Robust model-free adaptive iterative learning control for an autonomous bus trajectory tracking system.

A robust model-free adaptive iterative learning control (R-MFAILC) algorithm is proposed in this work to address the issue of laterally controlling an autonomous bus. First, according to the periodic repetitive working characteristics of autonomous buses, a novel dynamic linearized method used in the iterative domain is utilized, and a time-varying data model with a pseudo gradient (PG) is given. Then, the R-MFAILC controller is designed with a proposed adaptive attenuation factor. The proposed algorithm's advantage lies in the R-MFAILC controller, which solely utilizes the input and output data of the regulated entity. Moreover, the R-MFAILC controller has strong robustness and can handle the nonlinear measurement disturbances of the system. In simulations based on the Truck-Sim simulation platform, the effectiveness of the proposed algorithm is verified. A rigorous mathematical analysis is employed to demonstrate the stability and convergence of the proposed algorithm.

Redox-induced controllable engineering of MnO2-MnxCo3-xO4 interface to boost catalytic oxidation of ethane.

Multicomponent oxides are intriguing materials in heterogeneous catalysis, and the interface between various components often plays an essential role in oxidations. However, the underlying principles of how the hetero-interface affects the catalytic process remain largely unexplored. Here we report a unique structure design of MnCoOx catalysts by chemical reduction, specifically for ethane oxidation. Part of the Mn ions incorporates with Co oxides to form spinel MnxCo3-xO4, while the rests stay as MnO2 domains to create the MnO2-MnxCo3-xO4 interface. MnCoOx with Mn/Co ratio of 0.5 exhibits an excellent activity and stability up to 1000 h under humid conditions. The synergistic effects between MnO2 and MnxCo3-xO4 are elucidated, in which the C2H6 tends to be adsorbed on the interfacial Co sites and subsequently break the C-H bonds on the reactive lattice O of MnO2 layer. Findings from this study provide valuable insights for the rational design of efficient catalysts for alkane combustion.

Dietary supplementation with ellagic acid improves the growth performance, meat quality, and metabolomics profile of yellow-feathered broiler chickens.

The aim of this research was to explore the effects of ellagic acid (EA) on growth performance, meat quality, and metabolomics profile of broiler chickens. 240 healthy yellow-feathered broilers were randomly divided into 4 groups (6 replicates/group and 10 broilers /replicate): 1) a standard diet (CON); 2) CON+0.01% EA; 3) CON+0.02% EA; 4) CON+0.04% EA. Compared with the CON group, dietary 0.02% EA increased linearly and quadratically the ADG and lowered F/G ratio from 29 to 56 d and from 1 to 56 d of age (P < 0.05). The EA groups had higher spleen index and showed linear and quadratic improve thymus index (P < 0.05). A total of 0.02% EA linearly and quadratically increased the leg muscle percentage and quadratically increased the breast muscle percentage (P < 0.05). Compared to the control diet, 0.02% EA decreased quadratically the L* and increased a* of breast muscle at 45 min postslaughter (P < 0.05), and quadratically decreased (P < 0.05) the b* and increased linearly and quadratically (P < 0.05) drip loss. Additionally, EA improved linearly and quadratically (P < 0.05) serum total protein concentration and reduced linearly and quadratically (P < 0.05) serum blood urea nitrogen concentration. A total of 0.02% EA quadratically increased catalase activity and decreased malondialdehyde concentration in breast muscle compared with the control diet (P < 0.05). 0.02% and 0.04% EA could linearly and quadratically increase (P < 0.05) the concentrations of histidine, leucine and essential amino acids (EAA), 0.02% EA could linearly and quadratically increase (P < 0.05) the concentrations of threonine, glutamate, and flavored amino acids in breast muscle. 0.02% EA linearly and quadratically improved the C20:3n6, C22:6n3, polyunsaturated fatty acid (PUFA) concentrations, and the ratio of PUFA to saturated fatty acids (SFA), but reduced the C16:0 and the SFA concentrations in breast muscle than the CON group (P < 0.05). The EA diet linearly increased (P = 0.035) and quadratically tended (P = 0.068) to regulate the C18:2n6c concentration of breast muscle. Metabolomics showed that alanine metabolism, aspartate and glutamate metabolism, arginine and proline metabolism, taurine and hypotaurine metabolism, and glycerophospholipid metabolism were the most differentially abundant. These results showed that EA supported moderate positive effects on growth performance, meat quality, and metabolomics profile of broilers.

Effects of Dietary Terminalia chebula Extract on Growth Performance, Immune Function, Antioxidant Capacity, and Intestinal Health of Broilers.

Terminalia chebula extract (TCE) has many physiological functions and is potentially helpful in maintaining poultry health, but its specific effect on the growth of broilers is not yet known. This research investigated the effects of dietary Terminalia chebula extract (TCE) supplementation on growth performance, immune function, antioxidant capacity, and intestinal health in yellow-feathered broilers. A total of 288 one-day-old yellow-feathered broilers were divided into four treatment groups (72 broilers/group), each with six replicates of 12 broilers. The broilers were given a basal diet of corn-soybean meal supplemented with 0 (control), 200, 400, and 600 mg/kg TCE for 56 d. The results demonstrated that, compared with the basal diet, the addition of TCE significantly increased (linear and quadratic, p < 0.05) the final body weight and overall weight gain and performance and decreased (linear and quadratic, p < 0.05) the feed-to-gain ratio in the overall period. Dietary TCE increased (linear, p < 0.05) the levels of IgM, IL-4, and IL-10 and decreased (linear and quadratic, p < 0.05) the level of IL-6 in the serum. Dietary TCE increased (linear and quadratic, p < 0.05) the levels of IL-2 and IL-4, decreased (linear and quadratic, p < 0.05) the level of IL-1ß, and decreased (linear, p < 0.05) the level of IL-6 in the liver. Dietary TCE increased (linear and quadratic, p < 0.05) the level of IgM and IL-10, increased (linear, p < 0.05) the level of IgG, and decreased (linear and quadratic, p < 0.05) the levels of IL-1ß and IL-6 in the spleen. Supplementation with TCE linearly and quadratically increased (p < 0.05) the catalase, superoxide dismutase, glutathione peroxidase, and total antioxidant capacity activities while decreasing (p < 0.05) the malonic dialdehyde concentrations in the serum, liver, and spleen. TCE-containing diets for broilers resulted in a higher (linear and quadratic, p < 0.05) villus height, a higher (linear and quadratic, p < 0.05) ratio of villus height to crypt depth, and a lower (linear and quadratic, p < 0.05) crypt depth compared with the basal diet. TCE significantly increased (linear, p < 0.05) the acetic and butyric acid concentrations and decreased (quadratic, p < 0.05) the isovaleric acid concentration. Bacteroidaceae and Bacteroides, which regulate the richness and diversity of microorganisms, were more abundant and contained when TCE was added to the diet. In conclusion, these findings demonstrate that supplementing broilers with TCE could boost their immune function, antioxidant capacity, and gut health, improving their growth performance; they could also provide a reference for future research on TCE.

Feedforward-feedback-enhanced model-free adaptive iterative learning control with measurement disturbance and data dropout for an autonomous bus trajectory tracking system.

This article presents an innovative enhanced model-free adaptive iterative learning control approach suited for autonomous bus trajectory tracking systems that may experience measurement disruptions and random data dropouts. Data loss can occur independently and randomly at different times and in different iterations with varying probabilities, leading to successive data dropouts on both the time and iteration axes. The proposed enhanced model-free adaptive iterative learning control controller incorporates a data compensation mechanism to compensate for missing data, ensuring excellent control performance. This data-driven control strategy requires only input/output data for controller design. The convergence and effectiveness of the proposed approach are verified through rigorous mathematical analysis and simulation outcomes.

Impact of miR-519d on the Biological Activity of Non-Small Cell Lung Cancer Cells and Its Mechanism.

Objective: This study aimed to investigate the impact of miR-519d on the biological activity of non-small cell lung cancer (NSCLC) cells and elucidate its underlying mechanism. Methods: An experimental study design was adopted, and a cell culture-based study was conducted. We obtained non-small cell lung cancer cell lines from the ATCC cell bank and categorized them into three groups: the miR group, the NSCLC group, and the Negative control group. Various methods, including flow cytometry, quantitative real-time polymerase chain reaction (qRT-PCR), Transwell assays, Western blotting, and the Cell Counting Kit-8 (CCK-8) assay, were employed to assess miR-519d expression, apoptosis, proliferation, migration, and nuclear factor-kappa B (NF-KB) p65 protein content, thus exploring the impact of miR-519d on the biological activity of NSCLC cells. Results: In the miR group, we observed the highest expression level of miR-519d in NSCLC cells. Furthermore, the miR group exhibited the greatest number of apoptotic cells and the highest apoptosis rate (P < .05). Notably, the Transwell assay revealed reduced migration of NSCLC cells in the miR group, while the NSCLC cells in the control group exhibited more migratory activity. The cell counts of NSCLC cells also significantly decreased in the miR group, with migration comparable to the Negative control group (P > .05). Western blot analysis indicated that NF-KB p65 protein expression was highest in the Negative control group but significantly reduced in the miR group (all P < .05). Conclusions: miR-519d is downregulated in NSCLC cells. Elevating the expression of miR-519d inhibits various biological activities of lung cancer cells, including migration and proliferation. The downregulation of NF-KB p65 likely mediates this inhibition.

Adiponectin inhibits LPS-induced nucleus pulposus cell pyroptosis through the miR-135a-5p/TXNIP signaling pathway.

Pyroptosis, a newly discovered programmed cell death process, is characterized by NLRP3 inflammasome activation and pro-inflammatory mediator release. Nucleus pulposus (NP) cell pyroptosis is an important cause of intervertebral disc degeneration (IDD). Adiponectin (APN) is an adipokine and has an anti-inflammatory effect. However, whether and how APN protects against NP cell pyroptosis remains unexplored. Our results showed that human degenerated NP tissue displayed a significant increase in the protein levels of NLRP3, caspase-1 and GSDMD-N. APN expression was down-regulated in human degenerated NP tissue and NP cells challenged with lipopolysaccharide (LPS). Lentivirus-mediated overexpression of APN increased miR-135a-5p levels, decreased thioredoxin-interacting protein (TXNIP) expression and its interaction with NLRP3, and inhibited pyroptosis in human NP cells stimulated with LPS. TXNIP was identified as a direct target of miR-135a-5p. The inhibitory effects of APN on pyroptosis were reversed by pretreatment with miR-135a-5p inhibitor or lentiviral vector expressing TXNIP in LPS-treated human NP cells. In summary, these data suggest that APN restrains LPS-induced pyroptosis through the miR-135a-5p/TXNIP signaling pathway in human NP cells. Increasing APN levels could be a new approach to retard IDD.

An improved compact-form antisaturation model-free adaptive control algorithm for a class of nonlinear systems with time delays.

To solve the time-delay problem and actuator saturation problem of nonlinear plants in industrial processes, an improved compact-form antisaturation model-free adaptive control (ICF-AS-MFAC) method is proposed in this work. The ICF-AS-MFAC scheme is based on the concept of the pseudo partial derivative (PPD) and adopts equivalent dynamic linearization technology. Then, a tracking differentiator is used to predict the future output of a time-delay system to effectively control the system. Additionally, the concept of the saturation parameter is proposed, and the ICF-AS-MFAC controller is designed to ensure that the control system will not exhibit actuator saturation. The proposed algorithm is more flexible, has faster output responses for time-delay systems, and solves the problem of actuator saturation. The convergence and stability of the proposed method are rigorously proven mathematically. The effectiveness of the proposed method is verified by numerical simulations, and the applicability of the proposed method is verified by a series of experimental results based on double tanks.

Data-Driven Distributed Information-Weighted Consensus Filtering in Discrete-Time Sensor Networks With Switching Topologies.

This article proposes a data-driven distributed filtering method based on the consensus protocol and information-weighted strategy for discrete-time sensor networks with switching topologies. By introducing a data-driven method, a linear-like state equation is designed by utilizing only the input and output (I/O) data without a controlled object model. In the identification step, data-driven adaptive optimization recursive identification (DD-AORI) is exploited to identify the recurrence of time-varying parameters. It is proved that for discrete-time switching networks, estimation errors of all nodes are ultimately bounded when data-driven distributed information-weighted consensus filtering (DD-DICF) is executed. The algorithm combines with the received neighbors and direct or indirect observations for the target node to produce modified gains, resulting in a novel state estimator containing an information interaction mechanism. Subsequently, convergence analysis is performed on the basis of the Lyapunov equation to guarantee the boundedness of DD-DICF estimate error. Simulations verify the performance of the DD-DICF against the theoretical results as well as in comparison with some existing filtering algorithms.

A Review of Nanomaterials with Different Dimensions as Lubricant Additives.

Lubricant additives can effectively enhance the performance and environmental adaptability of lubricants and reduce the energy loss and machine wear caused by friction. Nanomaterials, as important additive materials, have an essential role in the research and development of new lubricants, whose lubrication performances and mechanisms are not only related to their physical and chemical properties, but also influenced by the geometric shape. In this paper, the friction reduction and antiwear performances of nanomaterials as lubricant additives are first reviewed according to the classification of the dimensions, and their lubrication mechanisms and influence rules are revealed. Second, the recent research progress of composite nanomaterials as lubrication additives is introduced, focusing on their synergistic mechanism to improve the lubrication performance further. Finally, we briefly discuss the challenges faced by nanoadditives and provide an outlook on future research. The review expects to provide new ideas for the selection and development of lubricant additives to expand the application of nanoadditives.

A Novel Genetic Neural Network Algorithm with Link Switches and Its Application in University Professional Course Evaluation.

This study exploits a novel enhanced genetic neural network algorithm with link switches (EGA-NNLS) to model the professional university course evaluating system. Various indices should be employed to evaluate the learning effect of a professional course comprehensively and objectively, and the traditional artificial evaluation methods cannot achieve this goal. The presented data-driven modeling method, EGA-NNLS, combines a neural network with link switches (NN-LS) with an enhanced genetic algorithm (EGA) and the Levenberg-Marquardt (LM) algorithm. It employs an optimized network structure combined with EGA and NN-LS to learn the relationships between the system's input and output from historical data and uses the network's gradient information via the LM algorithm. Compared with the traditional backpropagation neural network (BPNN), EGA-NNLS achieves a faster convergence speed and higher evaluation precision. In order to verify the efficiency of EGA-NNLS, it is applied to a collection of experimental data for modeling the professional university course evaluating system.

Distributed fusion estimation for multisensor systems with non-Gaussian but heavy-tailed noises.

Student's t distribution is a useful tool that can model heavy-tailed noises appearing in many practical systems. Although t distribution based filter has been derived, the information filter form is not presented and the data fusion algorithms for dynamic systems disturbed by heavy-tailed noises are rarely concerned. In this paper, based on multivariate t distribution and variational Bayesian estimation, the information filter, the centralized batch fusion, the distributed fusion, and the suboptimal distributed fusion algorithms are derived, respectively. The centralized fusion is given in two forms, namely, from t distribution based filter and the proposed t distribution based information filter, respectively. The distributed fusion is deduced by the use of the newly derived information filter, and it has been demonstrated to be equivalent to the centralized batch fusion. The suboptimal distributed fusion is obtained by a parameter approximation from the derived distributed fusion to decrease the computation complexity. The presented algorithms are shown to be the generalization of the classical Kalman filter based traditional algorithms. Theoretical analysis and exhaustive experimental analysis by a target tracking example show that the proposed algorithms are feasible and effective.

Esterification over Acid-Treated Mesoporous Carbon Derived from Petroleum Coke.

Multistep activation of a Canadian oilsands petroleum coke that yields an acidified mesoporous carbon catalyst is reported. Microporous-activated carbon (APC; ∼2000 m2/g), obtained by thermochemical activation of petroleum coke using KOH, was impregnated with ammonium heptamolybdate and activated by carbothermal hydrogen reduction (CHR). The resulting Mo2C, supported on high-mesopore volume (V meso ∼0.4 cm3/g) carbon, yields the desired mesoporous carbon catalyst (V meso ∼0.7 cm3/g) following acid washing. The effect of CHR temperature and the benefit of Mo2C loading on mesopore development is reported, and pore development models are discussed. The mesoporous carbons are active for the esterification of acetic acid and 1-butanol at 77 °C, and the butanol conversion correlates with the catalyst acidity, as measured by NH3-TPD.

A Novel Dual Successive Projection-Based Model-Free Adaptive Control Method and Application to an Autonomous Car.

In this paper, a novel model-free adaptive control (MFAC) algorithm based on a dual successive projection (DuSP)-MFAC method is proposed, and it is analyzed using the introduced DuSP method and the symmetrically similar structures of the controller and its parameter estimator of MFAC. Then, the proposed DuSP-MFAC scheme is successfully implemented in an autonomous car "Ruilong" for the lateral tracking control problem via converting the trajectory tracking problem into a stabilization problem by using the proposed preview-deviation-yaw angle. This MFAC-based lateral tracking control method was tested and demonstrated satisfactory performance on real roads in Fengtai, Beijing, China, and through successful participation in the Chinese Smart Car Future Challenge Competition held in 2015 and 2016.

Lazy-Learning-Based Data-Driven Model-Free Adaptive Predictive Control for a Class of Discrete-Time Nonlinear Systems.

In this paper, a novel data-driven model-free adaptive predictive control method based on lazy learning technique is proposed for a class of discrete-time single-input and single-output nonlinear systems. The feature of the proposed approach is that the controller is designed only using the input-output (I/O) measurement data of the system by means of a novel dynamic linearization technique with a new concept termed pseudogradient (PG). Moreover, the predictive function is implemented in the controller using a lazy-learning (LL)-based PG predictive algorithm, such that the controller not only shows good robustness but also can realize the effect of model-free adaptive prediction for the sudden change of the desired signal. Further, since the LL technique has the characteristic of database queries, both the online and offline I/O measurement data are fully and simultaneously utilized to real-time adjust the controller parameters during the control process. Moreover, the stability of the proposed method is guaranteed by rigorous mathematical analysis. Meanwhile, the numerical simulations and the laboratory experiments implemented on a practical three-tank water level control system both verify the effectiveness of the proposed approach.

Data-Driven Modeling for UGI Gasification Processes via an Enhanced Genetic BP Neural Network With Link Switches.

In this brief, an enhanced genetic back-propagation neural network with link switches (EGA-BPNN-LS) is proposed to address a data-driven modeling problem for gasification processes inside United Gas Improvement (UGI) gasifiers. The online-measured temperature of crude gas produced during the gasification processes plays a dominant role in the syngas industry; however, it is difficult to model temperature dynamics via first principles due to the practical complexity of the gasification process, especially as reflected by severe changes in the gas temperature resulting from infrequent manipulations of the gasifier in practice. The proposed data-driven modeling approach, EGA-BPNN-LS, incorporates an NN-LS, an EGA, and the Levenberg-Marquardt (LM) algorithm. The approach cannot only learn the relationships between the control input and the system output from historical data using an optimized network structure through a combination of EGA and NN-LS but also makes use of the networks gradient information via the LM algorithm. EGA-BPNN-LS is applied to a set of data collected from the field to model the UGI gasification processes, and the effectiveness of EGA-BPNN-LS is verified.

Extracting climate memory using Fractional Integrated Statistical Model: a new perspective on climate prediction.

Long term memory (LTM) in climate variability is studied by means of fractional integral techniques. By using a recently developed model, Fractional Integral Statistical Model (FISM), we in this report proposed a new method, with which one can estimate the long-lasting influences of historical climate states on the present time quantitatively, and further extract the influence as climate memory signals. To show the usability of this method, two examples, the Northern Hemisphere monthly Temperature Anomalies (NHTA) and the Pacific Decadal Oscillation index (PDO), are analyzed in this study. We find the climate memory signals indeed can be extracted and the whole variations can be further decomposed into two parts: the cumulative climate memory (CCM) and the weather-scale excitation (WSE). The stronger LTM is, the larger proportion the climate memory signals will account for in the whole variations. With the climate memory signals extracted, one can at least determine on what basis the considered time series will continue to change. Therefore, this report provides a new perspective on climate prediction.

A nonlinear dynamical model for atmospheric boundary layer turbulence.

Using the qualitative theory of nonlinear dynamical systems and the ergodic theory of chaos and strange attractors, we study a truncated-spectrum model of dynamical equations of the atmosphere. In the parameter plane (Re, Ri), the atmospheric motion states can be divided into four regions: O (basic), P (periodic), T (turbulent or chaotic), and T-P (transition of T and P). We analyze the routes to turbulence during the day and at night. Finally, we discuss the physical aspects of the occurrence of turbulence.