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Additional file 2: Figure S1. [Kaplan-Meier curves of overall survival for the preoperative groups of CEA (a), CA19-9 (b), and CA125 (c) in colorectal cancer patients]. Figure S2. [Kaplan-Meier curves of overall survival for the first postoperative groups of CEA (a), CA1 -9 (b), and CA125 (c) in colorectal cancer patients]. Figure S3. [Longitudinal trajectories of CEA (a), CA19-9 (b) and CA125 (c) of survived and died patients]. Figure S4. [The eigenfunctions of FPCA for CEA (a), CA19-9 (b), and CA125 (c)]. Figure S5. [The first five eigenfunctions for CEA (a), CA19-9 (b), and CA125 (c) based on MFPCA of the three markers]. Figure S6. [Variable importance of preoperative CEA&CA19-9&CA125 model (a) and longitudinal CEA&CA19-9&CA125 model (b)]. Figure S7. [ROC curves of the preoperative, postoperative and longitudinal CEA&CA19-9&CA125 models at 60 months after surgery for internal validation (a) and external validation (b)]. Figure S8. [Personalized dynamic prediction for the survival probability of patient A (a), patient B (b) and patient C (c) based on longitudinal CEA&CA19-9&CA125 model]. Figure S9. [AUC (a) and BS (b) of the prediction models with MSI status at 24 to 60 months after surgery for external validation].

Recently, the world has witnessed the most severe pandemic (COVID-19) in this century. Studies on epidemic prediction and simulation have received increasing attention. However, the current methods suffer from three issues. First, most of the current studies focus on epidemic prediction, which can not provide adequate support for intervention policy making. Second, most of the current interventions are based on population groups rather than fine-grained individuals, which can not make the measures towards the infected people and may cause waste of medical resources. Third, current simulations are not efficient and flexible enough for large-scale complex systems. In this paper, we propose a new epidemic simulation framework called HMES to address the above three challenges. The proposed framework covers a full pipeline of epidemic simulation and enables comprehensive fine-grained control in a large scale. In addition, we conduct experiments on real COVID-19 data. HMES demonstrates more accurate modeling of disease transmission up to 300 million people and up to 3 times acceleration compared to the state-of-the-art methods., Comment: UIC-2022, code available at https://github.com/hygeng/humanflow

Additional file 1. Characteristics of the included and excluded patients in YNCH and SYSU6.

China is well known for its wide and increasing commercial use of mobile social media for various purposes in different areas, ranging from online shopping to social networking. Such a popular commercial use was insightfully examined in relation to social relationship in the age of mobile internet, which enables people of either weak or strong connections to socialize anywhere anytime, leading to scenarios where mobile social media can be leveraged for profits. In what way can user experiences be guaranteed while platforms' value-added targets be achieved at the same time? In addressing that question, the authors of this chapter examined the commercial use of mobile social media in the context of complicated social networks. It is expected from the editor that further studies are to be carried out to comprehensively and comparatively examine the same topic in different countries or cultures.

Additional file 1: FigureS1. Association between preoperative CA19-9 status and overall survival. (a) overall population. (b) patients with normal preoperative CEA. (c) patientswith elevated preoperative CEA. Solid yellow lines are unadjustedhazard ratios, with dashed yellow lines showing 95% confidence intervalsderived from restricted cubic spline regressions. Reference lines for noassociation are indicated by the solid bold lines at a hazard ratio (HR) of 1.0. Dashed blue curves show the fraction of the population with different levels of preoperative CA19-9. Arrows indicate the concentration of preoperative CA19-9 with HR of 1.0. CA19-9, carbohydrate antigen 19-9; CEA, carcinoembryonic antigen; CI, confidence interval; E, number of events; HR, hazard ratio; N, number of patients. FigureS2. Kaplan‐Meier curves for overall survival according to the preoperative CA19-9 group. (a) overall population. (b) patients with normal preoperative CEA. (c) patientswith elevated preoperative CEA. CA19-9, carbohydrate antigen 19-9; CEA, carcinoembryonic antigen. FigureS3. Kaplan‐Meier curves according to the joint group of preoperative CEA and CA19-9 in colorectal cancer patients. (a) recurrence-free survival. (b) overall survival. CA19-9, carbohydrate antigen 19-9; CEA, carcinoembryonic antigen; OS, overall survival; RFS, recurrence-free survival. FigureS4. Forest plot for recurrence-free survival of preoperative CA 19-9 groups stratified by clinicopathological features based on the Cox models. P values for interaction were calculated using Cox regression model. HR and 95%CIs were given and visually represented by the squares and error bars. CA 19-9, carbohydrate antigen 19-9; CEA, carcinoembryonic antigen; CI, confidence interval; HR, hazard ratio. FigureS5. Forest plot for performance overallsurvival of preoperative CA19-9 groups stratified by clinicopathological features based on the Cox models. P values for interaction were calculated using Cox regression model. HR and 95%CIs were given and visually represented by the squares and error bars. CA19-9, carbohydrate antigen 19-9; CEA, carcinoembryonic antigen; CI, confidenceinterval; HR, hazard ratio. FigureS6. Kaplan‐Meier curves according to the joint group of preoperative CEA and CA19-9 in patients with stage II colorectal cancer. (a) recurrence-free survival.(b) overall survival. CA 19-9, carbohydrate antigen 19-9;CEA, carcinoembryonic antigen; OS, overall survival; RFS, recurrence-freesurvival.

Additional file 1. SI. Inclusion and exclusion criteria. SII. Annotation of ultrasound images. SIII. Details of theautomated segmentation model for breast ultrasound images. SIV. Data Standardization and Feature Extraction. SV. Feature Selection and Model Construction. SVI. Consistency evaluation of features by Bland-Altman. Fig. S1. Inclusion and exclusion criteria. Fig. S2. U-net architecture. Fig. S3. Flowchart of feature assessment and SUAS construction. Fig. S4. Principal component analysis plot and linear model for evaluating batch effect correction in three institutions. Fig. S5. Relative variable contribution in Model2 and Model3. Fig. S6. Clinical usefulness evaluation of Model2 and Model3. Fig. S7. The changing trend of entropy-related features in three institutions. Table S1. The ultrasonographic images acquisition parameters of the multiple centers. Table S2. Similarity and consistency evaluation between automated segmentation and manual segmentation. Table S3. Key features selected for construction of phasal signatures based on ultrasound images at each single time point. Table S4. Multivariate analysis of clinicopathological model, Model 2 (the early-treatment model) and Model 3 (the posttreatment model). Table S5. Prediction performance of the signatures. Table S6. AUCs of early-pretreatment model (Model 2) and post-treatment model (Model 3) in breast cancer subtypes. Table S7. NRI test for prediction improvements of SUAS compared to single-time signature in multiple cohorts. Table S8. IDI test for prediction improvements of SUAS compared to single-time signature in multiple cohorts. Table S9. Prediction performance of the signatures and SUAS in randomly split datasets.

Additional file 1.

Additional file 1: Note S1. Immunohistochemical staining. Table S1. The coding, partial regression coefficient and estimated 5-year baseline cumulative hazard of each prediction model. Figure S1. Comparison of automated and manual counting of positive cells. Figure S2. Determination of an optimal cut-off for CD3-score. Figure S3. Determination of an optimal cut-off for CD8-score. Figure S4. Kaplan-Meier curves of subgroup analyses

Additional file 2: Table S1.Baseline characteristics by participant site. Table S2.Multivariate analyses of recurrence-free survival in total population (Cox model). Table S3.Multivariate analyses of overall survival in total population (Cox model). Table S4.Interaction between preoperative CEA and CA19-9 with risk of outcomes. Table S5.Multivariate analyses of recurrence-free survival in colorectal cancer subgroup with CEA < 5 ng/ml (Cox model). Table S6.Multivariate analyses of recurrence-free survival in colorectal cancer subgroup with CEA ≥ 5 ng/ml (Cox model). Table S7. Multivariate analyses of overall survival in colorectal cancer subgroup with CEA < 5 ng/ml (Cox model). Table S8.Multivariate analyses of overall survival in colorectal cancer subgroup with CEA ≥ 5 ng/ml (Cox model). Table S9.A frailty model analysis of preoperative CA19-9 (cutoff: 37 U/ml) on colorectal cancer outcomes in total population. TableS10.Cox proportional hazard regression analysis of preoperative CA19-9 (cutoff:74 U/ml) on colorectal cancer outcomes in total population. Table S11.Relationship between preoperative CA19-9 and benefit from adjuvant chemotherapyin patients with stage II colorectal cancer.

Background. Poor balance is one of the risk factors for falls in patients with knee osteoarthritis (KOA), which is related to the symptoms. Electroacupuncture (EA) is one of the traditional Chinese conservative methods commonly used to improve the symptoms in patients with KOA. Objective. To assess whether EA increases the dynamic balance during stair negotiation among patients with KOA. Methods. A total of 40 KOA patients were assigned to two groups randomly (true electroacupuncture vs. mock electroacupuncture). Acupoints around the knee were selected in the true electroacupuncture (TEA) group with electrical stimulation (2 Hz). In the mock electroacupuncture (MEA) group, about 2 cm next to the above acupoints, the needles were inserted superficially without electrical stimulation. All the participants received 11 sessions of stimulation treatment in three weeks. The primary outcome was margin of stability (MOS). Secondary outcomes included hip kinematics and kinetics as well as pain. Results. There was no significant difference between the two groups for self-reported pain (p=0.585). During ascent, there was no difference between groups in MOS value in both directions, which was the anterior-posterior (A/P) direction and medial-lateral (M/L) direction at initial contact and toe-off as well as the midstance in the gait cycle, and no difference for the hip kinematics and kinetics between the groups was detected (p>0.05). For descent, at the toe-off event, the TEA group was more unstable as compared to the MEA group in the A/P direction (p=0.029) but not in the M/L direction, and the hip showed a larger internal rotator moment (p=0.049); at the midstance, the TEA group showed a lower abductor moment than the MEA group (p=0.003). Conclusions. Based on the assessment results from the chosen patients with KOA, the TEA did not demonstrate a significant effect in improving the dynamic balance during stair negotiation in comparison with the MEA. This finding does not support EA as a conservative treatment to improve the dynamic balance in such patients.

In this paper, a hierarchical energy management strategy (EMS) based on low-pass filter and equivalent consumption minimization strategy (ECMS) is proposed in order to lift energy sources lifespan, power performance and fuel economy for hybrid electrical vehicles equipped with fuel cell, battery and supercapacitor. As for the considered powertrain configuration, fuel cell serves as main energy source, and battery and supercapacitor are regarded as energy support and storage system. Supercapacitor with high power density and dynamic response acts during great power fluctuations, which relives stress on fuel cell and battery. Meanwhile, battery is used to lift the economy of hydrogen fuel. In higher layer strategy of the proposed EMS, supercapacitor is employed to supply peak power and recycle braking energy by using the adaptive low-pass filter method. Meantime, an ECMS is designed to allocate power of fuel cell and battery such that fuel cell can work in a high efficient range to minimize hydrogen consumption in lower layer. The proposed EMS for hybrid electrical vehicles is modeled and verified by advisor-simulink and experiment bench. Simulation and experiment results are given to confirm effectiveness of the proposed EMS of this paper.

In this paper, in order to extend battery lifespan and lift power performance in hybrid batteries/supercapacitors electrical vehicles, a new energy management strategy is proposed based on...

Additional file 1: Supplementary Methods.

Additional file 1: Figure S1. Study profile and IHC tiles dataset generation. Figure S2���S4. Kaplan���Meier survival analysis for patients stratified by TNM stage, age, sex, tumor site, MSI status, and treatment modalities. Figure S5. Using the pipeline to predict the prognosis of one patient with CD3 and CD8 WSIs. Figure S6. Kaplan���Meier survival curve of overall survival of stroma-low vs. stroma-high groups. Figure S7. Stroma-immune score vs. intraepithelial-immune score.

Simulation of the real-world traffic can be used to help validate the transportation policies. A good simulator means the simulated traffic is similar to real-world traffic, which often requires dense traffic trajectories (i.e., with a high sampling rate) to cover dynamic situations in the real world. However, in most cases, the real-world trajectories are sparse, which makes simulation challenging. In this paper, we present a novel framework ImInGAIL to address the problem of learning to simulate the driving behavior from sparse real-world data. The proposed architecture incorporates data interpolation with the behavior learning process of imitation learning. To the best of our knowledge, we are the first to tackle the data sparsity issue for behavior learning problems. We investigate our framework on both synthetic and real-world trajectory datasets of driving vehicles, showing that our method outperforms various baselines and state-of-the-art methods., Comment: Accepted by ECML-PKDD 2020, Best Applied Data Science Paper. 16 pages, 6 figures

The heavy traffic and related issues have always been concerns for modern cities. With the help of deep learning and reinforcement learning, people have proposed various policies to solve these traffic-related problems, such as smart traffic signal control systems and taxi dispatching systems. People usually validate these policies in a city simulator, since directly applying them in the real city introduces real cost. However, these policies validated in the city simulator may fail in the real city if the simulator is significantly different from the real world. To tackle this problem, we need to build a real-like traffic simulation system. Therefore, in this paper, we propose to learn the human routing model, which is one of the most essential part in the traffic simulator. This problem has two major challenges. First, human routing decisions are determined by multiple factors, besides the common time and distance factor. Second, current historical routes data usually covers just a small portion of vehicles, due to privacy and device availability issues. To address these problems, we propose a theory-guided residual network model, where the theoretical part can emphasize the general principles for human routing decisions (e.g., fastest route), and the residual part can capture drivable condition preferences (e.g., local road or highway). Since the theoretical part is composed of traditional shortest path algorithms that do not need data to train, our residual network can learn human routing models from limited data. We have conducted extensive experiments on multiple real-world datasets to show the superior performance of our model, especially with small data. Besides, we have also illustrated why our model is better at recovering real routes through case studies.

Based on a large number of data generated by the application of the Internet of things, the data information is processed and processed. The contents and methods of data processing in three-tier IOT structure are different. Upper layer: data communication and data communication; data acquisition and application. The data processing process reflects the purpose and mode of data processing in the Internet of things system. A large number of experiments have proved that the data processing methods and forms used in this paper have practical significance.

IntroductionStair ascent and descent require complex integration between sensory and motor systems; individuals with knee osteoarthritis (KOA) have an elevated risk for falls and fall injuries, which may be in part due to poor dynamic postural control during locomotion. Tai chi exercise has been shown to reduce fall risks in the ageing population and is recommended as one of the non-pharmocological therapies for people with KOA. However, neuromuscular mechanisms underlying the benefits of tai chi for persons with KOA are not clearly understood. Postural control deficits in performing a primary motor task may be more pronounced when required to simultaneously attend to a cognitive task. This single-blind, parallel design randomised controlled trial (RCT) aims to evaluate the effects of a 12-week tai chi programme versus balance and postural control training on neuromechanical characteristics during dual-task stair negotiation.Methods and analysisSixty-six participants with KOA will be randomised into either tai chi or balance and postural control training, each at 60 min per session, twice weekly for 12 weeks. Assessed at baseline and 12 weeks (ie, postintervention), the primary outcomes are attention cost and dynamic postural stability during dual-task stair negotiation. Secondary outcomes include balance and proprioception, foot clearances, self-reported symptoms and function. A telephone follow-up to assess symptoms and function will be conducted at 20 weeks. The findings will help determine whether tai chi is beneficial on dynamic stability and in reducing fall risks in older adults with KOA patients in community.Ethics and disseminationEthics approval was obtained from the Ethics Committee of the Affiliated Rehabilitation Hospital of Fujian University of Traditional Chinese Medicine (#2018KY-006–1). Study findings will be disseminated through presentations at scientific conferences or publications in peer-reviewed journals.Trial registration numberChiCTR1800018028.

In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones. However, a critical challenge in meta-learning is the task heterogeneity which cannot be well handled by traditional globally shared meta-learning methods. In addition, current task-specific meta-learning methods may either suffer from hand-crafted structure design or lack the capability to capture complex relations between tasks. In this paper, motivated by the way of knowledge organization in knowledge bases, we propose an automated relational meta-learning (ARML) framework that automatically extracts the cross-task relations and constructs the meta-knowledge graph. When a new task arrives, it can quickly find the most relevant structure and tailor the learned structure knowledge to the meta-learner. As a result, the proposed framework not only addresses the challenge of task heterogeneity by a learned meta-knowledge graph, but also increases the model interpretability. We conduct extensive experiments on 2D toy regression and few-shot image classification and the results demonstrate the superiority of ARML over state-of-the-art baselines., Comment: Accepted by ICLR 2020

With the increasing availability of traffic data and advance of deep reinforcement learning techniques, there is an emerging trend of employing reinforcement learning (RL) for traffic signal control. A key question for applying RL to traffic signal control is how to define the reward and state. The ultimate objective in traffic signal control is to minimize the travel time, which is difficult to reach directly. Hence, existing studies often define reward as an ad-hoc weighted linear combination of several traffic measures. However, there is no guarantee that the travel time will be optimized with the reward. In addition, recent RL approaches use more complicated state (e.g., image) in order to describe the full traffic situation. However, none of the existing studies has discussed whether such a complex state representation is necessary. This extra complexity may lead to significantly slower learning process but may not necessarily bring significant performance gain. In this paper, we propose to re-examine the RL approaches through the lens of classic transportation theory. We ask the following questions: (1) How should we design the reward so that one can guarantee to minimize the travel time? (2) How to design a state representation which is concise yet sufficient to obtain the optimal solution? Our proposed method LIT is theoretically supported by the classic traffic signal control methods in transportation field. LIT has a very simple state and reward design, thus can serve as a building block for future RL approaches to traffic signal control. Extensive experiments on both synthetic and real datasets show that our method significantly outperforms the state-of-the-art traffic signal control methods.

In this paper, an energy management strategy (EMS) for hybrid batteries and supercapacitor (SCs) electric vehicles (HBSEV) is considered for extending the batteries lifespan and lifting the power performance of HBSEV. In detail, firstly, vehicle dynamics model and hybrid energy storage source (HESS) models including batteries and SCs are adapted and simplified as fundamental of model prediction control (MPC) approach. Secondly, the EMS of HBSEV is designed for reducing batteries power variation and maintaining SCs SOC in a proper value based on MPC method combining adaptive method. That can enhance batteries lifespan and power performance. Finally, simulation results based on the proposed EMS are given, which show batteries lifespan and power performance for HBSEV are improved obviously compared with a real time average (RTA) strategy. Meanwhile, the SOC of batteries and SCs are maintained in a proper range.

In order to improve fuel economy further, an equivalent consumption minimization strategy (ECMS) is designed for the parallel hybrid electric vehicle (PHEV). Firstly, based on the relations among the battery state of charge (SOC) and the equivalent factor and fuel consumption, the equivalent fuel consumption minimization objective function is constructed. Secondly, a variable equivalent factor over real-time driving cycle is obtained by introducing the Pontryagin's Minimum Principle (PMP) and the penalty function of SOC. Finally, the simulation results show that compared with ECMS based on a PI controller, the ICE efficiency increases by 2% and the fuel consumption to travel 100 km decreases by 4 % with ECMS based on a variable equivalent factor.

The unique structures and properties of nanocarbons will provide fascinating applications in biomedicine. Here we report a new strategy to combine C60 with graphene for targeting phototherapy. Folic acid (FA) and polyethylene glycol (PEG) were conjugated onto graphene oxide (GO) via an imide linkage. Using nucleophilic addition, the FA–GO–PEG/C60 nanohybrid was then prepared. The versatile platform of FA–GO–PEG for C60 dramatically improved tumor targeting, which has been demonstrated by a cellular uptake assay. Owing to the doping effect and the inhibition of aggregation, the hybridization process also significantly enhanced the phototoxicity of FA–GO–PEG/C60. More importantly, the photothermal therapy (PTT) of FA–GO–PEG/C60 can cause obvious cell damage, which may further enhance the photodynamic therapy (PDT) efficacy against cancer cells. As expected, the combined PDT and PTT treatment with FA–GO–PEG/C60 showed remarkably improved and synergistic effects compared to PTT or PDT alone. This study presents the potential of nanocarbons for synergistic phototherapy of cancer.

The superlarge Jinchang gold deposit is located in the joint area between the Taipingling uplift and the Laoheishan depression of the Xingkai Block in both eastern Jilin and eastern Heilongjiang Province. Wall rocks of the gold deposits are the Neoproterozoic Huangsong Group of metamorphic rocks. Yanshanian magmatism in this region can be divided into 5 phases, the diorite, the graphic granite, the granite, the granite porphyry and the diorite porphyrite, which resulted in the magmatic domes and cryptoexplosive breecia chimney followed by large-scale hydrothermal alteration. Gold mineralization is closely related to the fourth and fifth phase of magmatism. According to the occurrences, gold ores can be subdivided into auriferous pyritized quartz vein, auriferous quartz-pyrite vein, auriferous polymetallic sulfide quartz vein and auriferous pyritized calcite vein. The ages of the gold deposit are ranging from 122.53 to 119.40 Ma. The ore bodies were controlled by a uniform tectono-magmatic hydrothermal alteration system that the ore-forming materials were deep derived from and the ore-forming fluids were dominated by magmatic waters with addition of some atmospheric water in the later phase of mineralization. Gold mineralization took place in an environment of medium to high temperatures and medium pressures. Ore-forming fluids were the K+-Na+-Ca2+-Cl−-SO42- type and characterized by medium salinity or a slightly higher, weak alkaline and weak reductive. Au in the ore-forming fluids was transported as complexes of [Au (HS)2]−, [AuCl2]−, [Au(CO2)]− and [Au(HCO3)2]−. Along with the decline of temperatures and pressures, the ore-forming fluids varied from acidic to weak acidic and then to weak alkaline, which resulted in the dissociation of the complex and finally the precipitation of the gold.

The electrical equipment loss caused by the lightning and power’s increasing demand is reduced by monitoring the extent of the surge protector failure through measuring its leakage current on-line. Proposed a method of monitoring the surge protector’s failure degree online based on mutual inductance current sensor, established its mathematical model, analyzed the influence of coil turns, load resistance and stray capacitance on the amplitude-frequency and phase-frequency characteristic. And used impact and sinusoidal signal to composite a motivic signal to test the optimized current sensor’s time-domain response feature by simulation. Experiments show: while the range of measured current is 0.2~9 mA, the mutual inductance current sensor designed can resist the impact of 50 kA high current, the current measurement error can be less than 0.11 mA, meet requirement of the leakage current on-line monitoring.

Rivaroxaban is a novel Xa inhibitor with an encouraging anti-thrombosis effect. The aim of this study is to assess whether rivaroxaban is superior to enoxaparin in venous thromboembolism prevention after knee- or hip-joint replacement.We searched for reports of randomized controlled trials on rivaroxaban versus enoxaparin in venous thromboembolism prophylaxis after knee- or hip-joint replacement in the Cochrane library, Embase, Pubmed, the Ovid database, and Chinese databases including VIP, CNKI, and CBM. Correlated data was extracted and analyzed.Eight studies involving 15246 patients were included, and all were randomized controlled studies. The methodological quality of six of the trials was generally moderate, while that of the remaining two was considered high quality. 10mg rivaroxaban daily is more effective than 40 mg/30 mg enoxaparin daily after the joint replacement in respect of the incidence of venous thromboembolism (P0.0001, RR = 0.38; P = 0.05, RR = 0.77, respectively). No significant difference between 10mg rivaroxaban daily and 40 mg/30 mg enoxaparin daily were found in major postoperative bleeding (P = 0.45, RR = 1.31;P = 0.34, RR = 1.61, respectively). With respect to other outcomes, rivaroxaban is not inferior to enoxaparin, while extended therapy with rivaroxaban (30 d) is more effective than short-term therapy (15 d) in relation to the incidence of venous thromboembolism (1.36% versus 10.13%).Rivaroxaban is superior to enoxaparin in venous thromboembolism prophylaxis after hip- or knee-joint replacement. Extended therapy--longer than 30 d--is recommended.

Measuring instrument for compensating brake master cylinder is mainly to measure the place, dimension and figure of the compensated hole and at the same time to measure the quality of machining and foundry by image for inner exterior of brake master cylinder. The measuring instrument adopt high differentiate rate image technique of CCD and using high precision drive framework to finish the measure assignment. It has some virtues, such as easy to handle, precision is high, automatous degree is good, and eliminate the influence of subjective complication. It is high-powered exactitude measuring instrument which gathered optics, mechanics, electronics and computer language. The measuring instrument for compensating brake master cylinder is made up of computer data acquire and process system, control system, CCD system, optical grating bit shift sensor and micro-machine drive system. The whole system adopt human-computer interactive measurement, when the cylinder was retracting ,we can install the workpiece to be measure to orientation fixture, and then the cylinder hold out and compaction the workpiece to be measure. To the effect that the orientation surface of workpiece to be measure can close to orientation fixture, then the step motor drive ball leading screw circumgyrate, then drive the endoscopy to move, meanwhile worm gear drive the bar circumgyrate, bases on the result of image CCD, then confirm the place of sampling point, note the information of grating ruler, save the quality information of cylinder inner exterior, by way of the member content of the date-base.

CCTV News, the first national 24-hour news channel in China, was launched in 2003. Three years later, it has become the leading 24-hour news channel in China, a milestone in the history of TV news broadcast in China. The results of this study show that the Chinese government has given much leeway to the news media at the micro level, but it has not given up its media control at the macro level. Although the news media in China have become more and more compatible with the global practices, the fundamental principle of sticking to the party line has not changed. And it will remain influential in determining what to broadcast and what not to broadcast for years to come as long as the fundamental political system does not change. ​Master of Mass Communication

A C60-polydopamine-graphene nanohybrid is prepared by a facile approach via polydopamine chemistry. The hybrid displays synergistic photodynamic and photothermal cancer cell killing effects under single light irradiation.

Most outlier detection algorithms are based on lazy learning or imply quadratic complexity. Both characteristics make them unsuitable for big data and stream data applications and preclude their applicability in systems that must operate autonomously. In this paper we propose a new algorithm-called SDO (Sparse Data Observers)-to estimate outlierness based on low density models of data. SDO is an eager learner; therefore, computational costs in application phases are severely reduced. We perform tests with a wide variation of synthetic datasets as well as the main datasets published in the literature for anomaly detection testing. Results show that SDO satisfactorily competes with the best ranked outlier detection alternatives. The good detection performance coupled with a low complexity makes SDO highly flexible and adaptable to stand-alone frameworks that must detect outliers fast with accuracy rates equivalent to lazy learning algorithms.

The rise of big data, especially social media data (e.g., Twitter, Facebook, Youtube), gives new opportunities to the understanding of human behavior. Consequently, novel computing methods for mining patterns in social media data are therefore desired. Through applying these approaches, it has become possible to aggregate public available data to capture triggers underlying events, detect on-going trends, and forecast future happenings. This thesis focuses on developing methods for social media analysis. Specifically, five directions are proposed here: 1) semi-supervised detection for targeted-domain events, 2) topical interaction study among multiple datasets, 3) discriminative learning about the identifications for common and distinctive topics, 4) epidemics modeling for flu forecasting with simulation via signals from social media data, 5) storyline generation for massive unorganized documents. Ph. D.