Analysis of recreational land and open space using ERTS-1 data

There are no author-identified significant results in this report

Grown organic matter as a fuel raw material resource

An extensive search was made on biomass production from the standpoint of climatic zones, water, nutrients, costs and energy requirements for many species. No exotic species were uncovered that gave hope for a bonanza of biomass production under culture, location, and management markedly different from those of existing agricultural concepts. A simulation analysis of biomass production was carried out for six species using conventional production methods, including their production costs and energy requirements. These estimates were compared with data on food, fiber, and feed production. The alternative possibility of using residues from food, feed, or lumber was evaluated. It was concluded that great doubt must be cast on the feasibility of producing grown organic matter for fuel, in competition with food, feed, or fiber. The feasibility of collecting residues may be nearer, but the competition for the residues for return to the soil or cellulosic production is formidable

Inferring the Origin Locations of Tweets with Quantitative Confidence

Social Internet content plays an increasingly critical role in many domains, including public health, disaster management, and politics. However, its utility is limited by missing geographic information; for example, fewer than 1.6% of Twitter messages (tweets) contain a geotag. We propose a scalable, content-based approach to estimate the location of tweets using a novel yet simple variant of gaussian mixture models. Further, because real-world applications depend on quantified uncertainty for such estimates, we propose novel metrics of accuracy, precision, and calibration, and we evaluate our approach accordingly. Experiments on 13 million global, comprehensively multi-lingual tweets show that our approach yields reliable, well-calibrated results competitive with previous computationally intensive methods. We also show that a relatively small number of training data are required for good estimates (roughly 30,000 tweets) and models are quite time-invariant (effective on tweets many weeks newer than the training set). Finally, we show that toponyms and languages with small geographic footprint provide the most useful location signals.Comment: 14 pages, 6 figures. Version 2: Move mathematics to appendix, 2 new references, various other presentation improvements. Version 3: Various presentation improvements, accepted at ACM CSCW 201

Automatic Synonym Discovery with Knowledge Bases

Recognizing entity synonyms from text has become a crucial task in many entity-leveraging applications. However, discovering entity synonyms from domain-specific text corpora (e.g., news articles, scientific papers) is rather challenging. Current systems take an entity name string as input to find out other names that are synonymous, ignoring the fact that often times a name string can refer to multiple entities (e.g., "apple" could refer to both Apple Inc and the fruit apple). Moreover, most existing methods require training data manually created by domain experts to construct supervised-learning systems. In this paper, we study the problem of automatic synonym discovery with knowledge bases, that is, identifying synonyms for knowledge base entities in a given domain-specific corpus. The manually-curated synonyms for each entity stored in a knowledge base not only form a set of name strings to disambiguate the meaning for each other, but also can serve as "distant" supervision to help determine important features for the task. We propose a novel framework, called DPE, to integrate two kinds of mutually-complementing signals for synonym discovery, i.e., distributional features based on corpus-level statistics and textual patterns based on local contexts. In particular, DPE jointly optimizes the two kinds of signals in conjunction with distant supervision, so that they can mutually enhance each other in the training stage. At the inference stage, both signals will be utilized to discover synonyms for the given entities. Experimental results prove the effectiveness of the proposed framework

Regions in Covid-19 recovery

Covid-19 is undoubtedly a regional crisis, spatially uneven in its impacts. While it is too soon to talk about a transition ‘from pandemic to recovery’, with attention switching to regional development priorities and the implications of Covid-19 on regional policy, planning and development, increasingly we will need to focus on regions in their recovery phase. In this article we ask four leading researchers what this recovery phase will mean for regions. Opening the way for future discussion perspectives on regional economic recovery, resilience planning, building healthy and just places, and overcoming the ‘shadow’ pandemic indicate how this recovery phase is unfolding and what we would benefit from doing differently to ‘build back better’ and overcome ‘wicked problems’ preventing more inclusive, just and sustainable regional futures

A CTMC study of collisions between protons and H2+H_2^+ molecular ions

We study numerically collisions between protons and $H_2^+$ molecular ions at intermediate impact energies by using the Classical Trajectory Monte Carlo method (CTMC). Total and differential cross sections are computed. The results are compared with: a) the standard one electron--two nucleon scattering, and b) the quantum mechanical treatment of the $H^{+} - H^{+}_{2}$ scattering.Comment: ReVTeX, 5 pages + 5 figs. (EPS) To be published in Physica Script

Active compounds and distinctive sensory features provided by American ginseng (Panax quinquefolius L.) extract in a new functional milk beverage

American ginseng (Panax quinquefolius L.) has recognized neurocognitive effects, and a ginsenoside-rich extract of the root of the plant has been shown to improve cognitive functions in young adults. This study aimed at assessing the chemical and sensory profiles of a UHT-treated, low-lactose functional milk containing American ginseng. Individual ginsenosides in the milk were analyzed by HPLC. Descriptive sensory analysis was performed by a trained panel to quantitatively document sensory changes resulting from the addition of ginseng and the UHT process on flavored and unflavored milks. Consumer acceptance of the product was also investigated. Total ginsenoside content in the UHT-treated milk enriched with the ginseng extract after UHT process treatment was 7.52. mg/100. g of milk, corresponding to a recovery of 67.6% compared with the content in the unprocessed extract. The intake of 150 to 300. mL of this ginseng-enriched milk provides the amount of total ginsenosides (11.5 to 23. mg) necessary to improve cognitive function after its consumption. Both the presence of ginsenosides and their thermal treatment affected some sensory properties of the milk, most notably an increase in bitterness and metallic taste, the appearance of a brownish color, and a decrease in milky flavor. Levels of brown color, bitterness, and metallic taste were highest in the industrially processed ginseng-enriched milk. The bitterness attributable to ginseng extract was reduced by addition of vanilla flavor and sucralose. A consumer exploratory study revealed that a niche of consumers exists who are willing to consume this type of product.The financial support of the Ministry of Science and Innovation of Spain (Madrid, Spain) for the project SENIFOOD (CENIT Programme) and for the contract with A. Tárrega (Juan de la Cierva Programme) is acknowledged. We gratefully acknowledge Juan Duato Aguilar, from Naturex Spain S.L. (Quart de Poblet, Spain), for his valuable technical support

Interactions Between EIP on AHA Reference Sites and Action Groups to Foster Digital Innovation of Health and Care in European Regions

The article describes some of the achievements of the European Innovation Partnership on Active and Healthy Ageing (EIP on AHA), after eight years in operation. These results were achieved thanks to the collaborative work of the action groups (AGs) and reference sites (RSs). RS regional ecosystems include key organisations committed to investing in innovation to foster active and healthy ageing. The AGs are groups of professionals committed to sharing their knowledge and skills in active and healthy ageing. This article reports on the approach used by the EIP on AHA to bring together experts and regions in identifying and addressing these challenges. Synergies between AGs offered substantial support to RSs, allowing regional health and care priorities and challenges to be identified and pursued through AG commitments. Building upon the experiences of the EIP on AHA, the Reference Sites Collaborative Network has set up a number of thematic action groups that bring together multidisciplinary experts from across Europe to address the main health and social care challenges at regional, national and European level