Monthly extended ocean predictions based on a convolutional neural network via the transfer learning method

Sea surface temperature anomalies (SSTAs) and sea surface height anomalies (SSHAs) are indispensable parts of scientific research, such as mesoscale eddy, current, ocean-atmosphere interaction and so on. Nowadays, extended-range predictions of ocean dynamics, especially in SSTA and SSHA, can provide daily prediction services in the range of 30 days, which bridges the gap between synoptic-scale weather forecasts and monthly average scale climate predictions. However, the forecast efficiency of extended range remains problematic. With the development of ocean reanalysis and satellite remote sensing products, large amounts datasets provide an unprecedented opportunity to use big data for the extended range prediction of ocean dynamics. In this study, a hybrid model, combing convolutional neural network (CNN) model with transfer learning (TL), was established to predict SSTA and SSHA at monthly scales, which makes full use of these data resources that arise from delayed gridding reanalysis products and real-time satellite remote sensing observations. The proposed model, where both ocean and atmosphere reanalysis datasets serve as the pretraining dataset and the satellite remote sensing observations are employed for fine-tuning based on the transfer learning (TL) method, can effectively capture the evolving spatial characteristics of SSTAs and SSHAs with low prediction errors over the 30 days range. When the forecast lead time is 30 days, the root means square errors for the SSTAs and SSHAs model results are 0.32°C and 0.027 m in the South China Sea, respectively, indicating that this model has not only satisfactory prediction performance but also offers great potential for practical operational applications in improving the skill of extended-range predictions

Study on the substitutability of nighttime light data for SDG indicators: a case study of Yunnan Province

Introdution: One crucial method to attain Sustainable Development Goals (SDGs) involves timely adjustment of development policies, promoting the realization of SDGs through a time-series assessment of the degree of accomplishment. In practical applications, data acquisition is a significant constraint in evaluating the SDGs, not only in China but across the globe. Hence, expanding data channels and exploring the feasibility of various data sources for sustainable development assessment are effective strategies to tackle the challenge of data acquisition.Methods: In light of this issue, this study selected Nighttime Light Data, a remote sensing data source closely linked to human social activities, as an alternative data source. Using Yunnan Province as an example, 16 localized indicators of social, economic, and environmental types were chosen. These indicators were then subjected to a correlation analysis with the Total Nighttime Light Index (TNLI). The relationships between different types of indicators and TNLI were analyzed at both temporal and spatial scales, thus identifying the indicators for which TNLI could serve as a suitable substitute measure.Results: The study indicates that when the SDG indicators are classified into economic, social and environmental categories, the total value of nighttime light presents a significant correlation and substitutability with economic indicators; significantly correlated with some social indicators, it can reveal the weak links in the development of underdeveloped areas; it is not significantly correlated with environmental indicators, while a trend correlation exists, which can provide some reference values.Discussion: This study has demonstrated the feasibility of using Nighttime Light Data for sustainable development assessment. It provides a novel evaluation method for countries that, despite a lack of resources for conducting sustainable development assessments, have a greater need for such assessments due to their lower economic development. Furthermore, a multitude of assessment methods can be developed based on Nighttime Light Data

The impact of SARS-Cov-2 infection on the periocular injection pain and hypersensitive reaction to botulinum toxin type A: results from clinical questionnaires

BackgroundThe COVID-19 pandemic has brought about significant changes in the medical field, yet the use of botulinum toxin type A has remained uninterrupted. Plastic surgeons must carefully consider the timing of administering botulinum toxin type A to patients who have recovered from COVID-19.MethodsA questionnaire survey was conducted among patients who had contracted and recovered from SARS-CoV-2 within a month. The survey aimed to investigate various indicators in patients who had received botulinum toxin A injections at the same site before and after their infection, including pain scores and allergic reactions and the occurrence of complications.ResultsThe pain scores of patients who contracted SARS-CoV-2 infection between 14-21 days post-infection exhibited significant variation from previous injections. However, patients who contracted the infection between 22-28 days post-infection did not exhibit significant variation from previous injections. Furthermore, the incidence of allergic reactions and complications following botulinum toxin injection within one month after contracting the infection did not significantly differ from that observed prior to infection.ConclusionAdministering botulinum toxin type A three weeks after COVID-19 recovery is a justifiable and comparatively secure approach

Autonomous motion and control of lower limb exoskeleton rehabilitation robot

Introduction: The lower limb exoskeleton rehabilitation robot should perform gait planning based on the patient’s motor intention and training status and provide multimodal and robust control schemes in the control strategy to enhance patient participation.Methods: This paper proposes an adaptive particle swarm optimization admittance control algorithm (APSOAC), which adaptively optimizes the weights and learning factors of the PSO algorithm to avoid the problem of particle swarm falling into local optimal points. The proposed improved adaptive particle swarm algorithm adjusts the stiffness and damping parameters of the admittance control online to reduce the interaction force between the patient and the robot and adaptively plans the patient’s desired gait profile. In addition, this study proposes a dual RBF neural network adaptive sliding mode controller (DRNNASMC) to track the gait profile, compensate for frictional forces and external perturbations generated in the human-robot interaction using the RBF network, calculate the required moments for each joint motor based on the lower limb exoskeleton dynamics model, and perform stability analysis based on the Lyapunov theory.Results and discussion: Finally, the efficiency of the APSOAC and DRNNASMC algorithms is demonstrated by active and passive walking experiments with three healthy subjects, respectively

The socio-spatial design of community and governance: Interdisciplinary urban design in China

This book proposes a new interdisciplinary understanding of urban design in China based on a study of the transformative effects of socio-spatial design and planning on communities and their governance. This is framed by an examination of the social projects, spaces, and realities that have shaped three contexts critical to the understanding of urban design problems in China: the histories of “collective forms” and “collective spaces”, such as that of the urban danwei (work-unit), which inform current community building and planning; socio-spatial changes in urban and rural development; and disparate practices of “spatialised governmentality”. These contexts and an attendant transformation from planning to design and from government to governance, define the current urban design challenges found in the dominant urban xiaoqu (small district) and shequ (community) development model. Examining the histories, transformations, and practices that have shaped socio-spatial epistemologies and experiences in China – including a specific sense of community and place that is rather based on a concrete “collective” than abstract “public” space and underpinned by socialised governance – this book brings together a diverse range of observations, thoughts, analyses, and projects by urban researchers and practitioners. Thereby discussing emerging interdisciplinary urban design practices in China, this book offers a valuable resource for all academics, practitioners, and stakeholders with an interest in socio-spatial design and development