北京信息职业技术学院（Beijing Information Technology College）位于北京市，是公办全日制普通高等职业技术学院，为国家示范性高等职业院校、国家现代学徒制试点单位、北京市示范性高职院校、北京市深化创新创业教育改革示范高校，CDIO工程教育联盟成员单位。学院创建于1954年，时为华北第四工业学校；1999年，北京市成人电子信息大学、北京无线电工业学校合并升格为北京信息职业技术学院。据2018年2月学校官网显示，学校有院本部（酒仙桥）、东校区（东坝）和南校区（花乡）等三个校区，总占地面积350亩，建筑面积21.8万平方米；固定资产总值9.25亿元，其中教学科研仪器设备总值3.69亿元，信息化设备2.33亿元；拥有校内实践基地46个，校外实习基地420处；设有10个教学机构，下辖42个专科专业；拥有教职工887人，全日制专科在校生5681人。2019年12月，被教育部、财政部列入第三类高水平专业群建设单位（A档）。
High concentrations of nitrogen (N) and phosphorus (P) in aquatic systems can readily induce eutrophication, posing significant ecological and human health risks. Establishing scientifically sound N and P water quality criteria is therefore essential for effective water resource management and algal bloom mitigation. Current approaches to establishing lake nutrient criteria primarily rely on determining reference conditions, a method that faces practical limitations due to the pervasive pollution affecting most water bodies. To address this challenge, this study introduces an innovative methodology for deriving N and P criteria based on algal growth potential analysis, which establishes the theoretical minimum nutrient thresholds required for bloom formation.We selected and cultured twenty-three dominant algal species identified from documented bloom events to assess their growth potential. Under optimized cultivation conditions, we determined the maximum N and P yield coefficients for each species, from which we subsequently calculated species-specific N and P criteria based on predetermined bloom thresholds. Using species sensitivity distribution (SSD) analysis, we derived protective nutrient criteria corresponding to the 5th percentile hazard concentration: 0.204 mg/L for N and 0.006 mg/L for P. These values are notably more stringent than those established in European and American guidelines, as well as previous Chinese studies. This study thus establishes a novel scientific framework for developing water quality standards, particularly applicable to urban lakes receiving reclaimed water with high N and P concentrations.
With the increasing popularity of social media, the emotions and opinions expressed by customers on online platforms have had a significant impact on companies’ customer relationship management. This study explores the application value and implementation strategies of social media sentiment analysis in customer loyalty management. Through a systematic review of existing literature, this paper first explains the basic principles and main methods of social media sentiment analysis, including machine learning, deep learning, and dictionary-based approaches. It then analyzes the application scenarios of sentiment analysis in identifying customer attitudes, predicting behavioral tendencies, and evaluating brand image. Based on empirical research, this paper proposes a customer loyalty management framework that integrates social media sentiment analysis, consisting of four key steps: data collection, sentiment identification, insight extraction, and strategy formulation. The results demonstrate that using sentiment analysis can effectively enhance companies’ understanding of customer needs, optimize product and service experiences, and thus strengthen customer loyalty. This paper also discusses potential technical challenges and ethical issues in implementation, providing theoretical guidance and practical recommendations for companies to better conduct customer relationship management in the digital age.
