The effect of medical material management system app on nursing workload and stress.

AIMS: To develop a clinical medical material management App for nurses, in order to reduce their workload and improve the efficacy of medical material management. DESIGN: The single-group pre- and post-test experimental design was adopted. METHODS: The subjects were nurses in the intensive care units of a regional hospital in Hsinchu City enrolled by purposive sampling. Single-group pre-tests and post-tests were conducted. The research period was from November 2019 to March 2020. The workload, stress, and information acceptance of 57 nurses before and after the intervention of the Medical Equipment App were collected. The research tools included a structured questionnaire, which includes open questions that cover the aspects of workload, stress, and information acceptance intention of nurses, as well as a demographic questionnaire, which collects the basic personal data, including gender, age, years of service, educational level, nursing ability level, use ability of IT products, and unit type. The results were analyzed and compared using SPSS, APP Inventor, and data mining modeling to determine the effects of the App. RESULTS: After employing the Shift Check App, the average workload of nurses was effectively reduced, in particular, the workload reduction of the N1 level nursing ability was greater than that of N2. In addition to satisfaction, the scores of information acceptance intention in all aspects, including behavioral intention, technology use intention, and contributing factors, all increased. CONCLUSION: The use of information technology products to assist medical material management in clinical practice has a significant effect on the load reduction of nurses and improvement of satisfaction. CLINICAL RELEVANCE: The App developed in this study can improve nurses' work satisfaction, quality of care and workload reduction.

Global-and-Local Context Network for Semantic Segmentation of Street View Images.

Semantic segmentation of street view images is an important step in scene understanding for autonomous vehicle systems. Recent works have made significant progress in pixel-level labeling using Fully Convolutional Network (FCN) framework and local multi-scale context information. Rich global context information is also essential in the segmentation process. However, a systematic way to utilize both global and local contextual information in a single network has not been fully investigated. In this paper, we propose a global-and-local network architecture (GLNet) which incorporates global spatial information and dense local multi-scale context information to model the relationship between objects in a scene, thus reducing segmentation errors. A channel attention module is designed to further refine the segmentation results using low-level features from the feature map. Experimental results demonstrate that our proposed GLNet achieves 80.8% test accuracy on the Cityscapes test dataset, comparing favorably with existing state-of-the-art methods.

Electron transport in high-resistance semiconductor nanowires through two-probe measurements.

Since the successful fabrication of semiconductor nanowires, various techniques have been developed to contact these nanowires and to probe their intrinsic electrical properties. Although many novel quasi one-dimensional materials such as Pb(1 - x)Mn(x)Se nanoarrays were recently produced, their intrinsic electron transport properties have not been extensively studied so far. In this work, we demonstrate that an ordinary source-drain configuration of field-effect transistors or the two-probe measurement can be applied to the exploration of the intrinsic properties of nanowires. This two-probe measurement approach also works on highly resistive nanowires without an Ohmic contact issue. By using this method, electron transport behavior, resistivity, and carrier concentrations of ZnO, InP, GaP, and Pb(1 - x)Mn(x)Se semiconductor nanowires have been investigated. Due to the tiny cross-section and few conducting channels, a nanomaterial usually reveals an ultra high resistance. This technique demonstrates a two-probe characterization of nanostructures, paving the simplest way toward electrical characterizations of all high-resistance nanomaterials such as deoxyribonucleic acid (DNA), molecules and organics.