A Differential Game Model of Tariff War

We present a simple two(-country) by two(-good) differental game model of international trade in which the governments of the two countries play a tariff-setting game. We explicitly derive a unilateral optimum tarifff rate and then a Markov-perfect equilibrium pair of tariff strategies (bilateral optimum tariff strategies) and compare the welfare level of each country among autarchic, free-trade, unilateral and bilateral optimum-tariff equilibria.Tariff-setting game, Durbale consumption good, Markov-perfect strategies, The rate of time preference

Hamiltonian decomposition for bulk and surface states

We demonstrate that a tight-binding Hamiltonian with nearest- and next-nearest-neighbor hopping integrals can be decomposed into bulk and boundary parts in a general lattice system. The Hamiltonian decomposition reveals that next nearest-neighbor hopping causes sizable changes in the energy spectrum of surface states even if the correction to the energy spectrum of bulk states is negligible. By applying the Hamiltonian decomposition to edge states in graphene systems, we show that the next nearest-neighbor hopping stabilizes the edge states.Comment: 5 pages, 4 figure

THE MID-ATLANTIC REGIONAL FOOD SYSTEM WEB SITE

Agribusiness,

E-BUSINESS APPLICATIONS OF THE MID-ATLANTIC REGIONAL FOOD SYSTEMS WEBSITE

Agribusiness, Research and Development/Tech Change/Emerging Technologies,

GGDs: Graph Generating Dependencies

We propose Graph Generating Dependencies (GGDs), a new class of dependencies for property graphs. Extending the expressivity of state of the art constraint languages, GGDs can express both tuple- and equality-generating dependencies on property graphs, both of which find broad application in graph data management. We provide the formal definition of GGDs, analyze the validation problem for GGDs, and demonstrate the practical utility of GGDs.Comment: 5 page

Morphology of the gastric mill teeth in dotillid crabs (Crustacea: Brachyura: Dotillidae) from Indonesia

The gastric mill is a prominent structure in the digestive system of brachyuran crabs, consisting of a median tooth plate and a pair of lateral tooth plates. Among crab species that are deposit feeders, the morphology and size of the gastric mill teeth are correlated with the preferred substrate types and food spectrum. In this study, we provide a detailed description of the morphology of the median and lateral teeth of the gastric mills in eight species of dotillid crabs from Indonesia, and compare them in relation to habitat preferences and molecular phylogeny. Ilyoplax delsmani, Ilyoplax orientalis, and Ilyoplax strigicarpus have comparatively simple shapes of their median and lateral teeth, with fewer teeth on each lateral tooth plate compared to Dotilla myctiroides, Dotilla wichmanni, Scopimera gordonae, Scopimera intermedia, and Tmethypocoelis aff. ceratophora, which have more complexly shaped median and lateral teeth, with a greater number of teeth on each lateral tooth plate. The number of teeth on lateral tooth correlates with habitat preference, that is, dotillid crabs inhabiting muddy substrata have fewer teeth on the lateral tooth plate, and those inhabiting sandy substrata have a more teeth. Phylogenetic analysis using partial COI and 16S rRNA genes supports that teeth morphology is similar among closely related species. Therefore, the description of median and lateral teeth of the gastric mill is expected to contribute to the systematic study of dotillid crabs

A meta-learning configuration framework for graph-based similarity search indexes

Similarity searches retrieve elements in a dataset with similar characteristics to the input query element. Recent works show that graph-based methods have outperformed others in the literature, such as tree-based and hash-based methods. However, graphs are highly parameter-sensitive for indexing and searching, which usually demands extra time for finding a suitable trade-off for specific user requirements. Current approaches to select parameters rely on observing published experimental results or Grid Search procedures. While the former has no guarantees that good settings for a dataset will also perform well on a different one, the latter is computationally expensive and limited to a small range of values. In this work, we propose a meta-learning-based recommender framework capable of providing a suitable graph configuration according to the characteristics of the input dataset. We present two instantiations of the framework: a global instantiation that uses the whole meta-database to train meta-models and a dataset-similarity-based instantiation that relies on clustering to generate meta-models tailored to datasets with similar characteristics. We also developed generic and tuned versions of the instantiations. The generic versions can satisfy user requirements in orders of magnitude faster than the traditional Grid Search. The tuned versions provide more accurate predictions at a higher cost. Our results show that the tuned methods outperform the Grid Search for most cases, providing recommendations close to the optimal one and being a suitable alternative, particularly for more challenging datasets

Nanoscale anisotropic structural correlations in the paramagnetic and ferromagnetic phases of Nd0.5Sr0.5 MnO3

We report x-ray scattering studies of short-range structural correlations and diffuse scattering in Nd0.5Sr0.5MnO3. On cooling, this material undergoes a series of transitions, first from a paramagnetic insulating (PI) to a ferromagnetic metallic (FM) phase, and then to a charge-ordered (CO) insulating state. Highly anisotropic structural correlations were found in both the PI and FM states. The correlations increase with decreasing temperature, reaching a maximum at the CO transition temperature. Below this temperature, they abruptly collapsed. Single-polaron diffuse scattering was also observed in both the PI and FM states suggesting that substantial local lattice distortions are present in these phases. We argue that our measurements indicate that nanoscale regions exhibiting layered orbital order exist in the paramagnetic and ferromagnetic phases of Nd0.5Sr0.5MnO3.Comment: 5 pages, 4 embedded figure

Synthesis and characterization of mixed oxide nanowires for gas sensing

A healthy and long-lasting life is the utmost wish of any living being thus aging. The aging phenomenon cannot be stopped but may be controlled to some extent when we live in appropriate environments. Usually, the outdoor environment is polluted by two means natural events (windblown dust, volcano eruptions, etc.) and man-made ones (burning of facile fuels, factories, volatile organic compounds, etc.). Pollution due to harmful air such as sulfur oxides (SO2), nitrogen oxides (NOX), carbon monoxide (CO), ammonia (NH3), methane (CH4), and volatile organic compounds (VOCs) is one of the significant issues since it is more sensitive to compromising the natural ecosystem and environment. So, exposure to these compounds worsens the aging phenomena of the living being (headache, fainting, skin and eye irradiation, respiratory infections, heart disease, lung cancer, and even superficial death). Therefore, it is necessary the detection these compounds in the environment. Accordingly, metal oxides (MOXs) gas sensors have conventionally been employed to detect and quantify harmful gases in both indoor and outdoor environments. However, one of the major problems with these sensors is achieving selective detection. Herein, we propose a novel design with two metal oxides (ZnO and Co3O4) that provide very high gas response together with superior selectivity. The proposed structure is a one-dimensional (1D) metal oxide composite; Co3O4/ZnO nanowires. The composite was prepared by in-situ thermal oxidation of metallic Co thin layer (50 nm) and evaporation of ZnO powder at a temperature of 800 ᵒC at a pressure of 0.15 mbar. The pressure was maintained by a controlled mixture of O2 and Ar. The morphological, compositional, and structural analyses are evidence of the successful growth of the Co3O4/ZnO composite nanowire with the root of Co3O4 and the tip with Pt (catalyzer) and Co3O4. The gas sensing characterization shows exciting sensing functionality towards acetone (C3H6O) compared to that of tested gases (C2H5OH, H2S, NH3, CO, NO2, and H2). The reported highest response (ΔG/G; G is the conductance) was above the value of 5000 toward 50 ppm (parts per million) C3H6O at 40 RH% air when working at 250 °C with the potential of detecting sub ppb (parts per billion) concentration levels of C3H6O. The very high C3H6O sensing performance together with exceptionally high selectivity of the sensor ascribed to Pt nanoparticle and the Co3O4 section on the tip of the Co3O4/ZnO. Moreover, the formation of heterojunctions, synergistic gas sensing, and the catalytic activity of the proposed design enhances the response of the sensors. Accordingly, scanning electron microscopic (SEM), transmission electron microscope (TEM), energy-dispersive X-ray spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD) characterization, and the sensing mechanisms are comprehensively discussed at the conference