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1.
Marion Börnhorst Jens Friedland Bjarne Kreitz Emanuele Moioli Gregor Wehinger 《化学,工程师,技术》2021,93(8):1210-1213
In this article, the results of the NaWuReT (Early Career Reaction Engineers) workshop on the topic “Are we doing relevant science?” are presented. The topics “(In)surmountable hurdles for Citizen Scientists in reaction engineering?” and “Circular Economy in reaction engineering with/for society?” were discussed. Therefrom, a variety of ideas and suggestions were extracted. 相似文献
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Omar S. Ahmed Charles Tardif Caroline Rouger Vessela Atanasova Florence Richard-Forget Pierre Waffo-Téguo 《Comprehensive Reviews in Food Science and Food Safety》2022,21(2):1161-1197
Mycotoxins are metabolites produced by molds that contaminate food commodities, are harmful to both humans and animals, as well as cause economic losses. Many countries have set regulatory limits and strict thresholds to control the level of mycotoxins in food and feedstuffs. New technologies and strategies have been developed to inhibit toxigenic fungal invasion and to decontaminate mycotoxins. However, many of these strategies do not sufficiently detoxify mycotoxins and leave residual toxic by-products. This review focuses on the use of phenolic compounds obtained from botanical extracts as promising bioagents to inhibit fungal growth and/or to limit mycotoxin yields. The mechanism of these botanicals, legislation concerning their use, and their safety are also discussed. In addition, recent strategies to overcome stability and solubility constraints of phenolic compounds to be used in food and feed stuffs are also mentioned. 相似文献
4.
《Journal of dairy science》2021,104(10):10896-10904
Dairy bull fertility is traditionally evaluated using semen production and quality traits; however, these attributes explain only part of the differences observed in fertility among bulls. Alternatively, bull fertility can be directly evaluated using cow field data. The main objective of this study was to investigate bull fertility in the Italian Brown Swiss dairy cattle population using confirmed pregnancy records. The data set included a total of 397,926 breeding records from 1,228 bulls and 129,858 lactating cows between first and fifth lactation from 2000 to 2019. We first evaluated cow pregnancy success, including factors related to the bull under evaluation, such as bull age, bull inbreeding, and AI organization, and factors associated with the cow that receives the dose of semen, including herd-year-season, cow age, parity, and milk yield. We then estimated sire conception rate using only factors related to the bull. Model predictive ability was evaluated using 10-fold cross-validation with 10 replicates. Interestingly, our analyses revealed that there is a substantial variation in conception rate among Brown Swiss bulls, with more than 20% conception rate difference between high-fertility and low-fertility bulls. We also showed that the prediction of bull fertility is feasible as our cross-validation analyses achieved predictive correlations equal to 0.30 for sire conception rate. Improving reproduction performance is one of the major challenges of the dairy industry worldwide, and for this, it is essential to have accurate predictions of service sire fertility. This study represents the foundation for the development of novel tools that will allow dairy producers, breeders, and artificial insemination companies to make enhanced management and selection decisions on Brown Swiss male fertility. 相似文献
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电子病历(EMRs)中包含着丰富的信息,如临床症状、诊断结果和药物疗效。命名实体识别(Named Entity Recognition, NER)旨在从非结构化文本中抽取命名实体,这也是从电子病历中抽取有价值信息的初始步骤。本文提出一种基于预训练模型RoBERTa-WWM(A Robustly Optimized BERT Pre-training Approach-Whole Word Masking)的命名实体识别方法。该方法引入预训练模型RoBERTa-WWM,利用其生成含有先验知识的语义表示。与BERT(Bidirectional Encoder Representations from Transformers)相比,RoBERTa-WWM生成的语义表示更适用于中文的命名实体识别任务,因为其在预训练阶段会进行全词掩码。RoBERTa-WWM生成的语义表示被依次输入双向长短时记忆(Bidirectional Long Short-Term Memory, BiLSTM)和条件随机场(Conditional Random Field, CRF)模型。实验结果表明,该方法在“2019全国知识图谱与语义计算大会(CCKS 2019)”数据集上可以有效提升F1值,提高中文电子病历中命名实体的识别效果。 相似文献
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北山国家地质公园具有重要地学研究价值,是保护母亲河行动生态教育基地之一。应用SWOT分析方法对公园开发、保护过程中面临的优势、劣势、机遇、威胁四方面进行动态分析,为旅游资源开发与保护提供科学参考。公园拥有得天独厚的生态环境、优越的地理位置,丰富的地质遗迹资源,为旅游开发、科普奠定了坚实基础。与此同时开发过程中旅游资源整合不够、地质公园遗迹保护缺失、地学文化挖掘力度不够等问题日益凸显。在当前旅游发展新形势下,旅游行业进入新时代、地学旅游得到新倡导、乡村振兴提出新思路,迎来新的机遇,也面临地质公园保护与开发矛盾、旅游市场的更新迭代、地质公园的受众群体多元化影响的新挑战。在当前时代背景下,在保证可持续发展前提下,建立健全基础服务设施与管理制度、规范地质公园建设、健全旅游配套设施和景区服务、开发精品地质科普旅游路线、打造乡村振兴示范区,助力北山国家地质公园新发展。 相似文献
7.
公共政策研拟,尤其是环境影响评估,不仅牵涉技术性科学层面,同时涵盖社会性价值评估与政策性决策判断。旨在探讨如何将科学、价值与判断等因素合理融合于环境影响评估作业过程中,并据此研拟一环境影响评估架构,作为中国台湾地区环保部门开发案审核依据之参考。本文所研拟之环境影响评估架构的理论基础包括社会选择理论、社会判断理论及多属性评估方法。通过社会选择理论分析环境政策制定的机制;通过社会判断理论解释科学与价值如何整合于政策制定及其可行性中;而通过多属性评估方法建立开发案评选的集体决策技术。以山坡地开发为例说明此架构在实践中的应用。 相似文献
8.
Eduardo H. Diniz Erica S. Siqueira Eric van Heck 《Information Technology for Development》2019,25(1):69-91
ABSTRACTCommunity currencies are used to pay for products or services within specific groups defined by geographical boundaries or specific common interests. Financial crises, social emergence in developing countries, and increased access to digital devices have stimulated a growing number of communities worldwide to develop digital currency projects. These projects use technologies ranging from traditional plastic cards to mobile phones and blockchain technologies. Following the design science research approach, this paper analyzes digital community currencies (DCCs) by developing a taxonomy based on platform architecture, governance, transactionality and virtuality. By investigating 22 DCC platforms around the world, 4 groups were distinguished: local, proprietary, commons and cyber. The identification of these four different groups of digital community currencies allows us to better discuss the potentials and limitations of each one of them. The presented taxonomy can be useful to researchers and practitioners both to explain and to design DCC platforms. Discussing each of the emerging categories from the proposed taxonomy helps us to provide insights into DCCs, offering a new theoretical frame for investigating the particular case of digital payment platforms. 相似文献
9.
Science of science has become a popular topic that attracts great attentions from the research community. The development of data analytics technologies and the readily available scholarly data enable the exploration of data-driven prediction, which plays a pivotal role in finding the trend of scientific impact. In this paper, we analyse methods and applications in data-driven prediction in the science of science, and discuss their significance. First, we introduce the background and review the current state of the science of science. Second, we review data-driven prediction based on paper citation count, and investigate research issues in this area. Then, we discuss methods to predict scholar impact, and we analyse different approaches to promote the scholarly collaboration in the collaboration network. This paper also discusses open issues and existing challenges, and suggests potential research directions. 相似文献
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