ALBERT

Description: A temperature sensor has been developed using an embedded system and a sensor head made of polymer-derived SiAlCN ceramics (PDCs). PDC is a promising material for measuring high temperature and the embedded system features low-power consumption, compact size, and wireless transmission. The developed temperature sensor has been experimentally tested to demonstrate the possibility of using such sensors for real world applications.

Description: Heavy oil transportation has become a highly technical operation facing complex difficulties. One of the major difficulties in the pipeline transportation is the high viscosity that requires efficient and economical ways to deal with. The typical polymer viscosity reducers are a negative problem during oil refinement process for their chemical properties. The objective of this study is to seek small molecular compound, different from the traditional polymers, to reduce the viscosity of the crude oil. In this work, a spirocompound, 3,9-diphenyl-2,4,8,10-tetraoxa-spiro[]undecane, was synthesized catalyzed by zeolite and modified zeolite, and the product was fully characterized by NMR, MS, and TG. Then, it was used as viscosity reducer for crude oil. The factors such as dosage and temperature on the viscosity behavior have been studied. The results showed a significant viscosity reduction at different temperature, and the most economical dosage is 500 ppm. The multiphenyl groups can interact with asphaltene by stacking, and the spirostructure can fix the stacking in different direction, which can prevent the agglomeration of wax crystals.

Description: The issues with downscaling the outputs of a global climate model (GCM) to a regional scale that are appropriate to hydrological impact studies are investigated using the random forest (RF) model, which has been shown to be superior for large dataset analysis and variable importance evaluation. The RF is proposed for downscaling daily mean temperature in the Pearl River basin in southern China. Four downscaling models were developed and validated by using the observed temperature series from 61 national stations and large-scale predictor variables derived from the National Center for Environmental Prediction–National Center for Atmospheric Research reanalysis dataset. The proposed RF downscaling model was compared to multiple linear regression, artificial neural network, and support vector machine models. Principal component analysis (PCA) and partial correlation analysis (PAR) were used in the predictor selection for the other models for a comprehensive study. It was shown that the model efficiency of the RF model was higher than that of the other models according to five selected criteria. By evaluating the predictor importance, the RF could choose the best predictor combination without using PCA and PAR. The results indicate that the RF is a feasible tool for the statistical downscaling of temperature.

Description: The coupling of groundwater movement and reactive transport during groundwater recharge with wastewater leads to a complicated mathematical model, involving terms to describe convection-dispersion, adsorption/desorption and/or biodegradation, and so forth. It has been found very difficult to solve such a coupled model either analytically or numerically. The present study adopts operator-splitting techniques to decompose the coupled model into two submodels with different intrinsic characteristics. By applying an upwind finite difference scheme to the finite volume integral of the convection flux term, an implicit solution procedure is derived to solve the convection-dominant equation. The dispersion term is discretized in a standard central-difference scheme while the dispersion-dominant equation is solved using either the preconditioned Jacobi conjugate gradient (PJCG) method or Thomas method based on local-one-dimensional scheme. The solution method proposed in this study is applied to the demonstration project of groundwater recharge with secondary effluent at Gaobeidian sewage treatment plant (STP) successfully.

Description: Intelligent manufacturing is a sustainable impetus to development of customization for new energy equipment. Reasonable principal-agent contract decision making can play a positive role in improving utility for customers and securing their long-term involvement. This paper establishes a multistage principal-agent model of intelligent customization for new energy equipment. The principal-agent and intelligent customization decisions, yielding risk avoidance and a better cost-benefit ratio, emerged from solving the model. A decision modification mechanism, used to improve intelligent customization output and consolidate customer trust, was proposed for agents. Research shows that overavoidance of customization risk by new energy equipment manufacturers reduces the efficiency of intelligent customization and aggravates the cost-benefit dilemma. Viewing intelligent customization as a long-term cooperative process, manufacturers can achieve a larger increase in equipment customization output at a smaller cost and enhance customer trust by adopting such a decision modification mechanism. The effect of this mechanism is obvious when the risk intensity is low. However, under the assumption of a multistage model, improvement of customer utility is mainly influenced by risk controllability. This paper provides solutions to the risk avoidance and cost-benefit dilemma problems of intelligent customization. The static principal-agent model was improved to a multistage model by setting the timeline. Long-term effectiveness of the decision modification mechanism was verified using this multistage model.

Description: Background. Patients with liver cirrhosis have a high risk of sepsis and a poor prognosis. Recently, a new standard for sepsis (Sepsis-3) has been proposed in the general population. The Coulter Lh 750 hematology analyzer can evaluate mean volume, conductivity, scatter, and distribution width of leukocyte. We tried to use Sepsis-3 criteria to study the diagnostic value of volume, conductivity, and scattering (VCS) parameters in sepsis and infection in patients with liver cirrhosis compared with traditional infection markers (PCT, IL-6, sCDl63). Methods. A blinded, cohort study was conducted in three different ED populations within three affiliated hospitals. A total of 249 patients with liver cirrhosis were enrolled in the study. According to the “Sepsis-3” consensus criteria, clinical history, and laboratory examination, the subjects were divided into sepsis (n=54), patients with infections (n=95), and patients without systemic infections (n=100). The blood samples of the patients were collected at the time of ED admission and were evaluated for the detection of sepsis. Results. The differences of MNV, MNS, MMV, MMS, MLV, NDW, and MDW in the three groups were statistically significant. In the diagnosis of sepsis in patients with liver cirrhosis, the sensitivity of combined detection of MMV and MDW was 88.89%; the specificity was 74%. This sensitivity was significantly better than the 83.3% achieved using 0.97 mg/L as the cutoff for sCD163. In the diagnosis of infection in cirrhosis, the sensitivity of combination of MNV and MMS was increased to 86.32%; the specificity was 92%. The sensitivity was the same as that achieved by using 0.31 ng/mL as the cutoff value of PCT, but the specificity increased. Conclusion. The leukocyte VCS parameter could be potential parameters for indicating sepsis and infection in patients with liver cirrhosis. The combined detection of MMV and MDW seemed to be helpful for the diagnosis of sepsis in these patients, and the combination of MNV and MMS could better indicate infection for them.

Description: The boiler-turbine unit is really a complex system in thermal power engineering due to its large-scale nonlinearity, unmeasured state, unknown disturbances, and constraints imposed on both controls and outputs. To design a controller with appropriate performance in above synthetical cases, this paper intends to propose an adaptively receding Galerkin optimal controller design method, in which, the mathematical dynamics of unit can be directly used as a predictive model without any linearization, and the unmeasured state in the predictive model is adaptively estimated using a predesigned state observer. With the help of a mathematical predictive model, optimal control law is then obtained based on a Galerkin optimization algorithm. Due to the application of the useful information measured at every sampling time instant, the proposed method can deal with the tracking problem with constraints rather than the stabilization problem that can be only done by the traditional Galerkin optimal control. Furthermore, it can also be easily extended to estimate and thus eliminate constant disturbances in an output channel using an independent model strategy. Some simulations suggest that satisfactory tracking performance can be achieved even when the unit experiences wide-range load change.

Description: Dental caries is a transmissible bacterial disease process, with cavities at the end, and caused by acids from bacterial metabolism. The essence of dental treatment is to clean and disinfect bacterial contamination from the tooth. In this work, we tried to demonstrate the cleaning and disinfecting effects of Nd:YAG laser irradiation on dental carious lesion and root canal in vitro. Acousto-optic Q-switched quasicontinuous and Cr3+:YAG crystal Q-switched pulse Nd:YAG lasers were employed to treat caries lesion and the root canal, respectively. Results showed that acousto-optic Q-switched quasicontinuous Nd:YAG laser irradiation and Cr3+:YAG crystal Q-switched pulse Nd:YAG laser irradiation could rapidly clean decayed material and bacterial contamination from dental carious lesion and the narrow tail end of root canal with minimally invasive in vitro, respectively. It was concluded that acousto-optic Q-switched quasicontinuous laser irradiation may be a rapid and effective alternative caries treatment, and Cr3+:YAG crystal Q-switched pulse Nd:YAG laser irradiation may be an effective method for canal cleaning and disinfecting during root canal therapy.

Description: The coupling of groundwater movement and reactive transport during groundwater recharge with wastewater leads to a complicated mathematical model, involving terms to describe convection-dispersion, adsorption/desorption and/or biodegradation, and so forth. It has been found very difficult to solve such a coupled model either analytically or numerically. The present study adopts operator-splitting techniques to decompose the coupled model into two submodels with different intrinsic characteristics. By applying an upwind finite difference scheme to the finite volume integral of the convection flux term, an implicit solution procedure is derived to solve the convection-dominant equation. The dispersion term is discretized in a standard central-difference scheme while the dispersion-dominant equation is solved using either the preconditioned Jacobi conjugate gradient (PJCG) method or Thomas method based on local-one-dimensional scheme. The solution method proposed in this study is applied to the demonstration project of groundwater recharge with secondary effluent at Gaobeidian sewage treatment plant (STP) successfully.

Description: For a class of continuous-time Markovian jump linear uncertain systems with partly known transition rates and input quantization, the state-feedback control design is considered. The elements in the transition rates matrix include completely known, boundary known, and completely unknown ones. First, an cost index for Markovian jump linear uncertain systems is introduced; then by introducing a new matrix inequality condition, sufficient conditions are formulated in terms of linear matrix inequalities (LMIs) for the control of the Markovian jump linear uncertain systems. Less conservativeness is achieved than the result obtained with the existing technique. Finally, a numerical example is given to verify the validity of the theoretical results.