Acetone Adsorption to Co3O4?111?Surface: A Density Functional Theory (DFT) Study

Acetone, as one of VOCs, is not only polluting to the environment, but also harmful to humans. Therefore, detecting acetone is an important topic in the field of gas sensing. The carbonyl functional group determines the chemical properties of acetone. Aldehydes also contain carbonyl functional group. In this paper, we have calculated adsorption energy, adsorption distance and transfer charge by DFT. The results showed that the top of Co3+ on Co3O4 (111) surface has the best selectivity for sensing acetone. Our study contributes to the further study of the sensing properties of p-type metal oxide semiconductor sensors

The Weighted Support Vector Machine Based on Hybrid Swarm Intelligence Optimization for Icing Prediction of Transmission Line

Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the people’s daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR). According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO), which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability

Applying DTN Routing for Reservation-Driven EV Charging Management in Smart Cities

Charging management for Electric Vehicles (EVs) on-the-move (moving on the road with certain trip destinations) is becoming important, concerning the increasing popularity of EVs in urban city. However, the limited battery volume of EV certainly influences its driver’s experience. This is mainly because the EV needed for intermediate charging during trip, may experience a long service waiting time at Charging Station (CS). In this paper, we focus on CS-selection decision making to manage EVs’ charging plans, aiming to minimize drivers’ trip duration through intermediate charging at CSs. The anticipated EVs’ charging reservations including their arrival time and expected charging time at CSs, are brought for charging management, in addition to taking the local status of CSs into account. Compared to applying traditionally applying cellular network communication to report EVs’ charging reservations,we alternatively study the feasibility of applying Vehicle-to-Vehicle (V2V) communication with Delay/Disruption Tolerant Networking (DTN) nature, due primarily to its flexibility and cost-efficiency in Vehicular Ad hoc NETworks (VANETs). Evaluation results under the realistic Helsinki city scenario show that applying the V2V for reservation reporting is promisingly cost-efficient in terms of communication overhead for reservation making, while achieving a comparable performance in terms of charging waiting time and total trip duration

An Integrated Clinical-mRNA-lncRNA-miRNA Signature for Muscle-Invasive Bladder Cancer Prognosis

An increasing number of evidence suggests that clinical variables alone are not enough to predict the survival of patients with muscle invasive bladder cancer (MIBC), and the expression of mRNAs, long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) also plays an important role in the onset of MIBC. This study aims to establish a more accurate model for predicting the overall survival of MIBC based on clinical information and genetic characteristics. In this study, the RNAs profiles and clinical variable data of patients with MIBC were downloaded from the Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis, differential expression analysis and elastic net-regulated Cox regression analysis were used to identify the clinical variables and RNAs (mRNAs, lncRNAs and miRNAs) related to the prognosis of MIBC. Prognostic models of MIBC were established by multivariate Cox regression and ridge regression analysis using the identified prognostic clinical variables and RNAs. Three clinical variables, 25 mRNAs, 3 lncRNAs and 2 miRNAs related to the prognosis of MIBC were identified, and an integrated signature, a clinical variable signature, and an mRNA-lncRNA-miRNA signature were established based on the identified clinical variables and/or RNAs. Among the three models, the integrated signature had the highest predictive accuracy (5-year the area under the curve (AUC)=0.835, 95%CI:0.776-0.894) among the three models (P 0.05). The patients in the TCGA MIBC cohort were classified into high- or low-risk groups by the integrated signature, and it was found that the patients in the low-risk group had a significantly longer overall survival time compared with the patients in the high-risk group (P 0.001). Applying published gene signatures and TCGA data, a new and more accurate integrated clinical-mRNA-lncRNA-miRNA signature for MIBC prognostic was established

RBF Neural Network Combined with Knowledge Mining Based on Environment Simulation Applied for Photovoltaic Generation Forecasting

Photovoltaic generation forecasting is one of the main tasks of the planning and operation in power system. Especially with the development of mico-grid, relative study on renewable energy generation gain more and more concerns. In this paper, a short-term forecasting model combining knowledge and intelligent algorithm is developed for photovoltaic array generation. Self-organizing map (SOM) is proposed to extract the relative knowledge, and to choose the most similar history situation and efficient data for wind power forecasting with numerical weather prediction (NWP). The historical data is classified into several groups, though which we could find the similar days and excavate the hidden rules. According to the data reprocessing, the selected samples will improve the forecast accuracy radial basis function network (RBF) trained by the class of the forecasting day is adopted to forecast the photovoltaic output accordingly. A case study is conducted to verify the effectiveness and the accuracy. Compared with the conventional BP neural network, the forecasting results demonstrate the method proposed in this paper can gain better forecasting performance with higher accuracy

Nanoscale Cathodoluminescence Spectroscopy Probing the Nitride Quantum Wells in an Electron Microcope

To gain a deeper understanding of the luminescence of multiquantum wells and the factors affecting it on a microscopic level, cathodoluminescence combined with scanning transmission electron microscopy and spectroscopy was used to reveal the luminescence of In0.15Ga0.85N five-period multiquantum wells. The composition-wave-energy relationship was established in combination with energy-dispersive X-ray spectroscopy , and the bandgaps of In0.15Ga0.85N and GaN in multiple quantum wells were extracted by electron energy loss spectroscopy to understand the features of cathodoluminescence luminescence spectra. The luminescence differences between different periods of multiquantum wells and the effects on the luminescence of multiple quantum wells owing to defects such as composition fluctuation and dislocations were revealed. Our study establishing the direct correspondence between the atomic structure of InxGa1-xN multiquantum wells and photoelectric properties, provides useful information for nitride applications.Comment: 13 pages,4 figure