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Myocardial infarction (MI) remains the most common cause of death worldwide. Many MI survivors will suffer from recurrent heart failure (HF), which has been recognized as a determinant of adverse prognosis. Despite the success of improved early survival after MI by primary percutaneous coronary intervention, HF after MI is becoming the major driver of late morbidity, mortality, and healthcare costs. The development of regenerative medicine has brought hope to MI treatment in the past decade. Mesenchymal stem cell (MSC)-derived exosomes have been established as an essential part of stem cell paracrine factors for heart regeneration. However, its regenerative power is hampered by low delivery efficiency to the heart. We designed, fabricated, and tested a minimally invasive exosome spray (EXOS) based on MSC exosomes and biomaterials. In a mouse model of acute myocardial infarction, EXOS improved cardiac function and reduced fibrosis, and promoted endogenous angiomyogenesis in the post-injury heart. We further tested the feasibility and safety of EXOS in a pig model. Our results indicate that EXOS is a promising strategy to deliver therapeutic exosomes for heart repair.

Smart hospitals are important components of smart cities. An intelligent medical system for brain tumor segmentation is required to construct smart hospitals. To achieve intelligent brain tumor segmentation, morphological variety and serious category imbalance must be managed effectively. Conventional deep neural networks have difficulty in predicting high-accuracy segmentation images due to these issues. To solve these problems, we propose using multimodal brain tumor images combined with the UNET and LSTM models to construct a new network structure with a mixed loss function to solve sample imbalance and describe an intelligent segmentation process to identify brain tumors. To verify the practicability of this algorithm, we used the open source Brain Tumor Segmentation Challenge dataset to train and verify the proposed network. We obtained DSCs of 0.91, 0.82, and 0.80; sensitivities of 0.93, 0.85, and 0.82; and specificities of 0.99, 0.99, and 0.98 in three tumor regions, including the whole tumor ( WT ), tumor core ( TC ), and enhanced tumor ( ET ). We also compared the results of the proposed network with those of other brain tumor segmentation methods, and the results showed that the proposed algorithm could segment different tumor lesions more accurately, highlighting its potential application value in the clinical diagnosis of brain tumors.

At present, medical images have played a more and more important role in clinical treatment. Lung images provide an important reference for doctors to make a diagnosis. Especially for surgical patients, a tumor can be accurately removed based on the full cognition about its size, position, and quantity. Therefore, computer-aided diagnosis for the analysis and treatment of a lot of lung tumor images is very important. Aiming at complexity and self-adaption of image segmentation in lung tumors, this article proposed a parallel deep learning algorithm with hybrid attention mechanism for image segmentation. First, lung parenchyma was extracted via preprocessing images. Then, images were input into hybrid attention mechanism and densely connected convolutional networks (DenseNet) module, respectively, where hybrid attention mechanism consisted of a spatial attention mechanism and a channel attention mechanism. Finally, four feasible solutions were proposed for the verification through changing the convolution quantity of dense block in DenseNet. The network structure with the better performance was achieved. The experimental results prove the parallel deep learning algorithm with hybrid attention mechanism performed well in image segmentation of lung tumors, and its accuracy can reach 94.61%.

Solar steam generation has attracted interest in water purification and seawater desalination as an efficient solar energy conversion method. However, the complex preparation process and limited efficiency restrict the practical applications of solar-generated steam. Here, we report a three-dimensional wood membrane inside coated with MoS2 (WM-H, with S defects) for efficient seawater desalination. The vertically aligned mesoporous wood channels and their inner microstructures increase the contact interface between water and MoS2 in wood. The formation of S defects allows the surface temperature of the membrane to reach ∼53 °C under 1 sun illumination. The MoS2-coated wood with S defects exhibited an excellent evaporation flux (1.46 kg m−2 h−1, one sun), with a solar thermal conversion efficiency of 82.5%. The adsorption peak values of Na+ on the two sides of MoS2 with defects are 2.17 times and 1.49 times higher than those of MoS2 without defects. The wood-based design provides an inexpensive way to capture solar energy and generate steam.

With the advancement of Internet of Things technology, we are able to collect massive people's trajectory data from various GPS services. These large amounts of trajectory records enable us to better understand human mobility patterns. Meanwhile, we are able to extract social relationships based on these digital records to provide personalized recommendation services, such as points of interests (POI) recommendation and friend recommendation. In this paper, we propose to recommend friends for taxi drivers based on vehicular trajectory records. For this purpose, we propose to construct a vehicular ad hoc network based on co-occurrence phenomenon. Furthermore, we take advantages of the network representation learning technique on the vehicular ad hoc network for learning driver vectors. Finally, potential friends are recommended based on the similarity of driver vectors. Extensive experimental results on two real-world datasets demonstrate that our proposed method has the best performance on friend recommendation compared with several state-of-the-art methods. To the best of our knowledge, this is the first attempt to recommend friends for taxi drivers based on vehicular ad hoc network representation learning.

We built a data-driven prediction model based on Time Series Neural Network regression model to explore the feasibility of utilizing functional connectivity (FC) to predict the continuous fatigue levels during underload task. When participants performed a 120-minute dull driving task, experimental data was recorded. The EEG and eye closure data used in this paper are from SJTU Emotion EEG Dataset, SEED. Unlike other researches, We proposed a functional brain networks method based on Phase Lag Index (PLI) to analyze EEG effectively and incorporate the temporal dependency of PERCLOS into model training. The functional connectivity corresponding to different fatigue degrees during whole experiment were analyzed. Meanwhile, taking the clustering coefficient (C) values, degree(D) and characteristic path length (L) values as input respectively while PERCLOS as output. The results suggest that brain network analysis approaches combined with Neural Network are effective to predicts individual mental fatigue during long time driving and can improve the performance of fatigue detection with a higher prediction correlation coefficient of 0.99 and a lower RMSE value of 0.01 on average.

Background Catecholamine excess arising from pheochromocytomas and paragangliomas (PPGLs) can cause a wide spectrum of cardiac manifestations, including acute cardiac complications (ACCs) and subclinical myocardial injuries (SMIs). In this study, we aimed to conduct a comprehensive analysis of ACCs and SMIs in a large cohort of patients with PPGLs. Methods We retrospectively analyzed the clinical data of consecutive patients with PPGLs admitted between January 2013 and July 2020 (n = 189). The prevalence of ACCs and SMIs and characteristics of patients identified with ACCs and SMIs were investigated. Moreover, comparisons were performed between patients with and without ACCs. Results Fourteen patients (7.4%) fulfilled the criteria for ACCs, including nine (4.8%) who presented with Takotsubo-like cardiomyopathy, four (2.1%) with heart failure with preserved ejection fraction, and finally one (0.5%) with catecholamine-induced cardiomyopathy. Compared to those without ACCs (n = 175), patients with ACCs had a higher prevalence of epinephrine-producing PPGLs (81.8% vs 33.9%, P = 0.006) and were more likely to show invasive behavior (61.5% vs 27.3%, P = 0.022) or hemorrhage/necrosis (53.9% vs 17.4%, P = 0.005) on histology. The apical sparing pattern (5/7, 71.4%) was the dominant impairment pattern of longitudinal strain (LS) for patients displaying Takotsubo-like cardiomyopathy. In patients without cardiac symptoms, a fairly high proportion (21/77, 27.3%) of patients who underwent screening for troponin and/or natriuretic peptide and/or echocardiography had SMIs. Conclusions One in every fourteen PPGL patients presented with ACCs, and in the patients with Takotsubo-like cardiomyopathy, the apical sparing pattern was the primary impairment pattern of LS. Additionally, nearly one-third of patients without symptoms had SMIs. The diagnosis of PPGLs should be considered in patients with acute reversible cardiomyopathy, especially in those exhibiting an apical sparing pattern of LS.

Gastric cancer (GC) is one of the infection-related cancers. Helicobacter pylori and Epstein-Barr virus (EBV) were established risk factors for GC. Recently, there are several reports showing the inconsistent association between hepatitis B virus (HBV) infection and the development of GC. To explore the relationship between HBV infection and the development of GC, we designed a meta-analysis of previous epidemiological studies, a hospital-based case-control study, followed by an immunohistochemistry (IHC) assay of HBV-exposed GC samples. We found that HBV infection was associated with an increased risk of GC based on the meta-analysis. No significant association between HBV infection and GC was detected according to our hospital-based case-control study. Histological examination showed that the gastric epithelium positive for HBx demonstrated a higher nuclear-cytoplasmic ratio compared to those HBx-negative cells. HBx and HBcAg were expressed more in tumors than those in normal counterparts in HBV-exposed subjects, and PD-L1 was lower in GC tissues from HBV carriers than those in HBV clearances. In conclusion, HBV infection may contribute to a higher risk for GC based on the meta-analysis and to the morphological atypia of gastric epithelium by the histological assessment, and GC patients among HBV carriers showed lower expression of PD-L1 may lose the chance for immune checkpoint blockade therapy.

Although promising results have been achieved in the area of traffic sign detection, little attention has been paid to text detection on traffic signs. In fact, in today’s popular driver-less automobile industry, traffic sign text which brings abundant and valuable traffic information plays an important and indispensable role. In this work, we design an effective detector for traffic sign text, whose pipeline only consists of a preprocessing module to tackle with some complex situations, a Fully Convolutional Network (FCN) in which a Scale-transfer layer is proposed to speed up the network and a simple post-processing step. Extensive experiments on the Chinese traffic sign text dataset (CTST-1600), ICDAR 2013 and MSRA-TD500 show that the proposed method has achieved the state-of-the-art results, which proves the ability of our detector on both particularity and universality applications. We collect the Chinese text-based traffic sign dataset named CTST-1600, and it can be found at https://github.com/pummi823/test/blob/master/CTST-1600 .

Based on the requirement of industrial automation, this paper proposes a three-dimensional reconstruction algorithm of the dynamic environment for industrial operation environment perception, which can be applied to many different industrial fields. According to the actual situation of the inner wall of the rolling mill, a method of lidar raw data preprocessing is proposed in this paper. The original lidar data is tagged and the detailed local point cloud information of interest is isolated. Loop detection is introduced into the algorithm to establish a globally consistent point cloud graph. The overall environment modeling algorithm is constructed to realize the real-time dynamic modeling of indoor environment in the actual industrial environment operation. The method has been evaluated by real-time reconstruction of the three-dimensional model of the specified area, which provides prior information for the operation of the robotic manipulator. We compare the performance of three-dimensional with loam, using the actual industrial environment, and show that our algorithm achieves better accuracy with reduced computational expense.

We report on space-selective crystallization of congruent and polar Sr2TiSi2O8 crystals in a stoichiometric SrO-TiO2-SiO2 glass induced by (1030 nm, 300 fs) femtosecond laser irradiation. This allows us to compare with non-congruent laser induced crystallization of polar LiNbO3, in non stoichiometric Li2O-Nb2O5-SiO2 glass and gain information on the mechanism of nanocrystals orientation with the laser polarization that we pointed out previously. Using scanning electron microscopy (SEM), Second-harmonic generation (SHG) and electron backscattered diffraction (EBSD), we studied the laser induced crystallization according to the laser processing parameters (pulse energy, pulse repetition rate, scanning speed). We found 1) a domain where the laser track is filled with crystals not perfectly textured (low energy), 2) a domain where an amorphous volume remain surrounded by a crystallized shell (high energy). This arises from Sr out-diffusion and may gives rise to crystallization of both SrTiO3 and Sr2TiSi2O8 phases at low speed. In the one-phase domain (at higher speed), it is found the possibility to elaborate a tube with a perfect fresnoite texture. Significant difference in size and morphology whereas crystallization threshold remains similar are discussed based on glases thermal propeties. Contrarily to Li2O-Nb2O5-SiO2 (LNS) glass, no domain of oriented nanocrystallization controlled by the laser polarization has been found in SrO-TiO2-SiO2 (STS) glass, which is attributed to the larger crystallization speed in STS glass. No nanogratings have also been found that is likely due to the congruency of the glass.

Background: Evidence on the association between physical performance and multimorbidity is scarce in Asia. This study aimed to identify multimorbidity patterns and their association with physical performance among older Chinese adults. Methods: Individuals aged &ge, 60 years from the China Health and Retirement Longitudinal Study 2011&ndash, 2015 (N = 10,112) were included. Physical performance was measured by maximum grip strength (kg) and average gait speed (m/s) categorized as fast (&gt, 0.8 m/s), median (&gt, 0.6&ndash, 0.8 m/s), and slow (&le, 0.6 m/s). Multimorbidity patterns were explored using exploratory factor analysis. Generalized estimating equation was conducted. Results: Four multimorbidity patterns were identified: cardio&ndash, metabolic, respiratory, mental&ndash, sensory, and visceral&ndash, arthritic. An increased number of chronic conditions was associated with decreased normalized grip strength (NGS). Additionally, the highest quartile of factor scores for cardio&ndash, metabolic (&beta, = &minus, 0.06, 95% Confidence interval (CI) = &minus, 0.07, &minus, 0.05), respiratory (&beta, 0.03, 95% CI = &minus, 0.05, &minus, 0.02), mental&ndash, sensory (&beta, 0.04, 0.03), and visceral&ndash, arthritic (&beta, 0.02) patterns were associated with lower NGS compared with the lowest quartile. Participants with &ge, 4 chronic conditions were 2.06 times more likely to have a slow gait speed. Furthermore, the odds ratios for the highest quartile of factor scores of four patterns with slow gait speed compared with the lowest quartile ranged from 1.26&ndash, 2.01. Conclusion: Multimorbidity was related to worse physical performance, and multimorbidity patterns were differentially associated with physical performance. A shift of focus from single conditions to the requirements of a complex multimorbid population was needed for research, clinical guidelines, and health-care services. Grip strength and gait speed could be targeted to routinely measure clinical performance among older adults with multimorbidity, especially mental&ndash, sensory disorders, in clinical settings.

Background Galcanezumab is a novel monoclonal antibody that target to calcitonin gene-related peptide (CGRP). It has been tested for the preventive treatment of migraine and episodic cluster headache by multiple randomized clinical trials (RCTs) and have been found to reduce headache frequency. Methods We systematically searched PubMed and Embase on Cochrane Central Register of Controlled Trials (CENTRAL) from the earliest date to August 1, 2019. Relative risk (RR) and weighted mean difference (WMD) were used to evaluate clinical outcomes. Results Seven studies were pooled with 3889 patients. Subcutaneous injection of Galcanezumab at 120 mg, 240 mg leads to a statistically significant response rate for the treatment of migraine compared with placebo (120 mg: RR = 1.51; 95% CI, 1.33 to 1.70; P P P = 0.425; 75% response: RR = 1.07; 95% CI, 0.94 to 1.23; P = 0.301; 100% response; RR = 1.06; 95% CI, 0.81 to 1.37; P = 0.682; MHD: RR = − 0.08; 95% CI, − 0.55 to − 0.40; P = 0.748) while related to a lower risk for adverse events for the treatment of migraine (120 mg RR = 1.06; 95% CI, 0.99 to 1.14; P = 0.084; 240 mg: RR = 1.17; 95% CI, 1.09 to 1.25; P P = 0.048). Conclusions Use of galcanezumab is related to a significantly reduced monthly headache frequency compared with placebo for the treatment of migraine and episodic cluster headache, 120 mg has the same treatment efficacy with 240 mg group while related to a lower risk for adverse effects for the treatment of migraine. 300 mg per month galcanezumab is effective for the prevention of episodic cluster headache with no significantly increased adverse events.

Bogies are an important component of high-speed trains. The level of mechanical performance of bogies has a major influence on the safety and reliability of high-speed train. Therefore, conducting fault diagnoses on bogies with big data is very important. Fault mechanisms of bogies are very complex, and feature signals are nonobvious. For these reasons, fault information of bogies cannot be effectively extracted using the traditional signal processing method. Therefore, this paper adopted the deep neural network to recognize faults in bogies. The deep neural network offers numerous benefits in this context. Using deep neural networks, fault information in a signal spectrum can be extracted in a selfadaptive method. This technique is free of dependence on extensive signal processing knowledge and diagnostic experience. Compared with the traditional intelligent diagnosis method, the deep neural network can obtain a higher diagnostic accuracy. Additionally, the deep neural network does not depend on the sample size, and it can obtain high diagnostic accuracy even when the sample size is relatively small. It also achieves very high diagnostic accuracy applied to high-speed trains with different speeds and different faults, which shows that the method is extensively applicable. Furthermore, the recognition accuracy rate of the deep neural network under normal conditions can reach 100%. This method provides a new paradigm for fault diagnosis of the high-speed train with big data and plays an important role in this field.

Firstly, the paper established a finite element model for a steel motor shell and computed related modals, vibration velocities, stress and strain respectively. Computational results show that the flange and end shield of the motor shell had the maximum vibration velocities and strain because these locations lacked the reinforcing ribs, while the maximum stress was mainly at joints between different structures. Secondly, the steel material was replaced by the aluminum alloy. Mechanical parameters of the motor shell were recomputed and compared with those of the steel structure. Results show that modal frequency on each order increased, which is good for avoiding the structural resonance. In addition, the maximum stress of the structure decreased by 4.4 MPa, and the maximum strain decreased by 0.27 mm, which could effectively improve the fatigue characteristics of the motor shell under long-term excitation. Then, the boundary element method was used to compute radiation noises of the motor shell in far field, where the radiation noise presented an obvious directivity. Finally, the paper proposed a GA-BP neural network model to predict the radiation noise of the motor and compared the prediction results with the boundary element. In the whole analyzed frequency band, the maximum difference between the neural network prediction and the real values did not exceed 5 dB, indicating that it is feasible to predict radiation noises of the motor by the neural network. Additionally, experiments were also conducted and compared with two kinds of numerical methods. Methods proposed in this paper provide some references for realizing the rapid noise reduction and light weight of motors.

China is defending the terrorist crime through the Anti-Terror Law and anti-terrorism criminal legislation. China"s Anti-Terror Law and the Criminal Code Amendment (9), which were promulgated in 2015, provide legal grounds for preventing and hurting ever-growing terrorist crimes. In particular, China"s amendment to the Criminal Code (9) is designed to rigorously enforce the legal framework for terrorist crimes, protect prejudicial rights that might be violated by serious terrorist crimes, and protect the penalties for terrorist crimes. However, China"s anti-terrorism legislation still has drawbacks such as lack of systematicity, limited regulatory boundaries, and lack of rigorous penalties for the establishment of anti-terrorism legislation. To counter this, China"s anti-terrorism legislation must strictly regulate the legal system of terrorist crimes, secure penalties, and prescribe anti-terrorism laws as professional chapters.

This paper presents computation of structural sound power and sound radiation modes, combined with structural dynamic equations to obtain the coupling relationship between sound and structures. As a result, the relationship between sound radiation modes of structures and structural vibration modes is established. The influence of the number and position of optimal secondary force sources on control of sound radiation modes is considered. Results show that sound radiation efficiency of sound radiation modes at the first order was more than that of sound radiation modes at other orders. The main diagonal element of coupling matrix between modes and sound radiation impedances was more than elements at other positions. Sound radiation modes at the first order were dominant sound radiation modes. When the number of secondary force sources was 4, the sound radiation power of structures was the lowest. Four force sources were taken as the basis to conduct on the related experiments in the anechoic chamber and compare with the computational result. Their results had a good consistency, which showed that the mentioned theory method was effective. Finally, the control strategy was applied to roofs of the vehicle. Experiments verified that sound pressure level of the driver in the low frequency was obviously improved, which remedied the defect of other optimization strategies for solving noises in the low frequency.

As quadruped robot is well-balanced, flexible and adaptable to environment, it has wide applications and has attracted the attention of numerous institutes. This paper combines theories of bionics and kinematics to design a quadruped robot and establish a gait control model for it. Firstly, it studies the bone structure of horse to analyze its structure advantages which are then applied to the design of the quadruped robot with the segment ratio of 1:1. Then the Kinematics formula and the traditional CPG gait control strategy are combined to establish the gait control model for quadruped robot which could output the joint control signal curves under stable gait.

Objectives Depressive symptoms are commonly seen among patients with multiple chronic somatic conditions, or somatic multimorbidity (SMM); however, little is known about the relationships between depressive symptoms and different SMM combinations. Our study aimed to delineate the patterns of SMM and their longitudinal associations with depressive symptoms among a nationally representative sample of middle-aged and older Chinese adults. Design We employed a longitudinal design. Setting and Participants Older adults (N = 10,084) aged ≥45 years from the China Health and Retirement Longitudinal Study 2011-2015 participated (mean age = 57.7 years at baseline; 53.3% men). Methods Sixteen chronic somatic conditions were ascertained at baseline via questionnaires. Depression was assessed with the Center for Epidemiological Studies Depression Scale at baseline and during follow-up. Patterns of SMM were identified via exploratory factor analyses. Generalized estimating equations were used to evaluate the longitudinal associations between patterns of SMM and the presence of depressive symptoms at follow-up. Results Compared with participants with no somatic condition, those with 1, 2, and 3 or more somatic conditions had a 21%, 66%, and 111% greater risk, respectively, for the presence of depressive symptoms. Increased factor scores for 4 patterns identified, cardio-metabolic pattern [adjusted odds ratio (AOR) 1.12, 95% confidence interval (CI) 1.06, 1.20], respiratory pattern (AOR 1.25, 95% CI 1.17, 1.33), arthritic-digestive-visual pattern (AOR 1.29, 95% CI 1.22, 1.37), and hepatic-renal-skeletal pattern (AOR 1.09, 95% CI 1.02, 1.16), were all associated with a higher risk of having depressive symptoms. Conclusions and Implications All SMM patterns were independently associated with depression among middle-aged and older Chinese adults, with greater odds for people with comorbid arthritic-digestive-visual conditions and respiratory conditions. Clinical practitioners should treat the middle-aged and older population under a multiple-condition framework combining SMM and mental disorders.

This paper proposes the use of dynamic substructure method of reduction of order to achieve effective reduction of feed system for high precision NC lathe feeding system rigid-flexible coupling model, namely the use of ADAMS to establish the rigid flexible coupling simulation model of high precision NC lathe, and then the vibration simulation of the period by using the FD 3D damper is very effective for feed system of bolt connection reduction of multi degree of freedom model. The vibration simulation calculation is more accurate, more quickly.

Media literacy amongst the younger generations and the role of schools faced with the spread of new youth practices are topics of increasing interest. The sample of the study included 534 students aged from 12 to 17. On the basis of media literacy framework, this study first describes the current situation of media literacy and then explores the factors that affect the core ability of media literacy among youth students. The results of this study show that the majority of the students had relatively low media literacy and gender, area, diversification of media use, frequency of media use and media education has obviously positive correlation with media literacy. Although 80% of students think that they need media education, their primary interest is to improve their technical skills. We also found that the underlying factor structure of the students' responses matched the conceptual model (technological dimension, cognitive dimension and ethical dimension), lending content validity to the scale. Based on these findings, the researchers made a number of recommendations.

Rationale Kummell disease is described as avascular necrosis of a vertebral body that occurred in a delayed fashion after a minor trauma. Anterior, posterior, and anterior-posterior approaches have been reported. Nevertheless, there is no standard treatment for patients with Kummell disease. Patient concerns We reported a successful cement-augmented pedicle screw placement in a patient with Kummell disease. A 63-year-old woman with serious osteoporosis complained persistent back pain with progressive lower extremities weakness for almost 2 years. Diagnoses The diagnosis of Kummell disease was mainly depended on clinical symptoms and imaging examinations. Interventions The application of a cement-augmented pedicle screw was designed to treat this illness. Outcomes The operation was successful without any complications. The patient stated that symptoms were obviously improved in 1 week after operation. Lessons The application of a cement-augmented pedicle screw is an effective treatment option for Kummell disease.

This paper presents a behavioural model to describe the realisation of a system objective from the services provides by the components it groups. The behavioural model takes into account both normal and faulty situations and allows to diagnose component faults. As behavioural model can be difficult to build and apprehend for large scale systems, the paper proposes an algorithm to diagnose system faults from the behavioural models of its subsystems without building the entire system behavioural model.

The fault diagnosis is a real requirement for the low cost automation as it can detect any system's fault and avoid the accompanying lost of productivity. We propose in this paper a decentralized fault diagnosis approach in order to reduce the complexity of the model proposed by centralized fault diagnosis approaches, which increases exponentially with the number of system's components. This allows reducing the expensive exploitation cost.

The quality of magnetic resonance imaging (MRI) features is key to the classification of Alzheimer’s disease. However, relevant research has as yet paid little attention to asymmetric MR features. In this paper, the asymmetric MR features of multiple anatomical structures are extracted. The MR feature types include volume feature and several kinds of texture features. Subsequently, the extracted features are selected based on the wrapper feature selection method with chain-like agent genetic algorithm (CAGA) and support vector machine (SVM). Finally, the selected asymmetric MR features are used for classification of Alzheimer’s disease. Experimental results show that the extracted features have apparent asymmetrical characteristics. The asymmetric volume feature of single anatomical structure can have better discrimination capability than the whole volume feature of same anatomical structure. Single selected asymmetric MR feature has displayed a superior discrimination capability in regards to three conditions of Alzheimer’s disease. The improvement is very apparent compared to before feature selection and the p-value-based feature selection method. In conclusion, this proposed method offer a new kind of feature type and can improve the classification rate for diagnosis of Alzheimer’s disease.

This paper presents a new formal modeling framework for reconfigurable control. The main idea is to use the STRIPS (STanford Research Institute Problem Solver), a formal language for automated planning which can integrate the cause-effect knowledge and the automated reasoning mechanism into one model. A qualitative model based on a graph of states is chosen. Transitions between states are determined from the possible actions (as defined as controls to the actuators). Each action is modeled in terms of preconditions and effects. The STRIPS can qualitatively define the fault models without requiring detail and precise knowledge of faulty components. Automated planning is an interesting tool for reconfigurable control. Since the process may end up in any one of a very large number of states after a break-down, it is not realistic to have recovery plans designed in advance for all such states. It is, thus, desirable to invoke a planner in the case of break-downs, which finds a plan that brings the process back into normal operation again, and that is the nature of reconfigurable control.

We describe the preparation and structural characterization of ultra-small water-dispersible CdSe semiconductor nanocrystal quantum dots (QDs), which are covered by a double-shell structure from CdS and CdZnS through a one-pot microwave-assisted synthesis technique. Because of the radial increase of the respective valence- and conduction-band offsets, the resulting core–shell–shell CdSe/CdS/CdZnS QDs are well electronically passivated, which endows them with high-fluorescence quantum yield of 90 % and high crystallinity, as was investigated by optical characterization, X-ray diffraction, high-resolution transmission electron microscopy, and X-ray photoelectron spectroscopy. Also, due to the stepwise adjustment of the lattice parameters in the radial direction, the obtained QDs display remarkable photostability, acid stability, and oxidation stability both in the aqueous solutions and the intracellular environment. The cytotoxicity experiment further substantiates the good biocompatibility of the core–shell–shell particles, though the size of the obtained QDs was very small (about 3.2 nm). This implied that the CdSe/CdS/CdZnS core–shell–shell QDs can be used as a promising candidate for fluorescent QDs-based biological applications.

International audience; This paper continues to present the framework of multi-faults diagnosis from the preceding paper. There are two main contributions in this part. One is aimed at developing a new diagnostic process for continuous and dynamic system and its corresponding consistency-checking module. This diagnostic process and its consistency-checking module are all based on the models defined by STRIPS actions. The other is that the fault models are introduced into the consistency-checking module for preventing the impossible diagnoses. Moreover, the STRIPS can qualitatively define the fault models without requiring detail and precise knowledge about faulty components.

International audience; This paper is part I of a two part effort that is intended to present a framework of multi-faults diagnosis. Reiter [14] has proposed a consistency-based approach for multi-faults diagnosis. We extend his theory to deal with the dynamic and continuous systems and offer a necessary assumption and a formal demonstration. Multi-faults diagnosis is a partially observable problem because there is usually not enough information about faults. So the STRIPS, a classic technique of automated planning, is chosen to build the system model. It provides the reasoning ability for the multi-faults diagnosis when diagnosis is formalized as reasoning from effects to causes with causal knowledge.

With the gradually implementation of the strategy of revitalizing the Northeast Old Industrial Base, the propelling role played by the investment environment for the Northeast economic growth becomes more important. As the investors always make the analysis of the actual condition of the investment district before their investment, local governments, in order to attract foreign funds, should go into a full analysis of its investment environment so as to take certain measures to improve it. The investment environment of the Northeast area is in a comparatively disadvantageous condition and its economic growth is relatively dropped behind, which is one of the factors that hinder the economic development of this area. Therefore, at this critical historical period of the reform of the Northeast Old Industrial Base, it has become a prime project that we should make us clear about the effect of investment environment to the economic growth in the Northeast, focusing on improving it while developing vigorously the privately-run businesses, thus pushing forward the development of the whole area. By means of the systematic engineering approach, this paper tries to make a systematic analysis of and appraisal for the factors of the effect on the investment environment of the Old Northeast industrial Base through the application of the interpretative structural modeling. In addition, it finds out the major problems of restraining the investment environment in the Northeast area, and further provides new perspectives and methods for the solution of the problems.

Amorphous chalcogenide As 2 S 3 , As 2 Se 3 , GeS 2 , GeSe 2 , Ge 20 As 25 S 55 , Ge 20 As 25 Se 55 , Ge 10 As 40 S 20 Se 30 films were studied by femtosecond OHD-OKE. Ict was indicated that the third-order nonlinear susceptibility χ (3) was as large as 10 -12 esu, and they also exhibited a very fast response time shorter than 200 fs. Their nonlinear refractive indices and absorption were also calculated from the above results. The ultrafast response and large third-order nonlinearity in amorphous chalcogenide films are attributed to the ultrafast distortion of the electron cloud of atoms.