How ICT and R&D affect productivity? Firm level evidence for China

Based on an extended three-step CDM model, this paper addresses the impacts of research and development (R&D) and information and communication technology (ICT) on firm productivity for the World Bank innovation survey data of China. The study includes ICT investment and R&D as the two main inputs into innovation and productivity. We find that R&D and ICT investments positively affect product innovation and process innovation, with R&D being more important for innovation and productivity, and ICT being more important for innovation and no direct effect on productivity. We conclude that R&D and ICT investments increase the probability of product innovation and process innovation, which increase firm’s productivity, suggesting that R&D and ICT investments indirectly affect productivity through innovation

Long-Term Exposure to High Altitude Affects Voluntary Spatial Attention at Early and Late Processing Stages

The neurocognitive basis of the effect of long-term high altitude exposure on voluntary attention is unclear. Using event related potentials, the high altitude group (people born in low altitude but who had lived at high altitude for 3 years) and the low altitude group (living in low altitude only) were investigated using a voluntary spatial attention discrimination task under high and low perceptual load conditions. The high altitude group responded slower than the low altitude group, while bilateral N1 activity was found only in the high altitude group. The P3 amplitude was smaller in the high altitude compared to the low altitude group only under high perceptual load. These results suggest that long-term exposure to high altitudes causes hemispheric compensation during discrimination processes at early processing stages and reduces attentional resources at late processing stages. In addition, the effect of altitude during the late stage is affected by perceptual load

Comparison between New-Onset and Old-Diagnosed Type 2 Diabetes with Ketosis in Rural Regions of China

Objectives. Type 2 diabetes (T2D) with ketosis was common because of late diagnosis and lacking adequate treatment in rural regions of China. This study aimed to provide the data of T2D with ketosis among inpatients in a south-west border city of China. Methods. Data of 371 patients of T2D with ketosis who were hospitalized between January 2011 and July 2015 in Baoshan People’s Hospital, Yunnan, China, were analyzed. New-onset and old-diagnosed T2D patients presenting with ketosis were compared according to clinical characteristics, laboratory results, and chronic diabetic complications. Results. Overall, the blood glucose control was poor in our study subjects. Male predominated in both groups (male prevalence was 68% in new-onset and 64% in old-diagnosed groups). Overweight and obesity accounted for 50% in new-onset and 46% in old-diagnosed cases. Inducements of ketosis were 13.8% in new-onset and 38.7% in old-diagnosed patients. Infections were the first inducements in both groups. The prevalence of chronic complications of diabetes was common in both groups. Conclusions. More medical supports were needed for the early detection and adequate treatment of diabetes in rural areas of China

The explicit formula and parity for some generalized Euler functions

Utilizing elementary methods and techniques, the explicit formula for the generalized Euler function $\varphi_{e}(n)(e = 8, 12)$ has been developed. Additionally, a sufficient and necessary condition for $\varphi_{8}(n)$ or $\varphi_{12}(n)$ to be odd has been obtained, respectively

Generalizing across Temporal Domains with Koopman Operators

In the field of domain generalization, the task of constructing a predictive model capable of generalizing to a target domain without access to target data remains challenging. This problem becomes further complicated when considering evolving dynamics between domains. While various approaches have been proposed to address this issue, a comprehensive understanding of the underlying generalization theory is still lacking. In this study, we contribute novel theoretic results that aligning conditional distribution leads to the reduction of generalization bounds. Our analysis serves as a key motivation for solving the Temporal Domain Generalization (TDG) problem through the application of Koopman Neural Operators, resulting in Temporal Koopman Networks (TKNets). By employing Koopman Operators, we effectively address the time-evolving distributions encountered in TDG using the principles of Koopman theory, where measurement functions are sought to establish linear transition relations between evolving domains. Through empirical evaluations conducted on synthetic and real-world datasets, we validate the effectiveness of our proposed approach.Comment: 15 pages, 7 figures, Accepted by AAAI 2024. arXiv admin note: text overlap with arXiv:2206.0004