Potential dependence of OER/EOP performance on heteroatom-doped carbon materials by grand canonical density functional theory.

Revealing the effect of external applied potential on the reaction mechanism and product selectivity is of great significance in electrochemical studies. In this work, the grand canonical density functional theory method was applied to simulate the explicit electrocatalytic process of oxygen evolution reaction and electrochemical ozone production due to the O3 product sensitivity toward the applied potential. Over the Pt/Pd single atom embedded on B/N co-doped graphene (Pt/Pd-BNC) surface, crossover points of O2/O3 selectivity inversion were predicted to be 1.33 and 0.89 V vs standard hydrogen electrode, which were also consistent with the previous experimental results. An in-depth analysis of the energetic terms in the reaction free energies also found the considerable impact of the applied potential on the Helmholtz free energy term, with optimal potential predicted for the key elementary steps, and linear correlations between electrode potential (U) and reaction free energy were found for each elementary step. This study offers extensive knowledge on the potential effect on the O2/O3 selective formation on two-dimensional anode surfaces and provides new insights for investigating the reactivity/selectivity on electrode surfaces in real reaction conditions.

Study on the mechanism of puerarin against osteoarthritis from ferroptosis based on network pharmacology and bioinformatics.

Network pharmacology and bioinformatics were used to study puerarin's molecular mechanism in treating osteoarthritis from the perspective of ferroptosis, revealing a new treatment target. Ferroptosis-related targets were obtained from FerrDb. Puerarin action targets were retrieved from TCMSP, Pharmmappe, SwissTargetPrediction, and Targetnet databases, and supplemented with PubMed. The gene expression profiles of GSE12021, GSE55235, and GSE82107 were obtained using "Osteoarthritis" as the search term in the GEO database, and the differential expression gene screening analysis was performed for osteoarthritis. The intersection targets between puerarin, iron death, and osteoarthritis were obtained using Venn diagrams. GO and KEGG analyses were conducted with R software. Molecular docking and visualization of puerarin and core targets were performed using Autodock Vina and PyMol software. The effects of puerarin on the cell viability and the TNFα, IL6, and Ilß levels of human inflammation articular chondrocytes were tested in vitro experiments. Puerarin, ferroptosis, and osteoarthritis share four targets: PLIN2, PTGS2, VEGFA, and IL6. GO enrichment analysis showed that puerarin maintained the blood-brain barrier, regulated peptide serine phosphorylation, and had anti-inflammatory effects. KEGG analysis showed that puerarin's anti-inflammatory effects were mainly through VEGF, IL-17, C-type lectin receptor, HIF-1, TNF, and other signaling pathways. Puerarin closely bound PLIN2, PTGS2, VEGFA, and IL6 targets in molecular docking. In vitro, puerarin prevented osteoarthritis. Network pharmacology and bioinformatics explained puerarin's multi-target and multi-pathway treatment of OA, which may be related to ferroptosis, and confirmed its anti-inflammatory effect.

Gaussian Particle Filtering for Nonlinear Systems With Heavy-Tailed Noises: A Progressive Transform-Based Approach.

The Gaussian particle filter (GPF) is a type of particle filter that employs the Gaussian filter approximation as the proposal distribution. However, the linearization errors are introduced during the calculation of the proposal distribution. In this article, a progressive transform-based GPF (PT-GPF) is proposed to solve this problem. First, a progressive transformation is applied to the measurement model to circumvent the necessity of linearization in the calculation of the proposal distribution, thereby ensuring the generation of optimal Gaussian proposal distributions in sense of linear minimum mean-square error (LMMSE). Second, to mitigate the potential impact of outliers, a supplementary screening process is employed to enhance the Monte Carlo approximation of the posterior probability density function. Finally, simulations of a target tracking example demonstrate the effectiveness and superiority of the proposed method.

Learning an Autonomous Dynamic System to Encode Periodic Human Motion Skills.

Learning an autonomous dynamic system (ADS) encoding human motion rules has been shown as an effective way for human motion skills transfer. However, most existing approaches focus on goal-directed motion skills transfer, and the study on periodic motion skills transfer is rare. One popular approach for periodic motion skills transfer is learning periodic dynamic movement primitive (DMP); however, periodic DMP is sensitive to spatial disturbances due to the introduction of the phase parameters. To solve this issue, this brief presents a novel approach to learn an ADS with a stable limit cycle without introducing phase parameters. First, a data-driven Lyapunov function (energy function) is learned, such that one of its level surfaces is consistent with periodic human demonstration trajectories. Then, an ADS is learned by sequentially solving energy function-related constrained optimization problems. With a proper design of constraint functions, we can ensure that the trajectory generated by the ADS will converge to an energy function-level surface, of which the shape is similar to periodic human demonstration trajectories. Experiments are conducted to show the effectiveness of the proposed approach (PA).

Fast Attack Detection for Cyber-Physical Systems Using Dynamic Data Encryption.

To defend the cyber-physical system (CPSs) from cyber-attacks, this work proposes an unified intrusion detection mechanism which is capable to fast hunt various types of attacks. Focusing on securing the data transmission, a novel dynamic data encryption scheme is developed and historical system data is used to dynamically update a secret key involved in the encryption. The core idea of the dynamic data encryption scheme is to establish a dynamic relationship between original data, secret key, ciphertext and its decrypted value, and in particular, this dynamic relationship will be destroyed once an attack occurs, which can be used to detect attacks. Then, based on dynamic data encryption, a unified fast attack detection method is proposed to detect different attacks, including replay, false data injection (FDI), zero-dynamics, and setpoint attacks. Extensive comparison studies are conducted by using the power system and flight control system. It is verified that the proposed method can immediately trigger the alarm as soon as attacks are launched while the conventional χ2 detection could only capture the attacks after the estimation residual goes over the predetermined threshold. Furthermore, the proposed method does not degrade the system performance. Last but not the least, the proposed dynamic encryption scheme turns to normal operation mode as the attacks stop.

Quantitative softness and texture bimodal haptic sensors for robotic clinical feature identification and intelligent picking.

Replicating human somatosensory networks in robots is crucial for dexterous manipulation, ensuring the appropriate grasping force for objects of varying softness and textures. Despite advances in artificial haptic sensing for object recognition, accurately quantifying haptic perceptions to discern softness and texture remains challenging. Here, we report a methodology that uses a bimodal haptic sensor to capture multidimensional static and dynamic stimuli, allowing for the simultaneous quantification of softness and texture features. This method demonstrates synergistic measurements of elastic and frictional coefficients, thereby providing a universal strategy for acquiring the adaptive gripping force necessary for scarless, antislippage interaction with delicate objects. Equipped with this sensor, a robotic manipulator identifies porcine mucosal features with 98.44% accuracy and stably grasps visually indistinguishable mature white strawberries, enabling reliable tissue palpation and intelligent picking. The design concept and comprehensive guidelines presented would provide insights into haptic sensor development, promising benefits for robotics.

Analysis of the mechanism of curcumin against osteoarthritis using metabolomics and transcriptomics.

Curcumin, a polyphenolic compound derived from the turmeric plant (Curcuma longa), has been extensively studied for its anti-inflammatory and anti-proliferative properties. The safety and efficacy of curcumin have been thoroughly validated. Nevertheless, the underlying mechanism for treating osteoarthritis remains ambiguous. This study aims to reveal the potential mechanism of curcumin in treating osteoarthritis by using metabolomics and transcriptomics. Firstly, we validated the effect of curcumin on inflammatory factors in human articular chondrocytes. Secondly, we explored the cellular metabolism mechanism of curcumin against osteoarthritis using cell metabolomics. Thirdly, we assessed the differences in gene expression of human articular chondrocytes through transcriptomics. Lastly, to evaluate the essential targets and elucidate the potential mechanism underlying the therapeutic effects of curcumin in osteoarthritis, we conducted a screening of the proteins within the shared pathway of metabolomics and transcriptomics. Our results demonstrated that curcumin significantly decreased the levels of inflammatory markers, such as IL-ß, IL-6, and TNF-α, in human articular chondrocytes. Cell metabolomics identified 106 differential metabolites, including beta-aminopropionitrile, 3-amino-2-piperidone, pyrrole-2-carboxaldehyde, and various other components. The transcriptomic analysis yielded 1050 differential mRNAs. Enrichment analysis showed that the differential metabolites and mRNAs were significantly enriched in seven pathways, including glycine, serine, and threonine metabolism; pentose and glucuronate interconversions; glycerolipid metabolism; histidine metabolism; mucin-type o-glycan biosynthesis; inositol phosphate metabolism; and cysteine and methionine metabolism. A total of 23 key targets were identified to be involved in these pathways. We speculate that curcumin may alleviate osteoarthritis by targeting key proteins involved in glycine, serine, and threonine metabolism; inhibiting pyruvate production; and modulating glycolysis.

Learning From Human Demonstrations for Wheel Mobile Manipulator: An Unscented Model Predictive Control Approach.

Industry 4.0 requires new production models to be more flexible and efficient, which means that robots should be capable of flexible skills to adapt to different production and processing tasks. Learning from demonstration (LfD) is considered as one of the promising ways for robots to obtain motion and manipulation skills from humans. In this article, a framework that enables a wheel mobile manipulator to learn skills from humans and complete the specified tasks in an unstructured environment is developed, including a high-level trajectory learning and a low-level trajectory tracking control. First, a modified dynamic movement primitives (DMPs) model is utilized to simultaneously learn the movement trajectories of a human operator's hand and body as reference trajectories for the mobile manipulator. Considering that the auxiliary model obtained by the nonlinear feedback is hard to accurately describe the behavior of mobile manipulator with the presence of uncertain parameters and disturbances, a novel model is established, and an unscented model predictive control (UMPC) strategy is then presented to solve the trajectory tracking control problem without violating the system constraints. Moreover, a sufficient condition guaranteeing the input to state practical stability (ISpS) of the system is obtained, and the upper bound of estimated error is also defined. Finally, the effectiveness of the proposed strategy is validated by three simulation experiments.

Neurodynamics-based distributed model predictive control of a low-speed two-stroke marine main engine power system.

This article studies a steady operation optimization problem of a low-speed two-stroke marine main engine (LTMME) power system including a cooling water subsystem, a fuel oil subsystem and a main engine subsystem with input and state constraints. Firstly, a distributed model with coupling inputs and states is established for the LTMME power system according to laws of thermodynamics and kinetics. Further, an optimization problem of the LTMME power system is formulated to ensure the system to operate steadily, subjected to constraint conditions of the distributed model and the input and state bounds. Moreover, the optimization problem is rewritten as a quadratic programming problem, and an iterative distributed model predictive control (DMPC) scheme based on a primal-dual neural network (PDNN) method is used to obtain the optimal inputs within the constrained range. Finally, based on the actual data from an underway ocean vessel named Mingzhou 501 with an LTMME power system, a group of simulations are carried out to verify the effectiveness of the proposed approach.

Wavelet Packet Decomposition-Based Multiscale CNN for Fault Diagnosis of Wind Turbine Gearbox.

This article presents an intelligent fault diagnosis method for wind turbine (WT) gearbox by using wavelet packet decomposition (WPD) and deep learning. Specifically, the vibration signals from the gearbox are decomposed using WPD and the decomposed signal components are fed into a hierarchical convolutional neural network (CNN) to extract multiscale features adaptively and classify faults effectively. The presented method combines the multiscale characteristic of WPD with the strong classification capacity of CNNs, and it does not need complex manual feature extraction steps as usually adopted in existing results. The presented CNN with multiple characteristic scales based on WPD (WPD-MSCNN) has three advantages: 1) the added WPD layer can legitimately process the nonstationary vibration data to obtain components at multiple characteristic scales adaptively, it takes full advantage of WPD and, thus, enables the CNN to extract multiscale features; 2) the WPD layer directly sends multiscale components to the hierarchical CNN to extract rich fault information effectively, and it avoids the loss of useful information due to hand-crafted feature extraction; and 3) even if the scale changes, the lengths of components remain the same, which shows that the proposed method is robust to scale uncertainties in the vibration signals. Experiments with vibration data from a production wind farm provided by a company using condition monitoring system (CMS) show that the presented WPD-MSCNN method is superior to traditional CNN and multiscale CNN (MSCNN) for fault diagnosis.

On design of robust sliding mode observer for nonlinear networked time-delay systems with communication constraints.

For a class of nonlinear discrete-time networked systems with time-delay and communication constraints, this paper is concerned with the design of robust sliding mode observer (SMO), where only one sensor node is allowed to transmit information to remote observer. We focus on the design of SMO to guarantee the exponentially stable of estimation error system and have a desired H∞ disturbance attenuation level in presence of communication constraints. Firstly, a sensor selector is introduced such that only one sensor node is chosen and its measurement can be transmitted to remote SMO at each time instant. Then, a sufficient condition is derived by introducing a piece-wise Lyapunov functional and using the Jensen's Inequality, which ensures the prescribed performance of estimation error system in the sliding mode surface that we have defined. Moreover, the observer gain matrices can be obtained through solving some matrix inequalities given in the main results. Finally, a simulation study performed on the F404 aircraft engine state monitoring is introduced to validate the robust SMO design.

Training Deep Neural Network for Optimal Power Allocation in Islanded Microgrid Systems: A Distributed Learning-Based Approach.

Currently, numerical optimization methods are used to solve distributed optimal power allocation (OPA) problems for islanded microgrid (MG) systems. Most of them are developed based on rigorous mathematical derivation. However, the complexity of such optimization algorithms inevitably creates a gap between theoretical analysis and real-time implementation. In order to bridge such a gap, in this article we provide a new distributed learning-based framework to solve the real-time OPA problem. Specifically, inspired by the human-thinking scheme, distributed deep neural networks (DNNs) together with a dynamic average consensus algorithm are first employed to obtain an approximate OPA solution in a distributed manner. Then a distributed balance generation and demand algorithm is designed to fine-tune it to obtain the final optimal feasible solution. In addition, it is theoretically proved that the proposed DNN can well approximate one existing OPA algorithm (Guo et al. 2018), where quantitative numbers of at most how many hidden layers and neurons are provided. Several experimental case studies show that our proposed distributed learning framework can achieve similar optimal results to those obtained by using typical existing distributed numerical optimization methods while it is superior in terms of simplicity and real-time capability.

Secure dimensionality reduction fusion estimation against eavesdroppers in cyber-physical systems.

This paper studies the distributed dimensionality reduction fusion estimation problem for cyber-physical systems with limited bandwidth in presence of eavesdroppers. Since wireless communication is implemented by broadcasting, the eavesdroppers can collude to collect the data through anther communication networks. To protect data privacy, based on the physical processes and local estimation error covariance (EEC) matrix, an insertion method of artificial noise (AN) is developed such that only eavesdroppers' fusion EEC becomes worse. Meanwhile, the fusion center needs to decode the received signal due to the noise interference, while the successful decoding probability varies with signal to noise ratio. Subsequently, some criteria for the selection probabilities and the successful decoding probabilities are given to guarantee the effectiveness of the AN insertion strategy. Moreover, a sufficient condition of the designed AN power is derived to guarantee the confidentiality. Simulation examples are given to show the effectiveness of the proposed methods.

Distributed H∞ Estimation in Sensor Networks With Two-Channel Stochastic Attacks.

This paper is concerned with the distributed estimation problem in sensor networks subjected to unknown attacks. Network attacks are considered to exist in two classes of channels: 1) communication channels from the plant to sensors and 2) communication channels among sensors. The status of an attack is viewed as a stochastic phenomenon, and the transmitted information will be affected when the attacker successfully carries out an attack on the related data packet. Based on the sensors' own measurements and their neighbors' local information, a novel distributed estimation model against two-channel stochastic attacks is presented. A sufficient condition on the existence of the desired distributed H ∞ estimators is derived and the distributed estimator gains are designed by solving a linear matrix inequality. Two illustrative examples are provided to demonstrate the effectiveness of the new design techniques.

Set-Membership Estimation for Complex Networks Subject to Linear and Nonlinear Bounded Attacks.

This paper is concerned with the set-membership estimation problem for complex networks subject to unknown but bounded attacks. Adversaries are assumed to exist in the nonsecure communication channels from the nodes to the estimators. The transmitted measurements may be modified by an attack function with added noise that is determined by the adversary but unknown to the estimators. A novel set-membership estimation model against unknown but bounded attacks is presented. Two sufficient conditions are derived to guarantee the existence of the set-membership estimators for the cases that the attack functions are linear and nonlinear, respectively. Two strategies for the design of the set-membership estimator gains are presented. The effectiveness of the proposed estimator design method is verified by two simulation examples.

"Star" miR-34a and CXCR4 antagonist based nanoplex for binary cooperative migration treatment against metastatic breast cancer.

Invasion and metastasis of tumor cells is one of the major obstacles in cancer therapy. The process of tumor metastasis and diffusion is coordinated by multiple pathways associated with chemokine signals and migration microenvironment. In our previous work, chemokine CXC receptor 4 (CXCR4) antagonists showed significant anti-metastatic effects by blocking the CXCR4/stromal cell-derived factor-1(SDF-1) axis in pancreatic cancer and breast cancer. Here, we proposed to achieve migration chain-treatment for metastatic tumors by introducing a cell adhesion molecules CD44 inhibitor (Star miR-34a) to deprive of cell migration capability on the basis of CXCR4 antagonism (cyclam monomer, CM). Dextrin modified 1.8 k PEI with CM-end was prepared to deliver therapeutic miR-34a (named DPC/miR-34a) for efficient anti-metastasis by downregulating adhesion protein CD44 and targeting the CXCR4/SDF-1 axis. Additionally, reduced expression of the anti-apoptotic protein Bcl2 caused by miR-34a could enhance the anti-tumor efficacy of DPC/miR-34a nanoplex administration. Compared with inhibition of the CXCR4/SDF-1 axis or CD44 expression, the multidimensional therapy (DPC/miR-34a) exhibited considerable suppression of cancer cell invasion as assessed by an in vitro cell invasion assay and in vivo anti-metastasis model. Moreover, DPC/miR-34a demonstrated a superior antitumor and anti-metastatic efficacy both in lung metastatic model and orthotopic MDA-MB-231 tumor models, thus providing an efficient approach for combating metastatic tumors.

[Effect of Denitrification and Phosphorus Removal Microorganisms in Activated Sludge Bulking Caused by Filamentous Bacteria].

Activated sludge bulking or foaming caused by filamentous bacteria is a frequent problem in the operation and management of wastewater treatment plants. To clarify the effect of filamentous bacteria sludge bulking on the functional flora in the biological denitrification and phosphorus removal system, morphological identification and Illumina MiSeq sequencing were applied to investigate the distribution of key micro-flora from the non-bulking period, sludge bulking period, and biological foaming period in five municipal wastewater treatment plants. The results showed that the sludge bulking and biological foaming were caused by Microthrix parvicella when the maximum contents were 6% and 38%, respectively. The main bacteria for denitrification and phosphorus removal were Nitrosomonas, Nitrospira, Thauera, and Candidatus Accumulibacter phosphatis. Compared to the non-bulking period, the relative abundance of AOB and PAO was significantly decreased when the maximum contents were 54% and 47%, respectively, during the bulking period. In addition, the relative abundance of denitrifying bacteria was significantly increased when the maximum content was 73%. The fluctuation of micro-flora for denitrification and phosphorus removal was affected by the activated sludge bulking and was related to the treatment process and physiological characteristics of the bacteria.

Multisensor-Based Periodic Estimation in Sensor Networks With Transmission Constraint and Periodic Mixed Storage.

In this paper, we consider a periodic estimation problem in sensor networks with a shared communication channel. The transmission constraint is inevitable in a single-channel-based sensor network if the sensors are heterogeneous or deployed far away from each other. A novel stochastic competitive transmission strategy is presented to deal with the transmission constraint, such that the sensors communicate with the fusion center (FC) in a strict asynchronous manner. A periodic mixed storage strategy combing the zero-input and the hold-input mechanisms is presented to describe periodic updating of the stored information in the sensors' buffers. A recursive Kalman filtering algorithm is derived for the FC to periodically generate estimates of state variables describing an object by using a linear continuous-time stochastic model. Two simulation examples are presented to show the effectiveness of the proposed results.

[Variation characteristics of maize yield and fertilizer utilization rate on an upland yellow soil under long term fertilization].

An analysis was made on the 16-year experimental data from the long term fertilization, experiment of maize on a yellow soil in Guizhou of Southwest China. Four treatments, i. e. , no fertilization (CK), chemical fertilization (165 kg N x hm(-2), 82.5 kg P2O5 x hm(-2), and 82.5 kg K2O x hm(-2), NPK), organic manure (30555 kg x hm(-2), M), and combined applicatioin of chemical fertilizers and organic manure (NPKM), were selected to analyze the variation trends of maize yield and fertilizer use efficiency on yellow soil under effects of different long term fertilization modes, aimed to provide references for evaluating and establishing long term fertilization mode and promote the sustainable development of crop production. Overall, the maize yield under long term fertilization had an increasing trend, with a large annual variation. Treatment NPKM had the best yield-increasing effect, with the maize yield increased by 4075.71 kg x hm(-2) and the increment being up to 139.2%. Long term fertilization increased the fertilizer utilization efficiency of maize. In treatment M, the nitrogen and phosphorus utilization rates were increased significantly by 35.4% and 18.8%, respectively. Treatment NPK had obvious effect in improving potassium utilization rate, with an increment of 20% and being far higher than that in treatments M (8.7%) and NPKM (9.2%). The results showed that long term fertilization, especially the combined application of chemical fertilizers and organic manure, was of great importance in increasing crop yield and fertilizer use efficiency.

H∞ filtering for discrete-time genetic regulatory networks with random delays.

The H(∞) filtering problem is investigated in this paper for a class of discrete-time genetic regulatory networks (GRNs) with random delays. The addressed filtering problem is to estimate the concentrations of mRNA and protein, and the filtering error system is modeled as a Markovian switched system. By using a properly constructed Lyapunov function, a sufficient condition is derived in terms of linear matrix inequalities (LMIs), which can guarantee stochastic stabilization of the filtering error system. Then, an optimization problem with LMIs constraints is established to design an H(∞) filter which ensures an optimal H(∞) disturbance attenuation level. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed results.