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This study investigates the underlying mechanisms through which proactive environmental strategy develops organizational capabilities. The results of a survey of publicly listed companies in China reveal that proactive environmental strategy has a more positive influence on stakeholder integration capability than on innovation capability. Moreover, organizational learning plays a greater role in mediating the effect of proactive environmental strategy on innovation capability than on stakeholder integration capability, whereas cross‐functional coordination plays a greater role in mediating its effect on stakeholder integration capability than on innovation capability. These findings provide important implications on organizational capability building via proactive environmental strategy.

Background: We studied patients with coronavirus disease 2019 (COVID-19) infected by severe acute respiratory syndrome coronavirus 2, a virus that originated in Wuhan, China, and is spreading over the country including Jiangsu Province. We studied the clinical characteristics and therapies of severe cases in Jiangsu Province. Methods: A multicenter retrospective cohort study was conducted to analyze clinical, laboratory data and treatment of 60 severe cases with COVID-19 infection in Jiangsu Province between January 24, 2020 and April 20, 2020. The improvement and deterioration subgroups were compared to identify predictors of disease progression. Results: A total of 653 infected cases with COVID-19 were reported in Jiangsu Province, of which 60 severe cases were included in this study. Up until April 20, 2020, the mortality of severe patients was 0%. The median age was 57 years. The average body mass index of these patients was 25 kg/m 2. White blood cell counts decreased in 45.0% of patients, lymphopenia in 63.3%, thrombocytopenia in 13.3% and procalcitonin levels in 88.3% of the patients were less than 0.5 ng/mL. There were no statistically significant differences in immunoglobulin therapy and GCs therapy between the improvement and deterioration subgroups. Logistic regression analysis identified higher levels of troponin T (odds ratio [OR]: 1.04; 95% confidence interval [

Based on customer cognitive, affective and conative experiences in Internet online shopping, this study, from customers' perspectives, develops a conceptual framework for e-CRM to explain the psychological process that customers maintain a long-term exchange relationship with specific online retailer. The conceptual framework proposes a series of causal linkages among the key variables affecting customer commitment to specific online retailer, such as perceived value (as cognitive belief), satisfaction (as affective experience) and trust (as conative relationship intention). Three key exogenous variables affecting Internet online shopping experiences, such as perceived service quality, perceived product quality, and perceived price fairness, are integrated into the framework. This study empirically tested and supported a large part of the proposed framework and the causal linkages within it. The empirical results highlight some managerial implications for successfully developing and implementing a strategy for e-CRM.

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Accurate classification of power quality disturbance is the premise and basis for improving and governing power quality. A method for power quality disturbance classification based on time-frequency domain multi-feature and decision tree is presented. Wavelet transform and S-transform are used to extract the feature quantity of each power quality disturbance signal, and a decision tree with classification rules is then constructed for classification and recognition based on the extracted feature quantity. The classification rules and decision tree classifier are established by combining the energy spectrum feature quantity extracted by wavelet transform and other seven time-frequency domain feature quantities extracted by S-transform. Simulation results show that the proposed method can effectively identify six types of common single disturbance signals and two mixed disturbance signals, with fast classification speed and adequate noise resistance. Its classification accuracy is also higher than those of support vector machine (SVM) and k-nearest neighbor (KNN) algorithms. Compared with the method that only uses S-transform, the proposed feature extraction method has more abundant features and higher classification accuracy for power quality disturbance.

A fast concrete crack detector based on L2 sparse representation is proposed. Specifically, via dividing the existing concrete images, many representative crack and non-crack image regions are selected for the over-complete dictionary. To suppress the noise disturbances, discrete cosine transformation is to extract the frequency-domain characteristics of these regions. For one new concrete image, it is first divided into many non-overlapping regions, and their sparse coefficients are fast computed on the established over-complete dictionary. Moreover then, a pooling operation is to extract the difference value between their sum coefficients on the crack templates and those on the noncrack ones, and easily yet effectively select the crack candidates via the sign bit of their difference values. Experiments on the practical concrete images show that the algorithm has high precision and efficiency.

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