Analyse Property Data Through Visualisation

People’s activities create 2.5 Quintilian bytes of data every day (Marr, 2018). The examples of activities include shopping, sleeping, property purchasing, selling or leasing, etc. A large amount of data is usually with high-dimensional geometry and multivariate characters. Traditional text-based data may be able to record the facts of activities, but the hidden story behind the data may not be discovered. Data visualisation is an instrument for reasoning about quantitative information and allows us to analyse data behaviours by understanding data patterns, trends and correlations that could not be detected by the traditional text-based data. This paper focuses on analysing property data for six suburbs in Sydney using visualisation. Data with 31 elements from the three-year censuses were used to create visual patterns for analysis. Parallel coordinates and dashboard techniques are applied for data visualization for the selected six suburbs. The results suggest that the well-designed data graphics is a powerful tool, and property data visualisation provides us with visual access to huge amounts of data in easily digestible visuals

High compression ratio image processing techniques using combinations of WT and IFS

Many works of compressing image based on wavelet transformation have been presented in recent years. Also, the method of fractals is used in this field. The first method uses the pyramid subband decomposition, and the second takes advantage of the self-similarity between the basic image and subimages. We combine these two methods as a hybrid algorithm to complete the image compression. The experiments show that this method has a better performance than many others, especially, in a high compression ratio situation. A comparison with the famous embedded zero tree wavelet (EZW) algorithm shows that its performance is close to the EZW algorithm.published_or_final_versio

Associative classifier for uncertain data

Associative classifiers are relatively easy for people to understand and often outperform decision tree learners on many classification problems. Existing associative classifiers only work with certain data. However, data uncertainty is prevalent in many real-world applications such as sensor network, market analysis and medical diagnosis. And uncertainty may render many conventional classifiers inapplicable to uncertain classification tasks. In this paper, based on U-Apriori algorothm and CBA algorithm, we propose an associative classifier for uncertain data, uCBA (uncertain Classification Based on Associative), which can classify both certain and uncertain data. The algorithm redefines the support, confidence, rule pruning and classification strategy of CBA. Experimental results on 21 datasets from UCI Repository demonstrate that the proposed algorithm yields good performance and has satisfactory performance even on highly uncertain data

Correlation effects in the electronic structure of the Ni-based superconducting KNi2S2

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Highly reproducible SERS substrate based on polarization-free Ag nanoparticles decorated SiO2/Si core-shell nanowires array

SiO2/Si core-shell nanowires array coated with gap-rich silver nanoparticles were demonstrated as a highly reproducible surface-enhanced Raman scattering (SERS) substrate. SERS detection of a relative standard deviation of 8% for 10−4 M R6G with a spot size of ∼2 μm and 900 spots over an area of 150 × 150 μm2 was reported. The high reproducibility is ascribed to the polarization-independent electrical field distribution among three-dimensional nanowire structure with an optimized thickness of SiO2 shell layer.published_or_final_versio

Development of an ontology-based visual approach for property data analytics

Real estate is a complex market that consists of many layers of social, financial and economic data, including but not limited to price, rental, location, mortgage, demographic and housing supply data. The sheer number of real estate properties around the world means that property transactions produce an extraordinary amount of data that is increasing exponentially. Most of the data are presented through thousands of rows on a spreadsheet or described in long paragraphs that are difficult to understand. The emergent data visualisation techniques are intended to allow data to be processed and analytics to be displayed visually to enable an understanding of complex information and the identification of new patterns from the data. However, not all visualisation techniques can achieve such a thing. Most techniques are able to display only visual low-dimensional data. This paper introduces an ontology visualisation methodology to explore the ontologies of property data behaviour for multidimensional data. The visualisation combines real estate data statistical analysis with several high-dimensional data visualisation techniques, including parallel coordinates and stacked area charts. By using six residential suburbs in Sydney as a demonstration, we find that the developed data visualisation methodology can be applied effectively and efficiently to analyse complex real estate market behaviour patterns

High volumetric energy density capacitors based on new electrode material lanthanum nitride

This is the author accepted manuscript. The final version is available from the American Chemical Society via the DOI in this recordLaN is synthesized via calcining La2O3 in NH3 and studied as capacitive material for energy storage. A volumetric capacitance of 951.3 F cm-3 was found in 1 mol dm-3 Na2SO4 using a current density of 1 Ag-1, with less than 1% loss of capacitance being experienced after 5000 cycles. In addition, 87.3% of the initial capacitance remained at a current density of 10 A g-1. LaN exhibits high capacitance that is attributed to subsurface space charge accumulation with a possible electric double-layer capacitor component. A reversible electrode process ensures long cycle life and favorable electrical charge transfer. The assembled LaN symmetrical capacitor showed high volumetric energy densities, facilitating high-duty applications.National Natural Science Foundation of ChinaFoundation for Innovation Groups of Basic Research in Gansu Provinc

The sustained influence of prior experience induced by social observation on placebo and nocebo responses

Background: Social observation is one of the main ways to gain experience. Similar to first-person experience, observational experience affects the effectiveness of subsequent treatments. Yet, it is still undetermined whether the influence of social observation on placebo and nocebo effects to subsequent treatments remains even if related experience occurred a few days ago.</p

Temperature-vacuum swing adsorption for direct air capture by using low-grade heat

Direct air capture (DAC) is a promising carbon mitigation technology and will likely be part of extensive carbon removal portfolio. Adsorptive DAC is an appropriate option for carbon capture to utilize low-grade heat because of its desirable regeneration temperature and adaptability to be integrated with renewables. Building indoor environment with CO2 concentrations above 1000 ppm provides another suitable scenario for DAC. Herein, DAC using temperature-vacuum swing adsorption (TVSA) is presented and analyzed by integrating various low-grade heat sources in buildings. An amine-functionalized metal organic framework is selected for process simulation, and the performance is compared with those using other sorbents. It indicates that amine-functionalized material has advantages in CO2 productivity and purity. A techno-economic analysis is carried out to explore the benefit of the proposed DAC in buildings. The results show that regeneration by heat pumps at 373 K is the most competitive solution and has 176.7 $·tCO2−1 of the levelized cost of DAC (LCOD). Compared with conventional energy supply, solutions with low-grade heat utilization in buildings could achieve lower carbon intensity and increase by 5.2–25.0% in net LCOD. These results will provide practical guidelines for DAC application with lower energy penalties and costs