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Introduction: Inhibitors of programmed cell death 1 (PD1) and its ligand (PDL1) have exhibited favorable long-term survival in many types of advanced-stage cancer and current approvals have to date been granted in certain tumour types irrespective of PD-L1 status.Methods: We extracted the following information: study sample size, trial period, cancer types, intervention of treatment, type of PD-L1 antibody, immunohistochemistry (IHC) scoring method, number and percentage of PD-L1 < 1% population, and median follow- up time. PD-L1 expression was defined as percentage of number of PD-L1-stained tumor cells (TPS), area of tumor infiltrated by PD-L1-stained immune cells (IPS), number of PD-L1-stained cells (tumor cells, lymphocytes and macrophages; CPS). Different trials used distinct method to define low PD-L1 expression. The risk of bias of the included trials was assessed by using the Cochrane risk of bias tool for RCTs.Results: Here, a total of 34 trials were included to extract individual patient data (IPD) to evaluate the survival benefit of first line PD1/PDL1 inhibitors vs. standard-of-care (SOC) in patients with PDL1 < 1%. In term of anti-PD-1/PD-L1 monotherapy, OS (HR = 0.90, 0.81−1.01) and PFS (HR = 1.11, 0.97−1.27) between PD-1/PD-L1 inhibitor group and SOC group were comparable. In term of anti-PD-1/PD-L1 combination therapy, PD-1/PD-L1 inhibitor group exhibited longer OS (median 19.5 months vs. 16.3 months; HR = 0.83, 0.79−0.88, p < 0.001) and PFS than those of SOC group (median 8.11 months vs. 6.96 months; HR = 0.82, 0.77−0.87, p < 0.001).Subgroup analysis showed that survival benefit was mainly observed in non-small cell lung cancer (NSCLC) (HROS = 0.74; HRPFS = 0.69; p < 0.001), small-cell lung cancer (SCLC) (HROS = 0.58, p < 0.001; HRPFS = 0.55, p = 0.030), esophageal squamous cell carcinoma (ESCC) (HROS = 0.62, p = 0.005; HRPFS = 0.79, p < 0.001), melanoma (HROS = 0.53, p < 0.001) and nasopharyngeal carcinoma (NPC) (HRPFS = 0.35, p = 0.013).Conclusion: Anti-PD-1/PD-L1 combinational therapy rather than monotherapy exhibit survival benefit in the low PD-L1 population in the first-line setting, and the survival benefit was mainly observed in specific tumor types.

Hydroxyapatite (HA) is an important component of mammalian bones and teeth. Its superior biocompatibility, biological activity, and bone tissue regenerative properties make it an ideal material for bone tissue replacement and regeneration. The morphological characteristics of HA can affect its selection in various applications. In contrast, one-dimensional (1D) micro/nano-structured HA has received widespread attention in the field of biomedicine. In particular, HA ultra-long nanofibers possess excellent mechanical properties, such as flexibility and deformability, overcoming the brittleness of traditional HA ceramic materials. This work successfully synthesized HA ultra-long nanofibers using oleic acid as a template by a solvothermal method. Particularly, the effects of the initial Ca/P ratios (iCa/P) on the morphologies of HA products were investigated. The microstructure and properties of HA products were characterized by scanning electron microscopy (SEM), X-ray powder diffractometer (XRD) and Fourier transform infrared spectroscopy (FTIR). The results indicated that the uniform and flexible HA nanofibers can be obtained only under the appropriate iCa/P. In addition, the effect of iCa/P on the microstructures of the HA formation stages was investigated using transmission electron microscopy (TEM), and then the formation mechanism of HA nanofibers was proposed. In this process, it is found that HA nanofibers mainly grow selectively along the c-axis, while iCa/P precisely influences the preferential growth direction of HA products.

An ultra-low quiescent current output-capacitor-less low dropout (OCL-LDO) regulator for power-sensitive applications is proposed in this paper. To improve the gain of the OCL-LDO feedback loop, the error amplifier employs a combination of a cross-coupled input stage for boosting the equivalent input transconductance and a negative resistance technique to improve the gain. Meanwhile, in order to address the issue of transient response of the ultra-low quiescent current OCL-LDO, a sub-threshold slew-rate enhancement circuit is proposed in this paper, which consists of a transient signal input stage and a slew-rate current increase branch. The proposed OCL-LDO is fabricated in a 0.18 μm CMOS process with an effective area of 0.049 mm2. According to the measurement results, the proposed OCL-LDO has a maximum load current of 100 mA and a minimum quiescent current of 640 nA at an input voltage of 1.2 V and an output voltage of 1 V. The overshoot and undershoot voltages are 197 mV and 201 mV, respectively, and the PSR of the OCL-LDO is −72.4 dB at 1 kHz when the load current is 100 μA. In addition, the OCL-LDO has a load regulation of 7.6 μV/mA and a line regulation of 0.87 mV/V.

In recent years, TNS has attracted wide attention because of its simplicity in synthesis and high efficiency in ion exchange. The adsorption of cesium ions in aqueous solution by TNS was investigated in this study. Results show that the removal rate of Cs (I) is about 88% when pH = 5.00 ± 0.05, C0 = 10 ppm and CTNS = 0.1 g/L. The adsorption equilibrium is reached in about 20 minutes and best fits the pseudo-second order model, R2 = 0.9998; compared with the Freundlich isotherm adsorption model and Temkin model, the Langmuir model has the best fitting effect, R2 = 0.9903. The fitting results show the maximum adsorption capacity of TNS for Cs (I) is 200.00 mg/g. The main adsorption mechanism of TNS to cesium ion is ion exchange. Therefore, TNS can be used as a potential adsorbent for effectively adsorbing Cs-containing wastewater. HIGHLIGHTS The removal of Cs (I) from aqueous solutions by TNS was firstly studied.; TNS showed a high adsorption capacity for Cs (I).; The solution pH played a significant role in the adsorption process of Cs (I).; TNS exhibited an excellent selectivity for Cs (I) in the interference of common cations.; The adsorption mechanism was the ion exchange between the interlayer Na (I) of TNS and Cs (I).;

BackgroundSerum C-reactive protein (CRP) is a biomarker of an acute inflammatory response and has been successfully used as a prognostic predictor for several malignancies. However, the clinicopathological significance of CRP levels in hepatocellular carcinoma (HCC) patients being treated with PD-1 inhibitors remains unclear.MethodsSerum CRP levels were measured for a total of 101 HCC patients that had been treated with PD-1 inhibitors from July 2018 to November 2019. The clinicopathological data was retrospectively analyzed to identify any clinical implications between CRP levels and responses to PD-1 inhibitors and patients’ progression-free survival (PFS).ResultsThe median PFS was 8.87 months in the CRP-low subgroup and 3.67 months in the CRP-high subgroup (P = 0.009). Univariate and multivariate Cox regression analysis demonstrated that both serum CRP and AFP levels were independent risk factors for the PFS of HCC patients treated with PD-1 inhibitors (P < 0.05). Moreover, Cox regression analysis after Propensity Score Matching showed the similar results. A prognostic model combining CRP and AFP levels could significantly stratify HCC patients receiving PD-1 inhibitors into low-, intermediate-, and high-risk subgroups (P < 0.001). Patients in the risk subgroups reported similar overall response rates (P = 0.625) and significantly different disease control rates (low- vs. intermediate- vs. high-risk groups: 81.6% vs. 65.1% vs. 35%, respectively, P = 0.002).ConclusionsThe results of this study support the association between high serum CRP levels with the response and PFS for HCC patients receiving PD-1 inhibitors. Furthermore, the levels of both CRP and AFP in an HCC patient before treatment initiation show great potential for determining the efficacy of PD-1 inhibitors.

Burden surface distribution plays a key role in achieving an energy-efficient status of blast furnace (BF). However, actual adjustment of burden surface usually depends on the operator's experience when the production status changes. Meanwhile, due to the characteristics of high dimension, strong coupling, and distributed parameters, it is difficult to establish the accurate mechanism model for BF ironmaking process. Considering the aforementioned issues, this paper proposes an integrated multi-objective optimization framework for optimizing burden surface distribution based on the analysis of BF operation characteristics. Firstly, data-driven models are constructed for two objectives, i.e., gas utilization ratio (GUR) and coke ratio (CR), and two constraints using adaptive particle swarm optimization (APSO) based extreme learning machine (ELM), named APSO-ELM. Multi-objective optimization is subsequently carried out between GUR and CR using the multi-objective differential evolution algorithm (MODE) to generate the Pareto optimal solutions. Finally, TOPSIS is applied to select a best compromise solution among the Pareto optimal solutions for this optimization problem. Comprehensive experiments are presented to illustrate the performance of the proposed integrated multi-objective optimization framework. The experimental results demonstrate that the proposed framework can give a reasonable burden surface profile according to the production status changes to guarantee the BF operation more efficient and stable.

In this paper, we develop a data-driven algorithm to learn the Nash equilibrium solution for a two-player non-zero-sum (NZS) game with completely unknown linear discrete-time dynamics based on off-policy reinforcement learning (RL). This algorithm solves the coupled algebraic Riccati equations (CARE) forward in time in a model-free manner by using the online measured data. We first derive the CARE for solving the two-player NZS game. Then, model-free off-policy RL is developed to obviate the requirement of complete knowledge of system dynamics. Besides, on- and off-policy RL algorithms are compared in terms of the robustness against the probing noise. Finally, a simulation example is presented to show the efficacy of the presented approach.

Titanium based materials have been widely applied in bone-tissue engineering. However, inefficient bone repair remains to be solved due to the lack of neural network reconstruction at the bone-implant interface. Herein, we propose a functional surface modification approach to promote neurogenesis. Using an electrochemical technique and a hydrothermal approach, a potassium titanate nanorod-decorated titanium oxide (K2Ti6O13-TiO2) nanotube array is constructed on the surface of titanium implants. The K2Ti6O13-TiO2 hybrid nanotube array on titanium implants can enhance the osteogenic differentiation of mesenchymal stem cells due to the special nanostructures of titanium oxide nanorods. Meanwhile, the release of potassium ions is able to accelerate the neural differentiation of neural stem cells. This study provides a new approach to promote neuralization on the surface of implants, which is promising for future applications in constructing a fully functional interface in bone repair.

Patients with advanced-stage or treatment-resistant colorectal cancer (CRC) benefit less from traditional therapies; hence, new therapeutic strategies may help improve the treatment response and prognosis of these patients. Ferroptosis is an iron-dependent type of regulated cell death characterized by the accumulation of lipid reactive oxygen species (ROS), distinct from other types of regulated cell death. CRC cells, especially those with drug-resistant properties, are characterized by high iron levels and ROS. This indicates that the induction of ferroptosis in these cells may become a new therapeutic approach for CRC, particularly for eradicating CRC resistant to traditional therapies. Recent studies have demonstrated the mechanisms and pathways that trigger or inhibit ferroptosis in CRC, and many regulatory molecules and pathways have been identified. Here, we review the current research progress on the mechanism of ferroptosis, new molecules that mediate ferroptosis, including coding and non-coding RNA; novel inducers and inhibitors of ferroptosis, which are mainly small-molecule compounds; and newly designed nanoparticles that increase the sensitivity of cells to ferroptosis. Finally, the gene signatures and clusters that have predictive value on CRC are summarized.

Aberrant expression of genes contributes to the chemoresistance of colorectal cancer (CRC) treatment. This study aimed to identify genes associated with the chemoresistance of oxaliplatin-based chemotherapy in CRC patients and to construct a signature. Oxaliplatin resistance-related genes were screened by analyzing the gene profiles of cell lines and tissue samples that underwent oxaliplatin-based treatment. Oxaliplatin resistance-related genes were used to establish a signature. The association of the signature had clinical significance, so the prognostic value of the signature was analyzed. Independent cohorts and CRC cell lines were used to validate the value of the gene signature and the oxaliplatin-resistant genes. There were 64 oxaliplatin resistance-related genes identified after overlapping the genes from the dataset of oxaliplatin-treated CRC cells and the dataset of patients treated with oxaliplatin-based chemotherapy. A gene signature based on five oxaliplatin resistance-related genes was established. This gene signature effectively predicted the prognosis of CRC patients who underwent chemotherapy. No significant associations were found between the gene mutations and survival of the patients; however, two genes were associated with microsatellite instability status. Two external independent cohorts and CRC cell line experiments validated the prognostic values of the signature and expression of the genes after oxaliplatin treatment. In conclusion, the oxaliplatin resistance-related gene signature involving five genes was a novel biomarker for the prediction of the chemotherapy response and prognosis of CRC patients who underwent oxaliplatin-based chemotherapy.

When a traditional mixture factor analysis (MFA) model is used for multimode process monitoring, the determination of parameter is complex, and the construction of monitoring statistics only considers the expectation in probability distributions of factor space and residual space. In this paper, a novel fault detection method based on a variational Bayesian MFA model for multimode process is introduced. The parameters of the MFA model structure, namely the number of local factor analyzer and the reduced dimensionality inside each factor analyzer, can be easily obtained through the birth-and-death Markov chain Monte Carlo algorithm and the variational inference technique. After parameter estimation for the Bayesian MFA model is done, a new monitoring index called negative variational log likelihood is developed by utilizing the whole information in probability distribution functions of all parameters. At last, two case studies, including a numerical example and the Tennessee Eastman (TE) process, verify the effectiveness and feasibility of the proposed monitoring scheme.

This paper develops an integral value iteration (VI) method to efficiently find online the Nash equilibrium solution of two-player non-zero-sum (NZS) differential games for linear systems with partially unknown dynamics. To guarantee the closed-loop stability about the Nash equilibrium, the explicit upper bound for the discounted factor is given. To show the efficacy of the presented online model-free solution, the integral VI method is compared with the model-based off-line policy iteration method. Moreover, the theoretical analysis of the integral VI algorithm in terms of three aspects, i.e., positive definiteness properties of the updated cost functions, the stability of the closed-loop systems, and the conditions that guarantee the monotone convergence, is provided in detail. Finally, the simulation results demonstrate the efficacy of the presented algorithms.

This paper is focused on composite adaptive control design for master-slave teleoperation systems with a flexible-joint manipulator as the slave. Gravity compensation and time-varying delays are considered. For the master manipulator, the composite adaptive controller with a novel adaptation law is designed to guarantee the parameter estimation error and the tracking error converging to zero. For the slave manipulator with joint flexibility, the proportional plus damping control with gravity compensation is proposed. The stability criteria of the closed-loop teleoperation system are given in terms of linear matrix inequalities. Comprehensive simulation studies are provided to demonstrate the effectiveness of the proposed control scheme.

The whale optimization algorithm(WOA) is a novel meta-heuristic evolutionary algorithm inspired by the behavior of whales predation. An important factor to the success of WOA is the balancing between exploration and exploitation. In the WOA, the distance control parameter $a$ is the main factor to find an appropriate balance between exploration and exploitation. In the standard WOA, the distance control parameter $a$ is optimized by linear control strategy (LCS), but the process of whales predation is not simply linear process. To address the issue, this paper proposed a nonlinear control strategy based on arcsine function (NCS-Arcsin) to optimize WOA. The NCS-Arcsin is applied to adjust distance control parameter $a$ . The NCS-Arcsin is considered to accurately describe the process of whales predation. Experiments on twelve well-known benchmark functions show the NCS-Arcsin can significantly improve the exploration and exploitation capabilities of WOA. In addition, the performance of proposed NCS-Arcsin is compared with LCS and other have been proposed NCS. The experimental results show that the optimization effect of NCS-Arcsin is stronger than that of LCS and other NCS. The NCSs based on the arcsine function is the best NCS, which can significantly improve the optimize performance of WOA.

This paper proposes a novel adaptive control framework for nonlinear teleoperation systems with dynamic and kinematic uncertainties and time-varying time delays. Master-slave synchronization in the task space is achieved. Compared with the previous work, the developed method can simultaneously handle the unknown kinematics/dynamics and the asymmetric varying time delays in a unified framework, and the inverses of the robots' Jacobians are not needed in the control design, and thus the master and slave robots can be redundant and the control law will be implemented more efficiently. Based on Lyapunov-Krasovskii methods, the stability criteria are established by linear matrix inequalities. It is shown by simulation studies that the developed control law can guarantee the task-space synchronization between the master and the slave.

This paper presents a distributed optimal model reference adaptive control approach for solving containment control of heterogeneous multi-agent systems (MASs) with non-autonomous leaders. First, a fully distributed adaptive observer is designed to provide for each agent the desired reference trajectory by estimating the convex hull spanned by leaders. The distributed observer dynamics serves as a reference model for each follower to synchronize. The global communication graph information or the leader dynamics is not required to design the observer. In contrast to existing model reference adaptive controllers (MRAC) for single-agent systems and containment control solutions for MASs, the proposed MRAC approach imposes optimality and presents a distributed adaptive optimal solution to the containment control problem. To impose optimality, a performance function is defined based on the adaptive observers' states as well as the followers' local measurements. It is shown that considering non-autonomous leaders in this optimal control problem leads to solving inhomogeneous algebraic Riccati equations (AREs), instead of normal AREs in standard optimal control problems. To obviate the requirement of knowing the agents' dynamics, an off-policy reinforcement learning approach implemented on an actor-critic structure is utilized for solving the inhomogeneous ARE. A simulation example is conducted to illustrate the effectiveness of the presented method.

Rapid emergence of antibiotic-resistant bacteria has brought huge threat to global healthcare systems. Alternative strategies are urgently needed to fight against these superbugs. In this study, we synthesized a series of cobalt phosphide nanoarchitectures and characterized their physicochemical properties as well as their antibacterial activities. We found that all nanomaterials showed an impressive photothermal property as indicated by their strong near-infrared (NIR) absorption capacity. In particular, 1D-CoP nanowires exhibited the optimal photothermal efficiency due to their higher aspect ratio. Under NIR light illumination, the temperature of the 1D-CoP nanowires suspension was increased by 45.4 °C within 20 min. In contrast, the temperatures of 2D-CoP nanoplates and 3D-CoP nanocubes were increased by 25.5 °C and 26.9 °C, respectively. The growth of planktonic bacteria can be effectively inhibited by 99% within 30 min under NIR irradiation with the presence of 1D-CoP nanowires in suspension. In comparison, up to 60% of the bacteria could be killed when treated with 2D-CoP nanoplates and 3D-CoP nanocubes. Moreover, all nanomaterials displayed high cytocompatibility. This work emphasizes that the anisotropy plays an important role in governing the photothermal properties of NIR-driven materials. Furthermore, the application of CoP nanowires is a promising strategy to treat antibiotic-resistant bacteria.

Periprosthetic joint infection (PJI) is one of the most pivotal issues accounting for clinical failure of titanium (Ti)-based implants. Therefore, it is of great significance to develop an antibacterial coating with high biocompatibility on the Ti implant surface. In this article, a novel zinc-doped strontium phosphate coating, denoted as Sr-Zn-PCC, was deposited on Ti substrates by phosphate chemical conversion (PCC) method. The texture characterizations indicated that continuous coatings containing SrHPO _4 , Sr _3 (PO _4 ) _2 and SrZn _2 (PO _4 ) _2 phases were formed on the Ti surface. It is found that the doping of Zn could tune the surface microstructure, roughness, wettability and corrosion resistance of the coatings. The Sr-Zn-PCC coating showed the same bacterial adhesion property at short time (2 h), but tended to hinder biofilm formation on coatings after incubation for up to 24 h. Up to 80% of Staphylococcus aureus ( S. aureus ) were killed within 24 h by contact with the Sr-Zn-PCC surface. Moreover, cytocompatibility assay indicated that MC-3T3 cells had good adhesion, spreading properties on the coatings, suggesting that it is a promising alloy with both excellent antibacterial ability and high biocompatibility for the applications on orthopedic implants.

This paper exploits optimal position of the mobile sensor to improve the target tracking performance of wireless sensor networks and simultaneously localize both of the static sensor nodes and mobile sensor nodes when tracking the human motion. In our approach, mobile sensors collaborate with static sensors and move optimally to achieve the required detection performance. The accuracy of final tracking result is then improved as the measurements of mobile sensors have higher signal-to-noise ratios after the movement. Specifically, we can simultaneously localize the mobile sensor and static sensors position when localizing the human’s position based on augmented extended Kalman filters (EKF). In the algorithm, we develop a sensor movement optimization algorithm that achieves near-optimal system tracking performance. We also presented an sensor nodes management scheme in order to deduce the computation complexity when localizing the static sensor nodes. The effectiveness of our approach is validated by extensive simulations using the simulations.

Gas utilization ratio (GUR) is an important indicator that is used to evaluate the energy consumption of blast furnaces (BFs). Currently, the existing methods cannot predict the GUR accurately. In this paper, we present a novel data-driven model for predicting the GUR. The proposed approach utilized both the TS fuzzy neural network (TS-FNN) and the particle swarm algorithm (PSO) to predict the GUR. The particle swarm algorithm (PSO) is applied to optimize the parameters of the TS-FNN in order to decrease the error caused by the inaccurate initial parameter. This paper also applied the box graph (Box-plot) method to eliminate the abnormal value of the raw data during the data preprocessing. This method can deal with the data which does not obey the normal distribution which is caused by the complex industrial environments. The prediction results demonstrate that the optimization model based on PSO and the TS-FNN approach achieves higher prediction accuracy compared with the TS-FNN model and SVM model and the proposed approach can accurately predict the GUR of the blast furnace, providing an effective way for the on-line blast furnace distribution control.

The concentration of alumina in the electrolyte is of great significance during the production of aluminum. The amount of the alumina concentration may lead to unbalanced material distribution and low production efficiency and affect the stability of the aluminum reduction cell and current efficiency. The existing methods cannot meet the needs for online measurement because industrial aluminum electrolysis has the characteristics of high temperature, strong magnetic field, coupled parameters, and high nonlinearity. Currently, there are no sensors or equipment that can detect the alumina concentration on line. Most companies acquire the alumina concentration from the electrolyte samples which are analyzed through an X-ray fluorescence spectrometer. To solve the problem, the paper proposes a soft sensing model based on a kernel extreme learning machine algorithm that takes the kernel function into the extreme learning machine. K-fold cross validation is used to estimate the generalization error. The proposed soft sensing algorithm can detect alumina concentration by the electrical signals such as voltages and currents of the anode rods. The predicted results show that the proposed approach can give more accurate estimations of alumina concentration with faster learning speed compared with the other methods such as the basic ELM, BP, and SVM.

Gas utilization ratio (GUR) is an important indicator used to measure the operating status and energy consumption of blast furnaces (BFs). In this paper, we present a soft-sensor approach, i.e., a novel online sequential extreme learning machine (OS-ELM) named DU-OS-ELM, to establish a data-driven model for GUR prediction. In DU-OS-ELM, firstly, the old collected data are discarded gradually and the newly acquired data are given more attention through a novel dynamic forgetting factor (DFF), depending on the estimation errors to enhance the dynamic tracking ability. Furthermore, we develop an updated selection strategy (USS) to judge whether the model needs to be updated with the newly coming data, so that the proposed approach is more in line with the actual production situation. Then, the convergence analysis of the proposed DU-OS-ELM is presented to ensure the estimation of output weight converge to the true value with the new data arriving. Meanwhile, the proposed DU-OS-ELM is applied to build a soft-sensor model to predict GUR. Experimental results demonstrate that the proposed DU-OS-ELM obtains better generalization performance and higher prediction accuracy compared with a number of existing related approaches using the real production data from a BF and the created GUR prediction model can provide an effective guidance for further optimization operation.

This paper proposes a novel particle filter algorithm for vehicle tracking, which feeds observation information back to state model and integrates block symmetry into observation model. In view of the proposal distribution in traditional particle filter without considering the observation data, a new state transition model which takes the observation into account is presented, so that the allocation of particles is more familiar with the posterior distribution. To track the vehicles in background with similar colors or under partial occlusion, block symmetry is proposed and introduced into the observation model. Experimental results show that the proposed algorithm can improve the accuracy and robustness of vehicle tracking compared with traditional particle filter and Kernel Particle Filter.

In view of the fact that the traditional genetic algorithm easily falls into local optimum in the late iterations, an improved chaos genetic algorithm employed chaos theory and genetic algorithm is presented to optimize the low side-lobe for T-shaped MIMO radar antenna array. The novel two-dimension Cat chaotic map has been put forward to produce its initial population, improving the diversity of individuals. The improved Tent map is presented for groups of individuals of a generation with chaos disturbance. Improved chaotic genetic algorithm optimization model is established. The algorithm presented in this paper not only improved the search precision, but also avoids effectively the problem of local convergence and prematurity. For MIMO radar, the improved chaos genetic algorithm proposed in this paper obtains lower side-lobe level through optimizing the exciting current amplitude. Simulation results show that the algorithm is feasible and effective. Its performance is superior to the traditional genetic algorithm.

This paper proposes a human motion tracking approach for a daily life surveillance in a distributed wireless sensor network using ultrasonic range sensors. Because the human target often moves with high nonlinearity, the proposed approach applies the unscented Kalman filter (UKF) technique. A novel sensor node selection scheme at each time step considering both the tracking accuracy and the energy cost is presented. Experimental results in a real human motion tracking system show that the proposed approach can perform better tracking accuracy compared to the most recent human motion tracking scheme in the real testbed implementation.

The burden charging is the most important operation of the upper operations in blast furnace. The position and descent speed of burden layer can reflect the situation of blast furnace and can guide the operators in the next burden charging. In this paper, the descent speed prediction model of burden layer is established by extreme learning machine algorithm. The model can make single-step and multi-step predictions to the burden descent speed using real radar data and status information in blast furnace. In the simulation part, we collected the real production data in iron-making process and obtained the satisfied and accurate simulation results by employing the proposed scheme. [ABSTRACT FROM AUTHOR]