An Optimal Surveillance Measure Against Foot-and-Mouth Disease in the United States

Surveillance programs on farms and in the local environment provide an essential protection against the importation and spread of exotic diseases. Combined with border quarantine measures, these programs protect both consumers and producers from major health concerns and disease incursions that can potentially destroy local agricultural production and supporting industries, as well as generate substantial losses in trade and tourism. However, surveillance programs also impose costs in the form of expenditures on the surveillance program itself, along with the costs of disease management and eradication should an incursion occur. Taking border quarantine expenditures as given, this paper develops a stochastic optimal control model (with a jump-diffusion process) to determine the optimal level of surveillance activity against a disease incursion by minimizing the present value of the major direct and indirect costs of the disease, as well as the cost of the surveillance and disease management and eradication programs. The model is applied to the case of a potential entry and spread of Foot-and-Mouth Disease in the United States. Results show that current surveillance expenditures are far less than optimal.Surveillance measures, border quarantine, disease incursion and spread, Foot- and-Mouth disease, stochastic optimal control, Livestock Production/Industries, Q1, Q17, Q18,

The Effects of Climate Change on GDP by Country and the Global Economic Gains From Complying With the Paris Climate Accord

Computable general equilibrium (CGE) models are a standard tool for policy analysis and forecasts of economic growth. Unfortunately, due to computational constraints, many CGE models are dimensionally small, aggregating countries into an often limited set of regions or using assumptions such as static price-level expectations, where next period’s price is conditional only on current or past prices. This is a concern for climate change modeling, since the effects of global warming by country, in a fully disaggregated and global trade model, are needed, and the known future effects of global warming should be included in forward-looking forecasts for prices and profitability. This work extends a large dimensional intertemporal CGE trade model to account for the various effects of global warming (e.g., loss in agricultural productivity, sea level rise, and health effects) on Gross Domestic Product (GDP) growth and levels for 139 countries, by decade and over the long term, where producers look forward and adjust price expectations and capital stocks to account for future climate effects. The potential economic gains from complying with the Paris Accord are also estimated, showing that even with a limited set of possible damages from global warming, these gains are substantial. For example, with the comparative case of Representative Concentration Pathway 8.5 (4∘C), the global gains from complying with the 2∘C target (Representative Concentration Pathway 4.5) are approximately US$17,489 billion per year in the long run (year 2100). The relative damages from not complying to Sub-Sahara Africa, India, and Southeast Asia, across all temperature ranges, are especially severe

A Multiple Choices Reading Comprehension Corpus for Vietnamese Language Education

Machine reading comprehension has been an interesting and challenging task in recent years, with the purpose of extracting useful information from texts. To attain the computer ability to understand the reading text and answer relevant information, we introduce ViMMRC 2.0 - an extension of the previous ViMMRC for the task of multiple-choice reading comprehension in Vietnamese Textbooks which contain the reading articles for students from Grade 1 to Grade 12. This dataset has 699 reading passages which are prose and poems, and 5,273 questions. The questions in the new dataset are not fixed with four options as in the previous version. Moreover, the difficulty of questions is increased, which challenges the models to find the correct choice. The computer must understand the whole context of the reading passage, the question, and the content of each choice to extract the right answers. Hence, we propose the multi-stage approach that combines the multi-step attention network (MAN) with the natural language inference (NLI) task to enhance the performance of the reading comprehension model. Then, we compare the proposed methodology with the baseline BERTology models on the new dataset and the ViMMRC 1.0. Our multi-stage models achieved 58.81% by Accuracy on the test set, which is 5.34% better than the highest BERTology models. From the results of the error analysis, we found the challenge of the reading comprehension models is understanding the implicit context in texts and linking them together in order to find the correct answers. Finally, we hope our new dataset will motivate further research in enhancing the language understanding ability of computers in the Vietnamese language

Boundary layers and emitted excitations in nonlinear Schrodinger superflow past a disk

The stability and dynamics of nonlinear Schrodinger superflows past a two-dimensional disk are investigated using a specially adapted pseudo-spectral method based on mapped Chebychev polynomials. This efficient numerical method allows the imposition of both Dirichlet and Neumann boundary conditions at the disk border. Small coherence length boundary-layer approximations to stationary solutions are obtained analytically. Newton branch-following is used to compute the complete bifurcation diagram of stationary solutions. The dependence of the critical Mach number on the coherence length is characterized. Above the critical Mach number, at coherence length larger than fifteen times the diameter of the disk, rarefaction pulses are dynamically nucleated, replacing the vortices that are nucleated at small coherence length

Least Expected Time Paths in Stochastic Schedule-Based Transit Networks

We consider the problem of determining a least expected time (LET) path that minimizes the number of transfers and the expected total travel time in a stochastic schedule-based transit network. A time-dependent model is proposed to represent the stochastic transit network where vehicle arrival times are fully stochastically correlated. An exact label-correcting algorithm is developed, based on a proposed dominance condition by which Bellman’s principle of optimality is valid. Experimental results, which are conducted on the Ho Chi Minh City bus network, show that the running time of the proposed algorithm is suitable for real-time operation, and the resulting LET paths are robust against uncertainty, such as unknown traffic scenarios

Teaching practicum: The impacts on classroom management skills of novice lecturers

This research investigates the perspectives of the novice lecturers in the Faculty of English Language Teacher Education (FELTE) regarding Teaching Practicum’s impacts on their classroom management skills. Using survey research, data was gathered through questionnaires distributed among the whole population of 29 participants, followed by semi-structured interviews to gain a deeper insight into participants’ experience. The study’s results showed that novice lecturers generally made considerable progress in classroom management skills, especially in attention-drawing and response encouragement, creation of a motivating environment as well as physical and emotional interactions with students. On the other hand, it was discovered that inadequate duration and inappropriate timing generally impeded novice lecturers’ gains during their Teaching Practicum. However, some challenges related to supervisor and school choice turned out to be significant factors from which participants could derive considerable benefits, which is opposed to previous research. At the end of the study, some recommendations were suggested for future research on this topic

Characterization and utilization of pulp and paper mill sludge digesting thermophilic bacteria in composting process

Pulp and paper mill sludge (PPMS) was found to be poorly colonised with thermophilic microorganisms. However, evidence to support the need for inoculation to facilitate PPMS composting has only been demonstrated in one instance. In this study, we aimed to: screen and identify PPMS digesting thermophilic bacterial strains; investigate effects of the mixture of selected thermophilic bacterial strains on PPMS digestion; and utilize this mixture as start inoculum in PPMS composting and assess the quality of compost product. The results showed that eleven thermophilic bacterial strains were isolated from Bai Bang PPMS by the enrichment culture method. Among these, three strains which reflected high growth rates on the plates of Minimal Media Agar supplemented with Bai Bang PPMS and showed hydrolytic and ligninolytic activities on the agar plates containing appropriate inductive substrates were selected. Based on the morphological, biochemical characteristics and 16S rRNA gene sequencing, they were identified as Bacillus subtilis. The inoculation with the mixture of selected strains enhanced remarkably Bai Bang PPMS digestion. The dry weight decrease, volatile suspended solids removal, dehydrogenase and protease activities in the inoculated sludge were 2.1-, 1.5-, 1.3- and 1.2- fold higher, respectively, compared to the non-inoculated sludge. The assessment of compost quality based on stability using the alkaline trap method and maturity using the germination and root elongation test showed that the inoculated compost was stable and mature while the non-inoculated compost was unstable and immature. These thermophilic bacterial strains therefore have great potential for Bai Bang PPMS composting

Algorithm to Automatically Extract Body Sizes and Shapes

This study presents an algorithm to automatically extract the size and body shape of a 3D scanned model. The methods used in this research include factor analysis, linear regression equation, cluster analysis, and discriminant analysis. These are used to analyze the body’s shape and choose the best primary dimensions for establishing the sizing system table. Authors use fuzzy logic to establish the mathematical model. In this model, the input variables are the inseam height and the neck girth measurements, and the output variables are the numbers of the human size coding and body shape. In addition, the rotation matrix and the optimal function are used to write an algorithm to estimate the neck girth and inseam measurements. Furthermore, a simple approach based on vertices and surface normal vector data, together with optimal searching, is adapted to estimate the primary dimensions. This estimation algorithm, combined with the fuzzy logic model, makes the automated process of extracting the size and body shape possible. The findings of the study suggest a new research method for quickly informing people about their body shape. This supports purchasing clothes and designing tailored clothing. The automatic algorithm will be very useful for buying clothes face-to-face or online