A deep learning based detection algorithm for anomalous behavior and anomalous item on buses.

This paper proposes a new strategy for analysing and detecting abnormal passenger behavior and abnormal objects on buses. First, a library of abnormal passenger behaviors and objects on buses is established. Then, a new mask detection and abnormal object detection and analysis (MD-AODA) algorithm is proposed.

Hot syngas cleanup for pilot two-stage fluidized bed steam-oxygen biomass gasification plant.

Biomass gasification as a renewable energy technology has been a widely explored research and development area. The efficient and economic removal of harmful components, particularly tars, in raw syngas from the biomass gasifier is still a major challenge. In this study, a novel two-stage fluidized bed pilot-scale gasifier has been developed to enhance the steam-oxygen biomass gasification to generate low-tar syngas; while, a prototype hot syngas cleanup system has been designed, built and tested to further reduce the tar content and purify the syngas from the biomass gasifier for downstream applications.

Model-free adaptive consensus design for a class of unknown heterogeneous nonlinear multi-agent systems with packet dropouts.

This paper studies the consensus problem for a class of unknown heterogeneous nonlinear multi-agent systems via a network with random packet dropouts. Based on the dynamic linearization technique, novel model-free adaptive consensus protocols with the data compensation mechanism are designed for both leaderless and leader-following cases. The advantage of this approach is that only neighborhood input and output data of the agents are required in the protocol design.

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.

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.

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.

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

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 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.

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.

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.

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.

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.

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.

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.

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.

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 m/g), obtained by thermochemical activation of petroleum coke using KOH, was impregnated with ammonium heptamolybdate and activated by carbothermal hydrogen reduction (CHR). The resulting MoC, supported on high-mesopore volume ( ∼0.

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.

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.

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.

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.