Production of green surfactants: Market prospects

Science has greatly contributed to the advancement of technology and to the innovation of production processes and their applications. Cleaning products have become indispensable in today’s world, as personal and environmental hygiene is important to all societies worldwide. Such products are used in the home, in most work environments and in the industrial sectors. Most of the detergents on the market are synthesised from petrochemical products. However, the interest in reducing the use of products harmful to human health and the environment has led to the search for detergents formulated with natural, biodegradable surfactant components of biological (plant or microbiological) origin or chemically synthesised from natural raw materials usually referred to as green surfactants. This review addresses the different types, properties, and uses of surfactants, with a focus on green surfactants, and describes the current scenario as well as the projections for the future market economy related to the production of the different types of green surfactants marketed in the world.How to cite: Farias CBB, Almeida FCG, Silva IA, et al. Production of green surfactants: Market prospects. Electron J Biotechnol 2021;51. https://doi.org/10.1016/j.ejbt.2021.02.002     

Quality flaw prediction in Spanish Wikipedia: A case of study with verifiability flaws

In this work, we present the first quality flaw prediction study for articles containing the two most frequent verifiability flaws in Spanish Wikipedia: articles which do not cite any references or sources at all (denominated Unreferenced) and articles that need additional citations for verification (so-called Refimprove). Based on the underlying characteristics of each flaw, different state-of-the-art approaches were evaluated. For articles not citing any references, a well-established rule-based approach was evaluated and interesting findings show that some of them suffer from Refimprove flaw instead. Likewise, for articles that need additional citations for verification, the well-known PU learning and one-class classification approaches were evaluated. Besides, new methods were compared and a new feature was also proposed to model this latter flaw. The results showed that new methods such as under-bagged decision trees with sum or majority voting rules, biased-SVM, and centroid-based balanced SVM, perform best in comparison with the ones previously published.     

Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods

Cross-Company Churn Prediction (CCCP) is a domain of research where one company (target) is lacking enough data and can use data from another company (source) to predict customer churn successfully. To support CCCP, the cross-company data is usually transformed to a set of similar normal distribution of target company data prior to building a CCCP model. However, it is still unclear which data transformation method is most effective in CCCP. Also, the impact of data transformation methods on CCCP model performance using different classifiers have not been comprehensively explored in the telecommunication sector. In this study, we devised a model for CCCP using data transformation methods (i.e., log, z-score, rank and box-cox) and presented not only an extensive comparison to validate the impact of these transformation methods in CCCP, but also evaluated the performance of underlying baseline classifiers (i.e., Naive Bayes (NB), K-Nearest Neighbour (KNN), Gradient Boosted Tree (GBT), Single Rule Induction (SRI) and Deep learner Neural net (DP)) for customer churn prediction in telecommunication sector using the above mentioned data transformation methods. We performed experiments on publicly available datasets related to the telecommunication sector. The results demonstrated that most of the data transformation methods (e.g., log, rank, and box-cox) improve the performance of CCCP significantly. However, the Z-Score data transformation method could not achieve better results as compared to the rest of the data transformation methods in this study. Moreover, it is also investigated that the CCCP model based on NB outperform on transformed data and DP, KNN and GBT performed on the average, while SRI classifier did not show significant results in term of the commonly used evaluation measures (i.e., probability of detection, probability of false alarm, area under the curve and g-mean).     

逆向工程在产品设计中的应用

逆向工程作为一种现代设计方法能迅速地缩短产品设计的时间,增强企业在市场中的竞争力。它在产品设计中的具体步骤有:产品分析,数据采样,数据分析,数据修复,CAD模型重建。     

体育产业概念理解及分类的研究

体育产业概念及分类在国内外都有很大的争议，通过对体育产业进行分析，且根据我国国情对体育产业概念及分类进行科学的界定。     

论奥运项目的市场体系及产品特性

完善奥运项目市场体系和把握其产品特性是进行市场开发的基础.对奥运项目市场进行规范的划分和对其产品进行细致的分析,可以更好地提高服务质量,提高市场开发的效益.     

人体上下肢长预测方法的研究与应用

人体生长发育过程中，具有非等比性和向心律的特点，在生长发育结束以前测评出的形态比例关系，不能直接说明成熟后的情况。通过对7～18岁儿童少年发育状况的调研，提出了人体上下肢长与身高比例关系的预测方法。     

FANUC-OTDⅡ系统复合型固定循环指令的应用探讨

本文详细论述了FANUC-OTDII系统G70-G76指令的应用,并依据其应用场合给出针对性的实例。     

关于影响蔡民十项全能主要因素的分析及成绩的预测研究

应用灰色系统对十项全能的影响因素进行了分析,找出了影响其成绩变化的优势因素.并运用灰色动态模型进行了近期和短期的预测,结果证明这一方法对十项成绩的预测准确性较好,残差一般为±0.25% .     

我国体育用品企业如何开展网络营销活动

年轻一代网络化的生活方式和国际体育用品企业对网络的应用，促使我国体育用品企业开展网络营销活动。本文结合国内外著名体育用品企业开展网络营销的经验，从在线产品策略、在线价格策略和在线渠道策略、在线促销策略三个方面探讨我国体育用品企业如何开展网络营销活动。