Pemasaran Buah Stroberi dari Kelurahan Rurukan dan Rurukan Satu Kecamatan Tomohon Timur, Kota Tomohon

This study aims to find out the marketing channels of strawberries ranging from farmers in the Village Rurukan and Rurukan one to the supermarket. The research was conducted in Kelurahan Rurukan and Rurukan Satu East Tomohon Subdistrict Tomohon City for three months from May 2016 until July 2016. The data obtained are primary and secondary data. Primary data is data sourced from direct interviews with related parties, among others strawberry farmers who became the subject of research. Secondary data is data obtained by the researcher who sourced from documents from related agencies, such as: Rurukan urban village office and Rurukan Satu East Tomohon subdistrict Tomohon City, internet and library sources or literature book data is analyzed by using marketing margin, farmer's share and profit margin. Based on the results of marketing channel research as much as two channels of marketing channel I ranging from farmers to final consumers, marketing channels II farmers to the Supermarket divided into two places namely Cool Tomohon and Freshmart Bahu. The result of marketing Margin of strawberry fruit on channel I is Rp. 0 (0%). For marketing margin value II is Rp. 6500 (30, 23%) (Cool) and Rp. 13,000 (46, 42%) (Freshmart). This shows that the largest margin of marketing value occurs in the marketing channel II in the supermarket Freshmart. The largest profit margin in the strawberry fruit sequence that occurs in marketing channel I between farmers to consumers with profit margin of Rp. 13,250 (98.14%).*jnkd

ANALISIS KEUNTUNGAN USAHATANI SAYURAN SELADA HIDROPONIK PADA URBAN FARMING DI BATUKOTA KECAMATAN MALALAYANG KOTA MANADO

This research aims to analyze the benefits of hydroponic lettuce farming in Urban Farming in Batu Kota, Malalayang District, Manado City. The data analysis method used in this research is quantitative analysis. Quantitative analysis used is the analysis of farming profits by calculating the difference between revenues and costs used. The data used in this study are primary data and secondary data. Primary data were obtained through observation and interviews using direct questionnaires at the hydroponic Urban Farming site through direct interviews between researchers and respondents, namely business owners. Secondary data is the collection of data and research materials obtained from ebooks, ejournals, theses and other sources. The results showed that the revenue of the Hydroponic Lettuce business was Rp. 3,200,000, while the costs incurred in the production process of lettuce for 1.5 months is Rp. 913,609.20, so that the profit on the Urban Farming business in Batu Kota, Malalayang District, Manado City is Rp. 2,286,390.80, for 1.5 months/planting period

Optimal Design of Multilayer Fog Collectors.

The growing concerns over desertification have spurred research into technologies aimed at acquiring water from nontraditional sources such as dew, fog, and water vapor. Some of the most promising developments have focused on improving designs to collect water from fog. However, the absence of a shared framework to predict, measure, and compare the water collection efficiencies of new prototypes is becoming a major obstacle to progress in the field. We address this problem by providing a general theory to design efficient fog collectors as well as a concrete experimental protocol to furnish our theory with all the necessary parameters to quantify the effective water collection efficiency. We show in particular that multilayer collectors are required for high fog collection efficiency and that all efficient designs are found within a narrow range of mesh porosity. We support our conclusions with measurements on simple multilayer harp collectors.EPSR

Reducing PostPartum Hemorrhage Rates at Maine Medical Center

In the United States, approximately 700 women die each year from pregnancy related deaths and the most frequent cause of preventable maternal mortality is obstetric hemorrhage. The postpartum hemorrhage (PPH) rate at Maine Medical Center (MMC) is three times the national average. At our facility we care for the most complex patients in the State and we must decrease our rate to accurately reflect our expertise, knowledge and skills

Transcriptional landscape of the human and fly genomes: Nonlinear and multifunctional modular model of transcriptomes

Regions of the genome not coding for proteins or not involved in cis-acting regulatory activities are frequently viewed as lacking in functional value. However, a number of recent large-scale studies have revealed significant regulated transcription of unannotated portions of a variety of plant and animal genomes, allowing a new appreciation of the widespread transcription of large portions of the genome. High-resolution mapping of the sites of transcription of the human and fly genomes has provided an alternative picture of the extent and organization of transcription and has offered insights for biological functions of some of the newly identified unannotated transcripts. Considerable portions of the unannotated transcription observed are developmental or cell-type-specific parts of protein-coding transcripts, often serving as novel, alternative 5′ transcriptional start sites. These distal 5′ portions are often situated at significant distances from the annotated gene and alternatively join with or ignore portions of other intervening genes to comprise novel unannotated protein-coding transcripts. These data support an interlaced model of the genome in which many regions serve multifunctional purposes and are highly modular in their utilization. This model illustrates the underappreciated organizational complexity of the genome and one of the functional roles of transcription from unannotated portions of the genome. Copyright 2006, Cold Spring Harbor Laboratory Press © 2006 Cold Spring Harbor Laboratory Press

Machine Learning in Automated Text Categorization

The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form and the ensuing need to organize them. In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified documents, the characteristics of the categories. The advantages of this approach over the knowledge engineering approach (consisting in the manual definition of a classifier by domain experts) are a very good effectiveness, considerable savings in terms of expert manpower, and straightforward portability to different domains. This survey discusses the main approaches to text categorization that fall within the machine learning paradigm. We will discuss in detail issues pertaining to three different problems, namely document representation, classifier construction, and classifier evaluation.Comment: Accepted for publication on ACM Computing Survey

Embellishing Text Search Queries to Protect User Privacy

Users of text search engines are increasingly wary that their activities may disclose confidential information about their business or personal profiles. It would be desirable for a search engine to perform document retrieval for users while protecting their intent. In this paper, we identify the privacy risks arising from semantically related search terms within a query, and from recurring highspecificity query terms in a search session. To counter the risks, we propose a solution for a similarity text retrieval system to offer anonymity and plausible deniability for the query terms, and hence the user intent, without degrading the system’s precision-recall performance. The solution comprises a mechanism that embellishes each user query with decoy terms that exhibit similar specificity spread as the genuine terms, but point to plausible alternative topics. We also provide an accompanying retrieval scheme that enables the search engine to compute the encrypted document relevance scores from only the genuine search terms, yet remain oblivious to their distinction from the decoys. Empirical evaluation results are presented to substantiate the effectiveness of our solution. 1

Continuous Space Models for CLIR

[EN] We present and evaluate a novel technique for learning cross-lingual continuous space models to aid cross-language information retrieval (CLIR). Our model, which is referred to as external-data composition neural network (XCNN), is based on a composition function that is implemented on top of a deep neural network that provides a distributed learning framework. Different from most existing models, which rely only on available parallel data for training, our learning framework provides a natural way to exploit monolingual data and its associated relevance metadata for learning continuous space representations of language. Cross-language extensions of the obtained models can then be trained by using a small set of parallel data. This property is very helpful for resource-poor languages, therefore, we carry out experiments on the English-Hindi language pair. On the conducted comparative evaluation, the proposed model is shown to outperform state-of-the-art continuous space models with statistically significant margin on two different tasks: parallel sentence retrieval and ad-hoc retrieval.We thank German Sanchis Trilles for helping in conducting experiments with machine translation. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the GeForce Titan GPU used for this research. The research of the first author was supported by FPI grant of UPV. The research of the third author is supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the grant ALMAMATER (PrometeolI/2014/030).Gupta, P.; Banchs, R.; Rosso, P. (2017). Continuous Space Models for CLIR. Information Processing & Management. 53(2):359-370. https://doi.org/10.1016/j.ipm.2016.11.002S35937053

The Effect of Mindfulness-based Programs on Cognitive Function in Adults: A Systematic Review and Meta-analysis

Mindfulness-based programs (MBPs) are increasingly utilized to improve mental health. Interest in the putative effects of MBPs on cognitive function is also growing. This is the first meta-analysis of objective cognitive outcomes across multiple domains from randomized MBP studies of adults. Seven databases were systematically searched to January 2020. Fifty-six unique studies (n = 2,931) were included, of which 45 (n = 2,238) were synthesized using robust variance estimation meta-analysis. Meta-regression and subgroup analyses evaluated moderators. Pooling data across cognitive domains, the summary effect size for all studies favored MBPs over comparators and was small in magnitude (g = 0.15; [0.05, 0.24]). Across subgroup analyses of individual cognitive domains/subdomains, MBPs outperformed comparators for executive function (g = 0.15; [0.02, 0.27]) and working memory outcomes (g = 0.23; [0.11, 0.36]) only. Subgroup analyses identified significant effects for studies of non-clinical samples, as well as for adults aged over 60. Across all studies, MBPs outperformed inactive, but not active comparators. Limitations include the primarily unclear within-study risk of bias (only a minority of studies were considered low risk), and that statistical constraints rendered some p-values unreliable. Together, results partially corroborate the hypothesized link between mindfulness practices and cognitive performance. This review was registered with PROSPERO [CRD42018100904]