International capital movement towards the Spanish real estate sector

Purpose – The purpose of this paper is to examine the determinants that affect international capital flows (ICF) toward the Spanish real estate market over the period 1995 first quarter to 2017 fourth quarter. Design/methodology/approach – VECM methodology is used to analyze time series and panel methods using pooled EGLS regression. Findings – VECM parameter results for construction and real estate activities sectors, quickly suggesting a stable performance of capital flows toward Spanish real estate sector that the short-term fluctuation of foreign investment results contributes to the long-term equilibrium relatively soon. By applying the Monetary theory of Johnson, the model identifies a relevant role of M3 explaining capital flows to real estate, together with the lagged variables of construction and real estate activities capital flows, Spanish real interest rate and Spain’s economic growth rate; they are the significant determinants on capital movement to Spanish real estate sector. Interestingly, Spanish housing prices as an exogenous variable, directly, significantly and negatively affect real estate capital flows in all cases as a way to capture the assets price bubble. Practical implications – Findings highlight reasons affecting capital flows to real estate and construction activities to Spanish sectors which allow capital Funds to take into account those drivers in their investment decisions. Originality/value – This paper is the first attempt to analyze the determinants of ICF to Spanish real estate market; it has a significant meaning for both Spanish economy and international investors

The Nitrogen Budget of Coastal Eastern Guangdong in the Last 15 Years

Nitrogen pollution has caused severe ecological and environmental crisis, especially in densely populated coastal regions. Using a mathematical model based on statistical data series from industry, agriculture, environmental protection, and population in 2000, 2005, 2010, and 2015, this paper aims to estimate the nitrogen income and expenditure of coastal Eastern Guangdong, to reveal the temporal variation of the nitrogen budget in the coastal region with high agriculture intensity, and to suggest a management strategy for the local nitrogen control. The results show that: coastal Eastern Guangdong is a nitrogen surplus region, with nitrogen load and nitrogen flux varying in the range 276.01–299.60 kg N ha−1 yr−1 and 221.26–239.06 kg N ha−1 yr−1, respectively, during the period 2000–2015; from 2000 to 2015, the overall nitrogen surplus and the nitrogen surplus unit area showed an obvious upward trend, indicating that nitrogen pollution in the area was deteriorating; agricultural used fertilizer serves as the main contributor to nitrogen input, while water nitrogen accounts for the highest portion of nitrogen output; despite the fluctuation of nitrogen input and output, water nitrogen output steadily increased, suggesting a stronger water environment management requirement. This research provides reference for researchers and decision-makers in the ecological and environmental domain

A bi-directional strategy to detect land use function change using time-series Landsat imagery on Google Earth Engine:A case study of Huangshui River Basin in China

Constructed land, cropland, and ecological land are undergoing intense competition in many rapidly-developing regions. One of the major reasons to cause frequent land use (LU) conversions is the policy dynamics. The detection of such conversions is thus a prerequisite to understanding urban dynamics and how policies shape landscapes. This paper presents a bi-directional strategy to detect the LU change of the Huangshui River Basin of China from 1987 to 2018 using time-series Landsat imagery. We first initialized classification and optimization of remote sensing images using the Random Forest algorithm; We then detected bi-directional spatio-temporal changes based on the distribution probability of land-cover types. Our results reveal complicated dynamics underlying the net increase in urban and built-up land (UB) and the net decrease in cropland. In this area, due to the implementation of ecological compensation projects such as ecological migration and mine restoration, we found that on average 5.52 km2 of UB was converted into ecological land (forest, grassland and shrubland) every year, even though UB has expanded 3.6 times in the last 30 years with multiple conversions for cropland and ecological land. Meanwhile, 60% of lost cropland was converted to shrubland and grassland, and 40% was converted to UB. The accuracy of LU classification increases by 6.03% from 88.17%, and kappa coefficient increases by 2.41% from 85.16, compared to the existing initial results and uni-directional detection method. This study highlights the importance of the use of an effective remote sensing-based strategy for monitoring high-frequency LU changes in watershed areas with complicated human-nature interactions.</p

Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments

In the domain of causal inference research, the prevalent potential outcomes framework, notably the Rubin Causal Model (RCM), often overlooks individual interference and assumes independent treatment effects. This assumption, however, is frequently misaligned with the intricate realities of real-world scenarios, where interference is not merely a possibility but a common occurrence. Our research endeavors to address this discrepancy by focusing on the estimation of direct and spillover treatment effects under two assumptions: (1) network-based interference, where treatments on neighbors within connected networks affect one's outcomes, and (2) non-random treatment assignments influenced by confounders. To improve the efficiency of estimating potentially complex effects functions, we introduce an novel active learning approach: Active Learning in Causal Inference with Interference (ACI). This approach uses Gaussian process to flexibly model the direct and spillover treatment effects as a function of a continuous measure of neighbors' treatment assignment. The ACI framework sequentially identifies the experimental settings that demand further data. It further optimizes the treatment assignments under the network interference structure using genetic algorithms to achieve efficient learning outcome. By applying our method to simulation data and a Tencent game dataset, we demonstrate its feasibility in achieving accurate effects estimations with reduced data requirements. This ACI approach marks a significant advancement in the realm of data efficiency for causal inference, offering a robust and efficient alternative to traditional methodologies, particularly in scenarios characterized by complex interference patterns.Comment: conference pape

Effects of adhesive thickness on global and local Mode-I interfacial fracture of bonded joints

AbstractThe interfacial fracture of adhesively bonded structures is a critical issue for the extensive applications to a variety of modern industries. In the recent two decades, cohesive zone models (CZMs) have been receiving intensive attentions for fracture problems of adhesively bonded joints. Numerous global tests have been conducted to measure the interfacial toughness of adhesive joints. Limited local tests have also been conducted to determine the interface traction-separation laws in adhesive joints. However, very few studies focused on the local test of effects of adhesive thickness on the interfacial traction-separation laws. Interfacial toughness and interfacial strength, as two critical parameters in an interfacial traction-separation law, have important effect on the fracture behaviors of bonded joints. In this work, the global and local tests are employed to investigate the effect of adhesive thickness on interfacial energy release rate, interfacial strength, and shapes of the interfacial traction-separation laws. Basically, the measured laws in this work reflect the equivalent and lumped interfacial fracture behaviors which include the cohesive fracture, damage and plasticity. The experimentally determined interfacial traction-separation laws may provide valuable baseline data for the parameter calibrations in numerical models. The current experimental results may also facilitate the understanding of adhesive thickness-dependent interface fracture of bonded joints

Improved Algebraic Algorithm On Point Projection For Bézier Curves

International audienceThis paper presents an improved algebraic pruning method for point projection for Bézier curves. It first turns the point projection into a root finding problem, and provides a simple but easily overlooked method to avoid finding invalid roots which is obviously irrelative to the closest point. The continued fraction method and its expansion are utilized to strengthen its robustness. Since NURBS curves can be easily turned into Bézier form, the new method also works with NURBS curves. Examples are presented to illustrate the efficiency and robustness of the new method

Ordered Semiconducting Nitrogen-Graphene Alloys

The interaction between substitutional nitrogen atoms in graphene is studied by performing first principles calculations. The nearest neighbor interaction between nitrogen dopants is highly repulsive because of the strong electrostatic repulsion between nitrogen atoms, which prevents the full phase separation in nitrogen doped graphene. Interestingly, there are two relatively stable nitrogen-nitrogen pairs due to the anisotropy charge redistribution induced by nitrogen doping. We reveal two stable semiconducting ordered N doped graphene structures C3N and C12N through the cluster expansion technique and particle swarm optimization method. In particular, C12N has a direct band gap of 0.98 eV. The heterojunctions between C12N and graphene nanoribbons might be promising organic solar cells

Effects of Dimethylaminoethanol and Compound Amino Acid on D-Galactose Induced Skin Aging Model of Rat

A lasting dream of human beings is to reverse or postpone aging. In this study, dimethylaminoethanol (DMAE) and compound amino acid (AA) in Mesotherapy were investigated for their potential antiaging effects on D-galactose induced aging skin. At 18 days after D-gal induction, each rat was treated with intradermal microinjection of saline, AA, 0.1% DMAE, 0.2% DMAE, 0.1% DMAE + AA, or 0.2% DMAE + AA, respectively. At 42 days after treatment, the skin wound was harvested and assayed. Measurement of epidermal and dermal thickness in 0.1% DMAE + AA and 0.2% DMAE + AA groups appeared significantly thicker than aging control rats. No differences were found in tissue water content among groups. Hydroxyproline in 0.1% DMAE + AA, 0.2% DMAE + AA, and sham control groups was much higher than all other groups. Collagen type I, type III, and MMP-1 expression was highly upregulated in both 0.1% DMAE + AA and 0.2% DMAE + AA groups compared with aging control. In contrast, TIMP-1 expression levels of various aging groups were significantly reduced when compared to sham control. Coinjection of DMAE and AA into target tissue has marked antiaging effects on D-galactose induced skin aging model of rat

Synchronisation among short-term rental markets, co-movements and cycles in 39 European cities

This paper presents new evidence of the short-term rental market's prices and transactions from a daily time-series perspective in 39 European cities from 2015 to 2020. It uses Airbnb micro datasets to build time-series cycles by extracting the original observations containing total bookings (rent transactions), rental units supply, and asking rent, with a daily periodicity. The cycles show the periods in which short-rental activity was more relevant for each city, and the level of rents across Europe. The paper provides empirical evidence of a long-term relationship among the city variables (tested via mean and variance). Causality supporting co-movements across cities was found by estimating a short-term naïve market equilibrium model using the vector error correction model approach, supporting the hypothesis that the short-term rental market performs according to housing-market principles. Short-run elasticities among rents and contracts across the 39 cities show causal evidence of co-movements among rents and the supply and demand of properties. The market adjustment on the supply side estimates new units responding to changes in prices within 15 lags (days) and longer (350 lags) from the demand side, equivalent to eight to nine months. Evidence of the pandemic's limited effect on housing supply and prices' positive effect is also provided. A robust negative weekend impact on prices was found, suggesting stronger market relevance on weekdays

Miniaturized Computational Photonic Molecule Spectrometer

Miniaturized spectrometry system is playing an essential role for materials analysis in the development of in-situ or portable sensing platforms across research and industry. However, there unavoidably exists trade-offs between the resolution and operation bandwidth as the device scale down. Here, we report an extreme miniaturized computational photonic molecule (PM) spectrometer utilizing the diverse spectral characteristics and mode-hybridization effect of split eigenfrequencies and super-modes, which effectively eliminates the inherent periodicity and expands operation bandwidth with ultra-high spectral resolution. These results of dynamic control of the frequency, amplitude, and phase of photons in the photonic multi-atomic systems, pave the way to the development of benchtop sensing platforms for applications previously unfeasible due to resolution-bandwidth-footprint limitations, such as in gas sensing or nanoscale biomedical sensing