Based on grey language multi-attribute group decision making, a kernel and grey scale scoring function is put forward according to the definition of grey language and the meaning of the kernel and grey scale. The function introduces grey scale into the decision-making method to avoid information distortion. This method is applied to the grey language hesitant fuzzy group decision making, and the grey correlation degree is used to sort the schemes. The effectiveness and practicability of the decision-making method are further verified by the industry chain sustainable development ability evaluation example of a circular economy. Moreover, its simplicity and feasibility are verified by comparing it with the traditional grey language decision-making method and the grey language hesitant fuzzy weighted arithmetic averaging (GLHWAA) operator integration method after determining the index weight based on the grey correlation.

Background ICIs have remarkably affected the treatment strategies for numerous malignancies, including lung cancer. However, only a fraction of patients experience durable responses to ICIs; thus, there is an urgent need to identify the parameters related to ICI therapeutic effects. In this study, we investigated nutritional status surrogates and several serum markers to estimate the efficacy of ICIs. Materials and methods The records of 66 patients with stage III/IV lung cancer who received ICIs were retrospectively analyzed. Features of patients’ clinical pathology, including age, sex, histology, line of treatment, BMI, serum albumin, serum creatinine, and serum inflammatory markers such as LMR and PLR, were examined. Progression-free survival was the primary endpoint. Relationships among categorical variables were assessed by the chi-squared test. Survival analysis was performed using the Kaplan–Meier method followed by the log-rank test. Cox multivariate analysis was performed to analyze the association between each variable and the survival time of patients. Results The patients with BMI ≥ 25 (kg/m2), serum ALB≥37 (g/dL), serum creatinine ≥61.8 (μmol/L), LMR ≥ 2.12 had a significantly prolonged PFS in comparison with BMI<25 (kg/m2), ALB<37 (g/dL), creatinine<61.8 (μmol/L), LMR<2.12 (p < 0.05). No statistically significant difference was detected between patients with PLR < 135 and PLR ≥ 135 (p = 0.612). Multivariate analysis revealed that ALB≥37 (g/dL) and creatinine ≥ 61.8 (μmol/L) were associated with prolonged PFS, while statistical significance was not achieved in the BMI groups. Conclusions The current results indicated that high BMI is related to longer PFS in lung cancer patients treated with ICIs, which may be correlated with high levels of serum albumin and creatinine.

Decision tree algorithm is a common classification algorithm in data mining technology, and its results are usually expressed in the form of if-then rules. The C4.5 algorithm is one of the decision tree algorithms, which has the advantages of easy to understand and high accuracy, and the concept of information gain rate is added compared with its predecessor ID3 algorithm. After theoretical analysis, C4.5 algorithm is chosen to analyze the performance appraisal results, and the decision tree for performance appraisal is generated by collecting data, data preprocessing, calculating information gain rate, determining splitting attributes, and postpruning. The system is developed in B/S architecture, and an R&D project management system and platform that can realize performance assessment analysis are built by means of visualization tools, decision tree algorithm, and dynamic web pages. The system includes information storage, task management, report generation, role authority control, information visualization, and other management information system functional modules. They can realize the project management functions such as project establishment and management, task flow, employee information filling and management, performance assessment system establishment, report generation of various dimensions, management cockpit construction. With decision tree algorithm as the core technology, the system obtains scientific and reliable project management information with high accuracy and realizes data visualization, which can assist enterprises to establish a good management system in the era of big data.

With the widespread adoption of smartphones and IP geolocation technology, many enterprises offering e-commerce services are seeking to leverage offline environmental information to gain insight into consumer preferences and improve online operations. Among the various environmental variables, crowd density is a key factor that influences consumer behavior. This paper conducted three studies to explore how social crowding affects consumers' brand preferences in online consumption scenarios, utilizing secondary data, ANOVA, regression, and bootstrapping analysis. The results demonstrate that high social crowding has a significant positive impact on consumers' preferences for brands with logos that have clear boundaries. Furthermore, this effect is moderated by consumers' regulatory focus. In the context of e-commerce, the findings suggest that enterprises can benefit from leveraging offline environmental information to optimize online operations and improve brand recognition.