A Novel Low-Complexity Cascaded Model Predictive Control Method for PMSM

A novel low-complexity cascaded model predictive control method for permanent magnet synchronous motors is proposed to achieve a fast dynamic response to ensure the system’s steady-state performance. Firstly, a predictive speed controller based on an extended state observer is designed in the outer speed loop to improve the anti-interference ability of the system; then, a low-complexity three-vector predictive control algorithm is adopted in the current inner loop, taking into account the steady-state performance of the system and lower computational burden. Finally, a comparative analysis is conducted between the proposed method and traditional methods through simulation and experiments, proving that the proposed method performs well in dynamic and static performance. On this basis, the computational complexity of the current inner loop three-vector prediction algorithm is effectively reduced, indicating the correctness and effectiveness of the proposed method

Assessing the environmental impact of building life cycle: A carbon reduction strategy through innovative design, intelligent construction, and secondary utilization

The sustainable development of low-carbon buildings has aroused widespread concern in the whole society. This study aims to propose a three-step carbon reduction strategy based on innovative design, intelligent construction and secondary utilization, and provide a new solution for carbon reduction in the construction industry. Based on the life cycle assessment method, the system boundary and calculation model of carbon emission and carbon compensation in each stage of building life cycle are redefined, and the carbon reduction effect of the mixed three-step strategy is quantitatively analyzed through the case carbon emission data, which proves the scientific feasibility of the carbon reduction strategy in this paper. The research shows that it has a good carbon reduction effect whether it is adopting structural optimization aided design in the architectural design stage, upgrading the traditional construction technology with 3D printing intelligent construction technology, or effectively disposing of the construction waste for recycling

Data-Driven Building Energy Consumption Prediction Model Based on VMD-SA-DBN

Prediction of building energy consumption using mathematical modeling is crucial for improving the efficiency of building energy utilization, assisting in building energy consumption planning and scheduling, and further achieving the goal of energy conservation and emission reduction. In consideration of the non-linear and non-smooth characteristics of building energy consumption time series data, a short-term, hybrid building energy consumption prediction model combining variational mode decomposition (VMD), a simulated annealing (SA) algorithm, and a deep belief network (DBN) is proposed in this study. In the proposed VMD-SA-DBN model, the VMD algorithm decomposes the time series into different modes to reduce the fluctuation of the data. The SA-DBN prediction model is built for each mode separately, and the DBN network structure parameters are optimized by the SA algorithm. The prediction results of each model are aggregated and reconstructed to obtain the final prediction output. The validity and prediction performance of the proposed model is evaluated on a publicly available dataset, and the results show that the proposed new model significantly improves the accuracy and stability of building energy consumption prediction compared with several typical machine learning methods. The mean absolute percent error (MAPE) of the VMD-SA-DBN model is 63.7%, 65.5%, 46.83%, 64.82%, 44.1%, 36.3%, and 28.3% lower than that of the long short-term memory (LSTM), gated recurrent unit (GRU), VMD-LSTM, VMD-GRU, DBN, SA-DBN, and VMD-DBN models, respectively. The results will help managers formulate more-favorable low-energy emission reduction plans and improve building energy efficiency

Intranasal administration of dantrolene increased brain concentration and duration.

Dantrolene has been demonstrated to be neuroprotective for multiple neurodegenerative diseases. However, dantrolene's limited penetration into the CNS hampers its effectiveness as a neuroprotective agent. Here, we studied whether the intranasal administration of dantrolene provided better penetration into the brain than the commonly used oral approach. C57BL/6 mice, aged 2-4 months, received a single dose of either intranasal or oral dantrolene (5mg/kg). Inhibition of dantrolene clearance from the brain was examined by co-administration with P-gp/BCRP inhibitors, nimodipine or elacridar. The concentration of dantrolene in the brain and plasma was measured at 10, 20, 30, 50, 70, 120, 150 and 180 minutes after administration. Separate cohorts of mice were given intranasal dantrolene (5mg/kg) or vehicle, 3 times/ week, for either 3 weeks or 4 months, to examine potential adverse side effects on olfaction and motor coordination, respectively. We found that Dantrolene concentrations were sustained in the brain after intranasal administration for 180 min, while concentrations fell to zero at 120 min for oral administration. Chronic use of intranasal dantrolene did not impair olfaction or motor function in these mice. Blood brain barrier pump inhibitors did not further increase dantrolene peak concentrations in the brain. Our results suggested that Intranasal administration of dantrolene is an effective route to increase its concentration and duration in the brain compared to the oral approach, without any obvious side effects on olfaction or motor function

Comparative binding character of two general anaesthetics for sites on human serum albumin.

Propofol and halothane are clinically used general anaesthetics, which are transported primarily by HSA (human serum albumin) in the blood. Binding characteristics are therefore of interest for both the pharmacokinetics and pharmacodynamics of these drugs. We characterized anaesthetic-HSA interactions in solution using elution chromatography, ITC (isothermal titration calorimetry), hydrogen-exchange experiments and geometric analyses of high-resolution structures. Binding affinity of propofol to HSA was determined to have a K(d) of 65 microM and a stoichiometry of approx. 2, whereas the binding of halothane to HSA showed a K(d) of 1.6 mM and a stoichiometry of approx. 7. Anaesthetic-HSA interactions are exothermic, with propofol having a larger negative enthalpy change relative to halothane. Hydrogen-exchange studies in isolated recombinant domains of HSA showed that propofol-binding sites are primarily found in domain III, whereas halothane sites are more widely distributed. Both location and stoichiometry from these solution studies agree with data derived from X-ray crystal-structure studies, and further analyses of the architecture of sites from these structures suggested that greater hydrophobic contacts, van der Waals interactions and hydrogen-bond formation account for the stronger binding of propofol as compared with the less potent anaesthetic, halothane

Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study

BackgroundIt is believed that smoking is not the cause of approximately 53% of lung cancers diagnosed in women globally. ObjectiveThe study aimed to develop and validate a simple and noninvasive model that could assess and stratify lung cancer risk in nonsmoking Chinese women. MethodsBased on the population-based Cancer Screening Program in Urban China, this retrospective, cross-sectional cohort study was carried out with a vast population base and an immense number of participants. The training set and the validation set were both constructed using a random distribution of the data. Following the identification of associated risk factors by multivariable Cox regression analysis, a predictive nomogram was developed. Discrimination (area under the curve) and calibration were further performed to assess the validation of risk prediction nomogram in the training set, which was then validated in the validation set. ResultsIn sum, 151,834 individuals signed up to take part in the survey. Both the training set (n=75,917) and the validation set (n=75,917) were comprised of randomly selected participants. Potential predictors for lung cancer included age, history of chronic respiratory disease, first-degree family history of lung cancer, menopause, and history of benign breast disease. We displayed 1-year, 3-year, and 5-year lung cancer risk–predicting nomograms using these 5 factors. In the training set, the 1-year, 3-year, and 5-year lung cancer risk areas under the curve were 0.762, 0.718, and 0.703, respectively. In the validation set, the model showed a moderate predictive discrimination. ConclusionsWe designed and validated a simple and noninvasive lung cancer risk model for nonsmoking women. This model can be applied to identify and triage people at high risk for developing lung cancers among nonsmoking women

Liquid–liquid phase reaction between crystal violet and sodium hydroxide: kinetic study and precipitate analysis

To investigate reaction order and kinetic parameters of the reaction between crystal violet (CV) and sodium hydroxide (NaOH), various concentrations of the reactants were applied. The present work also verifies the unknown solid product produced under highly concentrated conditions. The reaction orders of CV and NaOH were determined to be 1 and 1.08 by pseudo rate method, respectively, with a rate constant, k, of 0.054 [(M−1.08) s−1]. In addition to pseudo rate method, the half-life approach was used to calculate the overall reaction order to verify the accuracy of pseudo rate method. The overall reaction order was determined to be 1.9 by the half-life method. The overall reaction order based on the two methods studied was approximately 2. The precipitate formation was observed when high concentrations of CV (0.01–0.1 M) and NaOH (1.0 M) were applied. Fourier transform infrared (FTIR) spectroscopy was used to compare the spectra of the precipitate generated and a commercial solvent violet 9 (SV9). Based on the FTIR spectra, it was confirmed that the molecular structure of the precipitate matched that of solvent violet 9

Anesthetic-Induced Neurodegeneration Mediated via Inositol 1,4,5-Trisphosphate ReceptorsS⃞

The commonly used general anesthetic isoflurane induces widespread neurodegeneration in the developing mammalian brain through poorly understood mechanisms. We have investigated whether excessive Ca2+ release from the endoplasmic reticulum via overactivation of inositol 1,4,5-trisphosphate receptors (InsP3Rs) is a contributing factor in such neurodegeneration in rodent primary cultured neurons and developing rat brain. We also investigated the correlation between isoflurane exposure and cognitive decline in rats at 1 month of age. Our results show that isoflurane increases cytosolic calcium in the primary cortical neurons through release from the endoplasmic reticulum and influx from the extracellular space. Pharmacological inhibition of InsP3R activity and knockdown of its expression nearly abolishes the isoflurane-mediated elevation of the cytosolic calcium concentration and cell death in rodent primary cortical and hippocampal neurons. Inhibition of InsP3R activity by its antagonist xestospongin C significantly inhibits neurodegeneration induced by isoflurane at clinically used concentration in the developing brain of postnatal day 7 rats. Moreover, our results show that isoflurane activates β-site amyloid β precursor protein-cleaving enzyme via activation of the InsP3R. We also noted that mice exposed to isoflurane during early postnatal development showed transient memory and learning impairments, which did not correlate well with the noted neuropathological defects. Taken together, our results suggest that Ca2+ dysregulation through overactivation of the InsP3R may be a contributing factor in the mechanism of isoflurane-induced neurodegeneration in rodent neuronal cell culture and during brain development