关键词： 搜索引擎   服务网站   网址   官方网站   假冒网站   中国知网   特此声明   投稿  

摘要： 近一段时间通过搜索引擎搜索《中南药学》,出现了很多假冒网站,骗取读者文章或者钱财,我刊声明,《中南药学》杂志的官方网站为:http://***/,作者可以直接输入我刊网址或者从中国知网首页“作者投稿服务”搜索《中南药学》杂志进行注册投稿。其他网址均为仿冒,我刊也未与任何投稿服务网站合作,请作者不要将自己的文章交给他人或者通过其他中间网站投向我刊,由此造成的损失,作者自己承担。特此声明!

关键词： 时代烙印   文史资料   人生经验   《星火》   原浆   搜索引擎   作品题材   征文  

摘要： 以"原浆散文"命名的散文征文,已在《星火》杂志持续了一年多。关于这次征文的初衷与命意,《星火》主编、作家范晓波先生曾有过清晰的表述:"作品题材不限,但要求使用作家的直接人生经验或社会观察,不依赖搜索引擎和文史资料,像乡村酿酒一样使用真本实料,高投入低产出,尤其期待经历了十年磨一剑的沉淀和发酵后,把个人经验和时代烙印融为一体的厚重之作。"

关键词： 视觉搜索引擎   技术接受模型   满意度   购买意愿  

摘要： 以大数据为代表的新技术的快速发展推动了视觉搜索引擎在电子商务领域的应用。为了揭示视觉搜索引擎如何发挥作用,文章结合淘宝拍立淘的特征,在技术接受模型的理论基础上引入感知精准性变量,构建研究模型,揭示视觉搜索引擎对消费者购买意愿的影响机制。研究结果表明:①消费者对视觉搜索引擎的感知有用性、感知易用性和感知精准性均能显著地提升消费者的满意度,进而有效地增强消费者的商品购买意愿;②视觉搜索引擎的感知易用性和感知精准性是感知有用性的重要决定因素。基于研究结论,文章为电子商务的发展提出一些可行性的建议。

关键词： 搜索引擎  

ISBN: (纸本)9787111670674

摘要： 本书分为4大部分。第一部分（第1-3章）：搜索推荐系统的基础。首先介绍数学与统计学是现代机器学习理论的基础；其次介绍搜索推荐系统的常识；最后，描述知识图谱相关基础理论。第二部分（第4-6章）：搜索系统的基本原理。主要内容包括：搜索系统框架及原理、主要算法以及搜索系统相关评价指标。第三部分（第7-9章）：推荐系统的基本原理。主要内容包括：推荐系统框架及原理、主要算法以及推荐系统相关评价指标。第四部分（第10-12章）：应用。首先介绍三种常见的搜索引擎工具；其次讲述搜索引擎和推荐系统两个方向的应用。

摘要： PURPOSE: To characterize Internet search engine patterns of American Internet users for common causes of blindness and low vision. DESIGN: A retrospective cross-sectional study. METHODS: Retrospective analysis with publicly available Google trends data from January 1, 2004, to January 1, 2020, using Google search engine. Patient population: Random sample of US and worldwide Internet users who searched for information on the topics of cataract, macular degeneration, glaucoma, diabetic retinopathy, and near- sightedness using the Google search engine. Main outcome measures: Percentage of searches related to disease and treatment education for each condition. RESULTS: Cataract searches most commonly pertain to treatment education (72.3%) and disease education (23.6%). Glaucoma, macular degeneration, and near-sightedness searches more commonly pertained to disease education (69.5%, 64.0%, 50.4% respectively) than treatment education (18.4%, 17.9%, 10.7% respectively). Diabetic retinopathy searches related to other diseases (41.5%), followed by disease education (33.5%) and treatment education (8.2%). Mean relative search frequency (RSF) values for queries were 66.7 +/- 13.3, 58.6 +/- 6.2, 33.3 +/- 6.7, 29.2 +/- 6.5, and 8.6 +/- 1.4 for cataract, glaucoma, near-sightedness, diabetic retinopathy, and macular degeneration, respectively, with all pairwise comparisons yielding statistically significant values (P<.001). RSF was found to be fairly well correlated with North American blindness prevalence by condition (r(2) = 0.5898). CONCLUSION: The search results of American Internet search users yield information on disease basics or treatment education for the disease. The most commonly searched queries for each condition yield different types of information with cataract queries presenting more commonly with treatment information. These results may inform future patient education practices. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Rich text content   Search engines   Solr index   Chinese word segmentation   Weighting factor  

摘要： In order to improve the search performance of rich text content, a cloud search engine system based on rich text content is designed. On the basis of traditional search engine hardware system, several hardware devices such as Solr index server, collector, Chinese word segmentation device and searcher are installed, and the data interface is adjusted. On the basis of hardware equipment and database support, this paper uses the open source Apache Tika framework to obtain the metadata of rich text documents, implements word segmentation according to the rich text content and semantics, and calculates the weight of each keyword. Input search keywords, establish a text index, use BM25 algorithm to calculate the similarity between keywords and text, and output the search results of rich text according to the similarity calculation results. The experimental results show that the design system has high recall rate, high throughput, and the construction time of each data item index in different files is short, which improves the search efficiency and search accuracy.

摘要： Bluetick offers a juridical research platform that enables lawyers to search for cases and jurisprudence efficiently. Most Dutch legal alternatives are still old-fashioned search engines. Bluetick wants to move towards a zero-search-based approach where the system learns about the user's preference and provides them with recommendations. For the user, this means that they have to spend less time searching for cases while still finding all the relevant material. To reach this goal of zero-search, the quality of the recommendations must be high. Therefore improvements in this area are believed to result in a more lucrative product. This report describes the process of improving the version of the recommender system that was already implemented by Bluetick. The main contributions are evaluated by their effect on the recommender system, and their role in creating a more maintainable, extensible and transparent product. The first contribution of the team was a refactor of the old system. Using classes and interfaces, the new version makes it easier to do advanced computations on the results, while the interface makes it easier for Bluetick to add additional parts on which recommendations can be based. Secondly, similar to many existing webshops, the new system provides the user with insight into why items are recommended. Lastly, the user is now able to provide the system with relevant law articles at the start, so that the recommender system can give recommendations before the first search.

关键词： Syndromic surveillance   Measles   Linear regression   Forecasting   Programming languages   Computational science  

摘要： Background In recent years new forms of syndromic surveillance that use data from the Internet have been proposed. These have been developed to assist the early prediction of epidemics in various cases and diseases. It has been found that these systems are accurate in monitoring and predicting outbreaks before these are observed in population and, therefore, they can be used as a complement to other methods. In this research, our aim is to examine a highly infectious disease, measles, as there is no extensive literature on forecasting measles using Internet data, Methods This research has been conducted with official data on measles for 5 years (2013-2018) from the competent authority of the European Union (European Center of Disease and Prevention - ECDC) and data obtained from Google Trends by using scripts coded in Python. We compared regression models forecasting the development of measles in the five countries. Results Results show that measles can be estimated and predicted through Google Trends in terms of time, volume and the overall spread. The combined results reveal a strong relationship of measles cases with the predicted cases (correlation coefficient R= 0.779 in two-tailed significance p< 0.01). The mean standard error was relatively low 45.2 (12.19%) for the combined results. However, major differences and deviations were observed for countries with a relatively low impact of measles, such as the United Kingdom and Spain. For these countries, alternative models were tested in an attempt to improve the results. Conclusions The estimation of measles cases from Google Trends produces acceptable results and can help predict outbreaks in a robust and sound manner, at least 2 months in advance. Python scripts can be used individually or within the framework of an integrated Internet surveillance system for tracking epidemics as the one addressed here.

关键词： proteomics   deisotoping   preprocessing   spectrum processing   MSFragger   open search   nonspecific search  

摘要： Deisotoping, or the process of removing peaks in a mass spectrum resulting from the incorporation of naturally occurring heavy isotopes, has long been used to reduce complexity and improve the effectiveness of spectral annotation methods in proteomics. We have previously described MSFragger, an ultrafast search engine for proteomics, that did not utilize deisotoping in processing input spectra. Here, we present a new, high-speed parallelized deisotoping algorithm, based on elements of several existing methods, that we have incorporated into the MSFragger search engine. Applying deisotoping with MSFragger reveals substantial improvements to database search speed and performance, particularly for complex methods like open or nonspecific searches. Finally, we evaluate our deisotoping method on data from several instrument types and vendors, revealing a wide range in performance and offering an updated perspective on deisotoping in the modern proteomics environment.

关键词： 搜索引擎   快速查询   内容推荐   全方位渗透   流水线   社交媒体   无线连接   智能应用  

摘要： "阿得拉社会"正渗透我们日常生活从数字化到智能化,生活世界正在悄无声息地发生着根本性的变化。一个层层过滤、慢慢引导、逐渐上瘾、摆脱不掉的"阿得拉社会"(阿得拉是一种会让人上瘾的"聪明药")正在全方位渗透我们的日常生活。在这个由信息组成的技术形态中,一方面,我们享受着社交媒体、搜索引擎、内容推荐等智能应用带来的无线连接、快速查询、精准定制;另一方面,在技术打造的"算法流水线"上,你看什么、听什么、去哪儿消费等,都逐渐交由算法决策。