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Electric water heaters (EWHs) are usually equipped with inbuilt thermostats to measure water temperature near the installed positions. Most of the existing EWHs have only two thermostats and often do not have water flow sensor installed. Estimation of state of heat energy (SOHE) inside the hot water tank, which is crucial for efficient control and operation of the EWH, under such imperfect system conditions is very challenging. This paper, therefore, designs a transactive EWH agent (TEWHA) to better estimate SOHE in presence of measurement errors and imperfect system knowledge. The TEWHA, built-in Python, approximates the EWH physics using limited measurements obtained from the ground truth GridLAB-D model. The performance of the TEWHA is validated against ground truth system for three scenarios: a) ideal condition where TEWHA has complete knowledge of the system, b) imperfect condition where TEWHA has incomplete system information, and c) noised condition where TEWHA measurements contain random errors. Finally, an optimization problem is formulated and solved to demonstrate how TEWHA could use the estimated and measured parameters for a transactive control of EWH.

Convolutional neural network (CNN) is widely used in Natural Language Processing (NLP) and has achieved relatively good results. Since the convolutional layer of CNN can be initialized by encoding important semantic features, CNN can extract important semantic features in the convolutional layer. The pooling layer of CNN uses the feature of max-pooling to filter the features, but it does not consider the key information in the sentence and the context semantic information in the text. Therefore, a novel neural network algorithm combining with the attention mechanism and the pooling layer is proposed in this paper to make the model pay attention to the keywords in the sentence and automatically maintain the most meaningful text message, thereby improving the performance of text classification. Our paper uses important information features to initialize the convolution filter so that the model can notice the important semantic features. The resulting feature is enhanced by combining the attention mechanism in the pooling layer, so that the model can maintain the important information features of the text. Experiments show that the proposed model achieves excellent results for multiple text classification tasks, including sentiment classification and topic classification.

The effects of additions of Co and Ni to the FeNbBCu alloys on the glass forming ability (GFA), soft magnetic properties and electrical resistivity of Fe-based amorphous alloys were studied by means of X-ray diffraction, differential scanning calorimetry, vibrating sample magnetometry and four-probe testing. The Fe80–x–y Nb5B14Cu1Co x Ni y alloys exhibit high thermal stability and excellent GFA with T x1 ≥ 735 K and T x1/T m ≥ 0.539. They also show excellent soft magnetic properties with high saturation magnetization of 93– 101 emu g–1 and low coercivity of 0.04 –2.37 Oe. The electrical resistivity of these alloys ranges from 121 μΩ cm to 163 μΩ cm. The newly developed FeNbBCu-CoNi glassy alloys not only possess good GFA and high thermal stability, but also have excellent magnetic properties, high electrical resistivity and low cost. With such promising properties, these alloys have potential for industrial applications.

This paper presents an Artificial Neural Network (ANN)-based building-level hourly electrical load forecasting method that takes into account HVAC set points as one of the input parameters, in addition to the historical building load and outdoor weather data. The data presented in this paper deal with cooling load only. ANN is used to train and test the dataset, and the ANN-based load forecasting model provides the predicted load for each hour of the day. Three training algorithms are explored, including Levenberg-Marquardt, Scaled Conjugate gradient back-propagation and Bayesian Regularization (BR). Findings indicate that the BR-based neural network offers the best performance in terms of forecasting accuracy. In addition, a case study using a commercial building in Chicago, Illinois is presented where performances of the developed ANN-based models are compared. The forecasting error is around 5% or less for hour-ahead load forecasting, and around 8% or less for 12-hour ahead load forecasting.

Quantifying energy savings and peak demand reduction potentials in a building due to adjustment of HVAC set point during a demand response (DR) period is critical for active DR program participation and optimal demand-side resource allocations. This has been a topic of great challenge given the complex nature of physical characteristics and HVAC system thermal dynamics in commercial buildings. Based on the building model developed in eQUEST validated against measured power consumption data from a smart meter, this paper investigates how energy savings and peak demand reduction potentials of a building are impacted by HVAC set point changes during a DR period. Simulation results reveal that the days with high outdoor temperature are expected to achieve a predictable percentage of daily energy savings and peak load reductions when HVAC set points are raised in summer. However, the inevitable demand restrike, i.e., increase in building peak demand after a DR event, is a concern.

DC injection is a critical issue in transformerless grid-connected inverters. DC injection control based on virtual capacitor has the advantages of low cost, low loss, high accuracy and easy implementation. In this paper, the principle of DC injection control based on virtual capacitor was analyzed. In addition, the applicable conditions, working process, steady state error and advantages were also discussed in detail. The design of the control parameters based on virtual capacitor was proposed in a grid-connected inverter with LCL filter. The robustness of the control parameters was also discussed. Simulation and experimental results verify the validity of the analysis and demonstrate that this research has a certain value in engineering applications.

Multi-arm star polyornithines PEI-P(Orn) n are prepared by grafting polyornithine arms onto branched polyethylenimine (PEI) with an M w of 600 via the ring-opening polymerization of N -carboxyanhydride of benzyloxycarbonyl ornithine. To enhance gene delivery efficiency and reduce cytotoxicity, the amino side groups on the polyornithine arms are partially guanidinated that transforms the ornithine units to arginine units. Thus, the guanidinated products G-PEI-P(Orn) n contain multiple poly(ornithine-co-arginine) arms. PEI-P(Orn) n and G-PEI-P(Orn) 71 mediate the transfection of pGL3 plasmid to 293T cells almost as efficient as 25 kDa PEI in serum-free medium. Notably, in contrast to the dramatically lowered efficiency of 25 kDa PEI in the presence of serum, the efficiency of G-PEI-P(Orn) 71 can be retained or even enhanced in the medium containing 10% serum. The improved serum-compatibility and high efficiency of the guanidinium-modified star polyornithines make them promising for gene delivery.

As a new generation of technology cloud computing has been widely applied to various fields, and owing to the problem of the security, the security problems of the data in the cloud computing platform has been becoming the focus of attention. Therefore, it is vital of cloud computing that to put forward a security mechanism to ensure the security of data in the environment of cloud computing. The related technology of Intrusion Detection is researched, and the thoughts of combining misuse detection and anomaly detection together are adopting. Then a method of anomaly detection based on data mining is given. It is the improved decision trees algorithm, and achieves a real-time updates of invading the signature database by misuse detection. It was improved through the experiment the application based on improved decision trees can detect the attack effectively and guarantee the security of the cloud computing.

Phenylboronic acid-functionalized amphiphilic block copolymer Pluronic-PMCC-BA was synthesized via ring-opening polymerization of 5-methyl-5-benzyloxycarbonyl-1,3-dioxan-2-one (MBC) with fumaric acid as a catalyst followed by the deprotection of carboxyl groups by catalyzed hydrogenation and the condensation of 3-aminophenylboronic acid with the copolymer side groups. Pluronic-PMCC-BA can form stable micelle solution by self-assembly in water. The phenylboronic acid groups are located at the shell of micelle as proved by (1)H NMR. The diameter of drug-free micelles is approximate 60 nm. Nano-spheres with narrow size distribution could be observed in the TEM image. MTT assay results show that Pluronic-PMCC-BA exhibits slight cytotoxicity when the polymer concentration is higher than 25 μg mL(-1). The toxicities of DOX@Pluronic-PMCC and DOX@Pluronic-PMCC-BA to COS7, HeLa, and HepG2 cell lines are similar with those of free DOX. Interestingly, phenylboronic acid groups located at the surface of Pluronic-PMCC-BA micelles can recognize HepG2 cells and promote the drug uptake of the cells, which are observed by confocal laser scanning microscopy (CLSM). The results imply that Pluronic-PMCC-BA would be a promising material for targeted drug delivery to the cancer cells.

Background The purpose of this study was to elucidate the possible beneficial effects of adiponectin (APN) on acute lung injury in a rat model of sepsis. Methods We subjected male Sprague-Dawley rats to cecal ligation and puncture (CLP) to establish sepsis models. We randomly animals divided into four groups: control (C), model (CLP), preemptive APN administration (APN plus CLP), and delayed APN administration (CLP plus APN). We killed the animals 24 h after CLP and collected blood samples to determine PaO 2 and PaCO 2 . Lung samples were taken for histologic assessment and measurement of myeloperoxidase activity. We measured neutrophil and macrophage count and cytokine production (tumor necrosis factor-α and macrophage inflammatory protein-2) in bronchoalveolar lavage fluid. Results Histology findings and lung injury score analysis revealed acute lung injury in rats in the CLP group, whereas those in the APN-treated group had mild lung injury. The effects of sepsis on the increasing cell number in bronchoalveolar lavage fluid as well as the wet/dry weight ratio, neutrophil infiltration, and myeloperoxidase activity of lung tissue were significantly attenuated by APN administration. Adiponectin also significantly alleviated hypoxemia and hypercapnia resulting from the development of lung injury. In addition, in APN-treated rats, the levels of pulmonary inflammatory molecule (macrophage inflammatory protein-2) and cytokine (tumor necrosis factor-α) were down-regulated compared with the CLP group. Conclusions Adiponectin administration ameliorates acute lung injury in a rat model of sepsis induced by CLP, no matter whether it is administrated before or after the onset of sepsis.

A water-soluble polycarbonate with dimethylamino pendant groups, poly(2-dimethylaminotrimethylene carbonate) (PDMATC), is synthesized and characterized. First, the six-membered carbonate monomer, 2-dimethylaminotrimethylene carbonate (DMATC), is prepared via the cyclization reaction of 2-(dimethylamino)propane-1,3-diol with triphosgene in the presence of triethylamine. Although the attempted ring-opening polymerization (ROP) of DMATC with Sn(Oct)(2) as a catalyst fails, the ROP of DMATC is successfully carried out with Novozym-435 as a catalyst to give water-soluble aliphatic polycarbonate PDMATC with low cytotoxicity and good degradability.

This paper presents an improved multistage variable-step incremental conductance method to achieve the maximum power point tracking (MPPT). Compared with the traditional variable-step incremental conductance method, this improved algorithm optimizes the selection of the step size so that the tracking of the maximum power point is more quickly and the steady-state performance is better. Furthermore, the algorithm can still guarantee the quickness and accuracy of MPPT when the environmental conditions change suddenly and tremendously. At last, matlab simulation was applied to compare the characteristics of multistage variable-step algorithm with that of the other. And the simulation results verified that the improved algorithm has better dynamic and steady-state performance.

A phenylboronic-acid-modified amphiphilic block polyether is prepared via reaction of polyglycidol-block-poly(ethylene oxide)-block-poly(propylene oxide)-block-poly(ethylene oxide)-block-polyglycidol (Pluronic-PG) with 2-(N,N-dimethylaminomethyl)-5-aminomethyl phenylboronic acid using phosgene as a coupling reagent. The boronic-acid-modified non-cationic polymer binds plasmid pGL3 effectively, forms sub-µm polymer/DNA complex particles, and greatly facilitates the cell uptake of the plasmid. The efficiency of the polymer as a gene vector is evaluated in vitro by transfection of pGL3 to HeLa, COS-7 and HepG2 cells. Pluronic-PG-BA enhances the transfection efficiency by 100 to 1000 times compared with Pluronic-PG. The presence of serum does not significantly affect the transfection efficiency.