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Summary Toroidal molecular magnets represent promising candidates for next-generation ultra-dense information storage. These wheel-like molecules are able to store one bit per molecule because of their insensitivity to homogeneous magnetic fields—one of the main sources of magnetic perturbations. However, synthesis of molecules possessing a well-defined and stable vortex arrangement of the on-site magnetic moments in the ground state represents a challenge. Here, we show that 16 magnetic metal ions can be alternately arranged into a macrocycle named {Fe8Dy8}. The net toroidal moment can be experimentally determined at 0.23 Tesla. Moreover, ab initio calculations were performed to reveal that ferromagnetic exchange interactions between the FeIII and DyIII metal centers are the key to generate this toroidal moment. This feature is significantly distinguished from the previously described dipole-dipole interaction-based single-molecule toroics (SMTs), showing the importance of exchange-coupling interactions in the design of next-generation SMTs.

The surge of medical and e-commerce applications has generated tremendous amount of data, which brings people to a so-called “Big Data” era. Different from traditional large data sets, the term “Big Data” not only means the large size of data volume but also indicates the high velocity of data generation. However, current data mining and analytical techniques are facing the challenge of dealing with large volume data in a short period of time. This paper explores the efficiency of utilizing the Normal Distribution ( ND ) method for splitting and processing large volume medical data in cloud environment, which can provide representative information in the split data sets. The ND-based new model consists of two stages. The first stage adopts the ND method for large data sets splitting and processing, which can reduce the volume of data sets. The second stage implements the ND-based model in a cloud computing infrastructure for allocating the split data sets. The experimental results show substantial efficiency gains of the proposed method over the conventional methods without splitting data into small partitions. The ND-based method can generate representative data sets, which can offer efficient solution for large data processing. The split data sets can be processed in parallel in Cloud computing environment.

Data carriers using spin waves in spintronic and magnonic logic devices offer operation at low power consumption and free of Joule heating yet requiring noncollinear spin structures of small sizes. Heterometallic rings can provide such an opportunity due to the controlled spin-wave transmission within such a confined space. Here, we present a series of {ScnGdn} (n = 4, 6, 8) heterometallic rings, which are the first Sc-Ln clusters to date, with tunable magnetic interactions for spin-wave excitations. By means of time- and temperature-dependent spin dynamics simulations, we are able to predict distinct spin-wave excitations at finite temperatures for Sc4Gd4, Sc6Gd6, and Sc8Gd8. Such a new model is previously unexploited, especially due to the interplay of antiferromagnetic exchange, dipole-dipole interaction, and ring topology at low temperatures, rendering the importance of the latter to spin-wave excitations.

It has been a challenge for solving the motor imagery classification problem in the brain informatics area. Accuracy and efficiency are the major obstacles for motor imagery analysis in the past decades since the computational capability and algorithmic availability cannot satisfy complex brain signal analysis. In recent years, the rapid development of Machine Learning (ML) methods has empowered people to tackle the motor imagery classification problem with more efficient methods. Among various ML methods, the Graph neural networks(GNNs) method has shown its efficiency and accuracy in dealing with inter-related complex networks. The use of GNN provides new possibilities for feature extraction from brain structure connection. In this paper, we proposed a new model called MCGNet+, which improves the performance of our previous model MutualGraphNet. In this latest model, the mutual information of the input columns forms the initial adjacency matrix for the cosine similarity calculation between columns to generate a new adjacency matrix in each iteration. The dynamic adjacency matrix combined with the spatial temporal graph convolution network(ST-GCN) has better performance than the unchanged matrix model. The experimental results indicate that MCGNet+ is robust enough to learn the interpretable features and outperforms the current state-of-the-art methods.

The increasing demands of surgical implantation highlight the significance of anti-infection of medical devices, especially antibiofilm contamination on the surface of implants. The biofilms developed by colonized microbes will largely hinder the adhesion of host cells, leading to failure in long-term applications. In this work, UV-responsive multilayers were fabricated by stepwise assembly of poly(pyrenemethyl acrylate- co-acrylic acid) (P(PA- co-AA)) micelles and chitosan on different types of substrates. Under UV irradiation, the cleavage of pyrene ester bonds in the P(PA- co-AA) molecules resulted in the increase of roughness and hydrophilicity of the multilayers. During this process, reactive oxygen species were generated in situ within 10 s, which destroyed the biofilms of Staphylococcus aureus, leading to the degradation of the bacterial matrix. The antibacterial rate was above 99.999%. The UV-irradiated multilayers allowed the attachment and proliferation of fibroblasts, endothelial cells, and smooth muscle cells, benefiting tissue integration of the implants. When poly(dimethylsiloxane) slices with the multilayers were implanted in vivo and irradiated by UV, the density of bacteria and the inflammatory level (judging from the number of neutrophils) decreased significantly. Moreover, formation of neo blood vessels surrounding the implants was observed after implantation for 7 days. These results reveal that the photoresponsive multilayers endow the implants with multifunctions of simultaneous antibiofilm and tissue integration, shedding light for applications in surface modification of implants in particular for long-term use.

Organometal halide perovskites (OHPs) are a kind of promising materials applied in solar cells. However, the toxicity of lead in OHPs is considered as an environmental problem. Herein, we report two lead-free two-dimensional (2D) iodide-based OHPs, namely (C6H16N2)2AgBiI8·H2O (AgBiI) and (C6H16N2)2CuBiI8·0.5H2O (CuBiI), where C6H14N2 = 1,4-cyclohexanediamine, with a double perovskite structure. These two bimetallic perovskites show the optical band gaps of 1.93 eV and 1.68 eV. In addition, these 2D perovskite materials can form smooth films through a simple one-step spin-coating approach. Photocurrent measurements under xenon lamp irradiation indicate obvious photoresponses, suggesting that these semiconducting materials have the potential for application in light harvesting.

The information analysis of the electroencephalogram (EEG) signal is carried out by granulation and reciprocal entropy (PeEn). The analysis of the EEG signal is obtained by experimental activity. Due to its complexity and multichannel characteristic, together with granular computing (GrC) and PeEn are used to analyze the EEG signal. The EEG signal consists of 32 channels of data and the experimental data are used to discriminate patterns, with experimental focus on considering real and thinking actions. The time-series EEG signals were granularized according to the changes in the signal and analyzed by PeEn coding and Fuzzy C-Means (FCM) algorithm. Because there are two main actions, i.e., left-handed, and right-handed actions were clearly delineated. In addition, we provide the GrC algorithm to prove the boundary problem with the help of Hilbert-Huang transform. The obtained results show an advanced approach for analyzing EEG signals, which can be the basis for solving complex multichannel data analysis.

In order to solve the problems of low accuracy of data integration results, low integration efficiency, and easy confusion between different types of data in traditional methods, a multimedia vocal education data integration system based on adaptive genetic algorithm was designed. Specifically, the designed system is divided into three parts: data source management module, system administrator module, and database management module. The synchronized multimedia vocal education data are first processed by the synchronous multimedia vocal education data processing and then integrated by an adaptive genetic algorithm. The experimental results show that the longest data transmission time of the system is 2.3 s, which is much lower than that of the traditional method, and the accuracy of the integration result is higher, and the probability of data integration confusion is lower, which all indicate that the designed system has better application performance.

Hash codes based on deep learning can effectively learn image features. For supervised deep learning methods, the label information of the image can be used to further learn the semantic information of the image. However, the current supervised deep learning methods often use 1 and 0 (or -1) to represent the similarity of two images. In fact, these two extreme values do not fully reflect the similarity between images. Thus, we proposed a novel similarity and ranking preserving deep hashing method (SRPDH). In order to enrich and more comprehensively reflect the semantic information between images, we refine the single-label information into multi-label information, and use Jaccard coefficient model to calculate the similarity between label information. In the loss function model, we use the cross entropy model and consider the loss caused by the binary quantization of the network output. The experimental results show that our method can further improve the mean average precision (MAP) of image retrieval compared with the existing methods.

Online teaching are facing dramatic challenges due to the COVID19 pandemic requiring massive online education. Students are experiencing mental and physical isolation during this period. This research aims to find an efficient way to discover students emotion status through EEG pattern recognition (PR). Traditional PR methods have been applied extensively in EEG recognition including Artificial Neuron Networks (ANN), Support Vector Machine(SVM), K-Nearest Neighbors (KNN), and so on. In this paper, a association rule-based PR method has been introduced through incorporating clustering and Apriori association rule methods. The experimental results demonstrate that the optimized association rule-based EEG PR model can improve real-time recognition efficiency. The proposed model can be used for identifying students cognitive statuses and improve educational performance in COVID19 period.

The dual functional implants of antibacteria and osteointegration are highly demanded in orthopedic and dentistry, especially for patients who suffer from diabetes or osteoporosis simultaneously. However, there is lack of the facile and robust method to produce clinically applicable implants with this dual function although coatings possessing single function have been extensively developed. Herein, hyperbranched poly-L-lysine (HBPL) polymers were covalently immobilized onto the alkali-heat treated titanium (Ti) substrates and implants by using 3-glycidyloxypropyltrimethoxysilane (GPTMS) as the coupling agent, which displayed excellent antibacterial activity against S. aureus and E. coli with an efficiency as high as 89.4% and 92.2% in vitro, respectively. The HBPL coating also significantly promoted the adhesion, spreading, proliferation and osteogenic differentiation of MC3T3-E1 cells in vitro. Furthermore, the results of a S. aureus infection rat model in vivo ulteriorly verified that the HBPL-modified screws had good antibacterial and anti-inflammatory abilities at an early stage of implantation and better osteointegration compared with the control Ti screws.

Improving Research and Development (RD and placed focus on the main business that is of great significance for companies’ development. However, identifying and developing innovative products are becoming a major difficulty in most R&D and manufacturing companies. This paper intends to apply K-means and K–modes clustering method to identify the most important factors impacting sales data and forecast the trend of video surveillance products through Decision Tree classification method based on a real video surveillance company, i.e. Zhejiang U technologies Company Limited (abbrev. U). Through this work we could improve the quality of decision-making before R&D projects started and effectively take product development as an investment to manage.

Multiple Criteria Decision-Making (MCDM) are often contradict between their goals and criteria. Compromised or satisfied solutions usually cannot meet the practical needs well. We found the problem lies on the assumption that goals and constraints are fixed and reasonable but in fact they are extendable in practice. We present an extension preprocessing model for MCDM based on basic-elements theory. Several steps for the preprocessing of MCDM is introduced to extend the constraints, criteria or goals to obtain win-win solutions by implication analysis and transformations. It gives a way for exploring win-win solutions by extending the multi-direction information and knowledge of the constraints or goals supported by data mining and Extenics.

Cell migration plays a pivotal role in many pathological and physiological processes. So far, most of the studies have been focused on 2-dimensional cell adhesion and migration. Herein, the migration behaviors of cell spheroids in 3D hydrogels obtained by polymerization of methacrylated hyaluronic acid (HA-MA) and fibrinogen (Fg) with different ratios were studied. The Fg could be released to the medium gradually along with time prolongation, achieving the dynamic change of hydrogel structures and properties. Three types of cell spheroids, i.e., endothelial cell (EC), smooth muscle cell (SMC), and EC-SMC spheroids, were prepared with 10,000 cells in each, whose diameters were about 343, 108, and 224 μ m, respectively. The composite hydrogels with an intermediate ratio of Fg allowed the fastest 3D migration of cell spheroids. The ECs-SMCs migrated longest up to 3200 μ m at day 14, whereas the SMC spheroids migrated slowest with a distance of only ~400 μ m at the same period of time. The addition of free RGD or anti-CD44 could significantly reduce the migration distance, revealing that the cell-substrate interactions take the major roles and the migration is mesenchymal dependent. Moreover, addition of anti-N-cadherin and MMP inhibitors also slowed down the migration rate, demonstrating that the degradation of hydrogels and cell-cell interactions are also largely involved in the cell migration. RT-PCR measurement showed that expression of genes related to cell adhesion and antiapoptosis, and angiogenesis was all upregulated in the EC-SMC spheroids than single EC or SMC spheroids, suggesting that the use of composite cell spheroids is more promising to promote cell-substrate interactions and maintenance of cell functions.

The application of biometric identification technology has been applied extensively in modern society. EEG pattern recognition method is one of the key biometric identification technologies for advanced secure and reliable identification technology. This paper introduces a novel EEG pattern recognition method based on Segmented EEG Graph using PLA (SEGPA) model, which incorporates the novel self-adjusting time series dependency method. In such a model, the dynamic time-dependency method has been applied in the recognition process. The preliminary experimental results indicate that the proposed method can produce a reasonable recognition outcome.

In any information technology enterprise, resource allocation and project scheduling are two important issues to reduce project duration, cost and risk in multi-project environments. This paper proposes an integrated and efficient computational method based on multi-objective particle swarm optimization to solve these two interdependent problems simultaneously. Minimizing the project duration, cost and maximizing the quality of resource allocation are all considered in our approach. Moreover, we suggest a novel non-dominated sorting vector evaluated particle swarm optimization (NSVEPSO). In order to improve its efficiency, this algorithm first uses a novel method for setting the global best position, and then executes a non-dominated sorting process to select new population. The performance of NSVEPSO is evaluated by comparison with SWTC_NSPSO, VEPSO and NSGA-III. The results of four experiments in the real scenario with small, medium and large data sizes show that NSVEPSO provides better boundary solutions and costs less time than the other algorithms.

Simultaneous Localization and Mapping (SLAM) has become a research hotspot in the field of robots in recent years. However, most visual SLAM systems are based on static assumptions which ignored motion effects. If image sequences are not rich in texture information or the camera rotates at a large angle, SLAM system will fail to locate and map. To solve these problems, this paper proposes an improved ORB-SLAM2 algorithm based on information entropy and sharpening processing. The information entropy corresponding to the segmented image block is calculated, and the entropy threshold is determined by the adaptive algorithm of image entropy threshold, and then the image block which is smaller than the information entropy threshold is sharpened. The experimental results show that compared with the ORB-SLAM2 system, the relative trajectory error decreases by 36.1% and the absolute trajectory error decreases by 45.1% compared with ORB-SLAM2. Although these indicators are greatly improved, the processing time is not greatly increased. To some extent, the algorithm solves the problem of system localization and mapping failure caused by camera large angle rotation and insufficient image texture information.

In order to process a compressed image, such as a JPG image, a common way is to decompress the image first to get each pixel, and then process it. In this paper, we propose a method for image enhancement in the process of decompression. The image is compressed by using two-dimensional F-shift (TDFS) and two dimensional Haar wavelet transform. To enhance the image during decompression, firstly we adjust the brightness of the whole image by modifying the approximation coefficient and enhance the decompressed low frequency component part using the contrast limited adaptive histogram equalization (CLAHE) method. Finally, we decompose the remaining data and do the last step of image enhancement. Contrast with CLAHE and the state-of-art method, our method can not only combination merits of the spatial domain method and the transform domain method, but also can reduce the process complexity and maintain the compressibility of the original image.

Selective exhaustion of over-expressed reactive oxygen species (ROS) is of great significance in the therapy of osteoarthritis (OA) due to the inhibiting effect on oxidative stress and inflammation. Herein, a ROS-scavenging and drug-release platform was prepared via encapsulating dexamethasone acetate (DA)-loaded ROS erasable poly(ethylene glycol)-b-polythioketal-b-poly(ethylene glycol) (PEG-PTK-PEG) micelles (PDM) into an injectable hydrogel. The hydrogel (HDH@PDM) was constructed by Schiff base reaction between hydrazide-grafted hyaluronic acid (HA-ADH) and aldehyde-modified dextran (Dex-ALH), achieving a self-healing property for viscosupplementation. The PDM imparted enhanced antioxidant capability to the hydrogel, which in turn endowed the PDM with prolonged retention and sustained DA release. The intraarticularly administered multifunctional injectable hydrogel potently diminished inflammation via depleting ROS and suppressing inflammatory cytokines as well as downregulating pro-inflammatory M1 macrophages ratio in a rat OA model. The developed therapeutic system significantly alleviated OA symptoms, embodying in excellent capability of preventing cartilage extracellular matrix degeneration with negligible toxicity in vivo.

Chaotic particle swarm optimization algorithm is improved by incorporating antibody concentration, adaptive propagation, optimization mechanism of the multi-population evolution strategy, elite particles chaotic traversal mechanism and constraint processing mechanism. In this paper, an improved adaptive propagation chaotic particle swarm optimization algorithm based on immune selection (IS-APCPSO algorithm for short) is proposed. The performance of several algorithms has been compared by multimodal function, functions with high dimensional and complex constraints, bi-level programming function and a classic example of traffic network optimization. The experimental results prove that the proposed algorithm in accelerating convergence rate, increasing the diversity of particles, and preventing premature phenomenon is effective. The novel algorithm is expected to be used in the model solution of large-scale complex traffic network optimization problem.

Bismuth-based hybrid materials are recently attempted as lead-free alternatives for CH 3 NH 3 PbI 3 in solar cell applications, but the power conversion efficiencies of such cells remain low, for which the discontinuous connectivity of the inorganic components can be an important reason. To confirm such a hypothesis we synthesized a series of organic-inorganic bismuth halides with distinct structure-property correlation. The isolated compounds formulated as (TMP)[BiI 5 ] 1, (TMP)[BiBr 5 ] 2 , (TMP)[BiCl 5 ] 3 and (TMP) 1.5 [Bi 2 I 7 Cl 2 ] 4 (TMP = N,N,N’,N’-Tetramethylpiperazine) show one-dimensional bismuth-halide connectivity for pure halide compounds ( 1 - 3 ); while two-dimensional bismuth-halide connectivity for the mixed-halogen compound 4 . The optical band gaps of these four compounds, which are ranging from 2.02 eV to 3.21 eV, roughly correspond to the halogens sp...

In this paper, research on electroencephalogram (EEG) is carried out through principal component analysis (PCA) and support vector machine (SVM). PCA is used to collect EEG data characteristics to discriminate the behaviors by SVM methodology. The actual EEG signals are obtained from 18 experimenters who raised hands with meditation and actual movement during the experiments. The 16-channel data from the experiments form one data set. In order to get principal component of EEG signal, 16 features are considered from each channel and normalized. Simulation results demonstrate that two behaviors – i.e., raising hands and meditation – can be clearly classified using SVM, which is also visualized by a 2-dimensional principal component plot. Our research shows that specific human actions and thinking can be efficiently classified based on EEG signals using machine learning techniques like PCA and SVM. The result can apply to make action only with thinking.

Reactive oxygen species (ROS) play an important role in the pathogenesis of numerous diseases including atherosclerosis, diabetes, inflammation and myocardial infarction (MI). In this study, a ROS-responsive biodegradable elastomeric polyurethane containing thioketal (PUTK) linkages was synthesized from polycaprolactone diol (PCL-diol ), 1,6-hexamethylene diisocyanate (HDI), and ROS-cleavable chain extender. The PUTK was electrospun into fibrous patches with the option to load glucocorticoid methylprednisolone (MP), which were then used to treat MI of rats in vivo. The fibrous patches exhibited suitable mechanical properties and high elasticity. The molecular weight of PUTK was decreased significantly after incubation in 1 mM H2O2 solution for 2 weeks due to the degradation of thioketal bonds on the polymer backbone. Both the PUTK and PUTK/MP fibrous patches showed good antioxidant property in an oxidative environment in vitro. Implantation of the ROS-responsive polyurethane patches in MI of rats in vivo could better protect cardiomyocytes from death in the earlier stage (24 h) than the non ROS-responsive ones. Implantation of the PUTK/MP fibrous patches for 28 days could effectively improve the reconstruction of cardiac functions including increased ejection fraction, decreased infarction size, and enhanced revascularization of the infarct myocardium.

Reactive oxygen species (ROS) have been considered the pivotal signaling molecules in many physiological processes, and are usually overproduced in various inflammatory tissues. Overproduction of ROS may disrupt cellular homeostasis, cause non-specific damage to critical components, and lead to a series of diseases. ROS are acknowledged as a type of emerging triggered event similar to acidic pH, overproduced enzymes, temperature and other specific stimuli found in pathological microenvironments. Recently, ROS-responsive biomaterials have been identified as a type of promising therapeutic substance to alleviate oxidative stress in tissue microenvironments, and for use as a vehicle triggered by inflammatory diseases to realize drug release under physiological oxidative microenvironments. In this review, we discuss mainly the mechanisms of ROS-responsive biomaterials with solubility switch and chemical degradation, and those ROS-responsive groups used in ROS-responsive biomaterials. The mechanism of ROS overproduction in pathophysiological conditions is introduced. The various applications of ROS-responsive biomaterials in tissue regeneration and disease therapy, such as cardiovascular diseases, osteoarthritis, chronic diabetic wounds, inflammatory bowel disease and other inflammatory diseases, are summarized.

Although the negative impacts of particulate matter (PM2.5) on human health have been well recognized, very few efforts have been paid to find new strategies to suppress the toxicity of PM2.5 both in vitro and in vivo. In this study, reactive oxygen species (ROS)-responsive nanoparticles made of poly(1,4-phenleneacetonedimethylene thioketal) (PPADT) were used to load immunosuppressant drug tacrolimus (FK506) with a drug loading efficiency of around 44%. The PPADT particles showed very good ROS-responsiveness and were degraded in an oxidation environment. By exhausting intracellular ROS, they could effectively suppress the toxicity of A549 lung epithelial cells and RAW264.7 macrophages induced by the PM2.5 particulates collected from three different regions in China. Moreover, the inflammatory response of PM2.5 could also be significantly suppressed, showing much better performance than the free FK506 drugs both in vitro and in vivo. This concept-proving research demonstrates the promising application for the ROS-sensitive drug release particles in dispelling the toxicity and suppressing the inflammation of PM2.5 pollutes, shedding a new light in the design and applications of stimuli-responsive systems in the bionanotechnology and healthcare fields.

The increasing number of traffic accidents caused by drowsy driving has drawn much attention for detecting driver’s status and alarming drowsy driving. Existing research indicates that the changes in the physiological characteristics can reflect fatigue status, particularly brain activities. Nowadays, the research on brain science has made significant progress, such as the analysis of EEG signal to provide technical supports for real world applications. In this paper, we analyze drivers’ EEG data sets based on the self-adjusting Dynamic Time Dependency (DTD) method for detecting drowsy driving. The proposed model, i.e. SEGAPA, incorporates the time window moving method and cluster probability distribution for detecting drivers’ status. The preliminary experimental results indicates the efficiency of the proposed method.

Excessive reactive oxygen species (ROS) were closely associated with the progression of osteoarthritis (OA) in terms of symptoms and aggravation. To combine the advantages of ROS-stimuli responsiveness with scavenging ability, a novel polythioketal urethane (PTKU) was synthesized by simple and direct reaction between polythioketal (PTK) and diisocyanate. Apart from the excellent mechanical properties, the PTKU could be fabricated into various formulations, for example, nanoparticles (NPs) loaded with anti-inflammatory drug, dexamethasone. The PTKU@DEX NPs were capable of scavenging several kinds of ROS, accompanying with the degradation of polymers. In vitro evaluation with macrophages revealed that the PTKU and PTKU@DEX NPs had low cytotoxicity, and could suppress the intracellular ROS and expression of hypoxia inducible factor-1α (HIF-1α). Injection of the PTKU and PTKU@DEX NPs to OA in vivo could significantly reduce the ROS level in articular cavity and alleviate destruction of oxidative stress, and resulted in a lower ratio of inflammatory M1 macrophages and a higher level of anti-inflammatory M2 macrophages. The synergistic effects of ROS-responsive drug delivery and ROS scavenging PTKU promoted significantly the therapeutic outcome of OA with a best score close to the normal cartilage, revealing the great promise of PTKU in treatment of OA in vivo.

As technologies for image processing, image enhancement can provide more effective information for later data mining and image compression can reduce storage space. In this paper, a smart enhancement scheme during decompression, which combined a novel two-dimensional F-shift (TDFS) transformation and a non-standard two-dimensional wavelet transform (NSTW), is proposed. During the decompression, the first coefficient s00 of the wavelet synopsis was used to adaptively adjust the global gray level of the reconstructed image. Next, the contrast-limited adaptive histogram equalization (CLAHE) was used to achieve the enhancement effect. To avoid a blocking effect, CLAHE was used when the synopsis was decompressed to the second-to-last level. At this time, we only enhanced the low-frequency component and did not change the high-frequency component. Lastly, we used CLAHE again after the image reconstruction. Through experiments, the effectiveness of our scheme was verified. Compared with the existing methods, the compression properties were preserved and the image details and contrast could also be enhanced. The experimental results showed that the image contrast, information entropy, and average gradient were greatly improved compared with the existing methods.

Research on electroencephalography (EEG) signals and their data analysis have drawn much attention in recent years. Data mining techniques have been extensively applied as efficient solutions for non-invasive brain&ndash, computer interface (BCI) research. Previous research has indicated that human brains produce recognizable EEG signals associated with specific activities. This paper proposes an optimized data sampling model to identify the status of the human brain and further discover brain activity patterns. The sampling methods used in the proposed model include the segmented EEG graph using piecewise linear approximation (SEGPA) method, which incorporates optimized data sampling methods, and the EEG-based weighted network for EEG data analysis, which can be used for machinery control. The data sampling and segmentation techniques combine normal distribution approximation (NDA), Poisson distribution approximation (PDA), and related sampling methods. This research also proposes an efficient method for recognizing human thinking and brain signals with entropy-based frequent patterns (FPs). The obtained recognition system provides a foundation that could to be useful in machinery or robot control. The experimental results indicate that the NDA&ndash, PDA segments with less than 10% of the original data size can achieve 98% accuracy, as compared with original data sets. The FP method identifies more than 12 common patterns for EEG data analysis based on the optimized sampling methods.

Tissue regeneration involves versatile types of cells. The accumulation and disorganized behaviors of undesired cells impair the natural healing process, leading to uncontrolled immune response, restenosis, and/or fibrosis. Cell-selective surfaces and interfaces can have specific and positive effects on desired types of cells, allowing tissue regeneration with restored structures and functions. This review outlines the importance of surfaces and interfaces of biomaterials with cell-selective properties. The chemical and biological cues including peptides, antibodies, and other molecules, physical cues such as topography and elasticity, and physiological cues referring mainly to interactions between cells-cells and cell-chemokines or cytokines are effective modulators for achieving cell selectivity upon being applied into the design of biomaterials. Cell-selective biomaterials have also shown practical significance in tissue regeneration, in particular for endothelialization, nerve regeneration, capture of stem cells, and regeneration of tissues of multiple structures and functions.

The human brain is an extremely complex study. In recent years, EEG (Electroencephalography) data sets have been studied extensively in the various fields, particularly in the area of brain study. Some psychological work has suggested that human brains can generate EEG signals that are based on individual entities. These EEG signals varies according to different identities. This paper suggests a weighted network method for brain pattern recognition. The data segmentation technique is deployed in the process of constructing weighted networks. The EEG data segmentation technique incorporates the normal distribution sampling method [1]. The EEG data sets are obtained from various experiments including shopping psychological EEG data sets, driving EEG data sets, etc. This research discovers the potential of generating an efficient network for brain wave pattern recognition. The future work will further extend the current work and applied the proposed method to the human-robotic control and security areas.

Reducing the resigned staff number has become a significant challenge in many companies. Nevertheless, resignation can in many cases help companies to establish a 'survival of the fittest' culture that can provide companies with competitive advantages. However, a high percentage of resigned staff will have a negative impact on a company's daily operation and, in worst cases, will cause organizational breakdown. Analyzing the factors that influence the resigned staff could enable solutions to be found to prevent such incidences occurring in companies. In this paper, a data analytical method has been employed based on a China Construction Bank Hangzhou sub-branch to find whether the salary standard could have a significant effect on the rate of resignation. Through this work we could improve the contingency plan to reduce the resignation percentage and build a more reasonable human resource management system.

The particle swarm optimization algorithm is improved by introducing the immune selection, adaptive propagation, multi-population evolution. An improved adaptive propagation chaotic particle swarm optimization algorithm based on immune selection (IS-APCPSO algorithm for short) is proposed in this paper. The performance of several algorithms has been compared by a classic example of traffic network optimization. It is proved that the improved algorithm in accelerating convergence rate, increasing the diversity of particles, and preventing premature phenomenon is effective. The novel algorithm is expected to be used in the model solution of large-scale complex traffic network optimization problem.