Economic Impacts of Soybean Rust on the US Soybean Sector

The spread of Asian Soybean Rust (ASR) represents a real threat to the U.S. soybean sector. We assess the potential impacts of ASR on domestic soybean production and commodity markets as well as the competitive position of the US in the soybean export market. We develop a mathematical stochastic dynamic sector model with endogenous prices to assess the economic impacts of ASR on US agriculture. The model takes into account the disease spread during the cropping season, the inherent uncertainty regarding the risk of infection, and the dichotomous decisions that farmers make (no treatment, preventive treatment, and curative treatment) facing the risk of infection. Our results suggest substantial impacts from potential ASR spread on agricultural output, prices and exports. Our simulation results suggest that substantial losses to the US soybean producers may be avoided by establishing effective soybean rust controls. ASR control policies can be particularly efficient if applied in the gateway regions on the path of the ASR spread. On the other hand, our results indicate a possible gradual shift in soybean production from lower-latitude states toward higher-latitude statesAsian Soybean Rust, Stochastic Models, Dynamic Models, Crop Production/Industries, C61, Q13,

Economic Impacts of Soybean Rust on the US Soybean Sector

The spread of Asian Soybean Rust (ASR) represents a real threat to the U.S. soybean sector. We assess the potential impacts of ASR on domestic soybean production and commodity markets as well as the competitive position of the US in the soybean export market. We develop a mathematical stochastic dynamic sector model with endogenous prices to assess the economic impacts of ASR on US agriculture. The model takes into account the disease spread during the cropping season, the inherent uncertainty regarding the risk of infection, and the dichotomous decisions that farmers make (no treatment, preventive treatment, and curative treatment) facing the risk of infection. Our results suggest substantial impacts from potential ASR spread on agricultural output, prices and exports. Our simulation results suggest that substantial losses to the US soybean producers may be avoided by establishing effective soybean rust controls. ASR control policies can be particularly efficient if applied in the gateway regions on the path of the ASR spread. On the other hand, our results indicate a possible gradual shift in soybean production from lower-latitude states toward higher-latitude states.Asian Soybean Rust, Stochastic Models, Dynamic Models, Agribusiness, Marketing, C61, Q13,

Strictly singular operators and isomorphisms of Cartesian products of power series spaces

V. P. Zahariuta, in 1973, used the theory of Fredholm operators to develop a method to classify Cartesian products of locally convex spaces. In this work we modify his method to study the isomorphic classification of Cartesian products of the kind E0p(a)×E¥ q(b) where 1 £ p,q £ ¥, p ¹ q, a = (an)n=1¥ and b = (bn)n=1¥ are sequences of positive numbers and E0p(a), E¥ q (b) are respectively lp-finite and lq-infinite type power series spaces

Teaching a New Dog Old Tricks: Resurrecting Multilingual Retrieval Using Zero-shot Learning

While billions of non-English speaking users rely on search engines every day, the problem of ad-hoc information retrieval is rarely studied for non-English languages. This is primarily due to a lack of data set that are suitable to train ranking algorithms. In this paper, we tackle the lack of data by leveraging pre-trained multilingual language models to transfer a retrieval system trained on English collections to non-English queries and documents. Our model is evaluated in a zero-shot setting, meaning that we use them to predict relevance scores for query-document pairs in languages never seen during training. Our results show that the proposed approach can significantly outperform unsupervised retrieval techniques for Arabic, Chinese Mandarin, and Spanish. We also show that augmenting the English training collection with some examples from the target language can sometimes improve performance.Comment: ECIR 2020 (short

Local Stellar Kinematics from RAVE data - VII. Metallicity Gradients from Red Clump Stars

We investigate the Milky Way Galaxy's radial and vertical metallicity gradients using a sample of 47,406 red clump stars from the RAVE DR4. This sample is more than twice the size of the largest sample in the literature investigating radial and vertical metallicity gradients. The absolute magnitude of Groenewegen (2008) is used to determine distances to our sample stars. The resulting distances agree with the RAVE DR4 distances Binney et al. (2014) of the same stars. Our photometric method also provides distances to 6185 stars that are not assigned a distance in RAVE DR4. The metallicity gradients are calculated with their current orbital positions ($R_{gc}$ and $Z$) and with their orbital properties (mean Galactocentric distance, $R_{m}$ and $z_{max}$), as a function of the distance to the Galactic plane: d[Fe/H]/d$R_{gc}=$-$0.047\pm0.003$ dex/kpc for $0\leq |Z|\leq0.5$ kpc and d[Fe/H]/d$R_m=$-$0.025\pm0.002$ dex/kpc for $0\leq z_{max}\leq0.5$ kpc. This reaffirms the radial metallicity gradient in the thin disc but highlights that gradients are sensitive to the selection effects caused by the difference between $R_{gc}$ and $R_{m}$. The radial gradient is flat in the distance interval 0.5-1 kpc from the plane and then becomes positive greater than 1 kpc from the plane. The radial metallicity gradients are also eccentricity dependent. We showed that d[Fe/H]/d$R_m=$-$0.089\pm0.010$, -$0.073\pm0.007$, -$0.053\pm0.004$ and -$0.044\pm0.002$ dex/kpc for $e_p\leq0.05$, $e_p\leq0.07$, $e_p\leq0.10$ and $e_p\leq0.20$ sub-samples, respectively, in the distance interval $0\leq z_{max}\leq0.5$ kpc. Similar trend is found for vertical metallicity gradients. Both the radial and vertical metallicity gradients are found to become shallower as the eccentricity of the sample increases. These findings can be used to constrain different formation scenarios of the thick and thin discs.Comment: 18 pages, including 16 figures and 6 tables, accepted for publication in PAS

To Invest or Not to Invest: Using Vocal Behavior to Predict Decisions of Investors in an Entrepreneurial Context

Entrepreneurial pitch competitions have become increasinglypopular in the start-up culture to attract prospective investors. As theultimate funding decision often follows from some form of social interaction,it is important to understand how the decision-making processof investors is influenced by behavioral cues. In this work, we examinewhether vocal features are associated with the ultimate funding decisionof investors by utilizing deep learning methods.We used videos of individualsin an entrepreneurial pitch competition as input to predict whetherinvestors will invest in the startup or not. We proposed models that combinedeep audio features and Handcrafted audio Features (HaF) and feedthem into two types of Recurrent Neural Networks (RNN), namely LongShort-Term Memory (LSTM) and Gated Recurrent Units (GRU). Wealso trained the RNNs with only deep features to assess whether HaFprovide additional information to the models. Our results show that it ispromising to use vocal behavior of pitchers to predict whether investorswill invest in their business idea. Different types of RNNs yielded similarperformance, yet the addition of HaF improved the performance

To Invest or Not to Invest: Using Vocal Behavior to Predict Decisions of Investors in an Entrepreneurial Context

Entrepreneurial pitch competitions have become increasinglypopular in the start-up culture to attract prospective investors. As theultimate funding decision often follows from some form of social interaction,it is important to understand how the decision-making processof investors is influenced by behavioral cues. In this work, we examinewhether vocal features are associated with the ultimate funding decisionof investors by utilizing deep learning methods.We used videos of individualsin an entrepreneurial pitch competition as input to predict whetherinvestors will invest in the startup or not. We proposed models that combinedeep audio features and Handcrafted audio Features (HaF) and feedthem into two types of Recurrent Neural Networks (RNN), namely LongShort-Term Memory (LSTM) and Gated Recurrent Units (GRU). Wealso trained the RNNs with only deep features to assess whether HaFprovide additional information to the models. Our results show that it ispromising to use vocal behavior of pitchers to predict whether investorswill invest in their business idea. Different types of RNNs yielded similarperformance, yet the addition of HaF improved the performance

A note on "The Economic Lot Sizing Problem with Inventory Bounds"

In a recent paper, Liu (2008) considers the lot-sizing problem with lower and upper bounds on the inventory levels. He proposes an O(n^2) algorithm for the general problem, and an O(n) algorithm for the special case with non-speculative motives. We show that neither of the algorithms provides an optimal solution in general. Furthermore, we propose a fix for the former algorithm that maintains the O(n^2) complexity

Microwave and ultrasound pre-treatments for ‘Rocha’ pear: impact on drying kinetics and selected quality attributes

info:eu-repo/semantics/acceptedVersio

Self-folding with shape memory composites

Origami-inspired manufacturing can produce complex structures and machines by folding two-dimensional composites into three-dimensional structures. This fabrication technique is potentially less expensive, faster, and easier to transport than more traditional machining methods, including 3-D printing. Self-folding enhances this method by minimizing the manual labor involved in folding, allowing for complex geometries and enabling remote or automated assembly. This paper demonstrates a novel method of self-folding hinges using shape memory polymers (SMPs), paper, and resistive circuits to achieve localized and individually addressable folding at low cost. A model for the torque exerted by these composites was developed and validated against experimental data, in order to determine design rules for selecting materials and designing hinges. Torque was shown to increase with SMP thickness, resistive circuit width, and supplied electrical current. This technique was shown to be capable of complex geometries, as well as locking assemblies with sequential folds. Its functionality and low cost make it an ideal basis for a new type of printable manufacturing based on two-dimensional fabrication techniques.National Science Foundation (U.S.) (award number CCF-1138967)National Science Foundation (U.S.) (award number EFRI-1240383