Sustaining urban growth through innovative capacity : Beijing and Shanghai in comparison

The authors examine the diverse prospects of innovative sectors in Beijing and Shanghai using available indicators and data collected for this study through surveys. Beijing is the first choice for companies locating in China, but foreign employees prefer Shanghai for living convenience and cultural amenities. While Shanghai lags behind Beijing in knowledge creation and the generation of startup companies in the innovative sectors, it takes the lead in the commercialization of technological innovations and the development of creative cultural industries. The municipal authorities of Beijing and Shanghai have improved the innovation environment of the cities, but certain elements still stunt the growth of innovative industries, which cannot be removed easily. Three kinds of knowledge-intensive enterprises included in innovative sectors in the survey are high-tech manufacturers, knowledge-intensive business services, and creative content providers. The survey found that the clustering of the firms arose from the attraction of preferential policies and the purchase by governments or state-owned enterprises of information technology products. The survey shows that interaction among firms is inadequate in the knowledge-based industrial clusters in both Beijing and Shanghai. Hence, it may be some time before clustering leads to substantial gains in collective efficiency for innovative industry in Beijing and Shanghai.ICT Policy and Strategies,Health Monitoring&Evaluation,Water and Industry,Environmental Economics&Policies,Banks&Banking Reform

An analysis of new-tech agglomeration in Beijing: a new industrial district in the making?

Industrial districts are usually referred to as spatially concentrated networks of small and medium-sized firms. These have been seen in Europe and North America, but, so far, have been almost undiscovered in developing countries. Based on the assumption of the strong embedding of the stable and 'pure' district model, in this paper we examine a new-tech agglomeration in Beijing, as a variant of such districts in the making, and explain it with the use of concepts adopted from the industrial districts school. The Beijing case represents an experiment in the conscious public creation of new industrial spaces founded on the spontaneous action of key individuals. Initially it progressed as an embryonic industrial district that, in its early development, appeared to contain all three elements of entrepreneurship: small firms, new firm formation, and innovativeness. However, it has eventually been stranded by a unique combination of weaknesses. These include strong hierarchical restraints from the state-owned institutions or firms on local networking, and direct global linkages with the multinationals, which expose local economies to volatile world competition. We pinpoint the necessity for a developing country to rest its development of industrial districts on self-sustained innovativeness, and highlights the difficulties encountered in such a process.

Network dynamics and cluster evolution: changing trajectories of the aluminium extrusion industry in Dali, China

Research on industrial clusters has shifted in a paradigmatic way from an exploration of structural ideal-types toward evolutionary explanations. Thus far, however, networks-the key concept in the former paradigm-and evolution-the focus in the latter-remain somewhat unconnected in the literature. This article addresses this gap by developing a comprehensive tri-polar analytical framework of cluster evolution. This framework combines the three concepts of context, network and action, allowing each to evolve in interaction with the others. The empirical analysis applies this framework to the aluminium extrusion industry cluster in Dali, Guangdong province, which has developed over a period of 30 years. Our study finds that with the formation of a new generation of entrepreneurs, previous kinship-based learning networks have disappeared, causing significant changes to action and interaction within and between firms.EconomicsGeographySSCI9ARTICLE1127-1551

Determining plastic slips in rate-independent crystal plasticity models through machine learning algorithms

Dislocation slip-based crystal plasticity models have been a great success in connecting the fundamental physics with the macroscopic deformation of crystalline materials. Pioneered by Taylor in his work on "plastic strain in metals" (Taylor, 1938), and further advanced by Bishop and Hill (1951a, 1951b), the Taylor-Bishop-Hill theory laid the foundation of today's constitutive models on crystal plasticity. An intriguing part of those modeling is to determine the active slip systems-which system to be involved in and how much it contributes to the deformation. In this paper, we developed a machine learning-based algorithm to determine accurately and efficiently the active slip systems in crystal plasticity constitutive models. Applications to the common three polycrystalline metals, face-centered cubic (FCC) copper, body-centered cubic (BCC) alpha-iron, and hexagonal close-packed (HCP) AZ31B, demonstrate that even a simple neural network could give rise to accurate and efficient results in comparing with traditional routines. There seems to be plenty of space for further reducing the computation time and hence scaling up the simulating samples

A Scene Text Detector for Text with Arbitrary Shapes

The performance of text detection is crucial for the subsequent recognition task. Currently, the accuracy of the text detector still needs further improvement, particularly those with irregular shapes in a complex environment. We propose a pixel-wise method based on instance segmentation for scene text detection. Specifically, a text instance is split into five components: a Text Skeleton and four Directional Pixel Regions, then restoring itself based on these elements and receiving supplementary information from other areas when one fails. Besides, a Confidence Scoring Mechanism is designed to filter characters similar to text instances. Experiments on several challenging benchmarks demonstrate that our method achieves state-of-the-art results in scene text detection with an F-measure of 84.6% on Total-Text and 86.3% on CTW1500

Negative health outcomes associated with food insecurity status in the United States of America: A systematic review of peer-reviewed studies

Introduction: Social determinants of health, such as food security, are an important target for health providers, particularly in the care of patients from underserved populations, including the uninsured and socially marginalized. Preliminary research has shown that food insecurity status (FIS) is associated with negative health outcomes. Objective: We aim to present a concise, yet comprehensive resource that lists the health outcomes associated with FIS. This guide is meant to provide innovative health providers with the tools needed to justify the importance of using FIS screening and treatment as a preventive medicine intervention. Methods: We conducted a systematic review of peer-reviewed manuscripts that studied FIS in the United States of America (USA) and at least one health outcome. We searched PubMed, Embase, Web of Science, and Scopus and had multiple reviewers examine each abstract and manuscript. We only retained peer-reviewed studies that contained USA data, directly measured FIS, and directly compared FIS to a health outcome. Results: The initial search yielded 1,817 manuscripts. After screening abstracts for duplicates and inclusion criteria, a total of 117 manuscripts were retained and fully examined. Several manuscripts showed significant association between FIS and neurologic, cardiac, endocrine, and pulmonary health outcomes. Studies in the USA population show robust associations between FIS and poor mental health (including depression, anxiety, sleep disorders, impaired cognitive functioning, and epilepsy), metabolic syndrome, hyperlipidemia, greater risk for bone fracture in children, higher risk of end-stage renal disease in patients with chronic kidney disease, self-reported poor health, and higher mortality in patients with the human immunodeficiency virus. Though other literature reviews show positive associations between FIS and health outcomes such as diabetes, body mass index, and hypertension, our systematic review showed mixed results. Conclusions: FIS leaves underserved populations at risk for negative health outcomes. More research should be done to examine the effects of FIS alleviation as a preventative medicine intervention

Anisotropic expansion and size-dependent fracture of silicon nanotubes during lithiation

Silicon nanotube anodes are notably promising for high-performance lithium-ion batteries due to their outstanding structural stability, but fundamental understanding about their structural evolution during lithiation still remains unclear. Here, the expansion and fracture behavior of lithiated silicon nanotubes is investigated, and the influences of the crystal phase, crystal orientation, inner radius, wall thickness, and thickness-radius ratio are demonstrated. Experiments and simulations demonstrate anisotropic expansion and outer-surface fractures of crystalline silicon nanotubes and isotropic expansion of amorphous ones. The inner holes of nanotubes undergo much less expansion than the outside. The fracture ratio and the maximum hoop stress are positively correlated with both wall thickness and inner radius for crystalline silicon nanotubes. Their competition gives rise to an optimal thickness-outer radius ratio of about 2/3, and the critical diameter reaches 700 nm correspondingly. This research provides significant insight into the lithiation behavior of silicon nanotubes, which could help to design improved silicon anodes

IUP-BERT: Identification of Umami Peptides Based on BERT Features

Umami is an important widely-used taste component of food seasoning. Umami peptides are specific structural peptides endowing foods with a favorable umami taste. Laboratory approaches used to identify umami peptides are time-consuming and labor-intensive, which are not feasible for rapid screening. Here, we developed a novel peptide sequence-based umami peptide predictor, namely iUP-BERT, which was based on the deep learning pretrained neural network feature extraction method. After optimization, a single deep representation learning feature encoding method (BERT: bidirectional encoder representations from transformer) in conjugation with the synthetic minority over-sampling technique (SMOTE) and support vector machine (SVM) methods was adopted for model creation to generate predicted probabilistic scores of potential umami peptides. Further extensive empirical experiments on cross-validation and an independent test showed that iUP-BERT outperformed the existing methods with improvements, highlighting its effectiveness and robustness. Finally, an open-access iUP-BERT web server was built. To our knowledge, this is the first efficient sequence-based umami predictor created based on a single deep-learning pretrained neural network feature extraction method. By predicting umami peptides, iUP-BERT can help in further research to improve the palatability of dietary supplements in the future

Framework for virtual education of COVID-19 vaccines for Mandarin-speaking learners: an educational intervention module [version 3; peer review: 2 approved]

Background: In the United States, patients with limited English proficiency face significant barriers to comprehending and acting upon health-related information, particularly during the COVID-19 pandemic. The ability of health professionals to communicate COVID-19-related information to Mandarin-speaking patients has proved critical in discussions about vaccine efficacy, side effects, and post-vaccine protection. Methods: The authors created a one-hour educational module to help Mandarin-speaking medical students better convey COVID-19 vaccine information to Mandarin-only speakers. The module is composed of an educational guide, which introduced key terminology and addressed commonly asked questions, and pre- and post-surveys. The authors recruited 59 Mandarin-speaking medical students all of whom had previously completed a medical Mandarin elective. The module and surveys were distributed and completed in August 2021. Data analysis measured the change in aggregate mean for subjective five-point Likert-scale questions and change in percent accuracy for objective knowledge-based questions. Results: 86.4% of participants were primary English speakers with variable levels of Mandarin proficiency. The educational module significantly improved participants' subjective comfort level in discussing the COVID-19 vaccine in English and Mandarin. The largest improvement in both English and Mandarin was demonstrated in participants' ability to explain differences between the COVID-19 vaccines, with an aggregate mean improvement of 0.39 for English and 1.48 for Mandarin. Survey respondents also demonstrated increased percent accuracy in knowledge-based objective questions in Mandarin. Conclusions: This module provides Mandarin-learning medical students with skills to deliver reliable information to the general population and acts as a model for the continued development of educational modules for multilingual medical professionals