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The partial space elevator is a space transportation system that conveys cargo by a climber moving along the tether that connects the main satellite and the end body. This work proposed a novel prescribed performance based dual-loop control strategy for configuration keeping of the partial space elevator in cargo transportation. The control loop I controls the libration angle of the climber by a newly proposed prescribed performance control law to track the desired state with bounded performance. The Lyapunov stability of the control law is proved analytically. Control loop II stabilizes the overall configuration of the PSE by applying thrust and deploying/retrieving the tether simultaneously at the end body. To enforce the constraint that the total tether length is unchanged at the end of transportation, an event-triggered loaning control algorithm is proposed to further improve the Control loop II. Numerical simulations validate the prescribed performance based dual-loop control strategy. The results show that the stable configuration of the PSE is kept effectively with no libration motion in the period of cargo transportation. • A new dual-loop control strategy to keep PSE configuration in cargo transportation. • A prescribed performance control law to keep the climber's libration angle. • An event-triggered loaning control to incorporate configuration constraint. [ABSTRACT FROM AUTHOR]

Summary: Background: To compare the ability of the Cox regression and machine learning algorithms to predict the survival of patients with Anaplastic thyroid carcinoma (ATC). Methods: Patients diagnosed with ATC were extracted from the Surveillance, Epidemiology, and End Results database. The outcomes were overall survival (OS) and cancer-specific survival (CSS), divided into: (1) binary data: survival or not at 6 months and 1 year; (2): time-to-event data. The Cox regression method and machine learnings were used to construct models. Model performance was evaluated using the concordance index (C-index), brier score and calibration curves. The SHapley Additive exPlanations (SHAP) method was deployed to interpret the results of machine learning models. Results: For binary outcomes, the Logistic algorithm performed best in the prediction of 6-month OS, 12-month OS, 6-month CSS, and 12-month CSS (C-index = 0.790, 0.811, 0.775, 0.768). For time-event outcomes, traditional Cox regression exhibited good performances (OS: C-index = 0.713; CSS: C-index = 0.712). The DeepSurv algorithm performed the best in the training set (OS: C-index = 0.945; CSS: C-index = 0.834) but performs poorly in the verification set (OS: C-index = 0.658; CSS: C-index = 0.676). The brier score and calibration curve showed favorable consistency between the predicted and actual survival. The SHAP values was deployed to explain the best machine learning prediction model. Conclusions: Cox regression and machine learning models combined with the SHAP method can predict the prognosis of ATC patients in clinical practice. However, due to the small sample size and lack of external validation, our findings should be interpreted with caution. [ABSTRACT FROM AUTHOR]

Abstract This paper presents the concept and experimental results of a kinematics-based incremental visual servo control approach for robotic manipulators with an eye-in-hand configuration to capture non-cooperative targets autonomously. The vision system is adopted to estimate the real time position and motion of the target by an integrated algorithm of photogrammetry and the adaptive extended Kalman filter. The unknown intercept point of trajectories of the target and the end-effector is dynamically predicted and updated based on the target estimates and is served as the desired position of the end-effector. An incremental control law is developed for the robotic manipulator to avoid multiple solutions of the robotic inverse kinematics. The end-effector is then controlled by the proposed control scheme to approach the dynamically estimated interception point directly. The proposed approach is validated experimentally by custom built robotic manipulator. To demonstrate the effectiveness of the proposed approach, uncertainties, such as, joint flexibility of the robotic manipulator, backlash of actuators, nonlinear target motion, camera mounting bias, etc., have not been considered in the control law. The experimental results show that the predicted minimum tracking time is reduced asymptotically as the end-effector approaches the target, which demonstrate the proposed control scheme is effective and reliable. The advantages of the proposed control approach are: it does not require a robotic dynamic model that most of the existing robotic control based on; it avoids the multiple solution problem in the inverse kinematics; it is insensitive to system uncertainties; and it is much easier for engineering implementation. Highlights • An incremental kinematic controller is presented for autonomous robotic capture. • Position and motion of non-cooperative target has been estimated accurately in real-time. • Multiple solutions problem of inverse kinematics is effectively avoided. • Validation experiments are performed on an eye-in-hand robotic manipulator. [ABSTRACT FROM AUTHOR]

Summary: Background: To compare the ability of the Cox regression and machine learning algorithms to predict the survival of patients with Anaplastic thyroid carcinoma (ATC). Methods: Patients diagnosed with ATC were extracted from the Surveillance, Epidemiology, and End Results database. The outcomes were overall survival (OS) and cancer-specific survival (CSS), divided into: (1) binary data: survival or not at 6 months and 1 year; (2): time-to-event data. The Cox regression method and machine learnings were used to construct models. Model performance was evaluated using the concordance index (C-index), brier score and calibration curves. The SHapley Additive exPlanations (SHAP) method was deployed to interpret the results of machine learning models. Results: For binary outcomes, the Logistic algorithm performed best in the prediction of 6-month OS, 12-month OS, 6-month CSS, and 12-month CSS (C-index = 0.790, 0.811, 0.775, 0.768). For time-event outcomes, traditional Cox regression exhibited good performances (OS: C-index = 0.713; CSS: C-index = 0.712). The DeepSurv algorithm performed the best in the training set (OS: C-index = 0.945; CSS: C-index = 0.834) but performs poorly in the verification set (OS: C-index = 0.658; CSS: C-index = 0.676). The brier score and calibration curve showed favorable consistency between the predicted and actual survival. The SHAP values was deployed to explain the best machine learning prediction model. Conclusions: Cox regression and machine learning models combined with the SHAP method can predict the prognosis of ATC patients in clinical practice. However, due to the small sample size and lack of external validation, our findings should be interpreted with caution. [ABSTRACT FROM AUTHOR]

Hand, foot, and mouth disease (HFMD), mainly caused by coxsackievirus A16 (CVA16) and enterovirus 71 (EV71) infections, remains a serious public health issue with thousands of newly diagnostic cases each year since 2008 in China. The mechanisms underlying viral infection, however, are elusive to date. In the present study, we systematically investigated the host cellular microRNA (miRNA) expression patterns in response to CVA16 and EV71 infections. Through microarray examination, 27 miRNAs (15 upregulated and 12 downregulated) were found to be coassociated with the replication process of two viruses, while the expression levels of 15 and 5 miRNAs were significantly changed in CVA16- and EV71-infected cells, respectively. A great number of target genes of 27 common differentially expressed miRNAs were predicted by combined use of two computational target prediction algorithms, TargetScan and MiRanda. Comprehensive bioinformatic analysis of target genes in GO categories and KEGG pathways indicated the involvement of diverse biological functions and signaling pathways during viral infection. These results provide an overview of the roles of miRNAs in virus-host interaction, which will contribute to further understanding of HFMD pathological mechanisms. [ABSTRACT FROM AUTHOR]

Multi-step travel time prediction is more reasonable in reality than one-step travel time prediction method, but it was done research in by few scholars. Meanwhile, the real-time travel time in future several time sections cannot be detected. In this paper, multi-step travel time estimation model and algorithm based on Kalman filtering (KF) were presented. This model took real-time and history travel time of the upper road and road itself into account. The algorithm found the nearest time in history data away from today. They can solve the problem that travel time in future are not be detected, and travel time in future several time sections can be predicted reasonably. Meanwhile, the time lag was solved effectively. Results of experiments show that the accuracy of several-step prediction algorithm is great, and it is meaningful in practical application. [ABSTRACT FROM AUTHOR]

Borderline personality disorder (BoPD or BPD) is highly prevalent and characterized by reactive moods, impulsivity, behavioral dysregulation, and distorted self-image. Yet the BoPD diagnosis is underutilized and patients with BoPD are frequently misdiagnosed resulting in lost opportunities for appropriate treatment. Automated screening of electronic health records (EHRs) is one potential strategy to help identify possible BoPD patients who are otherwise undiagnosed. We present the development and analytical validation of a BoPD screening algorithm based on routinely collected and structured EHRs. This algorithm integrates rule-based selection and machine learning (ML) in a two-step framework by first selecting potential patients based on the presence of comorbidities and characteristics commonly associated with BoPD, and then predicting whether the patients most likely have BoPD. Leveraging a large-scale US-based de-identified EHR database and our clinical expert's rating of two random samples of patient EHRs, results show that our screening algorithm has a high consistency with our clinical expert's ratings, with area under the receiver operating characteristic (AUROC) 0.837 [95% confidence interval (CI) 0.778–0.892], positive predictive value 0.717 (95% CI 0.583–0.836), accuracy 0.820 (95% CI 0.768–0.873), sensitivity 0.541 (95% CI 0.417–0.667) and specificity 0.922 (95% CI 0.880–0.960). Our aim is, to provide an additional resource to facilitate clinical decision making and promote the development of digital medicine. [ABSTRACT FROM AUTHOR]

Electric energy metering plays a crucial role in ensuring fair and equitable transactions between grid companies and power users. With the implementation of the State Grid Corporation's energy Internet strategy, higher requirements have been put forward for power grid companies to reduce costs and increase efficiency and user service capabilities. Meanwhile, the accuracy and real-time requirements for electric energy measurements have also increased. Electricity information collection systems are mainly used to collect the user-side energy metering data for the power users. Attributed to communication errors, communication delays, equipment failures and other reasons, some of the collected data is missed or confused, which seriously affects the refined management and service quality of power grid companies. How to deal with such data has been one of the important issues in the fields of machine learning and data mining. This paper proposes a collaborative fitting algorithm to solve the problem of missing collected data based on latent semantics. Firstly, a tree structure of user history data is established, and then the user groups adjacent to the user with missing data are obtained from this. Finally, the missing data are recovered using the alternating least-squares matrix factorization algorithm. Through numerical verification, this method has high reliability and accuracy in recoverying the missing data. [ABSTRACT FROM AUTHOR]

In recent years humanoid robots have been widely used in toy, performance, education and other service industries, but most biped robots walk slowly and have poor stability. The reason is that the driver parameters of the robot cannot properly match the walking gait algorithm, and the insufficient performance of the robot driver leads to the poor motion capability of the robot. In this paper, the optimization design process of biped robot parameters is studied and expounded, and its motion capability is improved by optimizing the driving parameters of the robot. Firstly, the contradiction between walking speed, stability and driver performance of biped robot is analysed. The performance evaluation functions of the three are further established, and the optimal parameter design to a certain extent is realized based on the multi-objective optimization method. Finally, combining with the physical simulation engine, the design parameters are simulated and checked, and the robot design process is completed through the guidance of simulation results. [ABSTRACT FROM AUTHOR]

Objective: To systematically profile metabolic alterations and dysregulated metabolic pathways in hyperuricemia and gout, and to identify potential metabolite biomarkers to discriminate gout from asymptomatic hyperuricemia. Methods: Serum samples from 330 participants, including 109 with gout, 102 with asymptomatic hyperuricemia, and 119 normouricemic controls, were analyzed by high‐resolution mass spectrometry–based metabolomics. Multivariate principal components analysis and orthogonal partial least squares discriminant analysis were performed to explore differential metabolites and pathways. A multivariate methods with Unbiased Variable selection in R (MUVR) algorithm was performed to identify potential biomarkers and build multivariate diagnostic models using 3 machine learning algorithms: random forest, support vector machine, and logistic regression. Results: Univariate analysis demonstrated that there was a greater difference between the metabolic profiles of patients with gout and normouricemic controls than between the metabolic profiles of individuals with hyperuricemia and normouricemic controls, while gout and hyperuricemia showed clear metabolomic differences. Pathway enrichment analysis found diverse significantly dysregulated pathways in individuals with hyperuricemia and patients with gout compared to normouricemic controls, among which arginine metabolism appeared to play a critical role. The multivariate diagnostic model using MUVR found 13 metabolites as potential biomarkers to differentiate hyperuricemia and gout from normouricemia. Two‐thirds of the samples were randomly selected as a training set, and the remainder were used as a validation set. Receiver operating characteristic analysis of 7 metabolites yielded an area under the curve of 0.83–0.87 in the training set and 0.78–0.84 in the validation set for distinguishing gout from asymptomatic hyperuricemia by 3 machine learning algorithms. Conclusion: Gout and hyperuricemia have distinct serum metabolomic signatures. This diagnostic model has the potential to improve current gout care through early detection or prediction of progression to gout from hyperuricemia. [ABSTRACT FROM AUTHOR]

In recent years humanoid robots have been widely used in toy, performance, education and other service industries, but most biped robots walk slowly and have poor stability. The reason is that the driver parameters of the robot cannot properly match the walking gait algorithm, and the insufficient performance of the robot driver leads to the poor motion capability of the robot. In this paper, the optimization design process of biped robot parameters is studied and expounded, and its motion capability is improved by optimizing the driving parameters of the robot. Firstly, the contradiction between walking speed, stability and driver performance of biped robot is analysed. The performance evaluation functions of the three are further established, and the optimal parameter design to a certain extent is realized based on the multi-objective optimization method. Finally, combining with the physical simulation engine, the design parameters are simulated and checked, and the robot design process is completed through the guidance of simulation results. [ABSTRACT FROM AUTHOR]

A self-referenced phase generation carrier (SR-PGC) algorithm is proposed in this paper. The influence related to modulation depth, light intensity and carrier phase delay can be theoretically eliminated. In classical PGC algorithm, the orthogonal components are obtained by mixing the detected signal with the proper multiples of carrier frequency and low-pass filtering, which are related to several parameters (modulation depth, light intensity, carrier phase delay, etc.). We notice that the differential signals of them also contain the parameters above. In the proposed SR-PGC algorithm, the orthogonal components are taken as the reference signals, and they are divided by their own differential signals, so that the parameters above can be eliminated in real-time. The algorithm was implemented in the field-programmable gate array-based electronic processing circuit, and a large dynamic range of 178.2 dB@200 Hz was achieved. Within 1 kHz bandwidth, the phase resolution reached 2 μ rad/Hz 1 / 2. A maximum light intensity rejection ratio of 37.0 dB was obtained. While the modulation depth is between 1 rad and 3.5 rad, the signal-to-noise-and-distortion (SINAD) and the total harmonic distortion (THD) can reach as good as 74.0 dB and −76.8 dB respectively, and the fluctuation of the SINAD and THD in 4 h were both within 1.2%. These experimental results prove the high stability of SR-PGC algorithm. • A self-referenced phase generation carrier (SR-PGC) algorithm is proposed. • The SR-PGC algorithm can recover phase-shift and suppress nonlinear distortion. • The demodulation system gets better phase resolution and larger dynamic range. • The experimental results prove the high stability of SR-PGC algorithm. [ABSTRACT FROM AUTHOR]