Jiangxi University of Science and Technology

About: Jiangxi University of Science and Technology is a education organization based out in Ganzhou, China. It is known for research contribution in the topics: Microstructure & Alloy. The organization has 6958 authors who have published 5576 publications receiving 50650 citations.

Landslide hazard assessment based on Bayesian optimization–support vector machine in Nanping City, China

Abstract: Landslide hazard assessment is critical for preventing and mitigating landslide disasters. The tuning of hyperparameters is of great importance to achieve better accuracy in a landslide hazard assessment model. In this study, a novel approach is proposed for landslide hazard assessment with support vector machine (SVM) as the primary model and Bayesian optimization (BO) algorithm as the parameter tuning method. This study describes 1711 historical landslide disaster points in Nanping City, and a total of 12 landslide conditioning factors including elevation, slope, aspect, curvature, lithology, soil type, soil erosion, rainfall, river, land use, highway, and railway were selected. The multicollinearity diagnosis was performed on the factors using the Spearman correlation coefficient. For model validation, 1711 landslides and 1711 non-landslides were collected as the dataset and divided into a training dataset (50 %) and a testing dataset (50 %). The performance of the model was evaluated by the confusion matrix and receiver operating characteristic (ROC) curve. The results of the confusion matrix accuracy and the area under the ROC curve showed that the BO-SVM model (89.53 %, 0.97) performed better than the SVM model (84.91 %, 0.93). In addition, the landslide hazard maps generated by the BO-SVM model had better overall results than that by the SVM model.

IoT sensing framework with inter-cloud computing capability in vehicular networking

Abstract: In order to improve convenience, efficiency, and safety in vehicular networking applications (VNA), we propose a novel business model based on platform production services (PPS), design an inter-cloud architecture, and then apply this emerging scheme to vehicle maintenance services (VMS). Both internet of things (IoT) sensing framework and inter-cloud computing architecture are the crucial factors in implementing PPS business model. In the proposed scheme, implementation concept, system architecture, and scalable applications are introduced. Then we design IoT sensing framework for VNA, including cloud services and computation level scalability, inter-cloud architecture supporting telematics applications, and telematics application scenarios. After dissecting mobile cloud computing forming mechanism, we carry out the semantic modeling analysis for inter-cloud service model, and then design a VMS event processing flow to allow the management and cooperation among diverse components by means of event manager. The performance evaluation exemplified by VMS is implemented by means of probabilistic methods. The results show that convenience and efficiency increase in VNA as compared to existing schemes.

C–H activation derived CPPs for photocatalytic hydrogen production excellently accelerated by a DMF cosolvent

Abstract: A series of four conjugated porous polymer CPPs (CPs1–4) with various geometries have all been readily synthesized via direct C–H arylation between bithiophene (BT) and 1,3,6,8-tetrabromopyrene (TBPy), 2,2′,7,7′-tetrabromo-9,9′-spirofluorene (TBSF), 1,3,5-tribromobenzene (TBB) and 1,4-dibromobenzene (DB) respectively. The photocatalytic hydrogen evolution from water splitting by these CPPs under visible light irradiation is studied in detail. An amazing hydrogen evolution rate (HER) of 95.85 μmol h−1 was successfully achieved by the four-directional Py-BT-based CPP (CP1, 6 mg), using ascorbic acid (AA) and N,N-dimethylformamide (DMF)/water as a sacrificial agent and co-solvents respectively. Such a HER value is 8-fold higher than that of applying commonly used MeOH/water as a co-solvent in the same photocatalytic system. Significantly, when a 0.5 wt% platinum (Pt) co-catalyst was added into the above photocatalytic system, an unprecedented HER of 184.86 μmol h−1 (6 mg) was further realized. The combination of atom-economically synthesized CPPs and accelerating the HER by means of simply tuning the composition of co-solvents provided herein represents a promising step toward accessing green energy by green chemistry.