Urban industrial relocation: The theory of edge cities

In recent years urban economists have focused their attention upon a 'newly recognized' phenomenon: edge cities. Such an urban growth pattern, although having its primary roots in the United States, can be an appropriate framework for examining European trends of urban industrial location. The objective of this study is to examine the relocation of firms from dominant industrial areas, for example, urban CBDs, to new locations at the urban outer boundaries. In this context, we develop in this paper a model based upon the theory of monopolistic competition ("Dixit and Stiglitz, 1977") that examines the economic relationships among firms at different locations. Such intra/inter relationships are examined from the point of view of complementarity. Complementarity in our case combines the two notions of firms' interaction with cumulative and reinforcing effects, and of coordination among firms in the local industrial organizations. Our interest in such a notion springs from the necessity to explain the spatial distribution of firms, particularly why firms in their location often choose to cluster. One of the explanations within the literature is that concentration in clusters is due to the need to share common infrastructures. However, this is just one of many possible explanations for this phenomenon. In our model, we will tackle this aspect of firm locations in clusters from the point of view of the elasticity of substitution. On the basis of the model we will formulate a policy framework regarding industrial suburbanization.

Bioseparation of Four Proteins from Euphorbia characias Latex: Amine Oxidase, Peroxidase, Nucleotide Pyrophosphatase/Phosphodiesterase, and Purple Acid Phosphatase

This paper deals with the purification of four proteins from Euphorbia characias latex, a copper amine oxidase, a nucleotide pyrophosphatase/phosphodiesterase, a peroxidase, and a purple acid phosphatase. These proteins, very different in molecular weight, in primary structure, and in the catalyzed reaction, are purified using identical preliminary steps of purification and by chromatographic methods. In particular, the DEAE-cellulose chromatography is used as a useful purification step for all the four enzymes. The purification methods here reported allow to obtain a high purification of all the four proteins with a good yield. This paper will give some thorough suggestions for researchers busy in separation of macromolecules from different sources

A Baseline for Shapley Values in MLPs: from Missingness to Neutrality

Being able to explain a prediction as well as having a model that performs well are paramount in many machine learning applications. Deep neural networks have gained momentum recently on the basis of their accuracy, however these are often criticised to be black-boxes. Many authors have focused on proposing methods to explain their predictions. Among these explainability methods, feature attribution methods have been favoured for their strong theoretical foundation: the Shapley value. A limitation of Shapley value is the need to define a baseline (aka reference point) representing the missingness of a feature. In this paper, we present a method to choose a baseline based on a neutrality value: a parameter defined by decision makers at which their choices are determined by the returned value of the model being either below or above it. Based on this concept, we theoretically justify these neutral baselines and find a way to identify them for MLPs. Then, we experimentally demonstrate that for a binary classification task, using a synthetic dataset and a dataset coming from the financial domain, the proposed baselines outperform, in terms of local explanability power, standard ways of choosing them

Uniswap and the rise of the decentralized exchange

Despite blockchain based digital assets trading since 2009, there has been a functional gap between (1) on-chain transactions and (2) trust based centralized exchanges. This is now bridged with the success of Uniswap, a decentralized exchange. Uniswap's constant product automated market maker enables the trading of blockchain token without relying on market makers, bids or asks. This overturns centuries of practice in financial markets, and constitutes a building block of a new decentralized financial system. We apply ARDL and VAR methodologies to a dataset of 999 hours of Uniswap trading, and conclude that its simplicity enables liquidity providers and arbitrageurs to ensure the ratio of reserves match the trading pair price. We find that changes in Ether reserves Granger causes changes in USDT reserves

Eigenvector-based Graph Neural Network Embeddings and Trust Rating Prediction in Bitcoin Networks

Given their strong performance on a variety of graph learning tasks, Graph Neural Networks (GNNs) are increasingly used to model financial networks. Traditional GNNs, however, are not able to capture higher-order topological information, and their performance is known to degrade with the presence of negative edges which may arise in many common financial applications. Considering the rich semantic inference of negative edges, excluding them as an obvious solution is not elegant. Alternatively, another basic approach is to apply positive normalization, however, this also may lead to information loss. Our work proposes a simple yet effective solution to overcome these two challenges by employing the eigenvectors with top-$k$ largest eigenvalues of the raw adjacency matrix for pre-embeddings. These pre-embeddings contain high-order topological knowledge together with the information on negative edges, which are then fed into a GNN with a positively normalized adjacency matrix to compensate for its shortcomings. Through comprehensive experiments and analysis, we empirically demonstrate the superiority of our proposed solution in a Bitcoin user reputation score prediction task

A machine learning approach to support decision in insider trading detection

Identifying market abuse activity from data on investors' trading activity is very challenging both for the data volume and for the low signal to noise ratio. Here we propose two complementary unsupervised machine learning methods to support market surveillance aimed at identifying potential insider trading activities. The first one uses clustering to identify, in the vicinity of a price sensitive event such as a takeover bid, discontinuities in the trading activity of an investor with respect to his/her own past trading history and on the present trading activity of his/her peers. The second unsupervised approach aims at identifying (small) groups of investors that act coherently around price sensitive events, pointing to potential insider rings, i.e. a group of synchronised traders displaying strong directional trading in rewarding position in a period before the price sensitive event. As a case study, we apply our methods to investor resolved data of Italian stocks around takeover bids.Comment: 42 pages, 16 Figure

Euphorbia characias: Phytochemistry and Biological Activities

The aim of this review is to summarize all the compounds identified and characterized from Euphorbia characias, along with the biological activities reported for this plant. Euphorbia is one of the greatest genera in the spurge family of Euphorbiaceae and includes different kinds of plants characterized by the presence of milky latex. Among them, the species Euphorbia characias L. is an evergreen perennial shrub widely distributed in Mediterranean countries. E. characias latex and extracts from different parts of the plant have been extensively studied, leading to the identification of several chemical components such as terpenoids, sterol hydrocarbons, saturated and unsaturated fatty acids, cerebrosides and phenolic and carboxylic acids. The biological properties range between antioxidant activities, antimicrobial, antiviral and pesticidal activities, wound-healing properties, anti-aging and hypoglycemic properties and inhibitory activities toward target enzymes related to different diseases, such as cholinesterases and xanthine oxidase. The information available in this review allows us to consider the plant E. characias as a potential source of compounds for biomedical research

The effect of heterogeneity on financial contagion due to overlapping portfolios

We consider a model of financial contagion in a bipartite network of assets and banks recently introduced in the literature, and we study the effect of power law distributions of degree and balance-sheet size on the stability of the system. Relative to the benchmark case of banks with homogeneous degrees and balance-sheet sizes, we find that if banks have a power law degree distribution the system becomes less robust with respect to the initial failure of a random bank, and that targeted shocks to the most specialized banks (i.e., banks with low degrees) or biggest banks increases the probability of observing a cascade of defaults. In contrast, we find that a power law degree distribution for assets increases stability with respect to random shocks, but not with respect to targeted shocks. We also study how allocations of capital buffers between banks affects the system’s stability, and we find that assigning capital to banks in relation to their level of diversification reduces the probability of observing cascades of defaults relative to size-based allocations. Finally, we propose a non-capital-based policy that improves the resilience of the system by introducing disassortative mixing between banks and assets

Antibacterial activity and molecular docking studies of a selected series of Hydroxy-3-arylcoumarins

Antibiotic resistance is one of the main public health concerns of this century. This resistance is also associated with oxidative stress, which could contribute to the selection of resistant bacterial strains. Bearing this in mind, and considering that flavonoid compounds are well known for displaying both activities, we investigated a series of hydroxy-3-arylcoumarins with structural features of flavonoids for their antibacterial activity against different bacterial strains. Active compounds showed selectivity against the studied Gram-positive bacteria compared to Gram-negative bacteria. 5,7-Dihydroxy-3-phenylcoumarin (compound 8) displayed the best antibacterial activity against Staphylococcus aureus and Bacillus cereus with minimum inhibitory concentrations (MICs) of 11 µg/mL, followed by Staphylococcus aureus (MRSA strain) and Listeria monocytogenes with MICs of 22 and 44 µg/mL, respectively. Moreover, molecular docking studies performed on the most active compounds against Staphylococcus aureus tyrosyl-tRNA synthetase and topoisomerase II DNA gyrase revealed the potential binding mode of the ligands to the site of the appropriate targets. Preliminary structure–activity relationship studies showed that the antibacterial activity can be modulated by the presence of the 3-phenyl ring and by the position of the hydroxyl groups at the coumarin scaffoldThis work was partially supported by a grant from the University of Cagliari (FIR) and by Galician Plan of Research, Innovation and Growth 2011–2015 (Xunta da Galicia Plan I2C, ED481B 2014/086–0 and ED481B 2018/007S