Do Honored Cities Achieve a Sustainable Development? A Quasi-Natural Experimental Study Based on “National Civilized City” Campaign in China

As a new model of urban governance with Chinese characteristics, the national honored cities from city evaluation competitions, represented by the “National Civilized City” campaign, has always been popular among Chinese cities. Can the honored cities of the campaigns achieve sustainable development, and how? Based on the five concepts of sustainable development, which are innovation, coordination, green, openness and sharing, this study sets up a comprehensive index to measure the sustainability of the growth of a city. Then, the data of 242 Chinese cities from 2011 to 2019 and the difference-in-differences (DID) approach are used to evaluate the impacts of the Civilized City honored in the campaigns on the sustainability of growth. The results show that: (1) the “Civilized City” honor promotes sustainable development in Chinese cities; (2) the mechanism analysis shows that the “Civilized City” honor contributes to the sustainability of growth by effectively promoting the level of industrial agglomeration in cities; (3) further heterogeneity analysis shows that the effect of the “Civilized City” honor on the sustainability of growth varies by city size, the administrative level and the location of the city. By providing the evidence of economic effects of the “Civilized City” honor, this research rationalizes the city campaigns run by the Chinese government and provides important enlightenment for the continuous improvement of the selection mechanism of the national honored cities to promote sustainable development

PCANN: Distributed ANN Architecture for Image Recognition in Resource-Constrained IoT Devices

As deployment of Internet of Things (IoT) devices gain momentum, there is an increased interest in implementing machine learning (ML) algorithms on IoT devices. Most of the existing ML solutions, however, rely on a central server to execute data-intensive ML models, because most devices in IoT systems do not have sufficient storage and computing resources. This paper presents a distributed Artificial Neural Networks (ANN) architecture, called PCANN, which allows execution of a complex image recognition task on a collection of resource-constrained IoT devices. Our solution separates a single ML model into multiple small modules that are executed by the distributed IoT devices. The solution effectively reduces storage and computing requirements for individual devices to store and process ML model. We design multiple PCANN models and utilize the models for human posture recognition as the case study. The experimental results show that the distributed PCANN architecture achieves comparable accuracy as the classical ANN model, while the average size of each PCANN module is largely reduced

A Study of Unilateral Upper Limb Fine Motor Imagery Decoding Using Frequency-Band Attention Network

Brain-computer interface (BCI) based motor imagery (MI) can assist stroke patients in upper limb rehabilitation and help restore motor function to a certain extent. However, the classical MI paradigm distinguishes different limbs and cannot effectively meet the needs of upper limb rehabilitation training for patients. Therefore, this paper designed a new paradigm for three motor imagery actions targeting different joints of the unilateral upper limb, and electroencephalogram (EEG) data from 20 healthy participants were collected for research analysis. A deep neural network model combining an attention mechanism for multiple frequency bands and a deep convolutional network were proposed to adaptively assign weight to the EEG data in different frequency bands. Then feature extraction was performed for each frequency band to learn further and to classify features. This model can obtain an average accuracy of 69.2% for the subject-independent case with the triple classification in the designed fine motor imagery (FMI) dataset, which is better than other controlled methods. Furthermore, ablation experiments were conducted for each module, demonstrating the effectiveness of each module. These results manifest the feasibility of our proposed method and the potential of FMI paradigm for BCI, providing a new training tool for upper limb rehabilitation after stroke

INTERMEDIUM-M encodes an HvAP2L-H5 ortholog and is required for inflorescence indeterminacy and spikelet determinacy in barley

Inflorescence architecture dictates the number of flowers and, ultimately, seeds. The architectural discrepancies between two related cereals, barley and wheat, are controlled by differences in determinacy of inflorescence and spikelet meristems. Here, we characterize two allelic series of mutations named intermedium-m (int-m) and double seed1 (dub1) that convert barley indeterminate inflorescences into wheat-like determinate inflorescences bearing a multifloreted terminal spikelet and spikelets with additional florets. INT-M/DUB1 encodes an APETALA2-like transcription factor (HvAP2L-H5) that suppresses ectopic and precocious spikelet initiation signals and maintains meristem activity. HvAP2L-H5 inhibits the identity shift of an inflorescence meristem (IM) to a terminal spikelet meristem (TSM) in barley. Null mutations in AP2L-5 lead to fewer spikelets per inflorescence but extra florets per spikelet. In wheat, prolonged and elevated AP2L-A5 activity in rAP2L-A5 mutants delays but does not suppress the IM−TSM transition. We hypothesize that the regulation of AP2L-5 orthologs and downstream genes contributes to the different inflorescence determinacy in barley and wheat. We show that AP2L-5 proteins are evolutionarily conserved in grasses, promote IM activity, and restrict floret number per spikelet. This study provides insights into the regulation of spikelet and floret number, and hence grain yield in barley and wheat.</p

High electron transfer of TiO₂ nanorod@carbon layer supported flower-like WS₂ nanosheets for triiodide electrocatalytic reduction

WS2-based nanomaterials have been extensively studied due to their unique catalytic properties. However, it is still a great challenge to prepare WS2-based electrocatalysts with both maximally active edge sites exposure and high electronic conductivity. In this work, we have engineered a 1D-2D multidimensional nanostructured TiO2 nanorod@carbon layer supported flower-like WS2 nanosheets (TNRs@C@WS2) electrocatalyst with abundant exposed active edge sites as well as high electron transfer abilities. The TNRs@C@WS2 was explored as a good catalyst for the triiodide reduction reaction. The assembled dye-sensitized solar cell achieves a high photoelectric conversion efficiency (7.15%) and comparable to that (7.18%) of Pt. This unique 1D-2D multidimensional nanostructure may open up new opportunities for a variety of applications in clean energy and catalysis.This work was financially supported by the Natural Science Foundation of China (61774033, 52002038), the Natural Science Foundation of Jiangsu (BK20170661), and the Science and Technology Project of Changzhou (CJ20200037). This work was also supported by the Engineering Research Center for Nanophotonics & Advanced Instrument, Ministry of Education, East China Normal University

Supervised two-stage transfer learning on imbalanced dataset for sport classification

Sport classification is a crucial step for content analysis in a sport stream monitoring system. Training a reliable sport classifier can be a challenging task when the data is limited in amount and highly imbalanced. In this paper, we introduce a supervised two-stage transfer learning (Two-Stage-TL) method to solve the data shortage problem. It can progressively transfer features from a source domain to the target domain using a properly selected bridge domain. For the class imbalance issue, we compare several existing methods and demonstrate that the log-smoothing class weight is the most applicable way for this specific problem. Extensive experiments are conducted using ResNet50, VGG16, and Inception-ResNet-v2. The results show that Two-Stage-TL outperforms classical One-Stage-TL and achieves the best performance using log-smoothing class weight. The in-depth analysis is useful for researchers and developers in solving similar problems

Identifying the functional form and operation rules of energy storage pump for a hydro-wind-photovoltaic hybrid power system

Coupling energy storage pumps with conventional hydropower plants is one of the most valuable methods to increase the consumption rate of renewable energy. There are few small-scale hybrid power systems configurating energy storage pumps in the world (e.g., Ikaria Island, Greece). However, whether the operating principle and configuration method are feasible and transferable for a large-scale renewable energy base is unclear. This study proposes specific operating principles and configuration method for a large-scale hybrid power system, demonstrating the feasibility by investigating typical scenarios of an engineering case in Qinghai Province, China. Assume the conventional hydropower plants, wind farms, and PV stations in the case are constructed and in operation completely. The configuration relationship between energy storage pump and hydropower is investigated by setting the unit of energy storage pump from 1 to 50, the per-kW investment cost from CNY5000/kW to CNY30000/kW under the constraint of individual capacity of 100 MW. Furthermore, the economic indicators of internal rate of return and dynamic payback period are introduced to evaluate the performance of the whole hybrid power system. The configuration consequence of 18–21 pumps shows a good relation with renewable energy. This study provides theoretical and technical support for planning relevant hybrid power station projects