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Tianqi Wang,Zhimin Yuan,Jun Yao 대한환경공학회 2018 Environmental Engineering Research Vol.23 No.1
In the present study, long-term heavy metals (HMs) contaminated soil samples from a well-known Pb/Zn smelting area in the southwest of China were collected, and physicochemical and biological characteristics of these samples were evaluated. Soil samples contained different concentrations of HMs, namely Pb, Zn, Cu, and Cd. Enzyme activity analyses combined with microcalorimetric analysis were used for soil microbial activity evaluation. Results showed that two soil samples, containing almost the highest concentrations of HMs, also shared the greatest microbial activities. Based on correlation coefficient analysis, high microbial activity in heavily HMs contaminated soil might be due to the high contents of soil organic matter and available phosphorus in these samples. High-throughput sequencing technique was used for microbial community structure analysis. High abundance of genera Sphingomonas and Thiobacillus were also observed in these two heavily contaminated soils, suggesting that bacteria belonging to these two genera might be further isolated from these contaminated soils and applied for future studies of HMs remediation. Results of present study would contribute to the evaluation of microbial communities and isolation of microbial resources to remediate HMs pollution.
Association between Type 2 Diabetes Mellitus and Brain Atrophy: A Meta-Analysis
Tianqi Zhang,Marnie Shaw,Nicolas Cherbuin 대한당뇨병학회 2022 Diabetes and Metabolism Journal Vol.46 No.5
Background: Type 2 diabetes mellitus (T2DM) is known to be associated with cognitive decline and brain structural changes. This study systematically reviews and estimates human brain volumetric differences and atrophy associated with T2DM.Methods: PubMed, PsycInfo and Cochrane Library were searched for brain imaging studies reporting on brain volume differences between individuals with T2DM and healthy controls. Data were examined using meta-analysis, and association between age, sex, diabetes characteristics and brain volumes were tested using meta-regression.Results: A total of 14,605 entries were identified; after title, abstract and full-text screening applying inclusion and exclusion criteria, 64 studies were included and 42 studies with compatible data contributed to the meta-analysis (n=31,630; mean age 71.0 years; 44.4% male; 26,942 control; 4,688 diabetes). Individuals with T2DM had significantly smaller total brain volume, total grey matter volume, total white matter volume and hippocampal volume (approximately 1% to 4%); meta-analyses of smaller samples focusing on other brain regions and brain atrophy rate in longitudinal investigations also indicated smaller brain volumes and greater brain atrophy associated with T2DM. Meta-regression suggests that diabetes-related brain volume differences start occurring in early adulthood, decreases with age and increases with diabetes duration.Conclusion: T2DM is associated with smaller total and regional brain volume and greater atrophy over time. These effects are substantial and highlight an urgent need to develop interventions to reduce the risk of T2DM for brain health.
Tianqi Liu,Dongyan Chen,Jun Hu 제어·로봇·시스템학회 2021 International Journal of Control, Automation, and Vol.19 No.5
In this paper, the resilient distributed filtering problem is addressed for discrete stochastic uncertain timevarying systems with missing measurements and stochastic uncertainties over wireless sensor networks. Some random variables governed by the Bernoulli distribution are used to model the missing measurements phenomenon of each sensor node. In addition, the stochastic uncertainties are characterized by the multiplicative noises and stochastic nonlinearities. By using the variance-constrained method, an appropriate filter gain is selected to minimize thetrace of the upper bound for the filter error covariance. Moreover, the resilient distributed filtering algorithm isdesigned and a new matrix simplification technique is introduced to deal with the sparsity of sensor networks topology. Finally, both the feasibility and effectiveness of the resilient distributed filtering algorithm are verified by anumerical simulation.
Tianqi Zhu,Jianliang Mao,Chuanlin Zhang,Linyan Han 제어·로봇·시스템학회 2024 International Journal of Control, Automation, and Vol.22 No.1
This paper presents a novel image-based visual servoing (IBVS) controller for a six-degree-of-freedom (6-DoF) robot manipulator by employing a fuzzy adaptive model predictive control (FAMPC) approach. The control strategy allows the robot to track the desired feature points adaptively and fulfill kinematic constraints appearing in a vision-guided task with different initial Cartesian poses. To this aim, the successive linearization method is firstly employed to transform the nonlinear IBVS model to the linear time-invariant (LTI) one at each sampling instant. The nonlinear optimization problem is therefore degraded into a convex quadratic programming (QP) problem. Subsequently, a fuzzy logic is exploited to tune the weighting coefficients in the cost function on the basis of image pixels changes at each step, endowing the reliable adaptation capabilities to different working environments. Experimental comparison tests performed on a 6-DoF robot manipulator with an eye-in-hand configuration are provided to demonstrate the efficacy of the proposed controller.
Tianqi Liu,Danwei Wang,Ronghu Chi 제어로봇시스템학회 2013 제어로봇시스템학회 국제학술대회 논문집 Vol.2013 No.10
In this paper, a neural network based terminal iterative learning control(NNTILC) method is proposed for a class of discrete time uncertain linear systems to track run-varying reference point The zero error initial condition in most of the previous work on terminal iterative learning control(TILC) is removed by the use of neural network. A radial basis function (RBF) neural network is developed to approximate the effect of initial state and reference on terminal output iteratively. By involving these information as well as the reference signal in the control scheme, the proposed NNTILC can drive the system to track run-varying reference point fast and precisely beyond the initial state variance and reference change. Stability and convergence of this approach are proved and computer simulation results are provided to confirm its effectiveness further.
Tianqi Liu,Chukwunonso O. Aniagor,Marcel I. Ejimofor,Matthew C. Menkiti,Yakubu M. Wakawa,Jie Li,Rachid Ait Akbour,Pow-Seng Yap,Sie Yon Lau,Jaison Jeevanandam 한국공업화학회 2023 Journal of Industrial and Engineering Chemistry Vol.117 No.-
Recently, there is an increasing concern on dye contamination in the aquatic environment. Elimination ofdye contaminants has gained significant attention among researchers due to their potential deleteriousrisk to human health and the ecosystem. Among the treatment technologies for dye removal from thewater system, the utilization of modified graphene oxide as an adsorbent has garnered increasingresearch interest due to its superior dye adsorption capacity. Hence, this review aims to comprehensivelypresent the classifications and hazards of dyes, types of preparation methods for modified graphene oxideand recent developments in the employment of modified graphene oxide to adsorb dyes from water. Additionally, the primary objective of this review is to emphasize on adsorption performances of modifiedgraphene oxide for dye removal in an aqueous medium, specifically focusing on the adsorption kinetics,adsorption isotherms and the effect of experimental parameters. Furthermore, the pertinentchallenges, tremendous opportunities and the future outlook of modified graphene oxide to be employedas a potential aqueous dye adsorbent were also discussed.
Tianqi Wang,Dong Eui Chang 제어로봇시스템학회 2019 제어로봇시스템학회 국제학술대회 논문집 Vol.2019 No.10
We present a training pipeline for the autonomous driving task given the current camera image and vehicle speed as the input to produce the throttle, brake, and steering control output. The simulator Airsim’s [1] convenient weather and lighting API provides a sufficient diversity during training which can be very helpful to increase the trained policy’s robustness. In order to not limit the possible policy’s performance, we use a continuous and deterministic control policy setting. We utilize ResNet-34 [2] as our actor and critic networks with some slight changes in the fully connected layers. Considering human’s mastery of this task and the high-complexity nature of this task, we first use imitation learning to mimic the given human policy and then leverage the trained policy and its weights to the reinforcement learning phase for which we use DDPG [3]. This combination shows a considerable performance boost comparing to both pure imitation learning and pure DDPG for the autonomous driving task.