Lei Ju

Exploring the effect of bisphenol S on sludge hydrolysis and mechanism of the interaction between bisphenol S and α-Amylase through spectrophotometric methods

Sewage sludge, as a very significant sources of BPS (up to 523mg/kg dw) introduction into the environment, must be handled properly. Therefore, it is important to access BPS removal and its effect on sludge treatment with the biological treatment. However, it is unclear for its effect on the hydrolysis of sludge. In this research, impact of BPS on sludge hydrolysis by α-Amylase is studied from the respect of component of soluble organic matter in sludge using three-dimensional fluorescence spectra. Enzyme activity assay suggests that sludge hydrolysis is inhibited due to the denaturation of α-Amylase with BPS exposure. In order to illuminate the interaction mechanism between BPS and α-Amylase, UV-vis, steady-state fluorescence, circular dichroism, synchronous fluorescence, light scattering spectra, enzyme activity assay and molecule docking techniques are applied. Results show that BPS interacts with α-Amylase by hydrophobic bond in the activity region of α-Amylase. This interaction not only causes an unfolding skeleton structure of α-Amylase and a less hydrophobic microenvironment of tyrosine and tryptophan residues, but also leads to a specific fluorophore quenching involving static and dynamic type. This work provides direct evidence about enzyme toxicity of BPS and establishes a new strategy to investigate the interaction between protein and BPS at a molecular level, which is helpful for clarifying the bioactivities of BPS.

Multi-spectral and thermodynamic analysis of the interaction mechanism between Cu2+ and α-amylase and impact on sludge hydrolysis

An increasing amount of heavy metals (e.g., Cu2+) is being discharged into sewage treatment plants and is accumulating in sludge, which is toxic to the enzyme in sludge or soil when the sludge is used as fertilizer, resulting in unfavorable effect on the biological treatment of sludge and the circulation and conversion of materials in soil. In this research, effect of Cu2+ on sludge hydrolysis by α-amylase is studied from the respect of concentration and components of soluble organic matter in sludge, using three-dimensional fluorescence spectra. Results show that Cu2+ exposure not only inhibits the hydrolysis of sludge due to the denaturation of α-amylase but also affects the components of soluble organic matter in sludge. In order to illuminate the interaction mechanism between Cu2+ and α-amylase (a model of hydrolase in sludge), multi-spectra and isothermal titration microcalorimetry techniques are applied. Results show that the secondary structure of α-amylase is changed as that the α-helical content increases and the structure loosens. The microenvironment of amino acid residue in α-amylase is changed that the hydrophobicity decreases and the polarity increases with Cu2+ exposure. Isothermal titration calorimetry results show that Van der Waals force and hydrogen bond exist in the interaction between Cu2+ and α-amylase. Results from this research would favor the development of advanced process for the biological treatment of sludge containing heavy metals.

Energy efficient task allocation for hybrid main memory architecture

Compared with the conventional dynamic random access memory (DRAM), emerging non-volatile memory technologies provide better density and energy efficiency. However, current NVM devices typically suffer from high write power, long write latency and low write endurance. In this paper, we study the task allocation problem for the hybrid main memory architecture with both DRAM and PRAM, in order to leverage system performance and the energy consumption of the memory subsystem via assigning different memory devices for each individual task. For an embedded system with a static set of periodical tasks, we design an integer linear programming (ILP) based offline adaptive space allocation (offline-ASA) algorithm to obtain the optimal task allocation. Furthermore, we propose an online adaptive space allocation (online-ASA) algorithm for dynamic task set where arrivals of tasks are not known in advance. Experimental results show that our proposed schemes achieve 27.01% energy saving on average, with additional performance cost of 13.6%.

Position prediction system based on spatio-temporal regularity of object mobility

Abstract With the accumulation of the vast amount of location data acquired by positioning devices embedded in mobile phones and cars, position prediction of moving objects has been an important research direction for many location-based services such as public transit forecasting and tourist behavior analysis. In this paper, a position prediction system has been proposed, which utilizes not only spatial but also temporal regularity of object mobility. Historical trajectory data of the object is used to extract personal trajectory patterns to obtain candidate next positions. Each of the candidate next positions is scored by the proposed Spatio-Temporal Regularity-based Prediction (STRP) algorithm according to time components of patterns and current time. The position with the highest score is considered as the predicted next position. Furthermore, a hybrid B/S and C/S architecture is employed to perform the real-time prediction and results display. An evaluation based on two different public trajectory data sets demonstrates that the proposed STRP algorithm achieves highly accurate position prediction. Moreover, the average accuracy rate of our prediction algorithm with one known position is about 86.8%, which is 43.9% better than the Markov-based algorithm.

Bayesian Monitoring Design for Streambed Heat Tracing: Numerical Simulation and Sandbox Experiments: Bayesian Monitoring Design for Streambed Heat Tracing: Numerical Simulation and Sandbox Experiments

Heat tracing methods have been widely employed for subsurface characterization. Nevertheless, there were very few studies regarding the optimal monitoring design for heat tracing in heterogeneous streambeds. In this study, we addressed this issue by proposing an efficient optimal design framework to collect the most informative diurnal temperature signal for Bayesian estimation of streambed hydraulic conductivities. The data worth (DW) was measured by the expected relative entropy between the prior and posterior distributions of the conductivity field. An adaptively refined Gaussian process surrogate was employed to alleviate the computational burden, resulting in at least three orders of magnitude of speed-up. The applicability of the optimal experimental design framework was evaluated by both numerical and sandbox experimental cases. Results showed that the most informative locations centered in the transition zones among the main patterns of the hydraulic conductivity field, while the most informative times centered in a short period after the minimum/maximum temperature appeared. With the fixed number of measurements, extending the calibration period was more beneficial than increasing the monitoring frequency in improving the estimation results. To our best knowledge, this work is the first study on Bayesian monitoring design for streambed characterization with the heat tracing method. The method and results can provide guidance on selecting monitoring strategies under budget-limited conditions.