This research aims at taking a further step in developing a methodology framework of innovation performance audit. With adopting the ideology of performance measurement system, a framework of key performance indicator(KPI) system including three level issues of what is input , what is done and what happens is designed for technological innovation audit, which can reflect the performance of technological innovation at firm s level comprehensively. Furthermore, a questionnaire survey on line is conducted to test the validity of the key performance indicator system.Based on data analysis, the KPI system is proved to be effective as a whole, of which most indicators not only are important, but also could be measured easily in firms. Finally, in-depth innovation performance audit of four hi-tech firms is taken as a case study to verify the validity of the KPI system.
From the perspective of the complex multi-factors that affect manufacturing green competitiveness, this study constructs a green competitiveness index measurement indicator system of manufacturing industry in Yangtze River Delta Urban Agglomeration, which includes five dimensions: economic creativity, technological innovation, energy and environmental protection, industrial structure optimization, and social service capabilities.The manufacturing green competitiveness index in Yangtze River Delta Urban Agglomeration in 2014-2018 is measured and analyzed by using the comprehensive evaluation model of gray correlation projection method based on the combined weights of FAHP and maximum deviation. The results show that manufacturing green competitiveness of Yangtze River Delta Urban Agglomeration generally shows a relatively stable and continuous improvement trend, but the regional differences are large: regional cities and general node cities have significantly lower manufacturing green competitiveness than the leading cities and hub cities, and the pace of industrial structure transformation and upgrading in the whole region also needs to be accelerated. Based on these results, this paper puts forward some policy recommendations for comprehensive development of Yangtze River Delta Urban Agglomeration manufacturing industry: focus on improving the effective guidance of the positive incentive effect of technological innovation on manufacturing green competitiveness level, and solving the problem of insufficient technological innovation achievement transformation benefits; replan regional space, strengthen the integration of all industrial resources, reducing homogeneous competition; strengthen the ecological co-construction of regional manufacturing and improve social service security level.
Through the establishment of a comprehensive evaluation index system, this paper analyzes the allocation of science and technology resources in the Yangtze River Delta urban agglomeration from 2014 to 2020, evaluates the allocation efficiency of science and technology resources from the perspective of multi input and output, and understands the advantages and disadvantages of regional resource allocation. The research results show that: (1) under the guidance of the national strategic policy of actively promoting the development of world-class urban agglomerations, the allocation efficiency of science and technology resources in various provinces and cities of the Yangtze River Delta continues to optimize, and the allocation level of some regions shows a rapid development trend; (2) R&D personnel and R&D funds are the core factors that affect the efficiency of science and technology resource allocation; (3) the marketization of resource allocation is helpful to improve its allocation efficiency; and (4) improving the transformation rate of scientific and technological achievements, opening up the channel for innovative products, technologies, and services to enter the market, and enabling innovative enterprises to make profits can provide strong and lasting incentives for the improvement of scientific and technological resource allocation efficiency. Based on the research conclusions, this paper puts forward countermeasures and suggestions to improve the allocation efficiency of scientific and technological resources in the Yangtze River Delta urban agglomeration from the aspects of human resources and material resources, and provides a theoretical reference for the coordinated and sustainable development of the Yangtze River Delta City Group under the background of the implementation of the urban agglomeration strategy and the construction of a scientific and technological infrastructure platform.
We investigated the sensitivity of regional tumor response prediction to variability in voxel clustering techniques, imaging features, and machine learning algorithms in 25 patients with locally advanced non-small cell lung cancer (LA-NSCLC) enrolled on the FLARE-RT clinical trial. Metabolic tumor volumes (MTV) from pre-chemoradiation (PETpre) and midchemoradiation FDG-PET images (PETmid) were subdivided into K-means or hierarchical voxel clusters by standardized uptake values (SUV) and 3D-positions. MTV cluster separability was evaluated by CH index, and morphologic changes were captured by Dice similarity and centroid Euclidean distance. PETpre conventional features included SUVmean, MTV / MTV cluster size, and mean radiation dose. PETpre radiomics consisted of 41 intensity histogram and 3D texture features (PET Oncology Radiomics Test Suite) extracted from MTV or MTV clusters. Machine learning models (multiple linear regression, support vector regression, logistic regression, support vector machines) of conventional features or radiomic features were constructed to predict PETmid response. Leave-one-out-cross-validated root-mean-squared-error (RMSE) for continuous response regression (ΔSUVmean) and area-under-receiver-operating-characteristic-curve (AUC) for binary response classification were calculated. K-means MTV 2-clusters (MTVhi, MTVlo) achieved maximum CH index separability (Friedman p<0.001). Between PETpre and PETmid, *
This paper considers an EOQ inventory model with presale policy for deteriorating items, in which the demand rate depends on both on-hand inventory and selling price. Under the assumption that all the presale orders are fully backlogged with waiting-time dependent rebate, this study develops several propositions and derives optimal pricing and ordering policy by designing an effective algorithm. Two numerical examples are also given to illustrate the effectiveness of the algorithm. Finally, the sensitivity analysis of the main parameters is provided.
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