AllPassive Hardware Setup associated with Multilayer Perceptron Classifiers

This particular cardstock determines the existence of globally surrounded established ways of any predator-prey design using attraction-repulsion airport taxis in the clean bounded domain from a measurements with Neumann border problems. Additionally, the world stabilizing regarding alternatives with unity charges in order to regular constant says will be obtained. With all the nearby selleck chemicals llc moment integrability in the L2-norm of options, we develop the essential electricity quotations and also gain the international boundedness of alternatives from the Moser new release. The world stableness associated with constant continuous declares is made depending on the Lyapunov useful technique.This specific paper handles a new two-species competing predator-prey system using density-dependent diffusion, i.at the., \begineqnarray*\label1a \left\ \beginsplit&u_t=\Delta (d_1(w)u)+\gamma_1uF_1(w)-uh_1(u)-\beta_1uv,&(x,t)\in \Omega\times (0,\infty),\\&v_t=\Delta (d_2(w)v)+\gamma_2vF_2(w)-vh_2(v)-\beta_2uv,&(x,t)\in \Omega\times (0,\infty),\\&w_t=D\Delta w-uF_1(w)-vF_2(w)+f(w),&(x,t)\in \Omega\times (0,\infty), \endsplit \right. \endeqnarray* under homogeneous Neumann boundary conditions in a smooth bounded domain $\Omega\subset \mathbbR^2$, with the nonnegative initial data $\left( u_0, v_0, w_0 \right) \in (W^1,p(\Omega))^3$ with $p>2$, where the parameters $D,\gamma_1,\gamma_2,\beta_1,\beta_2>0$, $d_1(w)$ and $d_2(w)$ are density-dependent diffusion functions, $F_1(w)$ and $F_2(w)$ are commonly called the functional response functions accounting for the intake rate of predators as the functions of prey density, $h_1(u)$ and $h_2(v)$ represent the mortality rates of predators, and $f(w)$ stands for the growth function of the prey. First, we rigorously prove the global boundedness of classical solutions for the above general model provided that the parameters satisfy some suitable conditions by means of $L^p$-estimate techniques. Moreover, in some particular cases, we establish the asymptotic stabilization and precise convergence rates of globally bounded solutions under different conditions on the parameters by constructing some appropriate Lyapunov functionals. Our results not only extend the previous ones, but also involve some new conclusions.The high accuracy of short-term power load forecasting has a pivotal role in helping power companies to construct reasonable production scheduling plans and avoid resource waste. In this paper, a multi-model short-term power load prediction method based on Variational mode decomposition (VMD)-improved whale optimization algorithm (IWOA)-wavelet temporal convolutional network (WTCN)-bidirectional gated recurrent unit (BiGRU)-attention and CatBoost model fusion is proposed. First, VMD was employed to decompose the load data into different intrinsic mode functions. Second, a WTCN was utilized to extract the load data features, and multi-dimensional feature factors were integrated into the BiGRU network for model training. Moreover, an attention mechanism was added to enhance the influence degree of important information. The WTCN-BiGRU-attention model is improved by the WOA algorithm to optimize the hyperparameters of the network. Finally, the model was fused with the predicted data of the CatBoost network by the mean absolute percentage error-reciprocal weight (MAPE-RW) algorithm to construct the best fusion model. Compared with other forecasting models, the proposed multi-model fusion method has higher accuracy in short-term power load forecasting using the public data set for an Australian region.This study explored the chemical and pharmacological mechanisms of Shao Yao Gan Cao decoction (SYGC) in the treatment of Sphincter of Oddi Dysfunction (SOD) through ultra-high-performance liquid chromatography coupled with Quadrupole Exactive-Orbitrap high-resolution mass spectrometry (UHPLC-Q Exactive-Orbitrap HR-MS), network pharmacology, transcriptomics, molecular docking and in vivo experiments. First, we identified that SYGC improves SOD in guinea pigs by increased c-kit expression and decreased inflammation infiltration and ring muscle disorders. Then, a total of 649 SOD differential genes were found through RNA sequencing and mainly enriched in complement and coagulation cascades, the B cell receptor signaling pathway and the NF-kappa B signaling pathway. By combining UHPLC-Q-Orbitrap-HRMS with a network pharmacology study, 111 chemicals and a total of 52 common targets were obtained from SYGC in the treatment of SOD, which is also involved in muscle contraction, the B cell receptor signaling pathway and the complement system. Next, 20 intersecting genes were obtained among the PPI network, MCODE and ClusterOne analysis. Then, the molecular docking results indicated that four active compounds (glycycoumarin, licoflavonol, echinatin and homobutein) and three targets (AURKB, KIF11 and PLG) exerted good binding interactions, which are also related to the B cell receptor signaling pathway and the complement system. Finally, animal experiments were conducted to confirm the SYGC therapy effects on SOD and verify the 22 hub genes using RT-qPCR. This study demonstrates that SYGC confers therapeutic effects against an experimental model of SOD via regulating immune response and inflammation, which provides a basis for future research and clinical applications.Intratumor heterogeneity hinders the success of anti-cancer treatment due to the interaction between different types of cells. To recapitulate the communication of different types of cells, we developed a mathematical model to study the dynamic interaction between normal, drug-sensitive and drug-resistant cells in response to cancer treatment. Based on the proposed model, we first study the analytical conclusions, namely the nonnegativity and boundedness of solutions, and the existence and stability of steady states. Furthermore, to investigate the optimal treatment that minimizes both the cancer cells count and the total dose of drugs, we apply the Pontryagin's maximum(or minimum) principle (PMP) to explore the combination therapy strategy with either quadratic control or linear control functionals. We establish the existence and uniqueness of the quadratic control problem, and apply the forward-backward sweep method (FBSM) to solve the optimal control problems and obtain the optimal therapy scheme.Recently, the two-parameter Xgamma distribution (TPXGD) is suggested as a new lifetime distribution for modeling some real data. The TPXGD is investigated in different areas and generalized to other forms by many of the researchers. The acceptance sampling plans are one of the main important statistical tools in production and engineering fields. In this paper, modified acceptance sampling plans for the TPXGD are proposed with the assumption that the lifetime is truncated at a predetermined level. The mean of the TPXGD model is utilized as a quality parameter. The variables of the acceptance sampling plans including the acceptance numbers, the minimum sample sizes, operating characteristic function and the producer's risk are investigated for various values of the model parameters. Numerical examples are offered to illustrate the process of the proposed plans. Also, a real data is fitted to the TPXGD and an application based on the suggested acceptance sampling plans is considered for explanation.In this paper, the Adomian decomposition method (ADM) and Picard technique are used to solve a class of nonlinear multidimensional fractional differential equations with Caputo-Fabrizio fractional derivative. The main advantage of the Caputo-Fabrizio fractional derivative appears in its non-singular kernel of a convolution type. The sufficient condition that guarantees a unique solution is obtained, the convergence of the series solution is discussed, and the maximum absolute error is estimated. Several numerical problems with an unknown exact solution are solved using the two techniques. A comparative study between the two solutions is presented. A comparative study shows that the time consumed by ADM is much smaller compared with the Picard technique.Regulatory elements in DNA sequences, such as promoters, enhancers, terminators and so on, are essential for gene expression in physiological and pathological processes. A promoter is the specific DNA sequence that is located upstream of the coding gene and acts as the "switch" for gene transcriptional regulation. Lots of promoter predictors have been developed for different bacterial species, but only a few are designed for Pseudomonas aeruginosa, a widespread Gram-negative conditional pathogen in nature. In this work, an ensemble model named SPREAD is proposed for the recognition of promoters in Pseudomonas aeruginosa. In SPREAD, the DNA sequence autoencoder model LSTM is employed to extract potential sequence information, and the mean output probability value of CNN and RF is applied as the final prediction. Compared with G4PromFinder, the only state-of-the-art classifier for promoters in Pseudomonas aeruginosa, SPREAD improves the prediction performance significantly, with an accuracy of 0.98, recall of 0.98, precision of 0.98, specificity of 0.97 and F1-score of 0.98.Brain community detection is an efficient method to represent the communities of brain networks. However, time-variable functions of the brain and the intricate brain community structure impose a great challenge on it. In this paper, a time-sequential graph adversarial learning (TGAL) framework is proposed to detect brain communities and characterize the structure of communities from brain networks. In the framework, a novel time-sequential graph neural network is designed as an encoder to extract efficient graph representations by spatio-temporal attention mechanism. Since it is difficult to capture the community structure, the measurable modularity loss is used to optimize by maximizing the modularity of the community. In addition, the framework employs an adversarial scheme to guide the learning of representation. The effectiveness of our model is shown through experiments on the real-world brain network datasets, and the great performance of brain community detection demonstrates the advantage of the proposed framework.A ligand when bound to a macromolecule (protein, DNA, RNA) will influence the biochemical function of that macromolecule. This observation is empirical and attributable to the association of the ligand with the amino acids/nucleotides that comprise the macromolecule. The binding affinity is a measure of the strength-of-association of a macromolecule for its ligand and is numerically characterized by the association/dissociation constant. However, despite being widely used, a mathematically rigorous explanation by which the association/dissociation constant can influence the biochemistry and molecular biology of the resulting complex is not available. Here, the ligand-macromolecular complex is modeled as a homo- or hetero-dimer with a finite and equal number of atoms/residues per monomer. The pairwise interactions are numeric, empirically motivated and are randomly chosen from a standard uniform distribution. The transition-state dissociation constants are the strictly positive real part of all complex eigenvalues of this interaction matrix, belong to the open interval (0,1), and form a sequence whose terms are finite, monotonic, non-increasing and convergent. The theoretical results are rigorous, presented as theorems, lemmas and corollaries and are complemented by numerical studies. An inferential analysis of the clinical outcomes of amino acid substitutions of selected enzyme homodimers is also presented. These findings are extendible to higher-order complexes such as those likely to occur in vivo. The study also presents a schema by which a ligand can be annotated and partitioned into high- and low-affinity variants. The influence of the transition-state dissociation constants on the biochemistry and molecular biology of non-haem iron (Ⅱ)- and 2-oxoglutarate-dependent dioxygenases (catalysis) and major histocompatibility complex (Ⅰ) mediated export of high-affinity peptides (non-enzymatic association/dissociation) are examined as special cases.Based on the panel data of China from 2003 to 2017, this paper applies the panel vector autoregressive (PVAR) model to the study of the influencing factors of carbon emissions. After the cross-section dependence test, unit root test and cointegration test of panel data, the dynamic relationship between energy consumption, economic growth, urbanization, financial development and CO2 emissions is investigated by using PVAR model. Then, we used the impulse response function tool to better understand the reaction of the main variables of interest, CO2 emissions, aftershocks on four factors. Finally, through the variance decomposition of all factors, the influence degree of a single variable on other endogenous variables is obtained. Overall, the results show that the four factors have a significant and positive impact on carbon emissions. In addition, variance decomposition also showed that energy consumption and economic growth strongly explained CO2 emissions. These results indicate that the financial, economic and energy sectors of China's provinces still make relatively weak contributions to reducing carbon emissions and improving environmental quality. Therefore, several policies are proposed and discussed.As an advanced technique, compressed sensing has been used for rapid magnetic resonance imaging in recent years, Two-step Iterative Shrinkage Thresholding Algorithm (TwIST) is a popular algorithm based on Iterative Thresholding Shrinkage Algorithm (ISTA) for fast MR image reconstruction. However TwIST algorithms cannot dynamically adjust shrinkage factor according to the degree of convergence. So it is difficult to balance speed and efficiency. In this paper, we proposed an algorithm which can dynamically adjust the shrinkage factor to rebalance the fidelity item and regular item during TwIST iterative process. The shrinkage factor adjusting is judged by the previous reconstructed results throughout the iteration cycle. It can greatly accelerate the iterative convergence while ensuring convergence accuracy. We used MR images with 2 body parts and different sampling rates to simulate, the results proved that the proposed algorithm have a faster convergence rate and better reconstruction performance. We also used 60 MR images of different body parts for further simulation, and the results proved the universal superiority of the proposed algorithm.In this work, we suggest a reduced distribution with two parameters of the modified Weibull distribution to avoid some estimation difficulties. The hazard rate function of the reduced distribution exhibits decreasing, increasing or bathtub shape. The suggested reduced distribution can be applied to many problems of modelling lifetime data. Some statistical properties of the proposed distribution have been discussed. The maximum likelihood is employed to estimate the model parameters. The Fisher information matrix is derived and then applied to construct confidence intervals for parameters. A simulation is conducted to illustrate the performance of maximum likelihood estimation. Four sets of real data are tested to prove the proposed distribution advantages. According to the statistical criteria, the proposed distribution fits the tested data better than some well-known two-and three-parameter distributions.Calcified aortic valve stenosis (CAVS) is caused by calcium buildup and tissue thickening that impede the blood flow from left ventricle (LV) to aorta. In recent years, CAVS has become one of the most common cardiovascular diseases. Therefore, it is necessary to study the mechanics of aortic valve (AV) caused by calcification. In this paper, based on a previous idealized AV model, the hybrid immersed boundary/finite element method (IB/FE) is used to study AV dynamics and hemodynamic performance under normal and calcified conditions. The computational CAVS model is realized by dividing the AV leaflets into a calcified region and a healthy region, and each is described by a specific constitutive equation. Our results show that calcification can significantly affect AV dynamics. For example, the elasticity and mobility of the leaflets decrease due to calcification, leading to a smaller opening area with a high forward jet flow across the valve. The calcified valve also experiences an increase in local stress and strain. The increased loading due to AV stenosis further leads to a significant increase in left ventricular energy loss and transvalvular pressure gradients. The model predicted hemodynamic parameters are in general consistent with the risk classification of AV stenosis in the clinic. Therefore, mathematical models of AV with calcification have the potential to deepen our understanding of AV stenosis-induced ventricular dysfunction and facilitate the development of computational engineering-assisted medical diagnosis in AV related diseases.In ecology, the impact of predators goes beyond killing prey, the mere presence of predators reduces the ability of prey to reproduce. In this study, we extend the predator-prey model with fear effect by introducing the state-dependent control with a nonlinear action threshold depending on the combination of the density of prey and its changing rate. We initially defined the Poincaré map of the proposed model and studied its fundamental properties. Utilizing the properties of the Poincaré map, periodic solution of the model is further investigated, including the existence and stability of the order-1 periodic solution and the existence of the order-k (k≥2) periodic solutions. In addition, the influence of the fear effect on the system's dynamics is explored through numerical simulations. The action threshold used in this paper is more consistent with the actual growth of the population than in earlier linear threshold studies, and the results show that the control objectives are better achieved using the action threshold strategy. The analytical approach used in this study provided several novel methods for analyzing the complex dynamics that rely on state-dependent impulsive.The basic reproduction number, $ R_0 $, plays a central role in measuring the transmissibility of an infectious disease, and it thus acts as the fundamental index for planning control strategies. In the present study, we apply a branching process model to meticulously observed contact tracing data from Wakayama Prefecture, Japan, obtained in early 2020 and mid-2021. This allows us to efficiently estimate $ R_0 $ and the dispersion parameter $ k $ of the wild-type COVID-19, as well as the relative transmissibility of the Delta variant and relative transmissibility among fully vaccinated individuals, from a very limited data. $ R_0 $ for the wild type of COVID-19 is estimated to be 3.78 (95% confidence interval [CI] 3.72-3.83), with $ k = 0.236 $ (95% CI 0.233-0.240). For the Delta variant, the relative transmissibility to the wild type is estimated to be 1.42 (95% CI 0.94-1.90), which gives $ R_0 = 5.37 $ (95% CI 3.55-7.21). Vaccine effectiveness, determined by the reduction in the number of secondary transmissions among fully vaccinated individuals, is estimated to be 91% (95% CI 85%-97%). The present study highlights that basic reproduction numbers can be accurately estimated from the distribution of minor outbreak data, and these data can provide further insightful epidemiological estimates including the dispersion parameter and vaccine effectiveness regarding the prevention of transmission.Epidemic models have been broadly used to comprehend the dynamic behaviour of emerging and re-emerging infectious diseases, predict future trends, and assess intervention strategies. The symptomatic and asymptomatic features and environmental factors for Lassa fever (LF) transmission illustrate the need for sophisticated epidemic models to capture more vital dynamics and forecast trends of LF outbreaks within countries or sub-regions on various geographic scales. This study proposes a dynamic model to examine the transmission of LF infection, a deadly disease transmitted mainly by rodents through environment. We extend prior LF models by including an infectious stage to mild and severe as well as incorporating environmental contributions from infected humans and rodents. For model calibration and prediction, we show that the model fits well with the LF scenario in Nigeria and yields remarkable prediction results. Rigorous mathematical computation divulges that the model comprises two equilibria. That is disease-free equilibrium, which is locally-asymptotically stable (LAS) when the basic reproduction number, $ \mathcalR_0 $, is $ 1 $. We use time-dependent control strategy by employing Pontryagin's Maximum Principle to derive conditions for optimal LF control. Furthermore, a partial rank correlation coefficient is adopted for the sensitivity analysis to obtain the model's top rank parameters requiring precise attention for efficacious LF prevention and control.Based on mathematical models, in-depth analysis about the interrelationship between agricultural CO2 emission and economic development has increasingly become a hotly debated topic. By applying two mathematical models including logarithmic mean divisia index (LMDI) and Tapio decoupling, this work aims to study the driving factor and decoupling trend for Chinese agricultural CO2 emission from 1996 to 2020. Firstly, the intergovernmental panel on climate change (IPCC) method is selected to estimate the agricultural CO2 emission from 1996 to 2020, and the LMDI model is adopted to decompose the driving factors of agricultural CO2 emission into four agricultural factors including economic development, carbon emission intensity, structure, and labor effect. Then, the Tapio decoupling model is applied to analyze the decoupling state and development trend between the development of agricultural economy and CO2 emission. Finally, this paper puts forward some policies to formulate a feasible agricultural CO2 emission reduction strategy. The main research conclusions are summarized as follows 1) During the period from 1996 to 2020, China's agricultural CO2 emission showed two stages, a rapid growth stage (1996-2015) and a rapid decline stage (2016-2020). 2) Agricultural economic development is the first driving factor for the increase of agricultural CO2 emission, while agricultural labor factor and agricultural production efficiency factor play two key inhibitory roles. 3) From 1996 to 2020, on the whole, China's agricultural sector CO2 emission and economic development showed a weak decoupling (WD) state. The decoupling states corresponding to each time period are strong negative decoupling (SND) (1996-2000), expansive negative decoupling (END) (2001-2005), WD (2006-2015) and strong decoupling (SD) (2016-2020), respectively.Diabetic retinopathy (DR) is one of the main leading causes of visual impairment worldwide. The current study elucidates the role of JQ1 in DR. A diabetic model was constructed by STZ injection and a high-fat diet. After establishment of the diabetic model, rats were assigned to treatment groups 1) control, 2) diabetic model, and 3) diabetic+JQ1 model. In vitro Transwell and wound-healing assays were used to measure BV2 cell viability by stimulation with low glucose and high glucose with or without JQ1 and 740Y-P. Pathological methods were used to analyze DR, and Western blotting was used to analyze protein expression. Identification of enriched pathways in DR was performed by bioinformatics. Histopathological examination demonstrated that JQ1 rescued the loss of retinal cells and increased the thickness of retinal layers in diabetic rats. JQ1 attenuated high glucose-stimulated BV2 microglial motility and migration. The bioinformatics analysis implied that the Pl3K-Akt signaling pathway was enriched in DR. JQ1 decreased the phosphorylation of PI3K and AKT as well as the immunostaining of PI3K in BV2 cells. 740Y-P (a PI3K agonist) significantly reversed the decrease in p-PI3K and p-AK in BV2 cells. Additionally, JQ1 decreased the protein expression of p-PI3K, p-AKT, and MMP2/9 and immunostaining of PI3K in retinal tissues of rats. JQ1 suppresses the PI3K/Akt cascade by targeting MMP expression, thus decreasing the viability and invasion capacity of retinal microglia, suggesting an interesting treatment target for DR.Considering that many prey populations in nature have group defense behavior, and the relationship between predator and prey is usually affected by environmental noise, a stochastic predator-prey model with group defense behavior is established in this paper. Some dynamical properties of the model, including the existence and uniqueness of global positive solution, sufficient conditions for extinction and unique ergodic stationary distribution, are investigated by using qualitative theory of stochastic differential equations, Lyapunov function analysis method, Itô formula, etc. Furthermore, the effects of group defense behavior and environmental noise on population stability are also discussed. Finally, numerical simulations are carried out to illustrate that the effects of environmental noise on both populations are negative, the appropriate group defense level of prey can maintain the stability of the relationship between two populations, and the survival threshold is strongly influenced by the intrinsic growth rate of prey population and the intensity of environmental noise.Image reconstruction is extremely important for computed tomography (CT) imaging, so it is significant to be continuously improved. The unfolding dynamics method combines a deep learning model with a traditional iterative algorithm. It is interpretable and has a fast reconstruction speed, but the essence of the algorithm is to replace the approximation operator in the optimization objective with a learning operator in the form of a convolutional neural network. In this paper, we firstly design a new iterator network (iNet), which is based on the universal approximation theorem and tries to simulate the functional relationship between the former and the latter in the maximum-likelihood expectation maximization (MLEM) algorithm. To evaluate the effectiveness of the method, we conduct experiments on a CT dataset, and the results show that our iNet method improves the quality of reconstructed images.African swine fever (ASF) is an acute, hemorrhagic and severe infectious disease caused by the African swine fever virus (ASFV), and leads to a serious threat to the pig industry in China. Yet the impact of the virus in the environment and contaminated swill on the ASFV transmission is unclear in China. Then we build the ASFV transmission model with the virus in the environment and swill. We compute the basic reproduction number, and prove that the disease-free equilibrium is globally asymptotically stable when $ R_0 1 $. Using the public information, parameter values are evaluated. PRCCs and eFAST sensitivity analysis reveal that the release rate of ASFV from asymptomatic and symptomatic infectious pigs and the proportion of pig products from infectious pigs to swill have a significant impact on the ASFV transmission. Our findings suggest that the virus in the environment and contaminated swill contribute to the ASFV transmission. Our results may help animal health to prevent and control the ASFV transmission.Aiming at improving the operating efficiency of air freight station, the problem of optimizing the sequence of inbound/outbound tasks meanwhile scheduling the actions of elevating transfer vehicles (ETVs) is discussed in this paper. First of all, the scheduling model in airport container storage area, which considers not only the influence of picking sequence, optimal ETVs routing without collision, but also the assignment of input and output ports, is established. Then artificial bee colony (ABC) is proposed to solve the above scheduling issue. For further balancing the abilities of exploration and exploitation, improved multi-dimensional search (IMABC) algorithm is proposed where more dimensions will be covered, and the best dimension of the current optimal solution is used to guide the evolutionary direction in the following exploitation processes. Numerical experiments show that the proposed method can generate optimal solution for the complex scheduling problem, and the proposed IMABC outperforms original ABC and other improved algorithms.A discrete stage-structured tick population dynamical system with diapause is studied, and spraying acaricides as the control strategy is considered in detail. We stratify vector populations in terms of their maturity status as immature and mature subgroups. The immature subgroup is divided into two categories normal immature and diapause immature. We compute the net reproduction number $ R_0 $ and perform a qualitative analysis. When $ R_0 1; $ the model has transcritical bifurcation if $ R_0 = 1. $ Moreover, we calculate the net reproduction numbers of the model with constant spraying acaricides and periodic spraying acaricides, respectively, and compare the effects of the two methods on controlling tick populations.Recently, researchers have become interested in modelling, monitoring, and treatment of hepatitis B virus infection. Understanding the various connections between pathogens, immune systems, and general liver function is crucial. In this study, we propose a higher-order stochastically modified delay differential model for the evolution of hepatitis B virus transmission involving defensive cells. Taking into account environmental stimuli and ambiguities, we presented numerical solutions of the fractal-fractional hepatitis B virus model based on the exponential decay kernel that reviewed the hepatitis B virus immune system involving cytotoxic T lymphocyte immunological mechanisms. Furthermore, qualitative aspects of the system are analyzed such as the existence-uniqueness of the non-negative solution, where the infection endures stochastically as a result of the solution evolving within the predetermined system's equilibrium state. In certain settings, infection-free can be determined, where the illness settles down tremendously with unit probability. To predict the viability of the fractal-fractional derivative outcomes, a novel numerical approach is used, resulting in several remarkable modelling results, including a change in fractional-order δ with constant fractal-dimension ϖ, δ with changing ϖ, and δ with changing both δ and ϖ. White noise concentration has a significant impact on how bacterial infections are treated.It is known that differences between potentials of soma, dendrites and different parts of neural structures may be the origin of electroencephalogram (EEG) waves. These potentials may be produced by some excitatory synapses and currents of charges between neurons and then thereafter may themselves cause the emergence of new synapses and electrical currents. These currents within and between neurons emit some electromagnetic waves which could be absorbed by electrodes on the scalp, and form topographic images. In this research, a model is proposed which formulates EEG topographic parameters in terms of the charge and mass of exchanged particles within neurons, those which move between neurons, the number of neurons and the length of neurons and synapses. In this model, by knowing the densities of the frequencies in different regions of the brain, one can predict the type, charge and velocity of particles which are moving along neurons or are exchanged between neurons.This paper shows how biological population dynamic models in the form of coupled reaction-diffusion equations with nonlinear reaction terms can be applied to heterogeneous landscapes. The presented systems of coupled partial differential equations (PDEs) combine the dispersal of disease-vector mosquitoes and the spread of the disease in a human population. Realistic biological dispersal behavior is taken into account by applying chemotaxis terms for the attraction to the human host and the attraction of suitable breeding sites. These terms are capable of generating the complex active movement patterns of mosquitoes along the gradients of the attractants. The nonlinear initial boundary value problems are solved numerically for geometries of heterogeneous landscapes, which have been imported from geographic information system data to construct a general-purpose finite-element solver for systems of coupled PDEs. The method is applied to the dispersal of the dengue disease vector for Aedes aegypti in a small-scale rural setting consisting of small houses and different breeding sites, and to a large-scale section of the suburban zone of a metropolitan area in Vietnam. Numerical simulations illustrate how the setup of model equations and geographic information can be used for the assessment of control measures, including the spraying patterns of pesticides and biological control by inducing male sterility.Aiming at the problems of low detection accuracy and slow speed caused by the complex background of tea sprouts and the small target size, this paper proposes a tea bud detection algorithm integrating GhostNet and YOLOv5. To reduce parameters, the GhostNet module is specially introduced to shorten the detection speed. A coordinated attention mechanism is then added to the backbone layer to enhance the feature extraction ability of the model. A bi-directional feature pyramid network (BiFPN) is used in the neck layer of feature fusion to increase the fusion between shallow and deep networks to improve the detection accuracy of small objects. Efficient intersection over union (EIOU) is used as a localization loss to improve the detection accuracy in the end. The experimental results show that the precision of GhostNet-YOLOv5 is 76.31%, which is 1.31, 4.83, and 3.59% higher than that of Faster RCNN, YOLOv5 and YOLOv5-Lite respectively. By comparing the actual detection effects of GhostNet-YOLOv5 and YOLOv5 algorithm on buds in different quantities, different shooting angles, and different illumination angles, and taking F1 score as the evaluation value, the results show that GhostNet-YOLOv5 is 7.84, 2.88, and 3.81% higher than YOLOv5 algorithm in these three different environments.Physics-informed neural networks (PINN) have lately become a research hotspot in the interdisciplinary field of machine learning and computational mathematics thanks to the flexibility in tackling forward and inverse problems. In this work, we explore the generality of the PINN training algorithm for solving Hamilton-Jacobi equations, and propose physics-informed neural networks based on adaptive weighted loss functions (AW-PINN) that is trained to solve unsupervised learning tasks with fewer training data while physical information constraints are imposed during the training process. To balance the contributions from different constrains automatically, the AW-PINN training algorithm adaptively update the weight coefficients of different loss terms by using the logarithmic mean to avoid additional hyperparameter. Moreover, the proposed AW-PINN algorithm imposes the periodicity requirement on the boundary condition and its gradient. The fully connected feedforward neural networks are considered and the optimizing procedure is taken as the Adam optimizer for some steps followed by the L-BFGS-B optimizer. The series of numerical experiments illustrate that the proposed algorithm effectively achieves noticeable improvements in predictive accuracy and the convergence rate of the total training error, and can approximate the solution even when the Hamiltonian is nonconvex. A comparison between the proposed algorithm and the original PINN algorithm for Hamilton-Jacobi equations indicates that the proposed AW-PINN algorithm can train the solutions more accurately with fewer iterations.The aim of this article is to analyze the delay influence on the attraction for a scalar tick population dynamics equation accompanying two disparate delays. Taking advantage of the fluctuation lemma and some dynamic inequalities, we derive a criterion to assure the persistence and positiveness on the considered model. Furthermore, a time-lag-dependent condition is proposed to insure the global attractivity for the addressed model. Besides, we give some simulation diagrams to substantiate the validity of the theoretical outcomes.Nowadays, object detection methods based on deep neural networks have been widely applied in autonomous driving and intelligent robot systems. However, weakly perceived objects with a small size in the complex scenes own too few features to be detected, resulting in the decrease of the detection accuracy. To improve the performance of the detection model in complex scenes, the detector of an improved CenterNet was developed via this work to enhance the feature representation of weakly perceived objects. Specifically, we replace the ResNet50 with ResNext50 as the backbone network to enhance the ability of feature extraction of the model. Then, we append the lateral connection structure and the dilated convolution to improve the feature enhancement layer of the CenterNet, leading to enriched features and enlarged receptive fields for the weakly sensed objects. Finally, we apply the attention mechanism in the detection head of the network to enhance the key information of the weakly perceived objects. To demonstrate the effectiveness, we evaluate the proposed model on the KITTI dataset and COCO dataset. Compared with the original model, the average precision of multiple categories of the improved CenterNet for the vehicles and pedestrians in the KITTI dataset increased by 5.37%, whereas the average precision of weakly perceived pedestrians increased by 9.30%. Moreover, the average precision of small objects (AP_S) of the weakly perceived small objects in the COCO dataset increase 7.4%. Experiments show that the improved CenterNet can significantly improve the average detection precision for weakly perceived objects.In this paper, we investigate the prespecified-time bipartite synchronization (PTBS) of coupled reaction-diffusion memristive neural networks (CRDMNNs) with both competitive and cooperative interactions. Two types of bipartite synchronization are considered leaderless PTBS and leader-following PTBS. With the help of a structural balance condition, the criteria for PTBS for CRDMNNs are derived by designing suitable Lyapunov functionals and novel control protocols. Different from the traditional finite-time or fixed-time synchronization, the settling time obtained in this paper is independent of control gains and initial values, which can be pre-set according to the task requirements. Lastly, numerical simulations are given to verify the obtained results.The spread of SARS-CoV-2 in the Canadian province of Ontario has resulted in millions of infections and tens of thousands of deaths to date. Correspondingly, the implementation of modeling to inform public health policies has proven to be exceptionally important. In this work, we expand a previous model of the spread of SARS-CoV-2 in Ontario, "Modeling the impact of a public response on the COVID-19 pandemic in Ontario, " to include the discretized, Caputo fractional derivative in the susceptible compartment. We perform identifiability and sensitivity analysis on both the integer-order and fractional-order SEIRD model and contrast the quality of the fits. We note that both methods produce fits of similar qualitative strength, though the inclusion of the fractional derivative operator quantitatively improves the fits by almost 27% corroborating the appropriateness of fractional operators for the purposes of phenomenological disease forecasting. In contrasting the fit procedures, we note potential simplifications for future study. Finally, we use all four models to provide an estimate of the time-dependent basic reproduction number for the spread of SARS-CoV-2 in Ontario between January 2020 and February 2021.In winter and spring, for greenhouses with larger areas and stereoscopic cultivation, distributed light environment regulation based on photosynthetic rate prediction model can better ensure good crop growth. In this paper, strawberries at flowering-fruit stage were used as the test crop, and the LI-6800 portable photosynthesis system was used to control the leaf chamber environment and obtain sample data by nested photosynthetic rate combination experiments under temperature, light and CO2 concentration conditions to study the photosynthetic rate prediction model construction method. For a small-sample, nonlinear real experimental data set validated by grey relational analysis, a photosynthetic rate prediction model was developed based on Support vector regression (SVR), and the particle swarm algorithm (PSO) was used to search the influence of the empirical values of parameters, such as the penalty parameter C, accuracy ε and kernel constant g, on the model prediction performance. The modeling and prediction results show that the PSO-SVR method outperforms the commonly used algorithms such as MLR, BP, SVR and RF in terms of prediction performance and generalization on a small sample data set. The research in this paper achieves accurate prediction of photosynthetic rate of strawberry and lays the foundation for subsequent distributed regulation of greenhouse strawberry light environment.As an indicator measured by incubating organic material from water samples in rivers, the most typical characteristic of water quality items is biochemical oxygen demand (BOD5) concentration, which is a stream pollutant with an extreme circumstance of organic loading and controlling aquatic behavior in the eco-environment. Leading monitoring approaches including machine leaning and deep learning have been evolved for a correct, trustworthy, and low-cost prediction of BOD5 concentration. The addressed research investigated the efficiency of three standalone models including machine learning (extreme learning machine (ELM) and support vector regression (SVR)) and deep learning (deep echo state network (Deep ESN)). In addition, the novel double-stage synthesis models (wavelet-extreme learning machine (Wavelet-ELM), wavelet-support vector regression (Wavelet-SVR), and wavelet-deep echo state network (Wavelet-Deep ESN)) were developed by integrating wavelet transformation (WT) with the different standalone models.ion of water pollutants on both stations, South Korea.Industrial internet security is a critical component of cyberspace safety. Furthermore, the encryption protocol is a critical component of cyberspace security. Due to the rapid development of industrial internet and edge computing, increasingly more devices are outsourcing their data to cloud servers to save costs. Edge devices should have a secure session key to reduce communication costs and share information. However, most key generation and storage are completed by a centralized third-party organization, which carries some security risks. In this context, this paper will propose a lightweight multi-dimensional virtual iteration of the group key agreement protocol. Group key agreement protocol allows for one-at-a-time encryption and timely key updates without the involvement of a trusted third party, and each device in the network can agreement a large number of keys. According to the analysis of this protocol, it has high security, rapid computation speed, and little storage space.This paper formulates and analyzes a general delayed mathematical model which describe the within-host dynamics of Human T-cell lymphotropic virus class I (HTLV-I) under the effect Cytotoxic T Lymphocyte (CTL) immunity. The models consist of four components uninfected CD$ 4^+ $T cells, latently infected cells, actively infected cells and CTLs. The mitotic division of actively infected cells are modeled. We consider general nonlinear functions for the generation, proliferation and clearance rates for all types of cells. The incidence rate of infection is also modeled by a general nonlinear function. These general functions are assumed to be satisfy some suitable conditions. To account for series of events in the infection process and activation of latently infected cells, we introduce two intracellular distributed-time delays into the models (ⅰ) delay in the formation of latently infected cells, (ⅱ) delay in the activation of latently infected cells. We determine a bounded domain for the system's solutions. es from the body. This gives some impression to develop two types of treatments, the first type aims to extend the intracellular delay periods, while the second type aims to activate and stimulate the CTL immune response.In the semi-supervised learning field, Graph Convolution Network (GCN), as a variant model of GNN, has achieved promising results for non-Euclidean data by introducing convolution into GNN. However, GCN and its variant models fail to safely use the information of risk unlabeled data, which will degrade the performance of semi-supervised learning. Therefore, we propose a Safe GCN framework (Safe-GCN) to improve the learning performance. In the Safe-GCN, we design an iterative process to label the unlabeled data. In each iteration, a GCN and its supervised version (S-GCN) are learned to find the unlabeled data with high confidence. The high-confidence unlabeled data and their pseudo labels are then added to the label set. Finally, both added unlabeled data and labeled ones are used to train a S-GCN which can achieve the safe exploration of the risk unlabeled data and enable safe use of large numbers of unlabeled data. The performance of Safe-GCN is evaluated on three well-known citation network datasets and the obtained results demonstrate the effectiveness of the proposed framework over several graph-based semi-supervised learning methods.We propose a new mathematical model to investigate the effect of the introduction of an exposed stage for the cats who become infected with the T. gondii parasite, but that are not still able to produce oocysts in the environment. The model considers a time delay in order to represent the duration of the exposed stage. Besides the cat population the model also includes the oocysts related to the T. gondii in the environment. The model includes the cats since they are the only definitive host and the oocysts, since they are relevant to the dynamics of toxoplasmosis. The model considers lifelong immunity for the recovered cats and vaccinated cats. In addition, the model considers that cats can get infected through an effective contact with the oocysts in the environment. We find conditions such that the toxoplasmosis disease becomes extinct. We analyze the consequences of considering the exposed stage and the time delay on the stability of the equilibrium points. We numerically solve the constructed model and corroborated the theoretical results.Digital transformation is a new driving force of enterprise efficiency reform. Enterprises' digital transformation can effectively improve their technological innovation efficiency, thereby promoting their high-quality development. Using the panel data of 930 Chinese A-share listed companies from 2015 to 2020, we have studied the impact and heterogeneity of digital transformation on enterprise technological innovation efficiency with a panel data model. Further, a mediating effect model and a moderating effect model were constructed to study the mechanism of digital transformation affecting the efficiency of enterprise technological innovation. The conclusions are as follows. First, enterprise digital transformation significantly improves the efficiency of enterprise technological innovation. Second, the impact of digital transformation on the efficiency of enterprise technological innovation is heterogeneous, which is reflected in two aspects the factor intensity and the nature of ownership. Third, financing constraints and equity concentration play a mediating and a moderating role, respectively, in the impact of digital transformation on the efficiency of enterprise technological innovation.In this paper, we describe an approach based on improved Hidden Markov Model (HMM) for fault diagnosis of underwater thrusters in complex marine environments. First, considering the characteristics of thruster data, we design a three-step data preprocessing method. Then, we propose a fault classification method based on HMMs trained by Particle Swarm Optimization (PSO) for better performance than methods based on vanilla HMMs. Lastly, we verify the effectiveness of the proposed approach using thruster samples collected from a fault emulation experimental platform. The experiments show that the PSO-based training method for HMM improves the accuracy of thruster fault diagnosis by 17.5% compared with vanilla HMMs, proving the effectiveness of the method.This paper addresses the robust enhancement problem in the control of robot manipulators. A new hierarchical multiloop model predictive control (MPC) scheme is proposed by combining an inverse dynamics-based feedback linearization and a nonlinear disturbance observer (NDO) based uncertainty compensation. By employing inverse dynamics-based feedback linearization, the multi-link robot manipulator was decoupled to reduce the computational burden compared with the traditional MPC method. Moreover, an NDO was introduced into the input torque signal to compensate and correct the errors from external disturbances and uncertainties, aiming to enhance the robustness of the proposed controller. The feasibility of the proposed hierarchical multiloop MPC scheme was verified and validated via simulation of a 3-DOF robot manipulator. Results demonstrate that the proposed controller provides comparative accuracy and robustness and extends the existing state-of-the-art algorithms for the trajectory tracking problem of robot manipulators with disturbances.