Ultimately, many mathematical simulations which verify the acquired endodontic infections results were shown.However several AI-based designs have been founded for COVID-19 diagnosis, your machine-based diagnostic space remains continuing, generating further efforts to be able to overcome this crisis very important. So, we attemptedto develop a brand-new characteristic variety (FS) method as a result of chronic requirement of the best method to pick functions and also to build a design to predict the actual COVID-19 computer virus through specialized medical texts. This study engages any fresh designed technique motivated with the flamingo’s behavior to find a near-ideal characteristic part with regard to precise proper diagnosis of COVID-19 people. The very best features are generally picked employing a two-stage. From the first stage, we carried out a phrase weighting approach, that that’s RTF-C-IEF, in order to quantify the value of the options taken out. The next stage consists of employing a recently produced function selection method referred to as improved upon binary flamingo search criteria (IBFSA), which in turn chooses the most important and pertinent characteristics regarding COVID-19 individuals. The particular recommended multi-strategy development procedure reaches the center of this study to enhance the search formula. The main target is to broaden the actual algorithm’s abilities by simply escalating range and assist studying the formula lookup area. Furthermore, a binary procedure was applied to further improve the particular overall performance of standard Financial services authority to really make it befitting binary FS problems. A pair of datasets, totaling 3053 along with 1446 cases, were used to gauge the proposed style in line with the Assist Vector Equipment (SVM) and other classifiers. The outcome indicated that IBFSA has got the very best overall performance compared to numerous earlier swarm algorithms. It had been observed, that the variety of feature subsets which are chosen has also been considerably decreased by 88% along with acquired the most effective world-wide best characteristics.Within this paper, all of us think about the quasilinear parabolic-elliptic-elliptic attraction-repulsion program Money \beginequation
onumber \left\ \beginsplit &u_t =
abla\cdot(D(u)
abla u)-\chi
abla\cdot(u
abla v)+\xi
abla\cdot(u
abla w),&\qquad &x\in\Omega,\,t>0, \\ & 0 = \Delta v-\mu_1(t)+f_1(u),&\qquad &x\in\Omega,\,t>0, \\ &0 = \Delta w-\mu_2(t)+f_2(u),&\qquad &x\in\Omega,\,t>0 \endsplit \right. \endequation $ under homogeneous Neumann boundary conditions in a smooth bounded domain $ \Omega\subset\mathbbR^n, \ n\geq2 $. The nonlinear diffusivity $ D $ and nonlinear signal productions $ f_1, f_2 $ are supposed to extend the prototypes $ \beginequation
onumber D(s) = (1+s)^m-1,\ f_1(s) = (1+s)^\gamma_1,\ f_2(s) = (1+s)^\gamma_2,\ s\geq0,\gamma_1,\gamma_2>0,m\in\mathbbR section Infectoriae . \endequation $ We proved that if $ \gamma_1 > \gamma_2 $ and $ 1+\gamma_1 signaling pathway -m > \frac2n $, then the solution with initial mass concentrating enough in a small ball centered at origin will blow up in finite time. However, the system admits a global bounded classical solution for suitable smooth initial datum when $ \gamma_2 less then 1+\gamma_1 less then \frac2n+m $.As an indispensable part of large Computer Numerical Control machine tool, rolling bearing faults diagnosis is particularly important. However, due to the imbalanced distribution and partially missing of collected monitoring data, such diagnostic issue generally emerging in manufacturing industry is still hardly to be solved. Thus, a multilevel recovery diagnosis model for rolling bearing faults from imbalanced and partially missing monitoring data is formulated in this paper. Firstly, a regulable resampling plan is designed to handle the imbalanced distribution of data. Secondly, a multilevel recovery scheme is formed to deal with partially missing. Thirdly, an improved sparse autoencoder based multilevel recovery diagnosis model is built to identify the health status of rolling bearings. Finally, the diagnostic performance of the designed model is verified by artificial faults and practical faults tests, respectively.Healthcare is the method of keeping or enhancing physical and mental well-being with its aid of illness and injury prevention, diagnosis, and treatment. The majority of conventional healthcare practices involve manual management and upkeep of client demographic information, case histories, diagnoses, medications, invoicing, and drug stock upkeep, which can result in human errors that have an impact on clients. By linking all the essential parameter monitoring equipment through a network with a decision-support system, digital health management based on Internet of Things (IoT) eliminates human errors and aids the doctor in making more accurate and timely diagnoses. The term “Internet of Medical Things” (IoMT) refers to medical devices that have the ability to communicate data over a network without requiring human-to-human or human-to-computer interaction. Meanwhile, more effective monitoring gadgets have been made due to the technology advancements, and these devices can typically record a few physiological signals simultaneously, including the electrocardiogram (ECG) signal, the electroglottography (EGG) signal, the electroencephalogram (EEG) signal, and the electrooculogram (EOG) signal. Yet, there has not been much research on the connection between digital health management and multi-modal signal monitoring. To bridge the gap, this article reviews the latest advancements in digital health management using multi-modal signal monitoring. Specifically, three digital health processes, namely, lower-limb data collection, statistical analysis of lower-limb data, and lower-limb rehabilitation via digital health management, are covered in this article, with the aim to fully review the current application of digital health technology in lower-limb symptom recovery.The utilization of molecular structure topological indices is currently a standing operating procedure in the structure-property relations research, especially in QSPR/QSAR study. In the past several year, generous molecular topological indices related to some chemical and physical properties of chemical compounds were put forward. Among these topological indices, the VDB topological indices rely only on the vertex degree of chemical molecular graphs. The VDB topological index of an $ n $-order graph $ G $ is defined as TI(G) = \sum\limits_1\leq i\leq j\leq n-1m_ij\psi_ij, $ where $ \\psi_ij\ $ is a set of real numbers, $ m_ij $ is the quantity of edges linking an $ i $-vertex and another $ j $-vertex. Numerous famous topological indices are special circumstance of this expression. f-benzenoids are a kind of polycyclic aromatic hydrocarbons, present in large amounts in coal tar. Studying the properties of f-benzenoids via topological indices is a worthy task. In this work the extremum $ TI $ of f-benzenoids with given number of edges were determined. The main idea is to construct f-benzenoids with maximal number of inlets and simultaneously minimal number of hexagons in $ \Gamma_m $, where $ \Gamma_m $ is the collection of f-benzenoids with exactly $ m $ $ (m\geq19) $ edges. As an application of this result, we give a unified approach of VDB topological indices to predict distinct chemical and physical properties such as the boiling point, $ \pi $-electrom energy, molecular weight and vapour pressure etc. of f-benzenoids with fixed number of edges.A two-dimensional diffusion process is controlled until it enters a given subset of $ \mathbbR^2 $. The aim is to find the control that minimizes the expected value of a cost function in which there are no control costs. The optimal control can be expressed in terms of the value function, which gives the smallest value that the expected cost can take. To obtain the value function, one can make use of dynamic programming to find the differential equation it satisfies. This differential equation is a non-linear second-order partial differential equation. We find explicit solutions to this non-linear equation, subject to the appropriate boundary conditions, in important particular cases. The method of similarity solutions is used.This paper presents a mixed active controller (NNPDCVF) that combines cubic velocity feedback with a negative nonlinear proportional derivative to reduce the nonlinear vibrating behavior of a nonlinear dynamic beam system. Multiple time-scales method treatment is produced to get the mathematical solution of the equations for the dynamical modeling with NNPDCVF controller. This research focuses on two resonance cases which are the primary and 1/2 subharmonic resonances. Time histories of the primary system and the controller are shown to demonstrate the reaction with and without control. The time-history response, as well as the impacts of the parameters on the system and controller, are simulated numerically using the MATLAB program. Routh-Hurwitz criterion is used to examine the stability of the system under primary resonance. A numerical simulation, using the MATLAB program software, is obtained to show the time-history response, the effect of the parameters on the system and the controller. An investigation is done into how different significant effective coefficients affect the resonance’s steady-state response. The results demonstrate that the main resonance response is occasionally impacted by the new active feedback control’s ability to effectively attenuate amplitude. Choosing an appropriate control Gaining quantity can enhance the effectiveness of vibration control by avoiding the primary resonance zone and unstable multi-solutions. Optimum control parameter values are calculated. Validation curves are provided to show how closely the perturbation and numerical solutions are related.The imbalanced data makes the machine learning model seriously biased, which leads to false positive in screening of therapeutic drugs for breast cancer. In order to deal with this problem, a multi-model ensemble framework based on tree-model, linear model and deep-learning model is proposed. Based on the methodology constructed in this study, we screened the 20 most critical molecular descriptors from 729 molecular descriptors of 1974 anti-breast cancer drug candidates and, in order to measure the pharmacokinetic properties and safety of the drug candidates, the screened molecular descriptors were used in this study for subsequent bioactivity, absorption, distribution metabolism, excretion, toxicity, and other prediction tasks. The results show that the method constructed in this study is superior and more stable than the individual models used in the ensemble approach.The purpose of the article is to investigate Dirichlet boundary-value problems of the fractional p-Laplacian equation with impulsive effects. By using the Nehari manifold method, mountain pass theorem and three critical points theorem, some new results are achieved under more general growth conditions. In addition, this paper weakens the commonly used p-suplinear and p-sublinear growth conditions.This research deals with formulating a multi-species eco-epidemiological mathematical model when the interacting species compete for the same food sources and the prey species have some infection. It is assumed that infection does not spread vertically. Infectious diseases severely affect the population dynamics of prey and predator. One of the most important factors in population dynamics is the movement of species in the habitat in search of resources or protection. The ecological influences of diffusion on the population density of both species are studied. The study also deals with the analysis of the effects of diffusion on the fixed points of the proposed model. The fixed points of the model are sorted out. The Lyapunov function is constructed for the proposed model. The fixed points of the proposed model are analyzed through the use of the Lyapunov stability criterion. It is proved that coexisting fixed points remain stable under the effects of self-diffusion, whereas, in the case of cross-diffusion, Turing instability exists conditionally. Moreover, a two-stage explicit numerical scheme is constructed, and the stability of the said scheme is found by using von Neumann stability analysis. Simulations are performed by using the constructed scheme to discuss the model’s phase portraits and time-series solution. Many scenarios are discussed to display the present study’s significance. The impacts of the transmission parameter The influence of residents’ income on mental health is complex, and there are heterogeneous effects of residents’ income on different types of mental health. Based on the annual panel data of 55 countries from 2007 to 2019, this paper divides residents’ income into three dimensions absolute income, relative income and income gap. Mental health is divided into three aspects subjective well-being, prevalence of depression and prevalence of anxiety. Panel Tobit model is used to study the heterogeneous impact of residents’ income on mental health. The results show that, on the one hand, different dimensions of residents’ income have a heterogeneous impact on mental health, specifically, absolute income has a positive impact on mental health, while relative income and income gap have no significant impact on mental health. On the other hand, the impact of different dimensions of residents’ income on different types of mental health is heterogeneous. Specifically, absolute income and income gap have heterogeneous effects on different types of mental health, while relative income has no significant impact on different types of mental health.Cooperation is an indispensable behavior in biological systems. In the prisoner’s dilemma, due to the individual’s selfish psychology, the defector is in the dominant position finally, which results in a social dilemma. In this paper, we discuss the replicator dynamics of the prisoner’s dilemma with penalty and mutation. We first discuss the equilibria and stability of the prisoner’s dilemma with a penalty. Then, the critical delay of the bifurcation with the payoff delay as the bifurcation parameter is obtained. In addition, considering the case of player mutation based on penalty, we analyze the two-delay system containing payoff delay and mutation delay and find the critical delay of Hopf bifurcation. Theoretical analysis and numerical simulations show that cooperative and defective strategies coexist when only a penalty is added. The larger the penalty is, the more players tend to cooperate, and the critical time delay of the time-delay system decreases with the increase in penalty. The addition of mutation has little effect on the strategy chosen by players. The two-time delay also causes oscillation.With the evolution of society, the world has entered a moderate stage of aging. Not surprisingly, the aging problem in the world is getting more intense, resulting in the increasing demand for higher-quality and well-organized medical and elderly care services. To cope with that, many researchers have dedicated themselves to advancing the medical care system based on data or platforms. However, they have ignored the life cycle, health service and management and the inevitable shift of living scenarios for the elderly. Therefore, the study aims to improve health conditions and enhance senior citizens’ life quality and happiness index. In this paper, we build a unified body for people in their old age, bridging the disconnection between medical care and elderly care and constructing the “five-in-one” comprehensive medical care framework. It should be mentioned that the system takes the human life cycle as its axis, relies on the supply side and supply chain management, integrates medicine, industry, literature and science as methods, and takes health service management as a requirement. Furthermore, a case study on upper limb rehabilitation is elaborated along the “five-in-one” comprehensive medical care framework to confirm the effectiveness of the novel system.Coronary artery centerline extraction in cardiac computed tomography angiography (CTA) is an effectively non-invasive method to diagnose and evaluate coronary artery disease (CAD). The traditional method of manual centerline extraction is time-consuming and tedious. In this study, we propose a deep learning algorithm that continuously extracts coronary artery centerlines from CTA images using a regression method. In the proposed method, a CNN module is trained to extract the features of CTA images, and then the branch classifier and direction predictor are designed to predict the most possible direction and lumen radius at the given centerline point. Besides, a new loss function is developed for associating the direction vector with the lumen radius. The whole process starts from a point manually placed at the coronary artery ostia, and terminates until tracking the vessel endpoint. The network was trained using a training set consisting of 12 CTA images and the evaluation was performed using a testing set consisting of 6 CTA images. The extracted centerlines had an average overlap (OV) of 89.19%, overlap until first error (OF) of 82.30%, and overlap with clinically relevant vessel (OT) of 91.42% with manually annotated reference. Our proposed method can efficiently deal with multi-branch problems and accurately detect distal coronary arteries, thereby providing potential help in assisting CAD diagnosis.Due to the complexity of three-dimensional (3D) human pose, it is difficult for ordinary sensors to capture subtle changes in pose, resulting in a decrease in the accuracy of 3D human pose detection. A novel 3D human motion pose detection method is designed by combining Nano sensors and multi-agent deep reinforcement learning technology. First, Nano sensors are placed in key parts of the human to collect human electromyogram (EMG) signals. Second, after de-noising the EMG signal by blind source separation technology, the time-domain and frequency-domain features of the surface EMG signal are extracted. Finally, in the multi-agent environment, the deep reinforcement learning network is introduced to build the multi-agent deep reinforcement learning pose detection model, and the 3D local pose of the human is output according to the features of the EMG signal. The fusion and pose calculation of the multi-sensor pose detection results are performed to obtain the 3D human pose detection results. The results show that the proposed method has high accuracy for detecting various human poses, and the accuracy, precision, recall and specificity of 3D human pose detection results are 0.97, 0.98, 0.95 and 0.98, respectively. Compared with other methods, the detection results in this paper are more accurate, and can be widely used in medicine, film, sports and other fields.The evaluation of the steam power system is very important for the operator to understand the operating status of the system, but the lack of consideration of the fuzziness of the complex system and the impact of the indicator parameters on the whole system makes the evaluation difficult. In this paper, an indicator system for evaluating the operation status of the experimental supercharged boiler is established. After discussing several methods of parameter standardization and weight correction, a comprehensive evaluation method based on the deterioration degree and health value is proposed while considering the deviation of the indicator and the fuzziness of the system. The comprehensive evaluation method, the linear weighting method and the fuzzy comprehensive evaluation method are respectively used to evaluate the experimental supercharged boiler. The comparison of the three methods shows that the comprehensive evaluation method is more sensitive to minor anomalies and faults and can draw quantitative health assessment conclusions.Chinese medical knowledge-based question answering (cMed-KBQA) is a vital component of the intelligence question-answering assignment. Its purpose is to enable the model to comprehend questions and then deduce the proper answer from the knowledge base. Previous methods solely considered how questions and knowledge base paths were represented, disregarding their significance. Due to entity and path sparsity, the performance of question and answer cannot be effectively enhanced. To address this challenge, this paper presents a structured methodology for the cMed-KBQA based on the cognitive science dual systems theory by synchronizing an observation stage (System 1) and an expressive reasoning stage (System 2). System 1 learns the question’s representation and queries the associated simple path. Then System 2 retrieves complicated paths for the question from the knowledge base by using the simple path provided by System 1. Specifically, System 1 is implemented by the entity extraction module, entity linking module, simple path retrieval module, and simple path-matching model. Meanwhile, System 2 is performed by using the complex path retrieval module and complex path-matching model. The public CKBQA2019 and CKBQA2020 datasets were extensively studied to evaluate the suggested technique. Using the metric average F1-score, our model achieved 78.12% on CKBQA2019 and 86.60% on CKBQA2020.Breast cancer occurs in the epithelial tissue of the gland, so the accuracy of gland segmentation is crucial to the physician’s diagnosis. An innovative technique for breast mammography image gland segmentation is put forth in this paper. In the first step, the algorithm designed the gland segmentation evaluation function. Then a new mutation strategy is established, and the adaptive controlled variables are used to balance the ability of improved differential evolution (IDE) in terms of investigation and convergence. To evaluate its performance, The proposed method is validated on a number of benchmark breast images, including four types of glands from the Quanzhou First Hospital, Fujian, China. Furthermore, the proposed algorithm is been systematically compared to five state-of-the-art algorithms. From the average MSSIM and boxplot, the evidence suggests that the mutation strategy may be effective in searching the topography of the segmented gland problem. The experiment results demonstrated that the proposed method has the best gland segmentation results compared to other algorithms.Aiming at the problem of on-load tap changer (OLTC) fault diagnosis under imbalanced data conditions (the number of fault states is far less than that of normal data), this paper proposes an OLTC fault diagnosis method based on an Improved Grey Wolf algorithm (IGWO) and Weighted Extreme Learning Machine (WELM) optimization. Firstly, the proposed method assigns different weights to each sample ac-cording to WELM, and measures the classification ability of WELM based on G-mean, so as to realize the modeling of imbalanced data. Secondly, the method uses IGWO to optimize the input weight and hidden layer offset of WELM, avoiding the problems of low search speed and local optimization, and achieving high search efficiency. The results show that IGWO-WLEM can effectively diagnose OLTC faults under imbalanced data conditions, with an improvement of at least 5% compared with existing methods.In this work, we deal with the initial boundary value problem of solutions for a class of linear strongly damped nonlinear wave equations $ u_tt-\Delta u -\alpha \Delta u_t = f(u) $ in the frame of a family of potential wells. For this strongly damped wave equation, we not only prove the global-in-time existence of the solution, but we also improve the decay rate of the solution from the polynomial decay rate to the exponential decay rate.In the current global cooperative production mode, the distributed fuzzy flow-shop scheduling problem (DFFSP) has attracted much attention because it takes the uncertain factors in the actual flow-shop scheduling problem into account. This paper investigates a multi-stage hybrid evolutionary algorithm with sequence difference-based differential evolution (MSHEA-SDDE) for the minimization of fuzzy completion time and fuzzy total flow time. MSHEA-SDDE balances the convergence and distribution performance of the algorithm at different stages. In the first stage, the hybrid sampling strategy makes the population rapidly converge toward the Pareto front (PF) in multiple directions. In the second stage, the sequence difference-based differential evolution (SDDE) is used to speed up the convergence speed to improve the convergence performance. In the last stage, the evolutional direction of SDDE is changed to guide individuals to search the local area of the PF, thereby further improving the convergence and distribution performance. The results of experiments show that the performance of MSHEA-SDDE is superior to the classical comparison algorithms in terms of solving the DFFSP.This paper is devoted to investigating the impact of vaccination on mitigating COVID-19 outbreaks. In this work, we propose a compartmental epidemic ordinary differential equation model, which extends the previous so-called SEIRD model [1,2,3,4] by incorporating the birth and death of the population, disease-induced mortality and waning immunity, and adding a vaccinated compartment to account for vaccination. Firstly, we perform a mathematical analysis for this model in a special case where the disease transmission is homogeneous and vaccination program is periodic in time. In particular, we define the basic reproduction number $ \mathcalR_0 $ for this system and establish a threshold type of result on the global dynamics in terms of $ \mathcalR_0 $. Secondly, we fit our model into multiple COVID-19 waves in four locations including Hong Kong, Singapore, Japan, and South Korea and then forecast the trend of COVID-19 by the end of 2022. Finally, we study the effects of vaccination again the ongoing pandemic by numerically computing the basic reproduction number $ \mathcalR_0 $ under different vaccination programs. Our findings indicate that the fourth dose among the high-risk group is likely needed by the end of the year.The modular intelligent robot platform has important application prospects in the field of tourism management services. Based on the intelligent robot in the scenic area, this paper constructs a partial differential analysis system for tourism management services, and adopts the modular design method to complete the hardware design of the intelligent robot system. Through system analysis, the whole system is divided into 5 major modules, including core control module, power supply module, motor control module, sensor measurement module, wireless sensor network module, to solve the problem of quantification of tourism management services. In the simulation process, the hardware development of wireless sensor network node is carried out based on MSP430F169 microcontroller and CC2420 radio frequency wireless communication chip, and the corresponding physical layer and MAC (Media Access Control) layer data definition and data definition of IEEE802.15.4 protocol are completed for software implementation, and data transmission and networking verification. The experimental results show that the encoder resolution is 1024P/R, the power supply voltage is DC5V5%, and the maximum response frequency is 100 kHz. The algorithm designed by MATLAB software can avoid the existing shortcomings and meet the real-time requirements of the system, which significantly improves the sensitivity and robustness of the intelligent robot.We consider the Poisson equation by collocation method with linear barycentric rational function. The discrete form of the Poisson equation was changed to matrix form. For the basis of barycentric rational function, we present the convergence rate of the linear barycentric rational collocation method for the Poisson equation. Domain decomposition method of the barycentric rational collocation method (BRCM) is also presented. Several numerical examples are provided to validate the algorithm.Human evolution is carried out by two genetic systems based on DNA and another based on the transmission of information through the functions of the nervous system. In computational neuroscience, mathematical neural models are used to describe the biological function of the brain. Discrete-time neural models have received particular attention due to their simple analysis and low computational costs. From the concept of neuroscience, discrete fractional order neuron models incorporate the memory in a dynamic model. This paper introduces the fractional order discrete Rulkov neuron map. The presented model is analyzed dynamically and also in terms of synchronization ability. First, the Rulkov neuron map is examined in terms of phase plane, bifurcation diagram, and Lyapunov exponent. The biological behaviors of the Rulkov neuron map, such as silence, bursting, and chaotic firing, also exist in its discrete fractional-order version. The bifurcation diagrams of the proposed model are investigated under the effect of the neuron model’s parameters and the fractional order. The stability regions of the system are theoretically and numerically obtained, and it is shown that increasing the order of the fractional order decreases the stable areas. Finally, the synchronization behavior of two fractional-order models is investigated. The results represent that the fractional-order systems cannot reach complete synchronization.With the development of national economy, the output of waste is also increasing. People’s living standards are constantly improving, and the problem of garbage pollution is increasingly serious, which has a great impact on the environment. Garbage classification and processing has become the focus of today. This topic studies the garbage classification system based on deep learning convolutional neural network, which integrates the garbage classification and recognition methods of image classification and object detection. First, the data sets and data labels used are made, and then the garbage classification data are trained and tested through ResNet and MobileNetV2 algorithms, Three algorithms of YOLOv5 family are used to train and test garbage object data. Finally, five research results of garbage classification are merged. Through consensus voting algorithm, the recognition rate of image classification is improved to 2%. Practice has proved that the recognition rate of garbage image classification has been increased to about 98%, and it has been transplanted to the raspberry pie microcomputer to achieve ideal results.The variation of nutrient supply not only leads to the differences in the phytoplankton biomass and primary productivity but also induces the long-term phenotypic evolution of phytoplankton. It is widely accepted that marine phytoplankton follows Bergmann’s Rule and becomes smaller with climate warming. Compared with the direct effect of increasing temperature, the indirect effect via nutrient supply is considered to be an important and dominant factor in the reduction of phytoplankton cell size. In this paper, a size-dependent nutrient-phytoplankton model is developed to explore the effects of nutrient supply on the evolutionary dynamics of functional traits associated with phytoplankton size. The ecological reproductive index is introduced to investigate the impacts of input nitrogen concentration and vertical mixing rate on the persistence of phytoplankton and the distribution of cell size. In addition, by applying the adaptive dynamics theory, we study the relationship between nutrient input and the evolutionary dynamics of phytoplankton.