An Investigation on Responsible Innovation in the Emerging Shared Bicycle Industry:Case study of a Chinese firm

In the current era of Industrial 4.0, open innovation, and the sharing economy, innovation ecosystems are formed through government-industry-university (triple helix) interaction. The concept of responsible innovation has emerged to explore how innovation can be conducted in a transparent, trustworthy, and sustainable way so as to respond to the public interest. While current literature provides a conceptual framework, details of how responsible innovation can be formed, developed, and sustained in the sharing economy, in particular in developing countries, have been under-explored. This paper aims to explore factors of responsible innovation, linking dimensions with business practice, and identify the dynamic stages of the industry life cycle. Through an in-depth case study of China’s shared bicycle industry and the firm Hellobike, this paper has prioritized factors which lead to responsibility, such as user safety and friendliness in product design, real-time operations combined with big data, collaboration between industry and local government for industry standardization, and user credit systems. It has enriched key dimensions based on literature and case studies and proposed dynamic interaction models for industry, government, users, and universities at different stages of responsible innovation in the shared bicycle sector. From this empirical study, future research areas have been identified

Antidiabetic effect of Tibetan medicine Tang-Kang-Fu-San in db/db mice via activation of PI3K/Akt and AMPK pathways

This study was to investigate the anti-diabetic effects and molecular mechanisms of Tang-Kang-Fu-San (TKFS), a traditional Tibetan medicine, in treating type 2 diabetes mellitus of spontaneous diabetic db/db mice. Firstly HPLC fingerprint analysis was performed to gain the features of the chemical compositions of TKFS. Next different doses of TKFS (0.5 g/kg, 1.0 g/kg, and 2.0 g/kg) were administrated via oral gavage to db/db mice and their controls for 4 weeks. TKFS significantly lowered hyperglycemia and ameliorated insulin resistance (IR) in db/db mice, indicated by results from multiple tests, including fasting blood glucose test, intraperitoneal insulin and glucose tolerance tests, fasting serum insulin levels and homeostasis model assessment of IR analysis as well as histology of pancreas islets. TKFS also decreased concentrations of serum triglyceride, total and low-density lipoprotein cholesterol, even though it did not change the mouse body weights. Results from western blot and immunohistochemistry analysis indicated that TKFS reversed the down-regulation of p-Akt and p-AMPK, and increased the translocation of Glucose transporter type 4 in skeletal muscles of db/db mice. In all, TKFS had promising benefits in maintaining the glucose homeostasis and reducing IR. The underlying molecular mechanisms are related to promote Akt and AMPK activation and Glucose transporter type 4 translocation in skeletal muscles. Our work showed that multicomponent Tibetan medicine TKFS acted synergistically on multiple molecular targets and signaling pathways to treat type 2 diabetes mellitus

Colored Permutation Statistics by Conjugacy Class

In this paper, we consider the moments of permutation statistics on conjugacy classes of colored permutation groups. We first show that when the cycle lengths are sufficiently large, the moments of arbitrary permutation statistics are independent of the conjugacy class. For permutation statistics that can be realized via $\textit{symmetric}$ constraints, we show that for a fixed number of colors, each moment is a polynomial in the degree $n$ of the $r$-colored permutation group $\mathfrak{S}_{n,r}$. Hamaker & Rhoades (arXiv 2022) established analogous results for the symmetric group as part of their far-reaching representation-theoretic framework. Independently, Campion Loth, Levet, Liu, Stucky, Sundaram, & Yin (arXiv, 2023) arrived at independence and polynomiality results for the symmetric group using instead an elementary combinatorial framework. Our techniques in this paper build on this latter elementary approach

Correction method by introducing cloud cover forecast factor in model temperature forecast

Objective temperature forecast products can achieve better forecast quality by using one-dimensional regression correction directly based on the present model temperature forecast product, and the forecast accuracy can be further improved by adding appropriate auxiliary factors. In this paper, ECMWF forecast products and ground observation data from Fujian are used to revise the surface temperature at 2 m by introducing a cloud cover forecast factor based on the model temperature forecast correction method. Analysis shows that the forecast deviation of daily maximum and minimum temperature after the revision of a single-factor forecast is obviously correlated with cloud cover. A variety of prediction schemes are designed, and the final scheme is determined through comparative testing. The following conclusions are drawn: all schemes based on cloud cover grouping can improve forecast performance, and the total cloud cover scheme is generally better than the low cloud cover scheme. There is a good positive correlation between the forecast deviation of maximum temperature and the mean total cloud cover; that is, the more cloud cover, the bigger the deviation. The minimum temperature is negatively correlated with cloud cover when the cloud cover is less than 40% and positively correlated for the rest. The absolute forecast deviations of the maximum and minimum temperatures are larger when the total cloud cover is less. Whether for Tmax or Tmin forecast, the binary regression scheme after grouping consistently showed the best performance, with the lowest MAE. The final scheme was used to forecast the maximum and minimum temperature in 2021, and most verification indicators showed improvement in most forecast periods. The forecast accuracy for the 36-h daily maximum and minimum temperature is 81.312% and 91.480%, respectively, which is 2.4%–2.6% higher than the single-factor regression scheme. The forecast skill scores (FSS) reach 0.065 and 0.086, indicating that the method can effectively improve forecast quality in a stable manner and can be used for practical forecasting

BN-embedded monolayer graphene with tunable electronic and topological properties

Finding an effective and controllable way to create a sizable energy gap in graphene-based systems has been a challenging topic of intensive research. We propose that the hybrid of boron nitride and graphene (h-BNC) at low BN doping serves as an ideal platform for band-gap engineering and valleytronic applications. We report a systematic first-principles study of the atomic configurations and band gap opening for energetically favorable BN patches embedded in graphene. Based on first-principles calculations, we construct a tight-binding model to simulate general doping configurations in large supercells. Unexpectedly, the calculations find a linear dependence of the band gap on the effective BN concentration at low doping, arising from an induced effective on-site energy difference at the two C sublattices as they are substituted by B and N dopants alternately. The significant and tunable band gap of a few hundred meVs, with preserved topological properties of graphene and feasible sample preparation in the laboratory, presents great opportunities to realize valley physics applications in graphene systems at room temperature