作用,有效提升大学生历史责任感、社会使命感与公民荣誉感。高校应基于英语课程教学实践需求,针对英语课程国家意识培养方式滞后、隐性教育实践能力不足问题进行深入探究,并结合新时期青年大学生教育培养新需求,制定科学教育实践策略,通过创新教育培养新方法、深化教育实践引领等举措,逐步完善大学英语课程育人培养体系,实现人工智能视域下大学英语教学中国家意识隐性教育高质量推进。

考虑小应变刚度特性的软土边界面模型与应用

基坑开挖导致周边土体经历复杂的加卸载应力路径,传统的本构模型难以同时反映此过程中土体的超固结和小应变刚度特性。在现有的边界面模型中,引入土体小应变刚度特性,提出软土边界面模型小应变刚度修正方法。基于一种高效的半隐式应力更新算法,将改进后的边界面模型采用UMAT子程序二次开发嵌入有限元软件ABAQUS中,并应用于某地铁车站深基坑开挖工程的数值模拟。分析结果表明,采用改进模型准确地反映了上海地区典型土层的小应变状态本构关系和土体的应变刚度相关性;由于改进的边界面模型能够同时反映土体超固结和小应变刚度特性,计算的围护结构变形与现场监测数据吻合较好。