What are the most important factors that influence the changes in London Real Estate Prices? How to quantify them?

Abstract. In recent years, real estate industry has captured government and public attention around the world. The factors influencing the prices of real estate are diversified and complex. However, due to the limitations and one-sidedness of their respective views, they did not provide enough theoretical basis for the fluctuation of house price and its influential factors. The purpose of this paper is to build a housing price model to make the scientific and objective analysis of London's real estate market trends from the year 1996 to 2016 and proposes some countermeasures to reasonably control house prices.  Specifically, the paper analyzes eight factors which affect the house prices from two aspects: housing supply and demand and find out the factor which is of vital importance to the increase of housing price per square meter. The problem of a high level of multicollinearity between them is solved by using principal components analysis.Keywords. Real estate market, Real estate price.JEL. L85, R30, R33

Graph ODE with Factorized Prototypes for Modeling Complicated Interacting Dynamics

This paper studies the problem of modeling interacting dynamical systems, which is critical for understanding physical dynamics and biological processes. Recent research predominantly uses geometric graphs to represent these interactions, which are then captured by powerful graph neural networks (GNNs). However, predicting interacting dynamics in challenging scenarios such as out-of-distribution shift and complicated underlying rules remains unsolved. In this paper, we propose a new approach named Graph ODE with factorized prototypes (GOAT) to address the problem. The core of GOAT is to incorporate factorized prototypes from contextual knowledge into a continuous graph ODE framework. Specifically, GOAT employs representation disentanglement and system parameters to extract both object-level and system-level contexts from historical trajectories, which allows us to explicitly model their independent influence and thus enhances the generalization capability under system changes. Then, we integrate these disentangled latent representations into a graph ODE model, which determines a combination of various interacting prototypes for enhanced model expressivity. The entire model is optimized using an end-to-end variational inference framework to maximize the likelihood. Extensive experiments in both in-distribution and out-of-distribution settings validate the superiority of GOAT

A Comprehensive Survey on Deep Graph Representation Learning

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future

Does the built environment of settlements affect our sentiments? A multi-level and non-linear analysis of Xiamen, China, using social media data

IntroductionHumans spend most of their time in settlements, and the built environment of settlements may affect the residents' sentiments. Research in this field is interdisciplinary, integrating urban planning and public health. However, it has been limited by the difficulty of quantifying subjective sentiments and the small sample size.MethodsThis study uses 147,613 Weibo text check-ins in Xiamen from 2017 to quantify residents' sentiments in 1,096 neighborhoods in the city. A multilevel regression model and gradient boosting decision tree (GBDT) model are used to investigate the multilevel and nonlinear effects of the built environment of neighborhoods and subdistricts on residents' sentiments.ResultsThe results show the following: (1) The multilevel regression model indicates that at the neighborhood level, a high land value, low plot ratio, low population density, and neighborhoods close to water are more likely to improve the residents' sentiments. At the subdistrict level, more green space and commercial land, less industry, higher building density and road density, and a smaller migrant population are more likely to promote positive sentiments. Approximately 19% of the total variance in the sentiments occurred among subdistricts. (2) The proportion of green space and commercial land, and the density of buildings and roads are linearly correlated with residents' sentiments. The land value is a basic need and exhibits a nonlinear correlation with sentiments. The plot ratio, population density, and the proportions of industrial land and the migrant population are advanced needs and are nonlinearly correlated with sentiments.DiscussionThe quantitative analysis of sentiments enables setting a threshold of the influence of the built environment on residents' sentiments in neighborhoods and surrounding areas. Our results provide data support for urban planning and implementing targeted measures to improve the living environment of residents

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Improved Deadbeat Predictive Control Based Current Harmonic Suppression Strategy for IPMSM

When the interior permanent magnet synchronous motor (IPMSM) is running, there are abundant harmonics in the stator current. In order to achieve the suppression of current harmonics, the current harmonic extraction method and current harmonic controller are studied in this paper. Firstly, a simple and accurate method for extracting current harmonics is proposed by means of multiple synchronous rotating frame transformation (MSRFT). Secondly, an improved deadbeat predictive control (IDPC) based current harmonic controller is designed after analyzing the advantages and disadvantages of traditional current harmonic controllers. Thirdly, IDPC-based current harmonic suppression strategy is proposed by combining the proposed current harmonic extraction method and the proposed current harmonic controller. The proposed strategy can still effectively achieve current harmonic suppression when the motor runs at low speed, medium speed and high speed and the controller parameters are mismatched with the motor parameters. Finally, the feasibility and effectiveness of the proposed strategy are verified by simulation and experiments

Improved Deadbeat Predictive Control Based Current Harmonic Suppression Strategy for IPMSM

When the interior permanent magnet synchronous motor (IPMSM) is running, there are abundant harmonics in the stator current. In order to achieve the suppression of current harmonics, the current harmonic extraction method and current harmonic controller are studied in this paper. Firstly, a simple and accurate method for extracting current harmonics is proposed by means of multiple synchronous rotating frame transformation (MSRFT). Secondly, an improved deadbeat predictive control (IDPC) based current harmonic controller is designed after analyzing the advantages and disadvantages of traditional current harmonic controllers. Thirdly, IDPC-based current harmonic suppression strategy is proposed by combining the proposed current harmonic extraction method and the proposed current harmonic controller. The proposed strategy can still effectively achieve current harmonic suppression when the motor runs at low speed, medium speed and high speed and the controller parameters are mismatched with the motor parameters. Finally, the feasibility and effectiveness of the proposed strategy are verified by simulation and experiments

Resolving the Thermoinduced Electrochemistry for an In-Depth Understanding of the STEP Degradation of SDBS

The solar thermal electrochemical process (STEP) has sustainably accounted for the solar thermo- and electrochemical oxidation of sodium dodecyl benzene sulfonate (SDBS) fully driven by solar energy, gaining a high efficiency with a fast rate by the combination of thermochemistry and electrochemistry. In this article, thermoinduced electrochemistry was resolved for an in-depth understanding of the STEP degradation of SDBS. We employed thermodependent cyclic voltammetry, temperature-dependent fluorescence-electrochemical spectroscopy, and time-dependent electrochemical current spectroscopy for studying the electrochemistry, including the reaction, pathway, and mechanism. First, thermodependent cyclic voltammetric spectra indicated that the SDBS in sodium chloride solution is oxidized via an indirect process initialized by active chlorine, substantially accelerating and completing the oxidation process. Second, temperature-dependent fluorescence-electrochemical spectra displayed the pathway and kinetics by finding the initial desulfonation and the subsequent breaking of the alkyl side chain and benzene ring. Finally, time-dependent electrochemical current spectra demonstrated that the initial desulfonation is the fast step by generating the high current and the subsequent breaking is the slow one by a low current response, which is in agreement with the temperature-dependent fluorescence-electrochemical spectra. A panoramic view is proposed and schemed for fully understanding the process and mechanism of the STEP degradation of SDBS. Moreover, the efficiency and effectiveness of SDBS degradation were proven to be significantly enhanced by using the STEP in outdoor and indoor tests. It is a novel and energy-free route for wastewater treatment, accomplished by the synergistic use of solar energy without any other input of energy

Influence of fishery management on trophic interactions and biomass fluxes in Lake Taihu based on a trophic mass-balance model exercise on a long-term data series

With increasing anthropogenic activities, freshwater ecosystems around the world are becoming increasingly affected by various pressures, including eutrophication, overfishing, and irrational stocking, which may have a negative impact on the food web structure. Despite the extensive research and proposed management measures for eutrophic lakes, there are only few analysis on long-term monitoring data regarding fishery resources. Additionally, there is a lack of evaluation and prediction of the effectiveness of current fish management policies. To remedy this, we analyzed long-term monitoring data from Lake Taihu, China, a severely eutrophicated lake with a skewed fish size structure exhibiting dominance of small individuals. We first constructed 14 Ecopath models to investigate how trophic interactions and biomass fluxes changed from 2007 to 2020. Subsequently, the Ecosim model was used to predict how the biomass of fish and the ecosystem network respond to the initiated 10-years fishing ban. Our results demonstrate long-term changes in fish biomass and ecosystem stability. The analyses revealed that 1) the biomass development in different feeding types of fish is controlled by human activities (mainly catches and stocking) and trophic interactions and 2) the rate of decline in ecosystem network stability slows down during the fishing ban. The primary focus of this study was to fill the gap in long-term serial studies of fish monitoring data and ecosystem stability in the lake and, for the first time, to predict the outcome of the fishing ban from an ecosystem perspective using the Ecosim model. Overall, our results emphasize the importance of rational stocking and fishing policies and provide a better understanding of the changes in the ecological dynamics in Lake Taihu of relevance for the management and restoration of the lake