环境污染诱致型群体性事件的过程——变量分析

农村环境污染诱致型群体性事件的变量包括怨恨变量、精英分子及其组织能力、理性计算三个方面。当前我国在现代化建设过程中存在着自然生态环境恶化、居民生活环境污染等问题,政府应该以科学发展观为指导,加强对农村的环境污染治理,真正构建环境良好的和谐社会。

Comparison between different reconstruction routes in esophageal squamous cell carcinoma

AIM:To compare postoperative complications and prognosis of esophageal squamous cell carcinoma patients treated with different routes of reconstruction. METHODS:After obtaining approval from the Medical Ethics Committee of the Sun Yat-Sen University Cancer Center, we retrospectively reviewed data from 306 consecutive patients with histologically diagnosed esophageal squamous cell carcinoma who were treated between 2001 and 2011. All patients underwent radical McKeown-type esophagectomy with at least two-field lymphadenectomy. Regular follow-up was performed in our outpatient department. Postoperative complica-tions and long-term survival were analyzed by treatment modality, baseline patient characteristics, and operative procedure. Data from patients treated via the retrosternal and posterior mediastinal routes were compared. RESULTS:The posterior mediastinal and retrosternal reconstruction routes were employed in 120 and 186 patients, respectively. Pulmonary complications were the most common complications experienced during the postoperative period (46.1% of all patients; 141/306). Compared to the retrosternal route, the posterior mediastinal reconstruction route was associated with a lower incidence of anastomotic stricture (15.8% vs 27.4%, P = 0.018) and less surgical bleeding (242.8 ± 114.2 mL vs 308.2 ± 168.4 mL, P < 0.001). The median survival time was 26.8 mo (range:1.6-116.1 mo). Upon uni/multivariate analysis, a lower preoperative albumin level (P = 0.009) and a more advanced pathological stage (pT; P = 0.006; pN; P < 0.001) were identified as independent factors predicting poor prognosis. The reconstruction route did not influence prognosis (P = 0.477). CONCLUSION:The posterior mediastinal route of reconstruction reduces incidence of postoperative complications but does not affect survival. This route is recommended for resectable esophageal squamous cell carcinoma.

Phase Transition Analysis Based Quality Prediction for Multi-phase Batch Processes

Batch processes are usually involved with multiple phases in the time domain and many researches on process monitoring as well as quality prediction have been done using phase information. However, few of them consider phase transitions, though they exit widely in batch processes and have non-ignorable impacts on product qualities. In the present work, a phase-based partial least squares (PLS) method utilizing transition information is proposed to give both online and offline quality predictions. First, batch processes are divided into several phases using regression parameters other than prior process knowledge. Then both steady phases and transitions which have great influences on qualities are identified as critical-to-quality phases using statistical methods. Finally, based on the analysis of different characteristics of transitions and steady phases, an integrated algorithm is developed for quality prediction. The application to an injection molding process shows the effectiveness of the proposed algorithm in comparison with the traditional MPLS method and the phase-based PLS method.

Multivariate Statistical Process Monitoring of an Industrial Polypropylene Catalyzer Reactor with Component Analysis and Kernel Density Estimation

Abstract Data-driven tools, such as principal component analysis （PCA） and independent component analysis （ICA） have been applied to different benchmarks as process monitoring methods. The difference between the two methods is that the components of PCA are still dependent while ICA has no orthogonality constraint and its latentvariables are independent. Process monitoring with PCA often supposes that process data or principal components is Gaussian distribution. However, this kind of constraint cannot be satisfied by several practical processes. To ex-tend the use of PCA, a nonparametric method is added to PCA to overcome the difficulty, and kernel density estimation （KDE） is rather a good choice. Though ICA is based on non-Gaussian distribution intormation, .KDE can help in the close monitoring of the data. Methods, such as PCA, ICA, PCA.with .KDE（KPCA）, and ICA with KDE,（KICA）, are demonstrated and. compared by applying them to a practical industnal Spheripol craft polypropylene catalyzer reactor instead of a laboratory emulator.

A local and global statistics pattern analysis method and its application to process fault identification

Traditional principal component analysis （PCA） is a second-order method and lacks the ability to provide higherorder representations for data variables. Recently, a statistics pattern analysis （SPA） framework has been incorporated into PCA model to make full use of various statistics of data variables effectively. However, these methods omit the local information, which is also important for process monitoring and fault diagnosis. In this paper, a local and global statistics pattern analysis （LGSPA） method, which integrates SPA framework and locality pre- serving projections within the PCK is proposed to utilize various statistics and preserve both local and global in- formation in the observed data. For the purpose of fault detection, two monitoring indices are constructed based on the LGSPA model. In order to identify fault variables, an improved reconstruction based contribution （IRBC） plot based on LGSPA model is proposed to locate fault variables. The RBC of various statistics of original process variables to the monitoring indices is calculated with the proposed RBC method. Based on the calculated RBC of process variables＇ statistics, a new contribution of process variables is built to locate fault variables. The simula- tion results on a simple six-variable system and a continuous stirred tank reactor system demonstrate that the proposed fault diagnosis method can effectively detect fault and distinguish the fault variables from normal variables.