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Solid polymer electrolytes are promising candidates for implementation in next-generation all-solid-state batteries (ASSBs) which could replace conventional batteries used today. However, the materialization of ASSBs on a mass scale is restricted by the low ionic conductivity and high interfacial resistance of solid electrolytes. In this work, succinonitrile (SN) with lithium (trifluoromethylsulphonyl)imide (LiTFSI) and Al-doped Li7La3Zr2O12 (Al-LLZO) nanoparticles were used to improve the ionic conductivity of a polyethylene oxide-based composite electrolyte. The Al-LLZO nanoparticles were synthesized by a facile synthesis process at low temperatures, which contributed to an enhancement in the ionic conductivity. A solid polymer electrolyte with 7.5 wt% of Al-LLZO and 15 wt% of SN achieved a high ionic conductivity of 4.17 × 10-4 Scm-1 at room temperature and a large value of 0.451 for the lithium-ion transport number at 60 °C. By adding 10 wt% SN and 10 wt% of Al-LLZO in the LiFePO4 cathode, the cell could operate at 25 oC with a specific capacity of 130 mAh g-1 and 89% capacity retention after 200 cycles at current density of 20 mA g-1. This study therefore proposes a solution to improve the ionic conductivity of solid polymer electrolytes in all-solid-state batteries.

The emergence of various disruptive technologies such as big data, Internet of Things, and artificial intelligence have instigated our society to generate enormous volumes of data. The effective, efficient, and transparent capture and fusion of knowledge from a massive amount data is becoming an increasingly popular and crucial topic. In this study, we aim to provide a broad, complete, and systematic overview of the definitions and challenges of the knowledge graph fusion, which represents a holistic approach for integrating, enhancing, and unifying knowledge graphs. Further, advanced techniques for handling knowledge graph fusion along with the pragmatic smart systems leveraging it are discussed as a part of multiple perspectives. We believe that this survey study can be used as a potential reference for system practitioners and researchers in surpassing current obstacles as well as shaping their future direction.

Given the large amount of data collected from social media, it is very difficult for users to identify social events and understand their societies. In this paper, we propose a novel method for i ) decomposing and discovering social events and i i ) representing social events and their relationships as a knowledge graph. In particular, the proposed method is based on Independent Component Analysis (ICA) and the SocioScope Knowledge Graph (SKG) model. To demonstrate the actual performance, the proposed method has been evaluated with the support of the SocioScope framework (Nguyen and Jung, 2018). Then, it was verified that the system can efficiently provide people with a high understandability and traceability of social events.

Plane detection and segmentation is one of the most crucial tasks in point cloud processing. The output from this process can be used as input for further processing steps, such as modelling, registration and calibration. However, the sparseness and heterogeneity of Mobile Laser Scanning (MLS) point clouds may lead to problems for existing planar surfaces detection and segmentation methods. This paper proposes a new method that can be applicable to detect and segment planar features in sparse and heterogeneous MLS point clouds. This method utilises the scan profile patterns and the planarity values between different neighbouring scan profiles to detect and segment planar surfaces from MLS point clouds. The proposed method is compared to the three most state-of-the-art segmentation methods (e.g. RANSAC, a robust segmentation method based on robust statistics and diagnostic principal component analysis – RDCPA as well as the plane detection method based on line arrangement). Three datasets are used for the validation of the results. The results show that our proposed method outperforms the existing methods in detecting and segmenting planar surfaces in sparse and heterogeneous MLS point clouds. In some instances, the state-of-the-art methods produce incorrect segmentation results for facade details which have a similar orientation, such as for windows and doors within a facade. While RDCPA produces up to 50% of outliers depending on the neighbourhood threshold, another method could not detect such features at all. When dealing with small features such as a target, some algorithms (including RANSAC) were unable to perform segmentation. However, the propose algorithm was demonstrated to detect all planes in the test data sets correctly. The paper shows that these mis-segmentations in other algorithms may lead to significant errors in the registration process of between 1.047 and 1.614 degrees in the angular parameters, whereas the propose method had only resulted in 0.462 degree angular bias. Furthermore, it is not sensitive to the required method parameters as well as the point density of the point clouds.

Social data is one of important material for representing social activities. However, efficiently collecting social data from multiple sources is extremely hard due its big-data attributes. Moreover, discovering hidden patterns between social data to understand our society is actually a big challenge. Especially, knowledge around us is not stable but dynamically change over time, therefore, we need to refresh it promptly. SocioScope is created as an automatically tool to overcome aforementioned problems. In addition, other researchers can utilize SocioScope as a framework for reducing their effort time with essential tasks (i.e., collecting data, pre-processing data, and analyzing data).

In this paper, we estimates the effective conductivity of heterogeneous materials constituted aggregates of different polygonal shapes mixed with a matrix material. The problem is considered in the context of periodic homogenization theory. The local problem is formulated using the Lippmann–Schwinger equation of polarization with optimal operator norm. The equation can be used to derive a Fast Fourier Transform (FFT) based iteration scheme and theoretical estimates of the overall properties. When applied to microstructure with polygonal inclusion, the results show a very good agreement between those approaches.

Chlorophyll a (Chl a) and Chlorophyll b (Chl b) are major pigments in the photosynthetic machinery in green plants. Despite their similarities in chemical structures, the relaxation dynamics as observed in the ultrafast methods, such as pump probe and multidimensional spectroscopy, demonstrate a distinct kinetics. Here, we employ the ultrafast two-dimensional electronic spectroscopy (2DES) method to characterize the frequency fluctuation correlation function (FFCF) for Chl a and Chl b in various solvent environments, through the Centre Line Slope (CLS) method. We observe that the FFCF decay for both Chl a and Chl b in the methanol solvent environment has a ~40 ps component that is absent in aprotic solvents, tetrahydrofuran and diethyl ether. We observe also that Chl b exhibit higher values of inhomogeneous broadening than Chl a.

Objectives Time to definitive care is important for trauma outcomes, thus many emergency medical services (EMS) systems in the world adopt response times of ambulances as a key performance indicator. The objective of this study is to examine the underlying risk factors that can affect ambulance response times (ART) for trauma incidents, so as to derive interventional measures that can improve the ART. Material and methods This was a retrospective study based on two years of trauma data obtained from the national EMS operations centre of Singapore. Trauma patients served by the national EMS provider over the period from 1 January 2011 till 31 December 2012 were included. ART was categorized into “Short” ( 8 min) response times. A modelling framework which leveraged on both multinomial logistic (MNL) regression models and Bayesian networks was proposed for the identification of main and interaction effects. Results Amongst the process-related risk factors, weather, traffic and place of incident were found to be significant. The traffic conditions on the roads were found to have the largest effect—the odds ratio (OR) of “Long” ART in heavy traffic condition was 12.98 (95% CI: 10.66–15.79) times higher than that under light traffic conditions. In addition, the ORs of “Long ART” under “Heavy Rain” condition were significantly higher (OR 1.58, 95% CI: 1.26–1.97) than calls responded under “Fine” weather. After accounting for confounders, the ORs of “Long” ART for trauma incidents at “Home” or “Commercial” locations were also significantly higher than that for “Road” incidents. Conclusion Traffic, weather and the place of incident were found to be significant in affecting the ART. The evaluation of factors affecting the ART enables the development of effective interventions for reducing the ART.