Self-Control Matters: Examining Indirect Use of Hospital Information Systems and its Control Mechanisms

Indirect use of hospital information systems (HIS), denoting the designated physicians’ indirect interaction with HIS through delegating HIT-related tasks to others, has become a popular phenomenon in hospitals. Indirect use frees physicians from busy work and helps them achieve improved productivity, however, agency theory and organization literature alert that the consequences of indirect use may departure from physicians’ expectation due to agency problems. To resolve such challenges, prior work places great emphasis on formal control and social control. Considering the nature of hospital setting, this study proposes that agent’s self-control could also be a prominent control mechanism. To obtain a comprehensive understanding, this study builds a research model to depict the impacts of the three control mechanisms and their interplays on indirect use performance. Proposed methodology and preliminary findings are also discussed in this study, which aims to provide insights about how to perform indirect use efficiently and effectively

ENHANCING WORK PERFORMANCE IN STABLE POST-ADOPTIVE STAGE: A SYSTEM USE-RELATED BEHAVIORS PERSPECTIVE

The success of enterprise systems (ES) hinges on the work performance of system users in the stable post-adoptive stage. With a high failure rate of ES implementation, it is crucial to explore factors that could enhance users’ work performance. Drawing on literature on IS post-adoption and system use-related behaviors, this study proposes a theoretical model to understand how different types of ES use-related behaviors (i.e., technology interaction behaviors, task-technology adaptation behaviors and individual adaptation behaviors) can induce better performance in the stable phase of post-adoption. A field survey involving 250 physicians was conducted to test the proposed research model. The results showed different effects of ES use-related behaviors on improving users’ work performance. Individual adaptation behaviors enhanced the user performance, while technology interaction behaviors and task-technology adaptation behaviors did not show significant effect on performance. Interestingly, individual adaptation and task-technology adaptation behaviors could moderate the relationship between system use and performance, yet in an opposite manner. This study offers important contributions to ES researchers and practitioners

The Application of Wavelet Neural Network in the Settlement Monitoring of Subway

The settlement monitoring of subway runs through the entire construction stage of subway. It is very important to predict the accurate settlement value for construction safety of subway. In this paper, the wavelet transform is used to denoise the settlement data. The auxiliary wavelet neural network, embedded wavelet neural network and single BP neural network are applied to predict the settlement of Tianjin subway. Compared with single BP neural network and auxiliary wavelet neural network, the embedded wavelet neural network model has a higher accuracy and better prediction effect. The embedded wavelet neural network is more valuable than the BP neural network model so it can be used in the prediction of subway settlemen

Exploring the core knowledge of business intelligence

With the rapid development of data analysis, there is few research on the core knowledge of business intelligence system (BIS). In order to fill this research gap, this paper collected the 1003 articles and 31345 references from the Web of Science database, and then applied co-citation analysis and factor analysis, to analyze their core knowledge. We identified 52 highly cited articles and obtained 9 core knowledge categories in the field of BI: BI success, IT acceptance and measurement, big data analysis,dataanalysis anddecision making,business strategy, BIS,consumer behavior, knowledge management, business adoption. Research shows that BISs are still in the growing trend and core knowledge helps researchers and managers better understand the core concepts and relevance of BI, so as to quickly discover possible research directionsinthisresearchfieldandusefulapplicationsintheenterprise

Effect of He Plasma Jet Versus Surface Plasma on the Metabolites of Acute Myeloid Leukemia Cells

Cold atmospheric plasma, including plasma jet and surface plasma, can promote the apoptosis of cancer cells without causing significant damage to surrounding normal cells, which was hopeful to be applied to the clinical cancer therapy. However, experimental plasma devices used directly to clinical experiments has challenges in technology and methods, especially the difference in killing tumor cells efficiency of these two common plasma sources. Therefore, it is great necessity to explore the differences in treating tumors between different plasma sources. This paper achieved good killing efficiency by using two kinds of cold atmospheric plasma generating devices, namely plasma jet and surface plasma treatment along acute myeloid leukemia (AML). The results showed that the He plasma jet kills leukemia cells more efficiently than surface plasma with the same voltage and frequency and the same time. By GC-TOFMS and metabolomics analysis, this paper compared the differential metabolites of leukemia cells treated by two plasma devices and the key metabolic pathways closely related to differential metabolites. Simultaneously, we found alanine, aspartate and glutamate metabolism was most correlated with a key differential metabolite, glutamine. It was found that the glutaminase activity of He plasma jet group was lower than that of surface plasma group, which might be a reason for He plasma jet group to kill tumor cells better. It was also worth noting that relative quantity of glucose metabolites of plasma jet treatment group was lower than that of surface plasma treatment group. This study provides the basis for clinical trials for future

Can proline dehydrogenase—a key enzyme involved in proline metabolism—be a novel target for cancer therapy?

Emerging evidence suggests that proline metabolism is important for regulating the survival and death of different types of cancer cells. Proline dehydrogenase (PRODH), an enzyme catalyzing proline catabolism, and the degradation products of proline by PRODH, such as ATP and ROS, are known to play critical roles in cancer progression. Notably, the role of PRODH in cancer is still complicated and unclear, and primarily depends on the cancer type and tumor microenvironment. For instance, PRODH induces apoptosis and senescence through ROS signaling in different types of cancers, while as a protumor factor, PRODH promotes malignant phenotypes of certain tumors under stresses such as hypoxia. In order to assess whether PRODH can serve as a novel target for cancer therapy, we will provide an overview of the biological functions of PRODH and its double-edged role in cancer in this article

Upgrading the quality of recycled aggregates from construction and demolitionwaste by using a novel brick separation and surface treatment method

Mixed recycled aggregates (MRA) from construction and demolition waste (CDW) with high-purity and environmental performance are required for highway construction application in base layer and precast concrete curbs. The main problematic constituents that reduce the quality level of the recycled aggregates applications are brick components, flaky particles, and attached mortar, which make up a large proportion of CDW in some countries. This paper studies the potential of brick separation technology based on shape characteristics in order to increase the recycled concrete aggregates (RCA) purity for MRA quality improvement. MRA after purification was also processed with surface treatment experiment by rotating in a cylinder to improve the shape characteristics and to remove the attached mortar. The purity, strength property, densities, water absorption ratio, shape index, and mortar removal ratio of MRA were studied before and after the use of the brick separation and surface treatment proposed in this study. Finally, the recycled aggregates upgradation solution was adopted in a stationary recycling plant designed for a length of 113 km highway construction. The properties of CDW mixed concrete for precast curbs manufacturing were conducted. The results indicate that problematic fractions (brick components, particle shape, and surface weakness) in the MRA were significantly reduced by using brick separation and surface treatment solution. Above all, it is very important that the proposed brick separation method was verified to be practically adopted in CDW recycling plant for highway base layer construction and concrete curbs manufacturing at a low cost

Weakly supervised conditional random fields model for semantic segmentation with image patches.

Image semantic segmentation (ISS) is used to segment an image into regions with differently labeled semantic category. Most of the existing ISS methods are based on fully supervised learning, which requires pixel-level labeling for training the model. As a result, it is often very time-consuming and labor-intensive, yet still subject to manual errors and subjective inconsistency. To tackle such difficulties, a weakly supervised ISS approach is proposed, in which the challenging problem of label inference from image-level to pixel-level will be particularly addressed, using image patches and conditional random fields (CRF). An improved simple linear iterative cluster (SLIC) algorithm is employed to extract superpixels. for image segmentation. Specifically, it generates various numbers of superpixels according to different images, which can be used to guide the process of image patch extraction based on the image-level labeled information. Based on the extracted image patches, the CRF model is constructed for inferring semantic class labels, which uses the potential energy function to map from the image-level to pixel-level image labels. Finally, patch based CRF (PBCRF) model is used to accomplish the weakly supervised ISS. Experiments conducted on two publicly available benchmark datasets, MSRC and PASCAL VOC 2012, have demonstrated that our proposed algorithm can yield very promising results compared to quite a few state-of-the-art ISS methods, including some deep learning-based models