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Air pollution is a major environmental urban issue, particularly in fast-growing cities in developing countries. Reducing air pollution is thus a challenge while evaluating the economic value of air quality is crucial for environmental policies made. However, few studies accurately estimate this value as they neglect the possible endogeneity issues, as well as the dynamic and heterogeneous effects of air pollution. Under the hedonic framework, we therefore assess the economic effect of fine particulate matter (PM2.5) on housing prices in Beijing, China. We construct a panel based on resale apartment transactions matched with average quarterly PM2.5 data between 2013 and 2019. To reduce the risk of an estimation bias, we apply an instrumental variable (IV) approach. Our results show that PM2.5 is negatively associated with housing prices. Households were willing to pay an extra 0.0852% per housing unit price for an average quarterly reduction in PM2.5 of 1 µg/m3. Furthermore, we argue that high-income dwellers tend to pay more for clean air. The negative effects of PM2.5 across regions are significant and different. Compared with that in the basic year 2013, the negative effect increases in the first 3 years and then decreases in the last 3 years. Our findings enhance our comprehension of the economic impact of air quality and make a valuable contribution to the nuanced understanding of willingness to pay for air quality, which is beneficial in assessing and optimizing environmental regulations. [ABSTRACT FROM AUTHOR]

Regional integration is increasingly used as a policy strategy to accelerate urban development and regional cooperation. This research assesses the effects of regional integration on housing prices to evaluate policy effectiveness for small and medium-sized cities on the peripheries of core cities. Taking as a case study the Chinese city of Kaifeng—a contiguous city in the Zhengzhou megaregion—we utilized hedonic house price modelling and spatial econometrics to investigate the effect of Kaifeng's integration with the core city on the dynamics and determinants of housing prices between 2001 and 2016. The results show that housing prices in Kaifeng increased significantly after the city's integration with Zhengzhou in 2005. Further, the results confirm that the regional integration had a significantly positive effect on housing prices, especially in border areas. Moreover, the new time-saving cross-border light rail system had more influence on the prices of nearby housing than the new expressway, and new urban districts with high-quality amenities led to a sharp rise in housing prices in Kaifeng. Our findings offer policymakers some guidance concerning regional cooperation and urban development. [ABSTRACT FROM AUTHOR]

The assessment of semantic similarity between lexical terms plays a critical part in semantic-oriented applications for natural language processing and cognitive science. The optimization of calculation models is still a challenging issue for improving the performance of similarity measurement. In this paper, we investigate WordNet-based measures including distance-based, information-based, feature-based and hybrid. Among them, the distance-based measures are considered to have the lowest computational complexity due to simple distance calculation. However, most of existing works ignore the meronymy relation between concepts and the non-uniformity of path distances caused by various semantic relations, in which path distances are simply determined by conceptual hyponymy relation. To solve this problem, we propose a novel model to calculate the path distance between concepts, and also propose a similarity measure which nonlinearly transforms the distance to semantic similarity. In the proposed model, we assign different weights in accordance with various relations to edges that link different concepts. On basis of the distance model, we use five structure properties of WordNet for similarity measurement, which consist of multiple meanings, multiple inheritance, link type, depth and local density. Our similarity measure is compared against state-of-the-art WordNet-based measures on M&C dataset, R&G dataset and WS-353 dataset. According to experiment results, the proposed measure in this work outperforms others in terms of both Pearson and Spearman correlation coefficients, which indicates the effectiveness of our distance model. Besides, we construct six additional benchmarks to prove that the proposed measure maintains stable performance. [ABSTRACT FROM AUTHOR]

As a valuable tool for text understanding, semantic similarity measurement enables discriminative semantic-based applications in the fields of natural language processing, information retrieval, computational linguistics and artificial intelligence. Most of the existing studies have used structured taxonomies such as WordNet to explore the lexical semantic relationship, however, the improvement of computation accuracy is still a challenge for them. To address this problem, in this paper, we propose a hybrid WordNet-based approach CSSM-ICSP to measuring concept semantic similarity, which leverage the information content(IC) of concepts to weight the shortest path distance between concepts. To improve the performance of IC computation, we also develop a novel model of the intrinsic IC of concepts, where a variety of semantic properties involved in the structure of WordNet are taken into consideration. In addition, we summarize and classify the technical characteristics of previous WordNet-based approaches, as well as evaluate our approach against these approaches on various benchmarks. The experimental results of the proposed approaches are more correlated with human judgment of similarity in term of the correlation coefficient, which indicates that our IC model and similarity detection approach are comparable or even better for semantic similarity measurement as compared to others. [ABSTRACT FROM AUTHOR]

A comparison of electrochemical performance between LiFeMnCrPO/C and LiMnPO/C cathode materials was conducted in this paper. The cathode samples were synthesized by a nano-milling-assisted solid-state process using caramel as carbon sources. The prepared samples were investigated by XRD, SEM, TEM, energy-dispersive X-ray spectroscopy (EDAX), powder conductivity test (PCT), carbon-sulfur analysis, electrochemical impedance spectroscopy (EIS), and galvanostatic charge-discharge cycling. The results showed that LiFeMnCrPO/C exhibited high specific capacity and high energy density. The initial discharge capacity of LiFeMnCrPO/C was 163.6 mAh g at 0.1C (1C = 160 mA g), compared to 112.3 mAh g for LiMnPO/C. Moreover, the Fe/Cr-substituted sample showed good cycle stability and rate performance. The capacity retention of LiFeMnCrPO/C was 98.84 % over 100 charge-discharge cycles, while it was only 86.64 % for the pristine LiMnPO/C. These results indicated that Fe/Cr substitution enhanced the electronic conductivity for the prepared sample and facilitated the Li diffusion in the structure. Furthermore, LiFeMnCrPO/C composite presented high energy density (606 Wh kg) and high power density (574 W kg), thus suggested great potential application in lithium ion batteries (LIBs). [ABSTRACT FROM AUTHOR]

Semantic-oriented service matching is one of the challenges in automatic Web service discovery. Service users may search for Web services using keywords and receive the matching services in terms of their functional profiles. A number of approaches to computing the semantic similarity between words have been developed to enhance the precision of matchmaking, which can be classified into ontology-based and corpus-based approaches. The ontology-based approaches commonly use the differentiated concept information provided by a large ontology for measuring lexical similarity with word sense disambiguation. Nevertheless, most of the ontologies are domain-special and limited to lexical coverage, which have a limited applicability. On the other hand, corpus-based approaches rely on the distributional statistics of context to represent per word as a vector and measure the distance of word vectors. However, the polysemous problem may lead to a low computational accuracy. In this paper, in order to augment the semantic information content in word vectors, we propose a multiple semantic fusion (MSF) model to generate sense-specific vector per word. In this model, various semantic properties of the general-purpose ontology WordNet are integrated to fine-tune the distributed word representations learned from corpus, in terms of vector combination strategies. The retrofitted word vectors are modeled as semantic vectors for estimating semantic similarity. The MSF model-based similarity measure is validated against other similarity measures on multiple benchmark datasets. Experimental results of word similarity evaluation indicate that our computational method can obtain higher correlation coefficient with human judgment in most cases. Moreover, the proposed similarity measure is demonstrated to improve the performance of Web service matchmaking based on a single semantic resource. Accordingly, our findings provide a new method and perspective to understand and represent lexical semantics. [ABSTRACT FROM AUTHOR]

A new aqueous sol was synthesized to prepare Ag-doped TiO/Ti composite tight ultrafiltration (UF) membranes. This sol was prepared based on water (>95 wt%), for which the hydrodynamic size of 8 nm could be obtained by tuning the electrostatic and steric repulsive forces, breaking the limit of the polymeric sol-gel method to prepare tight UF membranes. For the preparation of the sol, high water content can significantly reduce the use of organic solvents and is more environment-benign. Moreover, Ag doping can considerably improve the toughness of the supported membranes to efficiently prevent the TiO/Ti composite membranes from cracking and overcome the thermal expansion mismatch of ceramic membranes and their metallic supports. The prepared Ag-doped TiO/Ti composite membranes presented a molecular weight cut-off of 3 kDa with a pure water flux of 24 L m h bar and exhibited good dye removal capabilities. The proposed Ag doping method has considerable potential for the fabrication of integrated hybrid ceramic-metallic composite membranes. [ABSTRACT FROM AUTHOR]

There is significant interest in recent years in developing magnetic nanoparticles (MNPs) having multifunctional characteristics with complimentary roles. In this study, methotrexate (MTX) was conjugated on the iron oxide magnetic nanoparticles surface via a poly(ethyleneimine) self-assembled monolayer (MTX-MNPs). The novel platform combined cancer chemotherapy, hyperthermia and potential monitoring of the progression of disease through magnetic resonance imaging (MRI). The conjugation of MTX on the magnetite surface was confirmed by Fourier transform infrared spectroscopy and change of zeta potential. Transmission electron microscope (TEM) showed that MTX-MNPs were morphologically spherical. The average diameter of MTX-MNPs was 30.1 ± 5.2 nm determined by dynamic light scattering. Magnetic measurements revealed that the saturation magnetization of MTX-MNPs reached 68.8 emu/g and the nanoparticles were superparamagnetic. The MTX-MNPs had good heating properties in an alternating magnetic field. TEM results showed that a larger number of MTX-MNPs were internalized into the MCF-7 cellular cytoplasm compared with the MNPs. The MTX-MNPs demonstrated highly synergistic antiproliferative effects of simultaneous chemotherapy and hyperthermia in MCF-7 breast cancer cells. A significant negative contrast enhancement was observed with magnetic resonance phantom imaging for MCF-7 cells over L929cells, when both were cultured with the nanoconjugate. The MTX-MNPs with combined characteristics of thermochemotherapy and MRI could be of high clinical significance in the treatment of tumor. [ABSTRACT FROM AUTHOR]

Amphipathic polymer pullulan acetate (PA)-coated magnetic nanoparticles were prepared and characterized by various physicochemical means. The cytotoxicity and cellular uptake of the magnetic nanoparticles were examined. The hyperthermic effect of the magnetic nanoparticles on tumor cells was evaluated. Transmission electron microscopy (TEM) showed that the PA coated magnetic nanoparticles (PAMNs) had spherical morphology. Dynamic light scattering (DLS) showed that the size distribution of PAMNs was unimodal,with an average diameter of 25.8 nm ± 6.1 nm. The presence of the adsorbed layer of PA on the magnetite surface was confirmed by Fourier transform infrared (FTIR) spectroscopy. Magnetic measurements revealed that the saturation magnetization of the PAMNs reached 51.9 emu/g and the nanoparticles were superparamagnetic. Thermogravimetric analysis (TGA) showed that the Fe3O4 particles constituted 75 wt% of the PAMNs. The PAMNs had good heating properties in an alternating magnetic field. Cytotoxicity assay showed that PAMNs exhibited no significant cytotoxicity against L929 cells. TEM results showed that a large number of PAMNs were internalized into KB cells. PAMNs have good hyperthermia effect on KB cells in vitro by magnetic field induced hyperthermia. These novel magnetic nanoparticles have great potential as magnetic hyperthermia mediators. [ABSTRACT FROM AUTHOR]

Moutan Cortex essential oil (MCEO) is considered to be a promising botanical insecticide. However, like most oils, MECO has several limitations, including instability and poor solubility. Nanoencapsulation technology is an excellent strategy for stabilizing essential oils because of its controlled release, enhanced efficacy, and strengthened biological activity. The present study investigated the acaricidal efficacy of pure MCEO and its encapsulated nanoemulsion (NE) and mesoporous silica nanoparticles (MSNs) against the house dust mite Dermatophagoides farinae using contact bioassays, fumigant bioassays, repellent bioassays, and the observation of toxic symptoms. MCEO-MSNs obtained in the study successfully encapsulated MCEO with an encapsulation efficiency of 63.83%. The acaricidal mortality experiments revealed that MCEO-NE and MCEO-MSN showed more significant toxicity against D. farinae than did pure MCEO. The nanomaterials showed better larvicidal and nymphicidal activities than pure MCEO at a high concentration (12-h LC90). Notably, the repellent effect experiment showed that MCEO-NE and MCEO-MSN had long-term and stable repellent effects on D. farinae, indicating the sustained release and persistence of the nanomaterials. More toxicity symptoms were observed in the IM-type group than in the KD-type group, suggesting that the MCEO nanoparticles have adverse effects on the respiratory system. Nanomaterials and MCEO promoted superoxide dismutase (SOD) activity and inhibited acetylcholinesterase (AChE) activity in D. farinae. In addition, the binding sites of paeonol to SOD and AChE were found through molecular docking. These findings demonstrate the potential of MCEO as a biological acaricide, which merits further investigation. [ABSTRACT FROM AUTHOR]

As an information carrier with rich semantics, images contain more sentiment than texts and audios. So, images are increasingly used by people to express their opinions and sentiments in social network. The sentiments of the images are overall and should come from different regions. So, the recognition of the sentiment regions will help to concentrate on important factors the affect the sentiments. Meanwhile, deep learning method for image sentiment classification needs simple and efficient approach for simultaneously carrying out pruning and feature selection whilst optimizing the weights. Motivated by these observations, we design a region-based convolutional neural network using group sparse regularization for image sentiment classification: R-CNNGSR. The method obtains the initial sentiment prediction model through CNN using group sparse regularization to get compact neural network, and then automatically detect the sentiment regions by combining the underlying features and sentimental features. Finally, the whole image and the sentiment region are fused to predict the overall sentiment of the images. Experiment results demonstrate that our proposed R-CNNGSR significantly outperforms the state-of-the-art methods in image sentiment classification. [ABSTRACT FROM AUTHOR]