Abstract:Due to the fear to predation risk preys may decrease birth rate or flee high level of predators patch to prey-only patch at a cost of decreased resources. In this paper a predator-prey model with fear effect and patch structure is constructed to study how the fear and diffusion affect predator-prey dynamics. The stability of equilibria and existence of Hopf bifurcation are studied. The fear to predation risk can be modeled by two types of parameters: $k$, $a_{21}$ and $a_{12}$, where high level of fear leads to large $k$ and thus low level of prey's birth rate; high level of fear results in large prey's diffusive rate $a_{21}$ from predator-prey patch 1 to prey-only patch 2, and great hunger and bad ability of remembering fear in patch 1 cause large prey's diffusive rate $a_{12}$ from patch 2 to patch 1. Numerical simulations are as follows. (1) In some cases, large $k$ can stabilize the predator-prey system by excluding the existence of periodic solutions when $a_{21}$ is small. However, when $a_{21}$ is large the change of $k$ can not lead to periodic oscillations. In addition, when $a_{21}$ is larger, the predators will die out. Thus, the oscillation behavior or the persistence of predators may be overestimated if the diffusive behaviors $a_{21}$ is weakened or ignored. (2) Under some situations, the change of $a_{12}$ causes Hopf bifurcations twice. This implies that the oscillation behavior may be underestimated or overestimated if the diffusive behavior $a_{12}$ is weakened. %Numerical simulations show that high levels of fear (or low birth rate of preys) can stabilize the predator-prey system by excluding the existence of periodic solutions. However, high level of dispersal caused by fear from predator-prey patch to predator-free patch can stabilize the predator-prey system. On the contrary, high level of dispersal caused by starvation from predator-free patch to predator-prey patch can induce periodic oscillations. % These conclusions imply that the oscillation behavior may be underestimated or overestimated according to whether the fear effect to predation risk (or low birth rate of preys) or dispersal caused by starvation dominates if the dispersal is ignored.

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	1. Dynamics of a Predator-Prey Model with Fear Effect and Patch Structure
	LAN Zi-Teng,ZHANG Yu-Wei,WEN Luo-Sheng,ZHANG Tian-Ran
	Mathematics 16 March 2024
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	Abstract:Due to the fear to predation risk preys may decrease birth rate or flee high level of predators patch to prey-only patch at a cost of decreased resources. In this paper a predator-prey model with fear effect and patch structure is constructed to study how the fear and diffusion affect predator-prey dynamics. The stability of equilibria and existence of Hopf bifurcation are studied. The fear to predation risk can be modeled by two types of parameters: $k$, $a_{21}$ and $a_{12}$, where high level of fear leads to large $k$ and thus low level of prey's birth rate; high level of fear results in large prey's diffusive rate $a_{21}$ from predator-prey patch 1 to prey-only patch 2, and great hunger and bad ability of remembering fear in patch 1 cause large prey's diffusive rate $a_{12}$ from patch 2 to patch 1. Numerical simulations are as follows. (1) In some cases, large $k$ can stabilize the predator-prey system by excluding the existence of periodic solutions when $a_{21}$ is small. However, when $a_{21}$ is large the change of $k$ can not lead to periodic oscillations. In addition, when $a_{21}$ is larger, the predators will die out. Thus, the oscillation behavior or the persistence of predators may be overestimated if the diffusive behaviors $a_{21}$ is weakened or ignored. (2) Under some situations, the change of $a_{12}$ causes Hopf bifurcations twice. This implies that the oscillation behavior may be underestimated or overestimated if the diffusive behavior $a_{12}$ is weakened. %Numerical simulations show that high levels of fear (or low birth rate of preys) can stabilize the predator-prey system by excluding the existence of periodic solutions. However, high level of dispersal caused by fear from predator-prey patch to predator-free patch can stabilize the predator-prey system. On the contrary, high level of dispersal caused by starvation from predator-free patch to predator-prey patch can induce periodic oscillations. % These conclusions imply that the oscillation behavior may be underestimated or overestimated according to whether the fear effect to predation risk (or low birth rate of preys) or dispersal caused by starvation dominates if the dispersal is ignored.
	TO cite this article:LAN Zi-Teng,ZHANG Yu-Wei,WEN Luo-Sheng, et al. Dynamics of a Predator-Prey Model with Fear Effect and Patch Structure[OL].[16 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762503


	2. Uterine Effects of Exposure to Polycyclic Aromatic Hydrocarbons (PAHs)
	Chen Shen,Hongyu Wang,Miaomiao Zhang,Xuechuan Hong
	Biology 15 March 2024
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	Abstract:Polycyclic aromatic hydrocarbons (PAHs) are a large group of toxic substances produced by the incomplete combustion of organic fuels. They are hydrocarbons with two or more benzene rings and are widely present in environment. Because PAHs cause damage to the respiratory, circulatory, reproductive, immune and cardiovascular systems, they have been the focus of national and international toxicologists. Women are highly susceptible to PAHs and their reproductive functions are extremely vulnerable to PAHs. A large body of reported literature suggests that PAHs exposure can produce uterine reproductive toxicity, such as hormonal disruption, abnormal cell proliferation, altered signaling pathways, regulate genes expression and cancer. PAHs can also cross the placental barrier and affect the zygote in the womb. This review summarizes the toxic effects of various PAHs on the uterus reported in the literature in recent years, and provides an in-depth analysis of the hazards of PAHs exposure on the uterus of the current generation and offspring at the molecular toxicology level.
	TO cite this article:Chen Shen,Hongyu Wang,Miaomiao Zhang, et al. Uterine Effects of Exposure to Polycyclic Aromatic Hydrocarbons (PAHs)[OL].[15 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762839

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	3. Dynamics and control of malaria transmission model with vaccination and patch structure
	ZENG Si-Jia, ZHANG Tian-Ran, ZHANG Tian-Ran
	Mathematics 15 March 2024
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	Abstract:Malaria is an infectious disease transmitted by mosquitoes, and this paper focuses on two diﬀerent regions due to diﬀerences in prevention and control measures such as vaccination. When a movement of population and mosquitoes occurs between two patches, under what conditions will malaria eventually extinct or continue to spread. This article takes the basic regeneration number as the threshold parameter. When $\mathscr{R}_0< 1$ the disease will die out and when $\mathscr{R}_0> 1$ the disease will persist. In section 5, through numerical simulation, we found that rational distribution of vaccine number between two patches can minimize $\mathscr{R}_0$ and minimize the total number of infections when the number of vaccine is limited. The results show that the distribution of vaccine number is diﬀerent from the conventional idea that the distribution is based on the patches population ratio. Due to the inﬂuence of population movement between patches, the optimal strategy for vaccine distribution needs to be based on the actual situation.
	TO cite this article:ZENG Si-Jia, ZHANG Tian-Ran, ZHANG Tian-Ran. Dynamics and control of malaria transmission model with vaccination and patch structure[OL].[15 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762506


	4. TransFuseNet: A Novel Multi-task Model for Community-Acquired Pneumonia Segmentation and Classification
	CHE PeiShuai,YIN Si-Xing,LI Shu-Fang
	Computer Science and Technology 13 March 2024
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	Abstract:Community-acquired pneumonia (CAP) poses a global public health challenge, and in the current environment of the pneumonia pandemic, timely and accurate diagnosis of different types of pneumonia is particularly crucial. Computed Tomography (CT) is an effective means of diagnosing pneumonia, and the use of artificial intelligence (AI) for diagnostic assistance can enhance clinical diagnostic efficiency. Therefore, this paper introduces a 3D multitask deep learning model called TransFuseNet to achieve real-time and accurate segmentation and classification of CAP.Specifically, the proposed network consists of two sub-networks: a 3D scSEU-Net sub-network for pneumonia lesion segmentation and a classification sub-network based on a fully convolutional Transformer. Both sub-networks share the same encoder, where the segmentation branch captures local features and spatial relationships, while the classification branch performs long-range modeling to capture global context information. Simultaneously, a loss function is introduced to enhance the interaction between the two sub-networks, balancing the importance of the two tasks.The retrospective dataset includes 180 patients who underwent thin-slice chest CT scans at a medical center in China. Numerous experiments demonstrate that the model achieved AUC: 0.989, DSC: 0.723, average accuracy: 0.927, precision: 0.889, sensitivity: 0.866, and specificity: 0.835 on the test set. The model shows no significant difference in pneumonia detection accuracy compared to radiologists.
	TO cite this article:CHE PeiShuai,YIN Si-Xing,LI Shu-Fang. TransFuseNet: A Novel Multi-task Model for Community-Acquired Pneumonia Segmentation and Classification[OL].[13 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762404


	5. Subtle dynamics of chaotic torsion pendulum: a detailed comparison between experiments and numerical simulations
	Xie Gui-Jin, GAO Jian, XIAO Jing-Hua, YAN Zi-Xiang
	Physics 13 March 2024
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	Abstract:We conduct a detailedexperimental and numerical study on the subtle dynamics of chaotic torsion pendulum (CTP).We first present experimental observations reported by students, and then propose a revised model of CTP based on laws of mechanics and insights about the experiment to understand these observations.Parameters of the revised model are fit using experimental data.The revised model agrees well with experimental observations.The subtle dynamics hidden in these phenomena are thoroughly exhibited through this study, hoping to provide more insights to the nonlinear nature of CTP.
	TO cite this article:Xie Gui-Jin, GAO Jian, XIAO Jing-Hua, et al. Subtle dynamics of chaotic torsion pendulum: a detailed comparison between experiments and numerical simulations[OL].[13 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762737


	6. Error Stairs of Reservoir Computing and its Applications
	JIA Lin-Yuan, GAO Jian, YAN Zi-Xiang, ZHAO Hui, ZHAO Hui, XIAO Jing-Hua
	Physics 13 March 2024
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	Abstract:In this paper we explore with learning capacity of reservoir computing with polynomial functions, and find a universal error stairs, which does not depend on the input signals, and can effectively describe the learning capability of the reservoir. Machine learning methods based on reservoir computing have shown superior performance in predicting the dynamics of complex systems. However, the generation process of the reservoir is often considered a `black box', so it is of great significance to characterize the learning capability of the reservoir. Based on the error stairs, we propose two indicators to characterize the learning capability of the reservoir, the highest order polynomial error and memory length, which describe the nonlinear processing ability and memory ability of the reservoir respectively. We validate and apply these two indicators through predictions on classical chaotic systems such as the Logistic map and the Lorenz system. These two indicators, for nonlinear processing ability and memory capacity respectively, provide a promising tool to study the learning capacity of reservoir computing and other machine learning method for dynamical systems.
	TO cite this article:JIA Lin-Yuan, GAO Jian, YAN Zi-Xiang, et al. Error Stairs of Reservoir Computing and its Applications[OL].[13 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762711


	7. Large sampling intervals for learning and predicting chaotic systems with reservoir computing
	XIE Qing-yan, YAN Zi-Xiang, GAO Jian, ZHAO Hui, ZHAO Hui, XIAO Jing-Hua
	Physics 13 March 2024
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	Abstract:Reservoir computing is an efficient artificial neural network with low training cost and low hardware overhead. It is widely used in time sequence information processing, such as waveform classification, speech recognition, time series prediction, etc. However, in practical applications, researchers can only use limited information from the system for predictions, and the sampling interval cannot be adjusted freely due to the limitations of the actual system. Based on the above situation, we demonstrate the impact of time and space sampling intervals on the short-term and long-term prediction capabilities of the reservoir computing and compare it with the existing numerical methods. It can be found that for chaotic systems, the reservoir computing can learn and reproduce the systems' states at almost five times larger spatio-temporal intervals compared to classical numerical methods, such as fourth-order Runge-Kutta and spectral methods. Our results show the captivity of reservoir computing in the applications with limitation of spatio-temporal intervals, and pave the way to reservoir-based fast numerical simulation methods.
	TO cite this article:XIE Qing-yan, YAN Zi-Xiang, GAO Jian, et al. Large sampling intervals for learning and predicting chaotic systems with reservoir computing[OL].[13 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762703


	8. A Multi-Document Inference Method Based on TR-BERT and Attention Networks
	ZHAO Jiaqi,LIN Rongheng
	Computer Science and Technology 13 March 2024
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	Abstract:In the task of multi-document inference, information related to the answer may reside across multiple relevant texts, and sometimes this information is not directly associated with the question. To address the challenge of balancing accuracy and efficiency in multi-document inference for the electric utility customer service scenario, this paper proposes a multi-level inference method based on pre-trained models and attention networks. The model utilizes pre-trained models to preserve the rich semantics extracted from paragraph texts and questions, and then evaluates the relevance of candidates through an attention mechanism. Furthermore, due to the real-time requirements of the question-answering scenario, we employ the TR-BERT pre-trained model based on dynamic token reduction and simplify the attention network. Experimental results on the WikiHop dataset demonstrate that the model overall exhibits advantages in both computational speed and accuracy, providing effective methodological support for the multi-turn question-answering functionality in intelligent question-answering systems.
	TO cite this article:ZHAO Jiaqi,LIN Rongheng. A Multi-Document Inference Method Based on TR-BERT and Attention Networks[OL].[13 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762558


	9. Named Entity Recognition in the Perovskite Field Based on Convolutional Neural Networks and MatBERT
	ZHANG Jiaxin,ZHANG Lingxue,SUN Yuxuan,LI Wei,QUHE Ruge
	Computer Science and Technology 13 March 2024
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	Abstract:Due to the significant increase in publications in the field of materials science, there has been a bottleneck in organizing material science knowledge and discovering new materials. The number of literature in the emerging field of perovskite materials has grown to a massive scale. It is necessary to compile information on the structure, properties, synthesis methods, characterization techniques, and applications of perovskite materials. To address this issue, we employ named entity recognition, a natural language processing technique, to extract important entities from perovskite material texts. In this paper, we propose a method based on convolutional neural networks (CNN) and MatBERT. Firstly, we utilize MatBERT, which has been pre-trained on a large amount of material science text, to generate contextualized word embeddings. Next, we extract feature information using a CNN model. Finally, a conditional random field (CRF) layer is used for decoding sequences in addition to calculating the training and validation loss. Experimental results demonstrate that the performance of our model on perovskite material dataset is improved by 1%~6% compared with BERT, SciBERT and MatBERT models. Through this model, we extract the entities of 2389 abstracts to obtain knowledge of perovskite materials.
	TO cite this article:ZHANG Jiaxin,ZHANG Lingxue,SUN Yuxuan, et al. Named Entity Recognition in the Perovskite Field Based on Convolutional Neural Networks and MatBERT[OL].[13 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762696


	10. Enhancing Persona Consistency in Dialogue Generation Algorithm with Retrieval Augmentation
	SHI Haozhe
	Computer Science and Technology 12 March 2024
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	Abstract:Open-domain dialogue systems are designed to fulfill people\'s daily communicative and emotional requirements, with the goal of cultivating long-term relationships with users. Yet, these systems encounter challenges in sustaining persona consistency, as responses generated at times are not logically aligned with the established character persona or preceding dialogues. This discrepancy undermines dialogue coherence and emotional engagement, consequently impeding the development of profound connections with users. Addressing this issue, this study introduce an innovative dialogue generation algorithm that incorporates retrieval-augmentation techniques. By forming an database of character information, the algorithm aids large language models in retrieving persona-relevant data during interactions, ensuring that responses consistently align with the character\'s defined persona. This method significantly mitigates the occurrence of generating inconsistent response, an "hallucination" effect. This study demonstrates the substantial impact of information optimization and filtering mechanisms on enhancing persona consistency within dialogue systems, as evidenced through comprehensive evaluation across three pivotal performance metrics: information relevance, faithfulness, and reponse relevance, facilitated by an integration of various retrieval strategies and information optimization techniques.
	TO cite this article:SHI Haozhe. Enhancing Persona Consistency in Dialogue Generation Algorithm with Retrieval Augmentation[OL].[12 March 2024] http://en.paper.edu.cn/en_releasepaper/content/4762669

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