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Investigating the chaotic dynamics in neural networks holds significant importance in elucidating brain-like neural activities and guiding brain-like learning. The multi-scroll chaos, due to its intricate topological structure, has garnered interest in the study of brain-like chaotic neural networks. Previous researches have primarily focused on ordinary multi-scroll attractors, while there has been little research on symmetric multi-scroll attractors. Symmetric attractors are typically more diverse and have more flexible evolutionary and higher stability which may lead to more stable system responses. The purpose of this paper is to investigate the symmetric multi-scroll phenomenon generated under the influence of the memristor controlled by multi-level-logic pulse in Hopfield Neural Network (HNN). Firstly, a memristive HNN capable of generating multi-scroll is proposed, serving as the foundation for studying the influence of multi-level-logic pulse. Through theoretical and numerical analysis, the dynamic behavior of the proposed memristive HNN is examined and simulation results reveal the emergence of multi-scroll attractors and initial offset coexisting behavior. Subsequently, a multi-level-logic pulse is introduced into the memristor to simulate one of its parameters. The experimental results reveal that the introduction of multi-level-logic pulse expands the original multi-scroll structure into a symmetric structure. Furthermore, it enlarges the chaotic parameter range of the system, which holds significant implications for the study of neural dynamics. Finally, the correctness of the proposed model is verified through hardware experiments. This study provides valuable guidance for neural dynamics researches and the application of memristors. [ABSTRACT FROM AUTHOR]

In this article, we consider the Einstein-scalar field Lichnerowicz equation - Δ u + h u = B u p - 1 + A u - p - 1 on any connected finite graph G = (V , E) , where A, B, h are given functions on V with A ≥ 0 , A ≢ 0 on V, and p > 2 is a constant. By using the classical variational method, topological degree theory and heat-flow method, we provide a systematical study on this equation by providing the existence results for each case: positive, negative and null Yamabe-scalar field conformal invariant, namely h > 0 , h < 0 and h = 0 respectively. [ABSTRACT FROM AUTHOR]

We revisit the well-known Brezis-Nirenberg problem - Δ u = u N + 2 N - 2 + ε u , in Ω , u > 0 , in Ω , u = 0 , on ∂ Ω , where ε > 0 and Ω ⊂ R N are a smooth bounded domain with N ≥ 3 . The existence of multi-bump solutions to above problem for small parameter ε > 0 was obtained by Musso and Pistoia (Indiana Univ Math J 51:541–579, 2002). However, to our knowledge, whether the multi-bump solutions are non-degenerate that is open. Here, we give some straightforward answer on this question under some suitable assumptions for the Green's function of - Δ in Ω , which enriches the qualitative analysis on the solutions of Brezis-Nirenberg problem and can be viewed as a generalization of Grossi (Nonlinear Differ Equ Appl 12:227–241, 2005) where the non-degeneracy of a single-bump solution has been proved. And the main idea is the blow-up analysis based on the local Pohozaev identities. [ABSTRACT FROM AUTHOR]

In the field of artificial neural networks, researchers often use the hyperbolic tangent function as an activation function to imitate the firing rules of biological neurons and to add nonlinear characteristics to neural networks. However, prior studies have neglected to consider the effect of the bias of the activation function, which represents the firing threshold of biological neurons, on the dynamical behaviors of neural networks. In this paper, we aim to study the influence of neuronal thresholds on dynamics of the Hopfield neural network (HNN). The bias of the activation function is used as the control parameter in this investigation. The proposed HNN model is analyzed through various methods, including phase portraits, 0-1 tests, Lyapunov exponent spectra, bifurcation diagrams, and bi-parameter dynamic maps. The results of the analysis illustrate that the firing threshold could induce a range of complex dynamical phenomena in the HNN, such as periodic attractors, chaotic attractors, and forward and reverse period doubling bifurcations. Furthermore, the hardware implementation of the proposed HNN model is successfully demonstrated through circuit simulations. These experiments confirm the consistency of the results obtained through numerical simulations. Finally, the potential application of the proposed HNN model is further explored by constructing an image encryption system. The results demonstrate that the chaotic attractor has good randomness properties and that its application in image encryption has a high level of security. This study may provide valuable insights into dynamics of the HNN model influenced by the neuron firing threshold and highlight the potential for practical applications of these models in engineering. [ABSTRACT FROM AUTHOR]

Hyperchaotic systems have been widely used in the field of communication and information security to generate random numbers due to their super-long sequences, pseudorandomness, and unpredictability. However, chaotic systems still have certain periodicity and security risks. To improve the reliability of chaotic random sequences, in this paper, a new method of generating chaotic random sequences based on random bilateral projection is proposed. Through random bilateral projection algorithm, the matrix formed by chaotic sequences is decomposed into a noiseless low-rank matrix, sparse matrix, and noise matrix, and the noise matrix is retained as a random sequence, which can effectively remove the regular factors to improve the randomness of the generated sequence. To verify the effectiveness of the proposed sequence generation method, we apply it to the compressive ghost imaging encryption system, and through simulation verified that compared with the existing algorithms, the proposed random sequence generation method has better efficiency and randomness, and can improve the security and efficiency of the compressive ghost imaging system. [ABSTRACT FROM AUTHOR]

In this work, a Pleasure–Arousal–Dominance (PAD) three-dimensional brain-like emotion generation system is proposed by simulating the brain tissue structures involved in emotion generation in the brain's limbic system. The system utilizes volatile memristors to simulate the activation and recovery process of neurons, and non-volatile memristors to simulate the synaptic weight changes. It combines the brain emotion learning model and the biological long short-term memory model to simulate the emotion generation process in the brain. The system employs the Dempster–Shafter (D–S) evidence theory for multimodal feature fusion, ultimately representing the generated human-like emotions in the PAD three-dimensional emotion expression space. Considering the differences in emotional information represented in each dimension of the PAD emotion expression space, this work proposes the use of the D–S evidence theory to calculate the weight values of multimodal evidence and each dimension of emotion signals. The system performs weighted summation for multimodal feature fusion, which is more biologically inspired and realistic. As a result, the generated emotion signals are more accurate, and the PAD three-dimensional emotion expression model enhances the capability and richness of emotion expression. The system processes multimodal input signals (text, speech, visual signals) to generate three-dimensional emotion signals (pleasure, arousal, and dominance signals), which correspond to specific emotions in a three-dimensional space. These signals can be visually represented as facial images using MATLAB. The simulation results from PSPICE indicate a nonlinear mapping relationship between the system's input and output. It shows that different inputs can generate distinct human-like emotions. [ABSTRACT FROM AUTHOR]

Compared with dissipative chaotic systems, conservative chaotic systems are more suitable for secure communication based on chaos. In this paper, an effective method for constructing a class of four-dimensional conservative chaotic systems is proposed by coupling four generalized Euler equations with four coupling parameters. Moreover, this method is also applicable to the construction of higher-dimensional Hamiltonian conservative systems. The chaotic and conservative properties of the system are verified in detail by relevant analytical methods, including equilibrium points, phase diagram, Lyapunov exponents, bifurcation diagram, energy, and transient quasi-periodic orbit. An interesting transient quasi-period phenomenon is observed in the new system. In addition, the Hamiltonian energy level affects the coexistence behavior of the new system. The coexistence phenomenon was analyzed from an energy perspective. With the increase in Hamiltonian energy level, the ergodicity and randomness increase. Besides, the system also has abundant coexistence orbitals at equal energy levels. Finally, the National Institute of Standards and Technology test verifies that the pseudo-random sequence based on the new system has good pseudo-randomness, and the hardware circuit of the system is realized by field programmable gate array, and the physical feasibility of the system is verified. This study demonstrates that the constructed class of Hamiltonian conservative chaotic systems is a good entropy source that can be applied to various chaotic embedded systems, including secure communication systems, cryptographic systems, and random number generators. [ABSTRACT FROM AUTHOR]

It is believed that local activation is the origin of all complexities, and the locally active memristive synaptic neural network can generate complex chaotic dynamic behaviors, such as hyperchaotic, multi-scroll, multi-stability and hidden dynamical behaviors. However, there are few studies on the simultaneous occurrence of multiple complex dynamic behaviors in neural networks. No chaotic system of multi-scroll hyperchaotic hidden attractors based on neural network has been found yet. To solve the problem, in this paper, we propose a new locally active memristive Hopfield neural network (HNN) model based on a multi-segment function, which is affected by electromagnetic radiation and external current. The multi-scroll hyperchaotic hidden attractors are found in the memristive HNN for the first time. Theoretical analysis and numerical simulation results show that the memristive HNN model has no equilibrium point, and the number of multi-scroll attractors is controlled by the state equation parameters of the memristive synapse. In addition, the structures and number of scrolls are also affected by electromagnetic radiation and external current. At the same time, under the appropriate parameter conditions, by modifying the initial value of the system, the memristive HNN has a controllable number of coexisting attractors, showing extreme multi-stability. Finally, a memristive HNN analog circuit is designed. The hardware experiment results reproduce the multi-scroll dynamics phenomenon, which verifies the correctness of the theoretical analysis and numerical simulation. [ABSTRACT FROM AUTHOR]

Hopfield neural networks are favored by academia and industrial fields due to their abundant dynamics. In this paper, the dynamical behavior of a small Hopfield neural network (HNN) simultaneously stimulated by electromagnetic radiation and multi-level-logic pulse is investigated. Firstly, a modified HNN with three neurons is presented by selecting appropriate synapse weight coefficients. And the system model of the HNN under electromagnetic radiation and an electrical pulse is constructed. Then its equilibrium stabilities and nonlinear dynamical phenomena are analyzed by using numerical analysis methods including phase portraits, Lyapunov exponents, and bifurcation diagrams. The research results show that the neural network affected by electromagnetic radiation and a multi-level-logic pulse signal can generate chaotic multi-scroll attractors, which has not been observed in the previous investigation for the Hopfield-type neural networks. In addition, the number of the scroll can be easily changed by adjusting the electrical pulse signal. Circuit simulations based on the designed neural network circuit are carried out to confirm the numerical simulations. Finally, an HNN-based image encryption scheme is designed from the perspective of engineering applications. Performance evaluations demonstrate that the proposed image cryptosystem has good security. [ABSTRACT FROM AUTHOR]

Memristor is one of the most promising emerging technologies to solve the von Neumann bottleneck problem due to its non-volatile and binary characteristics. This paper studies the design method of high-efficiency logic circuit based on memristor. First, a multiple-input-multiple-output (MIMO) logic circuit design scheme based on IMPLY and AND logic is proposed, which can derive multiple new efficient logic operation methods and complete complex logic with fewer steps and memristors. Second, in order to perform rapid interactive operations between different rows, an alternating crossbar array structure is designed which can quickly complete cross-row logic operations. Finally, a high-efficient full adder (FA) based on MIMO logic and alternating crossbar array is proposed. To accomplish 32-bit add operation, the proposed FA needs 160 memristors and only 41 steps. Compared with the state of art FA, our work has faster execution speed and fewer memristors. [ABSTRACT FROM AUTHOR]

Background: Pneumatosis cystoides intestinalis is a rare intestinal disease characterized by single or multiple air cysts in the submucosa or subserosa of the intestinal wall. It is often accompanied by other gastrointestinal diseases. The clinical manifestations were nonspecific, and the diagnosis mainly depended on colonoscopy, imaging, and other auxiliary examinations. Case presentation: The reason for pneumatosis cystoides intestinalis's occurrence remains unclear. Enteral nutrition deficiency, especially the use of glucosidase inhibitors, may be involved in the pathogenesis of pneumatosis cystoides intestinalis in this patient. Here, we report a rare case of pneumatosis cystoides intestinalis in an elderly man who had been taking acarbose for diabetes for a long time. Colonoscopy of the patient was normal before taking acarbose. Conclusion: In patients with diabetes who are treated with glucosidase inhibitors for gastrointestinal symptoms, the possibility of pneumatosis cystoides intestinalis should be considered, and a thorough gastrointestinal examination should be performed in such patients. [ABSTRACT FROM AUTHOR]

Due to the complex characteristics of chaotic system, such as initial value sensitivity, periodicity and pseudo-randomness, chaotic sequences generated by chaotic system is very suitable for image data encryption after appropriate processing. This paper focuses on chaotic image encryption based on the improved Joseph algorithm, and proposes a hyperchaotic image encryption system based on N + 2 ring Joseph algorithm and reversible cellular automata. The initial value of the chaotic system is generated by the SHA-512 hash value of the original image, which makes the proposed encryption algorithm highly sensitive to the original image. In addition, the proposed N + 2 ring Joseph algorithm is formed by N chaotic sequences rolling forward like gears when scrambling each pixel position of the image. It greatly hides the pixel information of the original text, and increases the scrambling effect and the difficulty of being cracked. Furthermore, in the diffusion stage, we add the reversible cellular automata technology. On the basis of image shuffling, each pixel is further associated with other pixels, so that small changes of in pixel values can produce an avalanche effect. Finally, we conducted simulation experiments and safety analysis. The results illustrate that the encryption algorithm proposed in this paper has good encryption performance, and can effectively resist statistical attacks, differential attacks, known plaintext attacks, ciphertext only attacks, select plaintext attacks and select ciphertext attacks. In conclusion, it is a practical and secure image encryption algorithm. [ABSTRACT FROM AUTHOR]

Image segmentation implementation provides simplified and effective feature information of image. Neural network algorithms have made significant progress in the application of image segmentation task. However, few studies focus on the implementation of hardware circuits with high-efficiency analog calculations and parallel operations for image segmentation problem. In this paper, a memristor-based competitive Hopfield neural network circuit is proposed to deal with the image segmentation problem. In this circuit, the memristive cross array is applied to store synaptic weights and perform matrix operations. The competition module based on the Winner-take-all mechanism is composed of the competition neurons and the competition control circuit, which simplifies the energy function of the Hopfield neural network and realizes the output function. Operational amplifiers and ABM modules are used to integrate operations and process external input information, respectively. Based on these designs, the circuit can automatically implement iteration and update of data. A series of PSPICE simulations are designed to verify the image segmentation capability of this circuit. Comparative experimental results and analysis show that this circuit has effective improvements both in processing speed and segmentation accuracy compared with other methods. Moreover, the proposed circuit shows good robustness to noise and memristive variation. [ABSTRACT FROM AUTHOR]

Anti-PD-1-based therapy has resulted in a minimal clinical response in malignant gliomas. Gliomas contain numerous glioma-associated microglia/macrophages (GAMs), reported to contribute to an immunosuppressive microenvironment and promote glioma progression. However, whether and how GAMs affect anti-PD-1 immunotherapy in glioma remains unclear. Here, we demonstrated that M1-like GAMs contribute to the anti-PD-1 therapeutic response, while the accumulation of M2-like GAMs is associated with therapeutic resistance. Furthermore, we found that PD-L1 ablation reverses GAMs M2-like phenotype and is beneficial to anti-PD-1 therapy. We also demonstrated that tumor-induced impairment of the antigen-presenting function of GAMs could limit the antitumor immunity of CD4+ T cells in anti-PD-1 therapy. Our study highlights the impact of GAMs activation on anti-PD-1 treatment and provides new insights into the role of GAMs in regulating anti-PD-1 therapy in gliomas. [ABSTRACT FROM AUTHOR]

We study the following nonlinear critical elliptic equation - Δ u + ϵ Q (y) u = u N + 2 N - 2 , u > 0 in R N , where ϵ > 0 is small and N ≥ 5. Assuming that Q(y) is periodic in y 1 with period 1 and has a local minimum at 0 satisfying Q (0) > 0 , we prove the existence and local uniqueness of infinitely many bubbling solutions of it. This local uniqueness result implies that some bubbling solutions preserve the symmetry of the potential function Q(y), i.e., the bubbling solution whose blow-up set is { (j L , 0 , … , 0) : j = 0 , ± 1 , ± 2 , … , ± m } must be periodic in y 1 provided that ϵ goes to zero and L is any positive integer, where m is the number of the bubbles which is large enough but independent of ϵ. [ABSTRACT FROM AUTHOR]

Generally, people tend to learn or recall pleasant experiences during positive feelings. Similarly, people tend to learn or recall unpleasant things during negative feelings. The research in psychological field has demonstrated that human memory is closely related to emotion. On one hand, emotion helps store the memory that possesses the same emotion valence, which is known as the mood congruency memory (MCM). On the other hand, memory stored in a certain emotional state will be associated easily when the same emotion occurs, which is called mood-dependent memory (MDM). Inspired by the mechanisms of MCM and MDM, a memristor-based circuit of emotion-affected associative memory neural network is proposed in this work. The designed circuit mainly contains MCM module and MDM module. The functions, such as learning, forgetting, variable learning rate, MCM effect, MDM effect, and time interval, are implemented by the circuit. The simulation results in PSPICE show that the proposed memristive circuit can learn and associate the memory based on emotional effects like humans. [ABSTRACT FROM AUTHOR]

Restrictions on heavy metals, especially chromium, have encouraged alternative tanning systems that can reduce environmental and human health risks from conventional chrome-based tanning. In this work, metal-free combination tanning was developed by using vegetable tannins and a triazine-based syntan containing active chlorine groups (SACC). Specifically, the relationship between leather performance (e.g., hydrothermal stability and organoleptic properties) and technical protocols (e.g., types and dose of tannins) was systematically established. The optimized protocol involving a unique procedure (i.e., 10% SACC pre-tanning, shaving, and 25% wattle tanning) endowed the leather with high shrinkage temperature (~ 92 °C) and met the Chinese standards for shoe upper leather (QB/T 1873-2010). Our method not only produces zero chrome-containing solid wastes, but also uses ~ 75% less tannin for leather manufacture. The excellent leather performance was ascribed to the synergistic effects, where SACC and wattle diffused into collagen fibrils and may bind to collagen via covalent, hydrogen and ionic bonding, locking the hierarchical structure of collagen from microfibrils to fiber bundles. Moreover, we summarized these findings and proposed a diffusion-binding-locking mechanism, providing new insights for current tanning theory. Together with the biodegradable spent tanning liquor, this approach will underpin the development of sustainable leather manufacture. [ABSTRACT FROM AUTHOR]

Short-chain fatty acids (SCFAs) are monocarboxylates produced by the gut microbiota (GM) and result from the interaction between diet and GM. An increasing number of studies about the microbiota–gut–brain axis (MGBA) indicated that SCFAs may be a crucial mediator in the MGBA, but their roles have not been fully clarified. In addition, there are few studies directly exploring the role of SCFAs as a potential regulator of microbial targeted interventions in ischemic stroke, especially for clinical studies. This review summarizes the recent studies concerning the relationship between ischemic stroke and GM and outlines the role of SCFAs as a bridge between them. The potential mechanisms by which SCFAs affect ischemic stroke are described. Finally, the beneficial effects of SFCAs-mediated therapeutic measures such as diet, dietary supplements (e.g., probiotics and prebiotics), fecal microbiota transplantation, and drugs on ischemic brain injury are also discussed. [ABSTRACT FROM AUTHOR]

In this paper, the quasi-synchronization problem of stochastic memristive neural networks (MNNs) subject to deception attacks is investigated via hybrid impulsive control. Deception attacks in the MNN synchronization model, which involve the attacker attempting to inject some false data into sensor-to-controller channels to destroy the control signal, are investigated from the perspective of network communication security. The attack conditions are described using stochastic variables that obey the Bernoulli distribution. Inspired by existing impulsive differential inequalities, a new inequality is proposed, which is useful for dealing with quasi-synchronization in impulsive systems. Thereafter, sufficient conditions and the error bound are obtained for validating the quasi-synchronization of stochastic MNNs subject to deception attacks based on the proposed inequality and Lyapunov stability theory. In the absence of an attack, the globally complete synchronization problem for stochastic MNNs is investigated. Additionally, the attack effects and their mitigation through control parameter design are discussed. Finally, the simulation results are presented to validate the theoretical analysis. [ABSTRACT FROM AUTHOR]

Purpose: Doxorubicin-induced cardiotoxicity (DIC) is a common side effect of doxorubicin chemotherapy, and a major mechanism of DIC is inflammation. However, no effective method exists to prevent DIC. In the present study, we investigated the cardioprotective effects of nicorandil against DIC using multiparametric cardiac magnetic resonance (CMR) imaging and elucidated the anti-inflammatory properties of nicorandil in rat models. Methods: Male Sprague-Dawley rats received four weekly intraperitoneal doxorubicin doses (4 mg/kg/injection) to establish the DIC model. After treatment with or without nicorandil (3 mg/kg/day) or diazoxide (10 mg/kg/day) orally, all the groups underwent weekly CMR examinations, including cardiac function and strain assessment and T2 mapping, for 6 weeks. Additionally, blood samples and hearts were collected to examine inflammation and histopathology. Results: According to our results, the earliest DIC CMR parameter in the doxorubicin group was T2 mapping time prolongation compared with the DIC rats treated with nicorandil (doxorubicin+nicorandil group) at week 2. Subsequently, the left ventricular ejection fraction (LVEF) and global peak systolic myocardial strain in the doxorubicin group were significantly reduced, and nicorandil effectively inhibited these effects at week 6. Our results were confirmed by histopathological evaluations. Furthermore, nicorandil treatment had a protective effect against the doxorubicin-induced inflammatory response. Interestingly, similar protective results were obtained using the KATP channel opener diazoxide. Conclusion: Collectively, our findings indicate that nicorandil application ameliorates DIC in rats with significantly higher cardiac function and myocardial strain and less fibrosis, apoptosis and inflammatory cytokine production. Nicorandil prevents T2 abnormalities in the early stages of DIC, showing a high clinical value for early nicorandil treatment in chemotherapy patients. [ABSTRACT FROM AUTHOR]

The ion exchange in neurons can trigger time-varying magnetic fields. According to the superposition field principle, each neuron is exposed to the integrated magnetic field generated by the other neurons. This paper considers the effect of magnetic field coupling between two neurons on neuron dynamics. The magnetic flux of the memristor describes the impact of the magnetic field. According to the different coupling types of neurons, the excitatory coupling between excitatory neurons. The inhibitory magnetic coupling between excitatory and inhibitory neurons is also considered. And then, the excitatory and inhibitory magnetic field coupling is studied under different external excitation currents. The excitatory magnetic field coupling can promote the firing of neurons. When the intensity of inhibitory magnetic field coupling is large enough, the neuronal firing mode is static. The firing mode of neurons can be changed by adjusting the coupling intensity. Therefore, magnetic field coupling can provide new insights into the mechanism of information interaction between neurons. Finally, the excitability and inhibition of magnetic field coupling are improved by comparing magnetic field coupling with synaptic coupling. These results indicate that magnetic field coupling has the same function as a synapse to some extent and has the characteristics of radiation propagation. [ABSTRACT FROM AUTHOR]

Memristive neurons and memristive neural networks constructed based on memristors have important research significance for revealing the mystery of the brain. This paper proposes a compound hyperbolic tangent cubic nonlinear memristor, which has nonvolatile memory characteristics and local activity characteristics. In particular, the memristor also has three stable pinched hysteresis loops under different initial values. The memristor is applied to Fitzhugh–Nagumo neuron and Hindmarsh–Rose neuron to establish five different memristive neural models, and a series of firing dynamics analysis are carried out on them. At the same time, we not only discuss multiple firing patterns on a single memristive neuron and double memristive coupled neurons, but also compare which neuron and which coupled neural network the proposed memristor is more suitable for, which is a lack of comprehensive investigation in the published research. Furthermore, digital circuit experiment is performed on the FPGA development board to verify the firing mechanism of these memristive neural models, which has potential application value for some practical projects. [ABSTRACT FROM AUTHOR]

How to improve the storage density and solve the sneak path current problem has become the key to the design of nonvolatile memristive memory. In this paper, a high storage density and high reading/writing speed 3D crossbar array non-volatile memory based on pure memristors is proposed. The main works are as follows: (1) an extensible memristive cluster is proposed, (2) a memristive switch is designed, and (3) a 3D crossbar array non-volatile memory is constructed. The memory cell of the 3D crossbar array non-volatile memory is constructed by pure memristors and can be extended by adding memristor in a memristive cluster or adding memristive clusters in a memory cell to realize multi-value storage. The memristive switch can effectively reduce the sneak path current effect. The pure memristive memory cell solves the conflict between the storage density and sneak path current effect and greatly improves the storage density of memory cells. Furthermore, the 3D cross-array structure allows different memory cells on the same layer or different layers to be read and written in parallel, which greatly improves the speed of reading and writing. Simulations with PSpice verifies that the proposed memristive cluster can realize stable multi-value storage, has higher storage density, faster reading and writing speed, fewer input ports and output ports, better stability, and lower power consumption. Moreover, the structure proposed in this paper can also be used in the circuit design of the neuromorphic network, logic circuit, and other memristive circuits. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the following critical elliptic equation - Δ u + V (y) u = u N + 2 N - 2 , u > 0 , in R N , u ∈ H 1 (R N) , where V(y) is a bounded non-negative function in R N. Assuming that V (y) = V (| y ^ | , y ∗) , y = (y ^ , y ∗) ∈ R 4 × R N - 4 and gluing together bubbles with different concentration rates, we obtain new solutions provided that N ≥ 7 , whose concentrating points are close to the point (r 0 , y 0 ∗) which is a stable critical point of the function r 2 V (r , y ∗) satisfying r 0 > 0 and V (r 0 , y 0 ∗) > 0. In order to construct such new bubble solutions for the above problem, we first prove a non-degenerate result for the positive multi-bubbling solutions constructed in Peng et al. (J Funct Anal 274:2606–2633, 2018) by some local Pohozaev identities, which is of great interest independently. Moreover, we give an example which satisfies the assumptions we impose. [ABSTRACT FROM AUTHOR]

Abstract Calcium-dependent protein kinases (CDPKs) can decode upstream Ca2+ flux changes and then phosphorylate downstream signaling molecules while transducing Ca2+ signaling, thereby engaging in plant growth, development, and biotic and abiotic stress responses. Although the CDPK gene family has been identified and analyzed in various plants, knowledge concerning CDPKs in woodland strawberry (Fragaria vesca) and their expression profiles in response to biotic stresses remain very limited. In this study, we identified 19 CDPK genes in F. vesca. Phylogenetic analysis separated the FvCDPKs into 4 subgroups. Gene structure and protein motif analysis for these FvCDPKs illustrated the different evolutionary relationships within subgroups. Collinearity analysis revealed 18 syntenic pairs between AtCDPKs and FvCDPKs and 5 syntenic pairs between OsCDPKs and FvCDPKs. Biotic stress-related cis-acting elements were detected in the promoter regions of FvCDPKs. Expression profiling of FvCDPKs was performed in different tissues under different biotic stresses, including Phytophthora cactorum (PC), Botrytis cinerea (BC) and strawberry vein banding virus (SVBV) exposure. The expression pattern of FvCDPKs during pathogen infection demonstrated their distinctive functional responses to biotic stresses. In summary, this study provides new insight into FvCDPKs and their response under biotic stresses. [ABSTRACT FROM AUTHOR]

Neural networks are favored by academia and industry because of their diversity of dynamics. However, it is difficult for ring neural networks to generate complex dynamical behaviors due to their special structure. In this paper, we present a memristive ring neural network (MRNN) with four neurons and one non-ideal flux-controlled memristor. The memristor is used to describe the effect of external electromagnetic radiation on neurons. The chaotic dynamics of the MRNN is investigated in detail by employing phase portraits, bifurcation diagrams, Lyapunov exponents and attraction basins. Research results show that the MRNN not only can generate abundant chaotic and hyperchaotic attractors but also exhibits complex multistability dynamics. Meanwhile, an analog MRNN circuit is experimentally implemented to verify the numerical simulation results. Moreover, a medical image encryption scheme is constructed based on the MRNN from a perspective of practical engineering application. Performance evaluations demonstrate that the proposed medical image cryptosystem has several advantages in terms of keyspace, information entropy and key sensitivity, compared with cryptosystems based on other chaotic systems. Finally, hardware experiment using the field-programmable gate array (FPGA) is carried out to verify the designed cryptosystem. [ABSTRACT FROM AUTHOR]

Wellbore instability is frequently encountered in the process of shale oil drilling. Due to the anisotropic strength characteristics of shale, most conventional models are not suitable for wellbore stability analysis in layered formation. In this paper, taking the continental shale in the north of Songliao Basin as the research object, the anisotropic elasticity and strength parameters of shale are measured experimentally. Based on pore elastic mechanics, an anisotropic wellbore stability model of layered shale is established, and the variation of collapse pressure under different formation conditions is analyzed. The results show that considering the elastic and strength anisotropy, the collapse pressure is the largest, and the influence of strength anisotropy is significantly greater than that of elastic anisotropy. The stability of horizontal wells drilled along the horizontal maximum principal stress is the optimal, followed by vertical wells and small angle directional wells. The actual drilling conditions of G101 well are basically consistent with the prediction results, which confirms the accuracy of the model. According to the traditional wellbore stability model, the equivalent density of collapse pressure in horizontal well section is 1.58 ~ 1.76 g/cm3. It is much lower than the collapse density of 1.86 g/cm3 calculated by the anisotropic model in this study. The findings of this study can help for better understanding of the mechanism of wellbore instability in horizontal wells in shale formations. The model can be used to guide the drilling engineering design of shale oil horizontal wells and reduce the losses caused by the instability of the wellbore. [ABSTRACT FROM AUTHOR]

The traditional Pavlov associative memory circuit includes reinforcement and extinction in the classical conditioned reflex. In fact, in addition to the reinforcement and extinction, classical conditioned reflex is accompanied by several higher-order effects. The blocking process is a very common phenomenon in organisms, and it is one of the higher-order effects. In this paper, a circuit based on memristor is designed to realize blocking based on Pavlov's associative memory. First of all, the circuit constructs a complete neural network circuit. The input neuron circuit composed of CMOS and other devices replaces the DC signal source and can generate pulse signals, making the circuit more bionic. Secondly, advanced neural activities such as learning memory, associative memory, and forgetting are realized. At the same time, the blocking process was realized on the basis of classical conditioned reflex, and the circuit successfully simulated the appearance and disappearance of the blocking phenomenon. Finally, the circuit simulation was carried out through PSPICE, and the simulation results proved the correctness of the circuit design. [ABSTRACT FROM AUTHOR]

In the existing affective associative memory neural network circuits, the change of emotions in the affective associative learning and forgetting processes is abrupt and the intensity of emotions is invariable. In fact, the transition from one emotion to another is a gradual process. In this paper, to realize the progressive changes of emotional intensity in the affective associative memory neural network, the gradual learning, gradual forgetting and gradual transferring processes of emotions are proposed and the memristor-based circuit of the affective associative memory neural network is designed. In the designed circuit, the firing frequency of output neurons is closely correlated with the intensity of emotions. The higher the firing frequency of output neurons, the stronger the emotional intensity. Based on the associative memory rule, the dynamical change of the synaptic weights leads to the gradual variation of the frequencies of output neurons. Thus, the function of variable emotional intensity can be realized and the gradual processes can be achieved. The PSPICE simulation results are given to verify that the proposed circuit could realize the affective learning, forgetting and transferring functions with gradual processes. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate radiomic models for preoperative prediction of intraductal component in invasive breast cancer (IBC-IC) using the intratumoral and peritumoral features derived from dynamic contrast-enhanced MRI (DCE-MRI). Methods: The prediction models were developed in a primary cohort of 183 consecutive patients from September 2017 to December 2018, consisting of 45 IBC-IC and 138 invasive breast cancers (IBC). The validation cohort of 111 patients (27 IBC-IC and 84 IBC) from February 2019 to January 2020 was enrolled to test the prediction models. A total of 208 radiomic features were extracted from the intratumoral and peritumoral regions of MRI-visible tumors. Then the radiomic features were selected and combined with clinical characteristics to construct predicting models using the least absolute shrinkage and selection operator. The area under the curve (AUC) of receiver operating characteristic, sensitivity, and specificity were used to evaluate the performance of radiomic models. Results: Four radiomic models for prediction of IBC-IC were built including intratumoral radiomic signature, peritumoral radiomic signature, peritumoral radiomic nomogram, and combined intratumoral and peritumoral radiomic signature. The combined intratumoral and peritumoral radiomic signature had the optimal diagnostic performance, with the AUC, sensitivity, and specificity of 0.821 (0.758–0.874), 0.822 (0.680–0.920), and 0.739 (0.658–0.810) in the primary cohort and 0.815 (0.730–0.882), 0.778 (0.577–0.914), and 0.738 (0.631–0.828) in the validation cohort. Conclusions: The radiomic model based on the combined intratumoral and peritumoral features from DCE-MRI showed a good ability to preoperatively predict IBC-IC, which might facilitate the individualized surgical planning for patients with breast cancer before breast-conserving surgery. Key Points: •·Preoperative prediction of intraductal component in invasive breast cancer is crucial for breast-conserving surgery planning. • Peritumoral radiomic features of invasive breast cancer contain useful information to predict intraductal components. •·Radiomics is a promising non-invasive method to facilitate individualized surgical planning for patients with breast cancer before breast-conserving surgery. [ABSTRACT FROM AUTHOR]

This paper compares two common environmental regulations: size control and intensity control. We compare the impacts of the two environmental regulations on firm competitiveness and market structure. Our findings show that size control and intensity control both improve firm competitiveness but through very different mechanisms. Intensity control motivates firms to innovate and develop cost-saving and environmentally friendly input and product portfolios to reduce pollution and improve competitiveness, which is consistent with the Porter hypothesis. In contrast, size control creates no incentive for surviving firms to innovate or reduce pollution, and it acts as a windfall benefit that grants these firms a less competitive market and thus firm competitiveness, a mechanism that is underexplored by the Porter hypothesis. Intensity control also results in a less competitive market but to a much smaller extent than size control. Therefore, intensity control performs better than size control from the perspective of social welfare. [ABSTRACT FROM AUTHOR]

In the multimedia big data, the demand for personalized multimedia recommendation algorithm is increasing to ease the multimedia information overload. The multimedia recommendation system has been applied in various industries and has been playing a significant role. With the development of multimedia big data, developing multimedia recommendation algorithms can effectively be used in multimedia data. However, a large number of prevailing recommendation systems cannot meet the multimedia recommendation requirements, since they ignore the user-item interactions with multimedia content. This essay realizes the multimedia recommendation based on probability graphical model, to deal with the cold start and data sparsity involved in collaborative filtering recommendation, proposing that add the user tag to user-item model. The essay optimizes the multimedia recommendation algorithm based on undirected graphical model and tests it with singular value decomposition, clustering and Naïve Bayes separately. The essay also builds the checklist recommendation model and experiments extensively for comparison with the conditional multimedia recommendation algorithm, by using PersonalRank algorithm based on random-walk to work out the weight coefficient of the user tag. At the same time, the essay enhances the probability-graph multimedia recommendation algorithm by dimensionality reduction and clustering, with the result of noticeably improved precision and recall. [ABSTRACT FROM AUTHOR]

In this paper, combining an approximating argument and some local Pohozaev identities, we obtain infinitely many weak solutions for a nonlocal problem with a Hardy potential and critical growth. [ABSTRACT FROM AUTHOR]

The study of dynamics on artificial neurons and neuronal networks is of great significance to understand brain functions and develop neuromorphic systems. Recently, memristive neuron and neural network models offer great potential in the investigation of neurodynamics. Many chaotic dynamics including chaos, transient chaos, hyperchaos, coexisting attractors, multistability, and extreme multistability have been researched based on the memristive neurons and neural networks. In this review, we firstly introduce the basic definition of chaotic dynamics and review several traditional artificial neuron and neural network models. Then we categorize memristive neuron and neural network models with different biological function mechanisms into five types: memristive autapse neuron, memristive synapse-coupled bi-neuron network, memristive synaptic weight neural network, neuron under electromagnetic radiation, and neural network under electromagnetic radiation. The modeling mechanisms of each type are explained and described in detail. Furthermore, the pioneer works and some recent important papers related to those types are introduced. Finally, some open problems in this field are presented to further explore future work. [ABSTRACT FROM AUTHOR]

In this communication, sulfate dodecyl sodium (SDS)-induced thermodynamics and conformational changes of collagen were studied. We used ultrasensitive differential scanning calorimetry (US-DSC) to directly monitor the thermal transition of collagen in the presence of SDS. The results show that SDS affects the conformation and thermal stability of collagen very differently depending on its concentrations. At CSDS ≤ 0.05 mM, the enhanced thermal stability of collagen indicates the stabilizing effect by SDS. However, a further increase of SDS leads to the denaturation of collagen, verifying the well-known ability of SDS to unfold proteins. This striking difference in thermodynamics and conformational changes of collagen caused by SDS concentrations can be explained in terms of their interactions. With increasing SDS, the binding of SDS to collagen can be dominated by electrostatic interaction shifting to hydrophobic interaction, and the latter plays a key role in loosening and unfolding the triple-helix structure of collagen. The important finding in the present study is the stabilizing effect of SDS on collagen molecules at extreme low concentration. [ABSTRACT FROM AUTHOR]

COVID-19 is now a severe threat to global health. Facing this pandemic, we developed a space-encoding microfluidic biochip for high-throughput, rapid, sensitive, simultaneous quantitative detection of SARS-CoV-2 antigen proteins and IgG/IgM antibodies in serum. The proposed immunoassay biochip integrates the advantages of graphene oxide quantum dots (GOQDs) and microfluidic chip and is capable of conducting multiple SARS-CoV-2 antigens or IgG/IgM antibodies of 60 serum samples simultaneously with only 2 μL sample volume of each patient. Fluorescence intensity of antigens and IgG antibody detection at emission wavelength of ~680 nm was used to quantify the target concentration at excitation wavelength of 632 nm, and emission wavelength of ~519 nm was used during the detection of IgM antibodies at excitation wavelength of 488 nm. The method developed has a large linear quantification detection regime of 5 orders of magnitude, an ultralow detection limit of ~0.3 pg/mL under optimized conditions, and less than 10-min qualitative detection time. The proposed biosensing platform will not only greatly facilitate the rapid diagnosis of COVID-19 patients, but also provide a valuable screening approach for infected patients, medical therapy, and vaccine recipients. [ABSTRACT FROM AUTHOR]

In this paper, A new 3D multi-scroll hidden attractor chaotic system is proposed. The proposed system has chaotic attractors with no equilibrium point, one stable equilibrium point and several stable equilibria. And these three types of hidden attractors can be obtained just through varying a parameter of the system. In the other hand, multi-scroll attractors are generated by a piecewise linear function. The phase diagrams and basins of attraction are respectively used to prove that this system has multi-scroll attractors and hidden attractors. There are also some other powerful tools to analyze the dynamical characteristics of this system like Lyapunov spectrums, bifurcation diagrams and Poincaré maps. This system has great application prospects in communication encryption due to the complex dynamic behaviors of the multi-scroll chaotic attractors and the security of the hidden attractors. Moreover, we accomplish the circuit experiment, and verify the feasibility of each case of the multi-scroll hidden attractor chaotic system. [ABSTRACT FROM AUTHOR]

Fractional calculus is closer to reality and has the same memory characteristics as memristor. Therefore, a fractional-order multistable locally active memristor is proposed for the first time in this paper, which has infinitely many coexisting pinched hysteresis loops under different initial states and wide locally active regions. Through the theoretical and numerical analysis, it is found that the fractional-order memristor has stronger locally active and memory characteristics and wider nonvolatile ranges than the integer-order memristor. Furthermore, this fractional-order memristor is applied in a chaotic system. It is found that oscillations occur only within the locally active regions. This chaotic system not only has complex and rich nonlinear dynamics such as infinitely many discrete equilibrium points, multistability and anti-monotonicity but also produces two new phenomena that have not been found in other chaotic systems. The first one is transient transition: the behavior of local chaos and local period transition alternately occurring. The second is state jump: the behavior of local period-4 oscillation or local chaotic oscillation jumping to local period-2 oscillation. Finally, the circuit simulation of the fractional-order multistable locally active memristive chaotic system using PSIM is carried out to verify the validity of the numerical simulation results. [ABSTRACT FROM AUTHOR]

Petroleum refinery wastewater (PRWW) that contains recalcitrant components as the major portion of constituents is difficult to treat by conventional biological processes. An effective and economical biological treatment process was established to treat industrial PRWW with an influent COD of over 2500 mg L−1 in this research. This process is mainly composed of internal circulation biological aerated filter (ICBAF), hydrolysis acidfication (HA), two anaerobic–aerobic (A/O) units, a membrane biological reactor (MBR), and ozone-activated carbon (O3-AC) units. The results showed that, overall, this system removed over 94% of the COD, BOD5, ammonia nitrogen (NH4+-N) and phosphorus in the influent, with the ICBAF unit accounting for 54.6% of COD removal and 83.6% of BOD5 removal, and the two A/O units accounting for 33.3% of COD removal and 9.4% of BOD5 removal. The degradation processes of eight organic pollutants and their removal via treatment were also analyzed. Furthermore, 26 bacteria were identified in this system, with Proteobacteria and Acidobacteria being the most dominant. Ultimately, the treatment process exhibited good performance in degrading complex organic pollutants in the PRWW. [ABSTRACT FROM AUTHOR]

The existing chaotic image encryption algorithms have common defects: (i) ciphertext does not participate in the generation processes of chaotic pseudo-random sequences and key sequences; (ii) the entire encryption process does not have a closed-loop structure. In order to solve above problems, in this paper, an image segmentation encryption algorithm based on hyperchaotic system is proposed. We decompose the scrambled sequence into three sequences of different lengths: S1, S2 and S3. Then, the initial values of the chaotic system are updated by the sequences S2 and S3 and using the updated initial value iterates the chaotic system and generates the key sequence K3, and the sequence S1 is encrypted by the sequence K3 to obtain the cipher sequence C1, using the sequences C1 and S3 updates the initial value of the chaotic system, and using the updated initial value iterates the chaotic system and generates the key sequence K4, and using the sequence K4 encrypts the sequence S2 to obtain the cipher sequence C2. Thus, ciphertext participates in the generation processes of chaotic pseudo-random sequences and key sequences, and the entire encryption process has a closed-loop structure. The experimental results show that the encryption algorithm has high security and sensitivity. [ABSTRACT FROM AUTHOR]

Legumes, unlike other plants, have the ability to establish symbiosis with nitrogen-fixing rhizobia. It has been theorized that a unique property of legume root cortical cells enabled the initial establishment of rhizobial symbiosis1–3. Here we show that a SHORTROOT–SCARECROW (SHR–SCR) stem cell program in cortical cells of the legume Medicago truncatula specifies their distinct fate. Regulatory elements drive the cortical expression of SCR, and stele-expressed SHR protein accumulates in cortical cells of M. truncatula but not Arabidopsis thaliana. The cortical SHR–SCR network is conserved across legume species, responds to rhizobial signals, and initiates legume-specific cortical cell division for de novo nodule organogenesis and accommodation of rhizobia. Ectopic activation of SHR and SCR in legumes is sufficient to induce root cortical cell division. Our work suggests that acquisition of the cortical SHR–SCR module enabled cell division coupled to rhizobial infection in legumes. We propose that this event was central to the evolution of rhizobial endosymbiosis. Repurposing of an SHR–SCR stem cell program in the legume root cortex enables rhizobial symbiosis. [ABSTRACT FROM AUTHOR]

In recent years, the deterioration of the environment has become a top concern in China. This paper investigates the factors that influence the satisfaction of urban residents with the local air and water quality in Chinese cities by using a unique dataset collected through an interview survey with about 25,000 participants. The study shows that a respondent's true rating could be systematically different from her stated choice in the survey. Empirical results reveal that respondents have a tendency to underreport their level of satisfaction. In addition, a respondent's true level of satisfaction is more likely to be lower if the individual is male and younger. Educational attainment is also correlated with a respondent's level of satisfaction. [ABSTRACT FROM AUTHOR]

Over the last few years, lots of chaotic image encryption schemes have been proposed. However, most of the schemes are permutation-diffusion architectures which still have some shortcomings, such as weak key streams, small key spaces, small information entropy, and so on. To eliminate the above weaknesses, in this paper, we propose a hyper-chaotic image encryption scheme based on quantum genetic algorithm (QGA) and compressive sensing (CS), which is a new image encryption scheme and has not been proposed so far. Firstly, QGA can update the population with the quantum rotation gate, which can enhance the randomness of the population and avoid falling into local optimum. Then compressive sensing technology is used to reduce data storage and speed up the encryption and decryption process. Moreover, we utilize the SHA-512 hash function of the plain image to calculate the initial values of the hyper-chaotic system, which is capable of enhancing the relationships between encryption schemes and plain images. The simulation experiments and security analysis reveal that the proposed scheme is more efficient in resisting statistical attack and plaintext attack and shows better performance in peak signal-to-noise ratio (PSNR) and information entropy compared with other image encryption schemes based on chaos theory. [ABSTRACT FROM AUTHOR]