High-Order Soliton Solutions and Hybrid Behavior for the (2 + 1)-Dimensional Konopelchenko-Dubrovsky Equations

In this paper, the evolutionary behavior of N-solitons for a (2 + 1)-dimensional Konopelchenko-Dubrovsky equations is studied by using the Hirota bilinear method and the long wave limit method. Based on the N-soliton solution, we first study the evolution from N-soliton to T-order (T=1,2) breather wave solutions via the paired-complexification of parameters, and then we get the N-order rational solutions, M-order (M=1,2) lump solutions, and the hybrid behavior between a variety of different types of solitons combined with the parameter limit technique and the paired-complexification of parameters. Meanwhile, we also provide a large number of three-dimensional figures in order to better show the degeneration of the N-soliton and the interaction behavior between different N-solitons.

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.

Modelling of vehicle interaction behavior during discretionary lane-changing preparation process on freeway

In order to increase the accuracy of microscopic traffic flow simulation,two acceleration models are presented to simulate car-following behaviors of the lane-changing vehicle and following putative vehicle during the discretionary lanechanging preparation( DLCP) process, respectively. The proposed acceleration models can reflect vehicle interaction characteristics. Samples used for describing the starting point and the ending point of DLCP are extracted from a real NGSIM vehicle trajectory data set. The acceleration model for a lanechanging vehicle is supposed to be a linear acceleration model.The acceleration model for the following putative vehicle is constructed by referring to the optimal velocity model,in which optimal velocity is defined as a linear function of the velocity of putative leading vehicle. Similar calibration,a hypothesis test and parameter sensitivity analysis were conducted on the acceleration model of the lane-changing vehicle and following putative vehicle,respectively. The validation results of the two proposed models suggest that the training and testing errors are acceptable compared with similar works on calibrations for car following models. The parameter sensitivity analysis shows that the subtle observed error does not lead to severe variations of car-following behaviors of the lane-changing vehicle and following putative vehicle.

Investigating the relationships between facets of work task and selection and query-related behavior

Purpose: This study aims to explore the relationships between different facets of work task and selection and query-related behavior.Design/methodology/approach:An experiment was conducted to explore the issue. The researcher recruited 24 participants and assigned six simulated work task situations to each of them. Each experiment lasted around 2 hours and was recorded by the software tool Morae.Findings: Time(frequency) and time(length) are more closely related to user’s selection and query-related behavior compared to the facet ‘process’ of work task. Knowledge level of work task topic, degree of work task difficulty, and subjective work task complexity are significantly correlated with selection and query-related behavior. Work task difficulty and work task complexity are different concepts. Subjective work task complexity, work task difficulty, and knowledge of work task topic are significantly correlated with user’s selection and query-related behavior.Research limitations/implications: The limitations of this study include a small sample size,limited work task situations, and possible spurious relationships. This study has implications in informing task-based information seeking/search/retrieval research and interactive information retrieval(IIR) systems design.Originality/values: Previous studies usually did not touch upon how different facets of work tasks affected interactive activities. Some studies examining task complexity and information behavior were concerned with how work tasks affect users’ behavior at information-seeking level, rather than at information search level. This study makes contribution to interactive information retrieval,task-based information search and retrieval, and personalization of IR.

Understanding the Interaction Between Space and Behavior: A Review of Behavior Geography

Behavior Geography Authors: Chai Yanwei, Ta Na Year: 2022 Publisher: Southeast University Press ISBN: 9787564193508(320,000 characters, in Chinese) In the context of a fluid society and cities in transition, the dynamic relationship between humans and their environment has received considerable attention in the field of geography.

Effect of organic binders on the activation and properties of indurated magnetite pellets

In the ironmaking process,the addition of an organic binder to replace a portion of bentonite has the potential to improve the per-formance of pellets.The interaction between original bentonite(OB)and organic binder was investigated.Results indicated that the micromor-phology of organic composite bentonite(OCB)became porous and the infrared difference spectrum exhibited a curved shape.In addition,the residual burning rates of OB and organic binder were determined to be 82.72%and 2.30%,respectively.Finally,the influence of OCB on the properties of pellets was investigated.The compressive strength of OCB-added green pellets(14.7 N per pellet)was better than that of OB-ad-ded pellets(10.3 N per pellet).Moreover,the range of melting temperature of OCB-added green pellets(173℃)was narrower than that of OB-added pellets(198℃).The compressive strength of OCB-added green pellets increased from 2156 to 3156 N per pellet with the increase in roasting temperature from 1200 to 1250℃.

Insider Attack Detection Using Deep Belief Neural Network in Cloud Computing

Cloud computing is a high network infrastructure where users,owners,third users,authorized users,and customers can access and store their information quickly.The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently.This cloud is nowadays highly affected by internal threats of the user.Sensitive applications such as banking,hospital,and business are more likely affected by real user threats.An intruder is presented as a user and set as a member of the network.After becoming an insider in the network,they will try to attack or steal sensitive data during information sharing or conversation.The major issue in today's technological development is identifying the insider threat in the cloud network.When data are lost,compromising cloud users is difficult.Privacy and security are not ensured,and then,the usage of the cloud is not trusted.Several solutions are available for the external security of the cloud network.However,insider or internal threats need to be addressed.In this research work,we focus on a solution for identifying an insider attack using the artificial intelligence technique.An insider attack is possible by using nodes of weak users’systems.They will log in using a weak user id,connect to a network,and pretend to be a trusted node.Then,they can easily attack and hack information as an insider,and identifying them is very difficult.These types of attacks need intelligent solutions.A machine learning approach is widely used for security issues.To date,the existing lags can classify the attackers accurately.This information hijacking process is very absurd,which motivates young researchers to provide a solution for internal threats.In our proposed work,we track the attackers using a user interaction behavior pattern and deep learning technique.The usage of mouse movements and clicks and keystrokes of the real user is stored in a database.The deep belief neural network is designed using a restricted Boltzmann machine(RBM)so that the layer of RBM communicates with the previous and subsequent layers.The result is evaluated using a Cooja simulator based on the cloud environment.The accuracy and F-measure are highly improved compared with when using the existing long short-term memory and support vector machine.

Integrability Tests and Some New Soliton Solutions of an Extended Potential Boiti-Leon-Manna-Pempinelli Equation

This paper is devoted to the study of a (2 + 1)-dimensional extended Potential Boiti-Leon-Manna-Pempinelli equation. Firstly, By means of the standard Weiss Tabor Carnevale approach and Kruskal’s simplification, we prove the painlevé non integrability of the equation. Secondly, A new breather solution and lump type solution are obtained based on the parameter limit method and Hirota’s bilinear method. Besides, some interaction behavior between lump type solution and N-soliton solutions (N is any positive integer) are studied. We construct the existence theorem of the interaction solution and give the process of calculation and proof. We also give a concrete example to illustrate the effectiveness of the theorem, and some spatial structure figures are displayed to reflect the evolutionary behavior of the interaction solutions with the change of soliton number N and time t.

BA-GNN: Behavior-aware graph neural network for session-based recommendation

Session-based recommendation is a popular research topic that aims to predict users’next possible interactive item by exploiting anonymous sessions.The existing studies mainly focus on making predictions by considering users’single interactive behavior.Some recent efforts have been made to exploit multiple interactive behaviors,but they generally ignore the influences of different interactive behaviors and the noise in interactive sequences.To address these problems,we propose a behavior-aware graph neural network for session-based recommendation.First,different interactive sequences are modeled as directed graphs.Thus,the item representations are learned via graph neural networks.Then,a sparse self-attention module is designed to remove the noise in behavior sequences.Finally,the representations of different behavior sequences are aggregated with the gating mechanism to obtain the session representations.Experimental results on two public datasets show that our proposed method outperforms all competitive baselines.The source code is available at the website of GitHub.

School Boarding and Students'Prosocial Behaviors:Evidence from Rural China

This study investigated the efects of boarding at school on students'prosocial behaviors in rural China using data from the National Children's Study of China.The instrumental variable(IV)approach was used to control for potential endogeneity,and the IVs were the proportion of boarding students in higher grades and the school area per student.The ordinary least squares and IV estimates showed that boarding students exhibited more prosocial behaviors,including compliance with rules,positive traits,and altruistic attitudes.These results were robust.Heterogeneity analyses suggested that students from low-income families,children who were not"left behind,"high-grade students,and female students were more likely to benefit from boarding.We found that these effects were primarily due to boarding students developing stronger feelings of trust and support from their peers and teachers and participating in more school-organized events and team activities.

A framework to evaluate whether to pool or separate behaviors in a multilayer network

A multilayer network approach combines different network layers,which are connected by interlayer edges,to create a single mathematical object.These networks can contain a variety of information types and represent different aspects of a system.However,the process for selecting which information to include is not always straightforward.Using data on 2 agonistic behaviors in a captive population of monk parakeets(Myiopsitta monachus),we developed a framework for investigating how pooling or splitting behaviors at the scale of dyadic relationships(between 2 individuals)affects individual-and group-level social properties.We designed 2 reference models to test whether randomizing the number of interactions across behavior types results in similar structural patterns as the observed data.Although the behaviors were correlated,the first reference model suggests that the 2 behaviors convey different information about some social properties and should therefore not be pooled.However,once we controlled for data sparsity,we found that the observed measures corresponded with those from the second reference model.Hence,our initial result may have been due to the unequal frequencies of each behavior.Overall,our findings support pooling the 2 behaviors.Awareness of how selected measurements can be affected by data properties is warranted,but nonetheless our framework disentangles these efforts and as a result can be used for myriad types of behaviors and questions.This framework will help researchers make informed and data-driven decisions about which behaviors to pool or separate,prior to using the data in subsequent multilayer network analyses.

Quality of experience evaluation of HTTP video streaming based on user interactive behaviors

User interactive behaviors play a dual role during the hypertext transfer protocol （HTTP） video service： reflection and influence. However, they are seldom taken into account in practices. To this end, this paper puts forward the user interactive behaviors, as subjective factors of quality of experience （QoE） from viewer level, to structure a comprehensive multilayer evaluation model based on classic network quality of service （QoS） and application QoS. First, dual roles of user behaviors are studied and the characteristics are extracted where the user experience is correlated with user interactive behaviors. Furthermore, we categorize QoE factors into three dimensions and build the metric system. Then we perform the subjective tests and investigate the relationships among network path quality, user behaviors, and QoE. Ultimately, we employ the back propagation neural network （BPNN） to validate our analysis and model. Through the simulation experiment of mathematical and BPNN, the dual effects of user interaction behaviors on the reflection and influence of QoE in the video stream are analyzed, and the QoE metric system and evaluation model are established.

Building trust networks in the absence of trust relations

User-specified trust relations are often very sparse and dynamic, making them difficult to accurately predict from online social media. In addition, trust relations are usually unavailable for most social media platforms.These issues pose a great challenge for predicting trust relations and further building trust networks. In this study,we investigate whether we can predict trust relations via a sparse learning model, and propose to build a trust network without trust relations using only pervasively available interaction data and homophily effect in an online world. In particular, we analyze the reliability of predicting trust relations by interaction behaviors, and provide a principled way to mathematically incorporate interaction behaviors and homophily effect in a novel framework,b Trust. Results of experiments on real-world datasets from Epinions and Ciao demonstrated the effectiveness of the proposed framework. Further experiments were conducted to understand the importance of interaction behaviors and homophily effect in building trust networks.

Interactions between engineered nanoparticles and dissolved organic matter: A review on mechanisms and environmental effects

Dissolved organic matter（DOM） is ubiquitous in the environment and has high reactivity.Once engineered nanoparticles（ENPs） are released into natural systems, interactions of DOM with ENPs may significantly affect the fate and transport of ENPs, as well as the bioavailability and toxicity of ENPs to organisms. However, because of the complexity of DOM and the shortage of useful characterization methods, large knowledge gaps exist in our understanding of the interactions between DOM and ENPs. In this article, we systematically reviewed the interactions between DOM and ENPs, discussed the effects of DOM on the environmental behavior of ENPs, and described the changes in bioavailability and toxicity of ENPs caused by DOM. Critical evaluations of published references suggest further need for assessing and predicting the influences of DOM on the transport,transformation, bioavailability, and toxicity of ENPs in the environment.

Anomaly Detection in Microblogging via Co-Clustering

Traditional anomaly detection on microblogging mostly focuses on individual anomalous users or messages. Since anomalous users employ advanced intelligent means, the anomaly detection is greatly poor in performance. In this paper, we propose an innovative framework of anomaly detection based on bipartite graph and co-clustering. A bipartite graph between users and messages is built to model the homogeneous and heterogeneous interactions. The proposed co- clustering algorithm based on nonnegative matrix tri-factorization can detect anomalous users and messages simultaneously. The homogeneous relations modeled by the bipartite graph are used as constraints to improve the accuracy of the co- clustering algorithm. Experimental results show that the proposed scheme can detect individual and group anomalies with high accuracy on a Sina Weibo dataset.

Interaction phenomena between lump and solitary wave of a generalized (3+1)-dimensional variable-coefficient nonlinear-wave equation in liquid with gas bubbles

In this paper, a generalized (3+1)-dimensional variable-coefficient nonlinear-wave equation is studied in liquid with gas bubbles. Based on the Hirota’s bilinear form and symbolic computation, lump and interaction solutions between lump and solitary wave are obtained,which include a periodic-shape lump solution, a parabolic-shape lump solution, a cubic-shape lump solution, interaction solutions between lump and one solitary wave, and between lump and two solitary waves. The spatial structures called the bright lump wave and the bright-dark lump wave are discussed. Interaction behaviors of two bright-dark lump waves and a periodic-shape bright lump wave are also presented. Their interactions are shown in some 3D plots.