Showing papers in "Mathematical Biosciences and Engineering in 2019"
TL;DR: A machine learning-based method was proposed to identify HBP, in which the samples were encoded by using the optimal tripeptide composition obtained based on the binomial distribution method.
Abstract: The soluble carrier hormone binding protein (HBP) plays an important role in the growth of human and other animals. HBP can also selectively and non-covalently interact with hormone. Therefore, accurate identification of HBP is an important prerequisite for understanding its biological functions and molecular mechanisms. Since experimental methods are still labor intensive and cost ineffective to identify HBP, it's necessary to develop computational methods to accurately and efficiently identify HBP. In this paper, a machine learning-based method was proposed to identify HBP, in which the samples were encoded by using the optimal tripeptide composition obtained based on the binomial distribution method. In the 5-fold cross-validation test, the proposed method yielded an overall accuracy of 97.15%. For the convenience of scientific community, a user-friendly webserver called HBPred2.0 was built, which could be freely accessed at http://lin-group.cn/server/HBPred2.0/ .
120 citations
TL;DR: This work proposes a simple and efficient full convolutional network based on DenseNet for remote sensing scene classification that improves classification performance significantly on UCM dataset, AID dataset, OPTIMAL-31 dataset and NWPU-RESISC45 dataset.
Abstract: The convolutional neural networks (CNN) applied in remote sensing scene classification have two common problems. One is that these models have large number of parameters, which causes over-fitting easily. The other is that the network is not deep enough, thus more abstract semantic information cannot be extracted. To solve these two problems, we propose a simple and efficient full convolutional network based on DenseNet for remote sensing scene classification. We construct a small number of convolutional kernels to generate a large number of reusable feature maps by dense connections, which makes the network deeper, but does not increase the number of parameters significantly. Our network is so deep that it has more than 100 layers. However, it has only about 7 million parameters, which is far less than the number of VGGos parameters. Then we incorporate an adaptive average 3D pooling operation in our network. This operation fixes feature maps of size 7 × 7 from the last DenseBlock to 1 × 1 and decreases the number of channels from 1024 to 512, thus the whole network can accept input images with different sizes. Furthermore, we design the convolutional layer instead of the fully connected layer that is used as a classifier usually, so that the output features of the network can be classified without flattening operation, which simplifies the classification operation. Finally, a good model is trained by exploiting pre-trained weights and data augmentation technology. Compared with several state-of-the-art algorithms, our algorithm improves classification performance significantly on UCM dataset, AID dataset, OPTIMAL-31 dataset and NWPU-RESISC45 dataset.
119 citations
TL;DR: The mechanism combing blockchain with regeneration coding is proposed to improve the security and reliability of stored data under edge computing and builds a global blockchain in the cloud service layer and local blockchain is built on the terminals of the Internet of things.
Abstract: Edge computing is an important tool for smart computing, which brings convenience to data processing as well as security problems. In particular, the security of data storage under edge computing has become an obstacle to its widespread use. To solve the problem, the mechanism combing blockchain with regeneration coding is proposed to improve the security and reliability of stored data under edge computing. Our contribution is as follows. 1) According to the three-tier edge computing architecture and data security storage requirements, we proposed hybrid storage architecture and model specifically adapted to edge computing. 2) Making full use of the data storage advantages of edge network devices and cloud storage servers, we build a global blockchain in the cloud service layer and local blockchain is built on the terminals of the Internet of things. Moreover, the regeneration coding is utilized to further improve the reliability of data storage in blockchains. 3) Our scheme provides a mechanism for periodically validating hash values of data to ensure the integrity of data stored in global blockchain.
96 citations
TL;DR: In this overview, the current state-of-the-art medical image analysis techniques in CAD research are presented, which focus on the convolutional neural network (CNN) based methods.
Abstract: Computer-aided detection or diagnosis (CAD) has been a promising area of research over the last two decades. Medical image analysis aims to provide a more efficient diagnostic and treatment process for the radiologists and clinicians. However, with the development of science and technology, data interpretation manually in the conventional CAD systems has gradually become a challenging task. Deep learning methods, especially convolutional neural networks (CNNs), are successfully used as tools to solve this problem. This includes applications such as breast cancer diagnosis, lung nodule detection and prostate cancer localization. In this overview, the current state-of-the-art medical image analysis techniques in CAD research are presented, which focus on the convolutional neural network (CNN) based methods. The commonly used medical image databases in literature are also listed. It is anticipated that this paper can provide researchers in radiomics, precision medicine, and imaging grouping with a systematic picture of the CNN-based methods used in CAD research.
90 citations
TL;DR: An improved Susceptible-Infected-Removed-Susceptible (SIRS) epidemic model on complex heterogeneous networks is constructed and the global stability of the endemic equilibrium is proved under some conditions.
Abstract: In this paper, by taking full consideration of demographics, transfer from infectious to sus-ceptible and contact heterogeneity of the individuals, we construct an improved Susceptible-Infected-Removed-Susceptible (SIRS) epidemic model on complex heterogeneous networks. Using the next generation matrix method, we obtain the basic reproduction number $\mathcal{R}_0$ which is a critical value and used to measure the dynamics of epidemic diseases. More specifically, if $\mathcal{R}_0$ 1, then there exists a unique endemic equilib-rium and the permanence of the disease is shown in detail. By constructing an appropriate Lyapunov function, the global stability of the endemic equilibrium is proved as well under some conditions. Moreover, the effects of three major immunization strategies are investigated. Finally, some numerical simulations are carried out to demonstrate the correctness and validness of the theoretical results.
66 citations
TL;DR: This work improves and construct a new DenseNet for CAPTCHA recognition (DFCR) that keeps the primary performance advantages of the DenseNets but also effectively reduces the memory consumption.
Abstract: Aiming at the problems of low efficiency and poor accuracy of traditional CAPTCHA recognition methods, we have proposed a more efficient way based on deep convolutional neural network (CNN). The Dense Convolutional Network (DenseNet) has shown excellent classification performance which adopts cross-layer connection. Not only it effectively alleviates the vanishing-gradient problem, but also dramatically reduce the number of parameters. However, it also has caused great memory consumption. So we improve and construct a new DenseNet for CAPTCHA recognition (DFCR). Firstly, we reduce the number of convolutional blocks and build corresponding classifiers for different types of CAPTCHA images. Secondly, we input the CAPTCHA images of TFrecords format into the DFCR for model training. Finally, we test the Chinese or English CAPTCHAs experimentally with different numbers of characters. Experiments show that the new network not only keeps the primary performance advantages of the DenseNets but also effectively reduces the memory consumption. Furthermore, the recognition accuracy of CAPTCHA with the background noise and character adhesion is above 99.9%.
66 citations
TL;DR: The proposed ensemble ResCNN applies the residual block which skips several blocks of convolutional layers by using shortcut connections, and can help to overcome vanishing/exploding gradient problem.
Abstract: Remaining useful life (RUL) estimation is one of the most important component in prognostic health management (PHM) system in modern industry. It defined as the length from the current time to the end of the useful life. With the rapid development of the smart manufacturing, the data-driven RUL approaches have been widely investigated in both academic and engineering fields. Deep learning, which is a new paradigm in machine learning, has been applied in the RUL related fields, and has achieved remarkable results. However, classical deep learning algorithms also encounter the vanishing/exploding gradient problem found in artificial neural network with gradient-based learning methods and backpropagation. In this research, a new residual convolutional neural network (ResCNN) is proposed. ResCNN applies the residual block which skips several blocks of convolutional layers by using shortcut connections, and can help to overcome vanishing/exploding gradient problem. What's more, the ResCNN is enhanced by using the k-fold ensemble method. The proposed ensemble ResCNN is conducted on the C-MAPSS data provided by NASA. The results show that the proposed ensemble ResCNN has achieved significant improvement in both the mean and the standard deviation of the prediction RUL values. The proposed ensemble ResCNN has also compared with other famous machine learning and deep learning methods, including Multilayer Perceptron, Support Vector Machines, Deep Belief Networks, Long Short-Term Memory Model, Convolutional Neural Network and many other methods in literatures. The comparison results show that ensemble ResCNN achieved the start-of-the-art results, and outperform almost all of them.
54 citations
TL;DR: It is observed that in the absence of fear effect, hunting cooperation can induce both supercritical and subcritical Hopf- bifurcations and fear factor can stabilize the predator-prey system by excluding the existence of periodic solutions and makes the system more robust compared to hunting cooperation.
Abstract: The predation strategy for predators and the avoidance strategy of prey are important topics in ecology and evolutionary biology. Both prey and predators adjust their behaviours in order to gain the maximal benefits and to increase their biomass for each. In the present paper, we consider a modified Leslie-Gower predator-prey model where predators cooperate during hunting and due to fear of predation risk, prey populations show anti-predator behaviour. We investigate step by step the impact of hunting cooperation and fear effect on the dynamics of the system. We observe that in the absence of fear effect, hunting cooperation can induce both supercritical and subcritical Hopf- bifurcations. It is also observed that fear factor can stabilize the predator-prey system by excluding the existence of periodic solutions and makes the system more robust compared to hunting cooperation. Moreover, the system shows two different types of bi-stabilities behaviour: one is between coexisting equilibrium and limit cycle oscillation, and another is between prey-free equilibrium and coexisting equilibrium. We also observe generalized Hopf-bifurcation and Bogdanov-Takens bifurcation in two parameter bifurcation analysis. We perform extensive numerical simulations for supporting evidence of our analytical findings.
49 citations
TL;DR: A systematic comparison of a number of epidemic outbreaks using phenomenological growth models indicates that the GLM model outperformed the other models in describing the great majority of the epidemic trajectories.
Abstract: Phenomenological models are particularly useful for characterizing epidemic trajectories because they often offer a simple mathematical form defined through ordinary differential equations (ODEs) that in many cases can be solved explicitly. Such models avoid the description of biological mechanisms that may be difficult to identify, are based on a small number of model parameters that can be calibrated easily, and can be utilized for efficient and rapid forecasts with quantified uncertainty. These advantages motivate an in-depth examination of 37 data sets of epidemic outbreaks, with the aim to identify for each case the best suited model to describe epidemiological growth. Four parametric ODE-based models are chosen for study, namely the logistic and Gompertz model with their respective generalizations that in each case consists in elevating the cumulative incidence function to a power p ∈ [0,1]. This parameter within the generalized models provides a criterion on the early growth behavior of the epidemic between constant incidence for p = 0, sub-exponential growth for 0 < p < 1 and exponential growth for p = 1. Our systematic comparison of a number of epidemic outbreaks using phenomenological growth models indicates that the GLM model outperformed the other models in describing the great majority of the epidemic trajectories. In contrast, the errors of the GoM and GGoM models stay fairly close to each other and the contribution of the adjustment of p remains subtle in some cases. More generally, we also discuss how this methodology could be extended to assess the "distance" between models irrespective of their complexity.
44 citations
TL;DR: An improved mask regional convolutional neural network which attach a Sobel filter to the mask branch of Mask R-CNN in this paper which outperforms some state-of-the-art methods on forgery detection and localization.
Abstract: The research on forgery detection and localization is significant in digital forensics and has attracted increasing attention recently. Traditional methods mostly use handcrafted or shallow-learning based features，but they have limited description ability and heavy computational costs. Recently, deep neural networks have shown to be capable of extracting complex statistical features from high-dimensional inputs and efficiently learning their hierarchical representations. In order to capture more discriminative features between tampered and non-tampered regions，we propose an improved mask regional convolutional neural network (Mask R-CNN) which attach a Sobel filter to the mask branch of Mask R-CNN in this paper. The Sobel filter acts as an auxiliary task to encourage predicted masks to have similar image gradients to the groundtruth mask. The overall network is capable of detecting two different types of image manipulations, including copy-move and splicing. The experimental results on two standard datasets show that the proposed model outperforms some state-of-the-art methods.
41 citations
TL;DR: This work shows how the control and monitoring of a solar powered smart irrigation system can be achieved using sensors and environmental data from an Internet of Everything (IoE) using the Radial Basis Function Network (RBFN).
Abstract: Water and food are two of the most important commodities in the world, which makes agriculture crucial to mankind as it utilizes water (irrigation) to provide us with food. Climate change and a rapid increase in population have put a lot of pressure on agriculture which has a snowball effect on the earth's water resource, which has been proven to be crucial for sustainable development. The need to do away with fossil fuel in powering irrigation systems cannot be over emphasized due to climate change. Smart Irrigation systems powered by renewable energy sources (RES) have been proven to substantially improve crop yield and the profitability of agriculture. Here we show how the control and monitoring of a solar powered smart irrigation system can be achieved using sensors and environmental data from an Internet of Everything (IoE). The collected data is used to predict environment conditions using the Radial Basis Function Network (RBFN). The predicted values of water level, weather forecast, humidity, temperature and irrigation data are used to control the irrigation system. A web platform was developed for monitoring and controlling the system remotely.
TL;DR: A predator-prey model with the cost of fear in prey reproduction term is formulated and various Turing patterns are presented and it is found that the change in the level of fear and diffusion coefficients alter these structures significantly.
Abstract: Fear can influence the overall population size of an ecosystem and an important drive for change in nature. It evokes a vast array of responses spanning the physiology, morphology, ontogeny and the behavior of scared organisms. To explore the effect of fear and its dynamic consequences, we have formulated a predator-prey model with the cost of fear in prey reproduction term. Spatial movement of species in one and two dimensions have been considered for the better understanding of the model system dynamics. Stability analysis, Hopf-bifurcation, direction and stability of bifurcating periodic solutions have been studied. Conditions for Turing pattern formation have been established through diffusion-driven instability. The existence of both supercritical and subcritical Hopf-bifurcations have been investigated by numerical simulations. Various Turing patterns are presented and found that the change in the level of fear and diffusion coefficients alter these structures significantly. Holes and holes-stripes mixed type of ecologically realistic patterns are observed for small values of fear and relative increase in the level of fear may reduce the overall population size.
TL;DR: The mathematical model of the flexible job shop scheduling problem with transportation time and processing time is established to minimize the maximum completion time and an improved genetic algorithm is used to solve the problem.
Abstract: In the practical production, after the completion of a job on a machine, it may be transported between the different machines. And, the transportation time may affect product quality in certain industries, such as steelmaking. However, the transportation times are commonly neglected in the literature. In this paper, the transportation time and processing time are taken as the independent time into the flexible job shop scheduling problem. The mathematical model of the flexible job shop scheduling problem with transportation time is established to minimize the maximum completion time. The FJSP problem is NP-hard. Then, an improved genetic algorithm is used to solve the problem. In the decoding process, an operation left shift insertion method according to the problem characteristics is proposed to decode the chromosomes in order to get the active scheduling solutions. The actual instance is solved by the proposed algorithm used the Matlab software. The computational results show that the proposed mathematical model and algorithm are valid and feasible, which could effectively guide the actual production practice.
TL;DR: It is shown via sensitivity analysis result that proper sanitation of the mosquito breeding sites and avoiding the mosquito bites are the key control measures to future dengue outbreaks in Taiwan.
Abstract: Dengue virus (DENV) infection is endemic in many places of the tropical and subtropical regions, which poses serious public health threat globally. We develop and analyze a mathematical model to study the transmission dynamics of the dengue epidemics. Our qualitative analyzes show that the model has two equilibria, namely the disease-free equilibrium (DFE) which is locally asymp- totically stable when the basic reproduction number (R0) is less than one and unstable if R0 > 1, and endemic equilibrium (EE) which is globally asymp-totically stable when R0 > 1. Further analyzes reveals that the model exhibit the phenomena of backward bifurcation (BB) (a situation where a stable DFE co-exists with a stable EE even when the R0 > 1), which makes the disease control more diffi-cult. The model is applied to the real dengue epidemic data in Kaohsiung and Tainan cities in Taiwan, China to evaluate the fitting performance. We propose two reconstruction approaches to estimate the time-dependent R0 , and we find a consistent fitting results and equivalent goodness-of-fit. Our findings highlight the similarity of the dengue outbreaks in the two cities. We find that despite the proximity in Kaohsiung and Tainan cities, the estimated transmission rates are neither completely synchronized, nor periodically in-phase perfectly in the two cities. We also show the time lags between the seasonal waves in the two cities likely occurred. It is further shown via sensitivity analysis result that proper sanitation of the mosquito breeding sites and avoiding the mosquito bites are the key control measures to future dengue outbreaks in Taiwan.
TL;DR: This paper shows how multiple sources are possible to share their data amongst a group of participants without revealing their own data to one another as well as the dealer in the encrypted domain.
Abstract: Healthcare industry is one of the promising fields adopting the Internet of Things (IoT) solutions. In this paper, we study secret sharing mechanisms towards resolving privacy and security issues in IoT-based healthcare applications. In particular, we show how multiple sources are possible to share their data amongst a group of participants without revealing their own data to one another as well as the dealer. Only an authorised subset of participants is able to reconstruct the data. A collusion of fewer participants has no better chance of guessing the private data than a non-participant who has no shares at all. To realise this system, we introduce a novel research upon secret sharing in the encrypted domain. In modern healthcare industry, a patientos health record often contains data acquired from various sensor nodes. In order to protect information privacy, the data from sensor nodes is encrypted at once and shared among a number of cloud servers of medical institutions via a gateway device. The complete health record will be retrieved for diagnosis only if the number of presented shares meets the access policy. The retrieval procedure does not involve decryption and therefore the scheme is favourable in some time-sensitive circumstances such as a surgical emergency. We analyse the pros and cons of several possible solutions and develop practical secret sharing schemes for IoT- based healthcare systems.
TL;DR: The issue of optimizing the dissemination protocol for each of the strategies for population replacement and sterile insect technique is addressed, and some properties of the optimal control are established and illustrated with numerical simulations.
Abstract: In the fight against vector-borne arboviruses, an important strategy of control of epidemic consists in controlling the population of the vector, Aedes mosquitoes in this case. Among possible actions, two techniques consist either in releasing sterile mosquitoes to reduce the size of the population (Sterile Insect Technique) or in replacing the wild population by one carrying a bacteria, called Wolbachia, blocking the transmission of viruses from insects to humans. This article addresses the issue of optimizing the dissemination protocol for each of these strategies, in order to get as close as possible to these objectives. Starting from a mathematical model describing population dynamics, we study the control problem and introduce the cost function standing for population replacement and sterile insect technique. Then, we establish some properties of the optimal control and illustrate them with numerical simulations.
TL;DR: An n-species stochastic model is proposed which considers the influences of the competitions and delayed diffusions among populations on dynamics of species, and the asymptotic stability in distribution of the model is investigated.
Abstract: In this study, we propose an n-species stochastic model which considers the influences of the competitions and delayed diffusions among populations on dynamics of species. We then investigate the stochastic dynamics of the model, such as the persistence in mean of the species, and the asymptotic stability in distribution of the model. Then, by using the Hessian matrix and theory of optimal harvesting, we investigate the optimal harvesting problem, obtaining the optimal harvesting effort and the maximum of expectation of sustainable yield (ESY). Finally, we numerically discuss some examples to illustrate our theoretical findings, and conclude our study by a brief discussion.
TL;DR: The specific ankle-foot orthoses fabricated by material PA12 have a significant effect on the improvement of velocity and stride length in people with stroke.
Abstract: The aim of present study is to investigate the feasibility of patient-specific ankle-foot orthoses fabricated using additive manufacturing (AM) techniques. Then, clinical performance of the AFOs manufactured using material PA12 was evaluated in stroke survivors based on gait analysis data. The ankle and foot were scanned by EinScan-Pro 3D scanner. The software Geomagic Studio was used for modifying the AFO model. After processing the original AFO model into the final required model, material PA12 were used to fabricate the AFOs by Multi Jet Fusion (MJF) technique. Finally, gait analysis of 12 stroke patients was conducted to compare the effects with and without AFO. It took 2 hours from processing the initial AFO model to the completion of final model, and the printing time was 8 hours. The printing thickness of the AFO was 1.2 mm. With respect to the temporal-spatial parameters, the velocity and stride length in the gait with AFO increased significantly as compared to the gait without AFO (P=0.001, P=0.002). The cadence increased, double limb support phase decreased, and the step length difference decreased in the gait with AFO; however, the difference was not statistically significant (P=0.117, P=0.075, P=0.051).This study confirmed the feasibility of patient-specific AFO fabricated by AM techniques, and demonstrated the process of modifying AFO models successfully. The specific ankle-foot orthoses fabricated by material PA12 have a significant effect on the improvement of velocity and stride length in people with stroke.
TL;DR: The potential of using a simple outbreak model in conjunction with the historical data about the cumulative forwarding users for nearcasting the propagation trend is illustrated.
Abstract: As the largest social media in China, the Sina-Microblog plays an important role in public opinion dissemination. Despite intensive efforts in understanding the information propagation dynamics, the use of a simple outbreak model to generate summative indices that can be used to characterize the time series of a single Weibo event has not been attempted. This work fills this gap, and illustrates the potential of using a simple outbreak model in conjunction with the historical data about the cumulative forwarding users for nearcasting the propagation trend.
TL;DR: A review of the literature on the ultrasonic detection methods of microcalcifications was conducted and the related principles and research status of these methods were introduced, and the characteristics and limitations of the various methods were discussed and analyzed.
Abstract: Breast microcalcifications are one of the important imaging features of early breast cancer and are a key to early breast cancer diagnosis. Ultrasound imaging has been widely used in the detection and diagnosis of breast diseases because of its low cost, nonionizing radiation, and real-time capability. However, due to factors such as limited spatial resolution and speckle noise, it is difficult to detect breast microcalcifications using conventional B-mode ultrasound imaging. Recent studies show that new ultrasound technologies improved the visualization of microcalcifications over conventional B-mode ultrasound imaging. In this paper, a review of the literature on the ultrasonic detection methods of microcalcifications was conducted. The reviewed methods were broadly divided into high-frequency B-mode ultrasound imaging techniques, B-mode ultrasound image processing techniques, ultrasound elastography techniques, time reversal techniques, high spatial frequency techniques, second-order ultrasound field imaging techniques, and photoacoustic imaging techniques. The related principles and research status of these methods were introduced, and the characteristics and limitations of the various methods were discussed and analyzed. Future developments of ultrasonic breast microcalcification detection are suggested.
TL;DR: The results demonstrate the importance of parameter identifiability in the validation and predictive ability of mathematical models of prostate tumor treatment and the use of biological constraints and additional types of measurements to reduce these uncertainties.
Abstract: The past two decades have seen the development of numerous mathematical models to study various aspects of prostate cancer in clinical settings. These models often contain large sets of parameters and rely on limited data sets for validation. The quantitative analysis of the dynamics of prostate cancer under treatment may be hindered by the lack of identifiability of the parameters from the available data, which limits the predictive ability of the model. Using three ordinary differential equation models as case studies, we carry out a numerical investigation of the identifiability and uncer- tainty quantification of the model parameters. In most cases, the parameters are not identifiable from time series of prostate-specific antigen, which is used as a clinical proxy for tumor progression. It may not be possible to define a finite confidence bound on an unidentifiable parameter, and the relative uncertainties in even identifiable parameters may be large in some cases. The Fisher information ma- trix may be used to determine identifiable parameter subsets for a given model. The use of biological constraints and additional types of measurements, should they become available, may reduce these uncertainties. Ensemble Kalman filtering may provide clinically useful, short-term predictions of pa- tient outcomes from imperfect models, though care must be taken when estimating "patient-specific" parameters. Our results demonstrate the importance of parameter identifiability in the validation and predictive ability of mathematical models of prostate tumor treatment. Observing-system simulation experiments, widely used in meteorology, may prove useful in the development of biomathematical models intended for future clinical application.
TL;DR: A detailed review of the basic concept of HS and a survey of its latest variants for function optimization and of the innovative applications of HS in the field of intelligent manufacturing based on about 40 recently published articles are provided.
Abstract: The harmony search (HS) algorithm is one of the most popular meta-heuristic algorithms. The basic idea of HS was inspired by the music improvisation process in which the musicians continuously adjust the pitch of their instruments to generate wonderful harmony. Since its inception in 2001, HS has attracted the attention of many researchers from all over the world, resulting in a lot of improved variants and successful applications. Even for today, the research on improved HS variants design and innovative applications are still hot topics. This paper provides a detailed review of the basic concept of HS and a survey of its latest variants for function optimization. It also provides a survey of the innovative applications of HS in the field of intelligent manufacturing based on about 40 recently published articles. Some potential future research directions for both HS and its applications to intelligent manufacturing are also analyzed and summarized in this paper.
TL;DR: A new negative correlation ensemble transfer learning method (NCTE) is proposed, and results show that NCTE has achieved a good results compared with other machine learning and deep learning method.
Abstract: With the development of the smart manufacturing, data-driven fault diagnosis has receiving more and more attentions from both academic and engineering fields. As one of the most important data-driven fault diagnosis method, deep learning (DL) has achieved remarkable applications. However, the DL based fault diagnosis methods still have the following two drawbacks: 1) One of the most major branch of deep learning is to construct the deeper structures, however the deep learning models in fault diagnosis is very shadow. 2) As stated by the no-free-lunch theorem, no single model can perform best on every dataset, and the individual deep learning model still suffers from the generalization ability. In this research, a new negative correlation ensemble transfer learning method (NCTE) is proposed. Firstly, the transfer learning based ResNet-50 is proposed to construct a deep learning structure that has 50 layers. Secondly, several fully-connected layers and softmax classifiers are trained cooperatively using negative correlation learning (NCL). Thirdly, the hyper-parameters of the proposed NCTE are determined by cross validation. The proposed NCTE is conducted on the KAT Bearing Dataset, and the prediction accuracy of NCTE is as high as 98.73%. This results show that NCTE has achieved a good results compared with other machine learning and deep learning method.
TL;DR: A delayed HIV-1 infection model with immune response is investigated, where a logistic growth is incorporated in the growth of the target cells, and the effect of delays on the infection dynamics is studied.
Abstract: In this paper, we investigate a delayed HIV-1 infection model with immune response. Though a logistic growth is incorporated in the growth of the target cells, our focus is on the effect of delays on the infection dynamics. We first study the existence of steady states, which depends on the basic reproduction number $R_0$. When $R_0\le 1$, there is only the infection-free steady state, which is globally asymptotically stable if $R_0 1$, besides the unstable infection-free steady state, there is a unique infected steady state. We then study the local stability of the infected steady state and local Hopf bifurcation at it. The theoretical analysis indicates that the dynamics scenario is complicated. For example, there can be three sequences of critical values, stability switches and double Hopf bifurcation can occur. Some of the theoretical results are also illustrated with numerical simulations.
TL;DR: Zhang et al. as discussed by the authors explored the potential mechanisms implicated with colorectal cancer and identified some key biomarkers using gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses.
Abstract: Colorectal cancer (CRC) is one of the most common malignancies, giving rise to serious financial burden globally. This study was designed to explore the potential mechanisms implicated with CRC and identify some key biomarkers. CRC-associated gene expression dataset (GSE32323) was downloaded from GEO database. The differentially expressed genes (DEGs) were selected out based on the GEO2R tool. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were employed to search the enriched pathways of these DEGs. Additionally, a protein-protein interaction (PPI) network was also constructed to visualize interactions between these DEGs. Quantitative Real-time PCR (qPCR) was further performed to valid the top5 up-regulated and top5 down-regulated genes in patients with CRC. Finally, the survival analysis of the top5 up-regulated and top5 down-regulated genes was conducted using GEPIA, aiming to clarify their potential effects on CRC. In this study, a total of 451 DEGs were captured (306 down-regulated genes and 145 up-regulated genes). Among these DEGs, the top5 up-regulated genes were DPEP1, KRT23, CLDN1, LGR5 and FOXQ1 while the top5 down-regulated genes were CLCA4, ZG16, SLC4A4, ADH1B and GCG. GO analysis revealed that these DEGs were mainly enriched in cell adhesion, cell proliferation, RNA polymerase II promoter and chemokine activity. KEGG analysis disclosed that the enriched pathway included mineral absorption, chemokine signaling pathway, transcriptional misregulation in cancer, pathways in cancer and PPAR signaling pathway. Survival analysis showed that the expression level of ZG16 may correlate with the prognosis of CRC patients. Furthermore, according to the connectivity degree of these DEGs, we selected out the top15 hub genes, namely MYC, CXCR1, TOP2A, CXCL12, SST, TIMP1, SPP1, PPBP, CDK1, THBS1, CXCL1, PYY, LPAR1, BMP2 and MMP3, which were expected to be promising therapeutic target in CRC. Collectively, our analysis unveiled potential biomarkers and candidate targets in CRC, which could be helpful to the diagnosis and treatment of CRC.
TL;DR: The presented CMPSO is more flexible to select the most appropriate solutions for hiding the sensitive information based on user's preference and has good performance than the traditional single-objective evolutionary approaches in terms of three side effects.
Abstract: Privacy-preserving data mining has become an interesting and emerging issue in recent years since it can, not only hide the sensitive information but still mine the meaningful knowledge at the same time. Since privacy-preserving data mining is a non-trivial task, which is also concerned as a NP-hard problem, several evolutionary algorithms were presented to find the optimized solutions but most of them focus on considering a single-objective function with the pre-defined weight values of three side effects (hiding failure, missing cost, and artificial cost). In this paper, we aim at designing a multiple objective particle swarm optimization method for hiding the sensitive information based on the density clustering approach (named CMPSO). The presented CMPSO is more flexible to select the most appropriate solutions for hiding the sensitive information based on user's preference. Extensive experiments are carried on two datasets to show that the designed CMPSO algorithm has good performance than the traditional single-objective evolutionary approaches in terms of three side effects.
TL;DR: Simulation studies are utilized to assess how misspecifying the error structure affects parameter estimation results, specifically bias and uncertainty, as a function of the level of random noise in the data and it is shown that issues of parameter uncertainty and bias in the presence of overdispersion can be mitigated with the inclusion of more data.
Abstract: The Poisson distribution is commonly assumed as the error structure for count data; however, empirical data may exhibit greater variability than expected based on a given statistical model. Greater variability could point to model misspecification, such as missing crucial information about the epidemiology of the disease or changes in population behavior. When the mechanism producing the apparent overdispersion is unknown, it is typically assumed that the variance in the data exceeds the mean (by some scaling factor). Thus, a probability distribution that allows for overdispersion (negative binomial, for example) may better represent the data. Here, we utilize simulation studies to assess how misspecifying the error structure affects parameter estimation results, specifically bias and uncertainty, as a function of the level of random noise in the data. We compare results for two parameter estimation methods: nonlinear least squares and maximum likelihood estimation with Poisson error structure. We analyze two phenomenological models the generalized growth model and generalized logistic growth model to assess how results of parameter estimation are affected by the level of overdispersion underlying in the data. We use simulation to obtain confidence intervals and mean squared error of parameter estimates. We also analyze the impact of the amount of data, or ascending phase length, on the results of the generalized growth model for increasing levels of overdispersion. The results show a clear pattern of increasing uncertainty, or confidence interval width, as the overdispersion in the data increases. While maximum likelihood estimation consistently yields narrower confidence intervals and smaller mean squared error, differences between the two methods were minimal and not practically significant. At moderate levels of overdispersion, both estimation methods yielded similar performance. Importantly, it is shown that issues of parameter uncertainty and bias in the presence of overdispersion can be mitigated with the inclusion of more data.
TL;DR: This paper proposes a new framework of robust digital watermarking for color images using combined embedding techniques of Discrete Fourier Transform (DFT) and Dual Tree Complex Wavelet Transform (DTCWT).
Abstract: Image watermarking focuses on hiding secret data into the cover image imperceptibly to protect the copyright of the original image. In this paper, we propose a new framework of robust digital watermarking for color images using combined embedding techniques of Discrete Fourier Transform (DFT) and Dual Tree Complex Wavelet Transform (DTCWT). The cover image is first divided into Y, U and V channels. The Y channel is then transformed by DFT and partitioned into the ring shapes. With an embedding key, we generate pseudo-random patterns to represent the watermark. These patterns are also transformed and partitioned. The watermark represented by the selection of patterns is then embedded into the rings of the DFT coefficients. We further embed a rectification watermark into the U channel, in which DTCWT is applied to achieve a capability of geometric distortion resilience. On the recipient's side, the detection and extraction of watermark can be successfully done. Compared with previous schemes, the proposed method is better on preserving the image quality. Meanwhile, the robustness against typical attacks is also stronger.
TL;DR: A delayed phytoplankton-zooplankton system with the coeffcient depending on delay gives the nonnegative and boundedness of solutions of the delay differential equations and the stability of periodic solution and bifurcation direction through the use of central manifold theory.
Abstract: In this paper, a delayed phytoplankton-zooplankton system with the coefficient depending on delay is investigated. Firstly, it gives the nonnegative and boundedness of solutions of the delay differential equations. Secondly, it gives the asymptotical stability properties of equilibria in the absence of time delay. Then in the presence of time delay, the existence of local Hopf bifurcation is discussed when the delay changes. In addition to that, the stability of periodic solution and bifurcation direction are also obtained through the use of central manifold theory. Furthermore, he global continuity of the local Hopf bifurcation is discussed by using the global Hopf bifurcation result of FDE. At last, some numerical simulations are presented to show the rationality of theoretical analyses.
TL;DR: Experience of updated biological knowledge that influenced mathematical modellings of rubella vaccination is taken into account to reflect about the tetravalent dengue vaccine and a discussion about the security of vaccination strategies.
Abstract: The only rubella vaccine available in North America is the RA27/3 strain (isolated from the kidney of a rubella-infected fetus and attenuated) licensed in 1979, which substituted HPV77/DE5 strain vaccine due to concerns about waning immunity. The first dengue vaccine (Dengvaxia CYDTDV) was first registered in Mexico in December, 2015, which is a live recombinant tetravalent dengue vaccine. Rubella vaccine was applied since 1969, but tetravalent dengue vaccine is being used in large scale nowadays. In the past, based on unavailable information regarded to rubella vaccine, mathematical models were used to design vaccination schemes in order to avoid congenital rubella syndrome (CRS). Currently, knowing that vaccine does not result in CRS, rubella vaccination is modelled as usual childhood infection. This experience of updated biological knowledge that influenced mathematical modellings of rubella vaccination is taken into account to reflect about the tetravalent dengue vaccine. We also address a discussion about the security of vaccination strategies.