Impact of R&D innovation and political background on corporate growth: A study based on private listed companies in China.

Based on a review of related concepts and theories this study investigates the different impacts of research and development (R&D) innovation and political background on corporate growth in a particular context. Unlike other studies, we integrate these two factors. We empirically analyze 6079 sets of data from 1292 A-share private manufacturing enterprises in Shanghai and Shenzhen from 2012 to 2019. The results show that these factors directly impact corporate growth and have heterogeneous effects at different enterprise growth levels. We find the effect of R&D innovation on corporate growth is more pronounced for young firms. These findings highlight the need for firms to adjust their investments in R&D innovation and political backgrounds at different stages of development to adapt to different markets and political environments.

Decision tree accelerated CTU partition algorithm for intra prediction in versatile video coding.

Versatile video coding (VVC) achieves enormous improvement over the advanced high efficiency video coding (HEVC) standard due to the adoption of the quadtree with nested multi-type tree (QTMT) partition structure and other coding tools. However, the computational complexity increases dramatically as well. To tackle this problem, we propose a decision tree accelerated coding tree units (CTU) partition algorithm for intra prediction in VVC. Firstly, specially designated image features are extracted to characterize the coding unit (CU) complexity. Then, the trained decision tree is employed to predict the partition results. Finally, based on our newly designed intra prediction framework, the partition process is early terminated or redundant partition modes are screened out. The experimental results show that the proposed algorithm could achieve around 52% encoding time reduction for various test video sequences on average with only 1.75% Bjontegaard delta bit rate increase compared with the reference test model VTM9.0 of VVC.

Remote Diabetic Foot Temperature Monitoring for Early Detection of Diabetic Foot Ulcers: A Cost-Effectiveness Analysis.

BACKGROUND: Foot temperature monitoring for the prevention and early detection of diabetic foot ulcers (DFU) is evidence-based and recommended in clinical practice. However, easy-to-use remote monitoring tools have been lacking, thereby preventing widespread adoption. OBJECTIVE: To evaluate the cost-effectiveness of remote foot temperature monitoring (RFTM) (Siren's Neurofabric™ Diabetic socks) in addition to standard of care (SoC) versus SoC alone for early detection of DFU with diabetic neuropathy and a moderate to high risk of DFU. METHODS: A payer perspective decision-tree analysis was conducted to compare expected DFU occurrence and subsequent amputation rates and costs between treatment strategies over one year. Inputs in the model were sourced from publicly available literature and relevant health technology assessments. One-way sensitivity analyses were performed for each model variable. RESULTS: In the base-case scenario, RFTM plus SoC was a dominant strategy compared to SoC alone. RFTM plus SoC was associated with cost savings of $38,593 per additional ulcer avoided versus SoC alone, and $8027 per patient per year on average compared to SoC alone. These results were highly robust to one-way sensitivity analysis; all scenarios remained dominant if compliance was ≥13%. CONCLUSION: RFTM is a cost-effective addition to SoC in patients with diabetic neuropathy at a moderate-to-high risk of DFU and subsequent amputation. Further, reduction in DFU and associated complications may result in improvements in the patient's quality of life and mental health. Future studies are needed to evaluate the compliance and reduction of DFU occurrence in patients on RFTM.