Prospect Theory Based Individual Irrationality Modelling and Behavior Inducement in Pandemic Control

Understanding and modeling individuals’behaviors during epidemics is crucial for effective epidemic control.However,existing research ignores the impact of users’irrationality on decision-making in the epidemic.Meanwhile,existing disease control methods often assume users’full compliance with measures like mandatory isolation,which does not align with the actual situation.To address these issues,this paper proposes a prospect theorybased framework to model users’decision-making process in epidemics and analyzes how irrationality affects individuals’behaviors and epidemic dynamics.According to the analysis results,irrationality tends to prompt conservative behaviors when the infection risk is low but encourages risk-seeking behaviors when the risk is high.Then,this paper proposes a behavior inducement algorithm to guide individuals’behaviors and control the spread of disease.Simulations and real user tests validate our analysis,and simulation results show that the proposed behavior inducement algorithm can effectively guide individuals’behavior.

A function-based behavioral modeling method for air combat simulation

Today’s air combat has reached a high level of uncertainty where continuous or discrete variables with crisp values cannot be properly represented using fuzzy sets. With a set of membership functions, fuzzy logic is well-suited to tackle such complex states and actions. However, it is not necessary to fuzzify the variables that have definite discrete semantics.Hence, the aim of this study is to improve the level of model abstraction by proposing multiple levels of cascaded hierarchical structures from the perspective of function, namely, the functional decision tree. This method is developed to represent behavioral modeling of air combat systems, and its metamodel,execution mechanism, and code generation can provide a sound basis for function-based behavioral modeling. As a proof of concept, an air combat simulation is developed to validate this method and the results show that the fighter Alpha built using the proposed framework provides better performance than that using default scripts.

用于学习和解释行人预期行为的群体交互场

Anticipating others’actions is innate and essential in order for humans to navigate and interact well with others in dense crowds.This ability is urgently required for unmanned systems such as service robots and self-driving cars.However,existing solutions struggle to predict pedestrian anticipation accurately,because the influence of group-related social behaviors has not been well considered.While group relationships and group interactions are ubiquitous and significantly influence pedestrian anticipation,their influence is diverse and subtle,making it difficult to explicitly quantify.Here,we propose the group interaction field(GIF),a novel group-aware representation that quantifies pedestrian anticipation into a probability field of pedestrians’future locations and attention orientations.An end-to-end neural network,GIFNet,is tailored to estimate the GIF from explicit multidimensional observations.GIFNet quantifies the influence of group behaviors by formulating a group interaction graph with propagation and graph attention that is adaptive to the group size and dynamic interaction states.The experimental results show that the GIF effectively represents the change in pedestrians’anticipation under the prominent impact of group behaviors and accurately predicts pedestrians’future states.Moreover,the GIF contributes to explaining various predictions of pedestrians’behavior in different social states.The proposed GIF will eventually be able to allow unmanned systems to work in a human-like manner and comply with social norms,thereby promoting harmonious human-machine relationships.

Nonlinear Auto-Companding Method for Behavioral Modeling of Switched-Current Circuits

A wavelet collocation method with nonlinear auto companding is proposed for behavioral modeling of switched current circuits.The companding function is automatically constructed according to the initial error distribution obtained through approximating the input output function of the SI circuit by conventional wavelet collocation method.In practical applications,the proposed method is a general purpose approach,by which both the small signal effect and the large signal effect are modeled in a unified formulation to ease the process of modeling and simulation.Compared with the published modeling approaches,the proposed nonlinear auto companding method works more efficiently not only in controlling the error distribution but also in reducing the modeling errors.To demonstrate the promising features of the proposed method,several SI circuits are employed as examples to be modeled and simulated.

Behavioral modeling of RF power amplifiers with time-delay feed-forward neural networks

A novel behavioral model using three-layer time-delay feed-forward neural networks （TDFFNN）is adopted to model radio frequency （RF）power amplifiers exhibiting memory nonlinearities. In order to extract the parameters, the back- propagation algorithm is applied to train the proposed neural networks. The proposed model is verified by the typical odd- order-only memory polynomial model in simulation, and the performance is compared with different numbers of taped delay lines（TDLs） and perceptrons of the hidden layer. For validating the TDFFNN model by experiments, a digital test bench is set up to collect input and output data of power amplifiers at a 60 × 10^6 sample/s sampling rate. The 3.75 MHz 16-QAM signal generated in the vector signal generator（VSG） is chosen as the input signal, when measuring the dynamic AM/AM and AM/PM characteristics of power amplifiers. By comparisons and analyses, the presented model provides a good performance in convergence, accuracy and efficiency, which is approved by simulation results and experimental results in the time domain and frequency domain.

Observation on the Effect of Parental Participation in Nursing Under the IMCHB Model in Neonatal Hypoxic-Ischemic Encephalopathy

Objective:To investigate the clinical effects of parental participation in nursing under the Interaction Model of Client Health Behavior(IMCHB)model in neonatal hypoxic-ischemic encephalopathy(HIE).Methods:The First Affiliated Hospital of Gannan Medical University included 46 newborns with HIE admitted from October 2021 to October 2023 into the study population.They were divided into a control group and an observation group according to the random number table method,with the control group adopting routine nursing,and the observation group implementing parental participation in nursing under the IMCHB model.The indicators of physical,intellectual,and psychomotor development of the two groups were compared before and after nursing.Results:The physical,intellectual,and psychomotor development of the observation group was higher than that of the control group after 3 months of nursing,and the difference was statistically significant(P<0.05).Conclusion:The implementation of the IMCHB model of parental participation in the clinical care of HIE neonates can further promote their physical,intellectual,and psychomotor development.

Non-Overlapping Conditions to Enable Multi- Dimensional Behavioral Models/DPDs for Multi-Band or Non-Continuous Carrier Aggregation Systems

To linearize the multi.band PAs/transmitters, a serial of multi.band predistortion models based on multi.dimensional architecture have been proposed. However, most of these models work properly only for the signals whose harmonic and intermodulation products of carriers' non.overlap with the interested fundamental bands. In this paper, the non.overlapping conditions for dual.band and tri.band signals are derived and denoted in the form of closed.form expression. It can be used to verify whether a given dual.band/multi.band signals can be linearized properly by these multi.dimensional behavioral models. Also the conditions can be used to plan the frequency spacing and maximum bandwidth of a multi.band or non.continuous carrier aggregation signal. Several dual.band and triband signals were tested on the same PA, by employing 2.D DPD and 3.D DPD behavioral models. The measurement results show that the signals which don't satisfy the non.overlapping conditions cannot be linearized well by the multi.dimensional behavioral models which does not take the harmonic and intermodulation products of carriers' into account.

ANIMAL BEHAVIORAL MODELS OF TINNITUS

The pathophysiology of tinnitus is poorly understood and treatments are often unsuccessful. A number of animal models have been developed in order to gain a better understanding of tinnitus. A great deal has been learned from these models re- garding the electrophysiological and neuroanatomical correlates of tinnitus following exposure to noise or ototoxic drugs. Re- liable behavioral data is important for determining whether such electrophysiological or neuroanatomical changes are indeed related to tinnitus. Of the many documented tinnitus animal behavioral paradigms, the acoustic startle reflex had been pro- posed as a simple method to identify the presence or absence of tinnitus. Several behavioral models based on conditioned re- sponse suppression paradigms have also been developed. In addition to determining the presence or absence of tinnitus, some of the behavioral paradigms have provided signs of the onset, frequency, and intensity of tinnitus in animals. Although none of these behavioral models have been proved to be a perfect model, these studies provide useful information on understanding the neural mechanisms underlying tinnitus.

Multislice behavioral modeling based on envelope domain for power amplifiers

An envelope domain multislice behavioral modeling is introduced. The tradition AM-AM and AM- PM characteristics of power amplifiers axe extended to envelope domain and base-band filter is applied to distortion complex envelope signal for description of the envelope memory effect. Using traditional one and two-tone tests, the coefficients of nonlinear model and the FIR filter can be extracted. At last the model has been applied to a 10 W WCDMA Power amplifier to predict its output signal. And simulation results show that the model output conforms very well to the traditional transistor level simulation results.

老年患者门诊预约挂号的影响因素:基于安德森健康行为模型的实证研究

目的基于安德森健康行为模型,研究老年患者门诊预约挂号的影响因素,为优化门诊预约挂号服务、推进老年患者预约挂号提供参考。方法以安德森健康行为模型为理论框架设计问卷,采用方便抽样法向上海某三级甲等综合医院门诊老年患者发放调查问卷。将各维度因素纳入二分类logistic回归模型,采用单因素分析和logistic回归分析探讨老年患者门诊预约挂号的影响因素。结果共回收282份有效问卷,预约挂号187人,占66.31%。曾使用过1~2种预约挂号服务、存在社会支持、距离医院路程80 min以上、收入为>5000~7500元、就诊科室预约率25.00%以上、有预约挂号使用意愿的老年患者更倾向于预约挂号。患慢性病数量在5种及以上的老年患者更不倾向于预约挂号。结论老年患者门诊预约需求较大且受多种因素影响。须关注老年人预约挂号“痛点”,关怀老年群体,共建支持网络,加强有效宣传,优化医疗资源配置,激发使用意愿。

Study on Volterra-Laguerre behavioral model for RF power amplifier

Voherra series behavioral model for radio frequency （RF） power amplifier （PA） has been widely used in system-level simulation, however, high computational complexity makes this kind of model limited to ＂weak＂ nonlinearity. In order to reduce the computational complexity and the number of coefficients of Volterra series kernels, a Volterra series improved behavioral model based on Laguerre orthogonal polynomials function, namely Voherra-Laguerre behavioral model, is proposed. Mathematical expressions of Volterra-Laguerre behavioral model is derived, and accuracy of the model is verified through comparison of measured and simulation output data from a freescale PA using MRF21030 transistor. Mathematical analysis and simulation results show that Voherra-Laguerre behavioral model has a simple structure, much less coefficients and better modeling performance than general Volterra series model. The model can be used more correctly for system-level simulation of RF PA with wideband signal.

Deep Neural Network Based Behavioral Model of Nonlinear Circuits

With the rapid growth of complexity and functionality of modern electronic systems, creating precise behavioral models of nonlinear circuits has become an attractive topic. Deep neural networks (DNNs) have been recognized as a powerful tool for nonlinear system modeling. To characterize the behavior of nonlinear circuits, a DNN based modeling approach is proposed in this paper. The procedure is illustrated by modeling a power amplifier (PA), which is a typical nonlinear circuit in electronic systems. The PA model is constructed based on a feedforward neural network with three hidden layers, and then Multisim circuit simulator is applied to generating the raw training data. Training and validation are carried out in Tensorflow deep learning framework. Compared with the commonly used polynomial model, the proposed DNN model exhibits a faster convergence rate and improves the mean squared error by 13 dB. The results demonstrate that the proposed DNN model can accurately depict the input-output characteristics of nonlinear circuits in both training and validation data sets.

Behavioral Model of Molecular Gap-Type Atomic Switches and Its SPICE Integration

Atomic switches can be used in future nanodevices and to realize conceptually novel electronics in new types of computer architecture because of their simple structure, ease of operation, stability, and reliability. The atomic switch is a single solid-state switch with inherent learning abilities that exhibits various nonlinear behaviors with network devices. However, previous studies focused on experiments and nonvolatile memory applications, and studies on the application of the physical properties of the atomic switch in computing were nonexistent. Therefore, we present a simple behavioral model of a molecular gap-type atomic switch that can be included in a simulator. The model was described by three simple equations that reproduced the bistability using a double-well potential and was able to easily be transferred to a simulator using arbitrary numerical values and be integrated into HSPICE. Simulations using the experimental parameters of the proposed atomic switch agreed with the experimental results. This model will allow circuit designers to explore new architectures, contributing to the development of new computing methods.

Detecting While Accessing:A Semi-Supervised Learning-Based Approach for Malicious Traffic Detection in Internet of Things

In the upcoming large-scale Internet of Things(Io T),it is increasingly challenging to defend against malicious traffic,due to the heterogeneity of Io T devices and the diversity of Io T communication protocols.In this paper,we propose a semi-supervised learning-based approach to detect malicious traffic at the access side.It overcomes the resource-bottleneck problem of traditional malicious traffic defenders which are deployed at the victim side,and also is free of labeled traffic data in model training.Specifically,we design a coarse-grained behavior model of Io T devices by self-supervised learning with unlabeled traffic data.Then,we fine-tune this model to improve its accuracy in malicious traffic detection by adopting a transfer learning method using a small amount of labeled data.Experimental results show that our method can achieve the accuracy of 99.52%and the F1-score of 99.52%with only 1%of the labeled training data based on the CICDDoS2019 dataset.Moreover,our method outperforms the stateof-the-art supervised learning-based methods in terms of accuracy,precision,recall and F1-score with 1%of the training data.

Identifying behavioral determinants and stage of readiness for performing knee massage among individuals with knee osteoarthritis:An observational study

Objective Patients who experience knee osteoarthritis or chronic knee pain can alleviate their symptoms by performing self-knee massage.Understanding the readiness and types of determinants needed to facilitate self-knee massage is needed to design effective,theory-informed interventions.The primary objective of this study was to apply the transtheoretical model of behavior change to identify how factors,which include the type of knee condition and pain level,predict an individual’s readiness to adopt self-knee massage.The secondary objective employed the capability,opportunity and motivation-behavior(COM-B)model to identify relevant determinants that are predictive of an individual’s readiness to undertake self-knee massage.Methods An observational study design was used to recruit individuals with knee osteoarthritis(n=270)and chronic knee pain(n=130).Participants completed an online survey that assessed the transtheoretical model of behavior change stages,COM-B determinants(capability,opportunity and motivation),along with self-administered massage behavior.Multivariate analysis of covariance and structural equation modeling were used to test the primary and secondary objective,respectively.Results Participants who had knee osteoarthritis scored higher on the action stage compared to those with chronic pain(P=0.003),and those who experienced greater level of pain scored higher in the contemplation(P<0.001)and action phases(P<0.001)of performing knee massage compared to those with milder pain.The COM-B structural equation model revealed self-administered knee massage to be predicted by capability(β=0.31,P=0.004)and motivation(β=0.29,P<0.001),but not opportunity(β=–0.10,P=0.39).Pain level predicted motivation(β=0.27,P<0.001),but not capability(β=0.09,P=0.07)or opportunity(β=0.01,P=0.83).Tests for mediating effects found that determinants of COM-B(motivation and capability)mediate between pain level and self-administered massage behavior(β=0.10,P=0.002).Conclusion Clinicians and researchers can expect that patients diagnosed with knee osteoarthritis or who have chronic knee pain are ready(action stage)or are considering the behavior(contemplation stage)of self-knee massage.Individuals who report having knee osteoarthritis or chronic knee pain should be coached to develop the skills to perform self-knee massage and helped to develop the motivation to carry out the therapy.

Co-Design of Monolithically Integrated Photo-Detector and Optical-Receiver

A behavioral model of the photodiode is presented.The model describes the relationship between photocurrent and incident optical power,and it also illustrates the impact of the reverse bias to the variation of the junction capacitance.According to this model,the photodiode and a CMOS receiver circuit are simulated and designed simultaneously under a universal circuit simulation environment.

Development of a PC-based Open Architecture Software-CNC System

As a key te..hnology in the field of advanced manufacturing, an open architecture controller is studied. In order to develop an open architecture software-CNC system on personal computer （PC） according to open modular architecture controllers （OMAC）. First, the software and hardware platform is chosen and software realization methodology for the CNC system is determined. Second, static modeling methods of an open architecture controller inclusive of object-oriented （OO） programming technology, dynamic link library （DLL） technology and system modules partition are investigated. Third, the dynamical behavioral modeling and the data flow representation of open architecture controller are discussed, which are both described in hierarchy fmite state machine （FSM） model. Fourth, a reusable software module model is established to develop software function module library. Finally, a 3-axis milling machine tool test-bed, named for HIT-CNC, is successfully designed by means of the constructed software function module library and the system configuring method. The experimental results show that, besides increasing the degree of reusability and openness, application of above-mentioned method- ology leads to significant decrease of development time as well as maintenance cost.

Modeling for Ethernet passive optical network receiver

A behavior model for the receiver of the Ethernet passive optical network（EPON） is presented. The model consists of a fiber, a photodetector, a transimpedance amplifier （TIA） followed by a limiting amplifier and a clock and data recovery＇ circuit （CDR）. Each sub-model is constructed based on the architecture of a circuit. The noise and jitter in each block such as shot noise, thermal noise, deterministic and random jitter are also considered. The performance of the whole receiver can be evaluated by the simulation of the behavior model, which is faster than the ordinary circuit model and more accurate than the analytical model. The whole model is implemented with C ＋＋ and simulated in Microsoft Visual C ＋＋ 6. 0. Using the Monte Carlo method, the EPON receiver is simulated. The simulation results show a good agreement with experimental ones.

Urban Green Space, Uneven Development and Accessibility: A Case of Dalian′s Xigang District

This study examines the socio-spatial context of uneven development and the residential accessibility of green space in Dalian of Liaoning Province, China. The social fairness was analyzed with a community scale as the basis. We combined social scientific methods with a GIS method using a behavior accessibility model from the perspectives of fairness of urban green space allocation based on social geography, geographic information science, management science and many other related discipline theories. The results show that: 1) Most of the urban green space distribution presents an unbalanced phenomenon, and it does not match with the population distribution; 2) We found some differences in the accessibility of the population with different attributes and opportunities to use and enjoy the urban green spaces, mainly due to: the dual social and spatial attributes of the residents and the serious stratum differentiation generated were the internal causes; the residential space differentiation and the pursuit of economic and real estate development were the direct causes; and unreasonable planning, in regard to the fact that government policies did not give consideration to efficiency and fairness, was also an important factor.

GLOBAL EXISTENCE AND ASYMPTOTIC BEHAVIOR FOR AN 1-D COMPRESSIBLE ENERGY TRANSPORT MODEL

In this article, the global existence and the large time behavior of smooth solutions to the initial boundary value problem for a degenerate compressible energy transport model are established.