On the impact of the digital economy on urban resilience based on a spatial Durbin model

Based on panel data from 31 provinces in China between 2011 and 2020, we empirically studied the impact of the digital economy on urban resilience using fixed-effects models, threshold-effects models and spatial Durbin models. Our research findings indicate that (1) the development of the digital economy has a significant positive impact on the enhancement of urban resilience; (2) the promotional effect of the digital economy on urban resilience varies significantly across different regions; (3) the promotional effect of the digital economy on urban resilience exhibits a typical double-threshold characteristic due to the different levels of development in digital financial inclusion and (4) the digital economy has a positive spillover effect on the urban resilience of surrounding areas. Therefore, we should actively promote the development of the digital economy and digital financial inclusion, making the digital economy a new driving force for promoting urban resilience

The impact of population agglomeration on ecological resilience: Evidence from China

Due to climate change and human activities, ecological and environmental issues have become increasingly prominent and it is crucial to deeply study the coordinated development between human activities and the ecological environment. Combining panel data from 31 provinces in China spanning from 2011 to 2020, we employed a fixed-effects model, a threshold regression model, and a spatial Durbin model to empirically examine the intricate impacts of population agglomeration on ecological resilience. Our findings indicate that population agglomeration can have an impact on ecological resilience and this impact depends on the combined effects of agglomeration and crowding effects. Also, the impact of population agglomeration on ecological resilience exhibits typical dual-threshold traits due to differences in population size. Furthermore, population agglomeration not only directly impacts the ecological resilience of the local area, but also indirectly affects the ecological resilience of surrounding areas. In conclusion, we have found that population agglomeration does not absolutely impede the development of ecological resilience. On the contrary, to a certain extent, reasonable population agglomeration can even facilitate the progress of ecological resilience

Network manufacturing systems : modelling, implementation and applications

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Research on a Tool Wear Monitoring Algorithm Based on Residual Dense Network

To accurately and efficiently detect tool wear values during production and processing activities, a new online detection model is proposed called the Residual Dense Network (RDN). The model is created with two main steps: Firstly, the time-domain signals for a cutting tool are obtained (e.g., using acceleration sensors); these signals are processed to denoise and segmented to provide a larger number of uniform samples. This processing helps to improve the robustness of the model. Secondly, a new deep convolutional neural network is proposed to extract features adaptively, by combining the idea of a recursive residual network and a dense network. Notably, this method is specifically tailored to the tool wear value detection problem. In this way, the limitations of traditional manual feature extraction steps can be avoided. The experimental results demonstrate that the proposed method is promising in terms of detection accuracy and speed; it provides a new way to detect tool wear values in practical industrial scenarios

Research Progresses on the Physiological and Pharmacological Benefits of Microalgae-Derived Biomolecules

Microalgae are a kind of photoautotrophic microorganism, which are small, fast in their growth rate, and widely distributed in seawater and freshwater. They have strong adaptability to diverse environmental conditions and contain various nutrients. Many scholars have suggested that microalgae can be considered as a new food source, which should be developed extensively. More importantly, in addition to containing nutrients, microalgae are able to produce a great number of active compounds such as long-chain unsaturated fatty acids, pigments, alkaloids, astaxanthin, fucoidan, etc. Many of these compounds have been proven to possess very important physiological functions such as anti-oxidation, anti-inflammation, anti-tumor functions, regulation of the metabolism, etc. This article aimed to review the physiological functions and benefits of the main microalgae-derived bioactive molecules with their physiological effects

Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-scale Winter Wheat Identification

Machine learning comprises a group of powerful state-of-the-art techniques for land cover classification and cropland identification. In this paper, we proposed and evaluated two models based on random forest (RF) and attention-based long short-term memory (A-LSTM) networks that can learn directly from the raw surface reflectance of remote sensing (RS) images for large-scale winter wheat identification in Huanghuaihai Region (North-Central China). We used a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images over one growing season and the corresponding winter wheat distribution map for the experiments. Each training sample was derived from the raw surface reflectance of MODIS time-series images. Both models achieved state-of-the-art performance in identifying winter wheat, and the F1 scores of RF and A-LSTM were 0.72 and 0.71, respectively. We also analyzed the impact of the pixel-mixing effect. Training with pure-mixed-pixel samples (the training set consists of pure and mixed cells and thus retains the original distribution of data) was more precise than training with only pure-pixel samples (the entire pixel area belongs to one class). We also analyzed the variable importance along the temporal series, and the data acquired in March or April contributed more than the data acquired at other times. Both models could predict winter wheat coverage in past years or in other regions with similar winter wheat growing seasons. The experiments in this paper showed the effectiveness and significance of our methods

Extracting 3D Indoor Maps with Any Shape Accurately Using Building Information Modeling Data

Indoor maps lay the foundation for most indoor location-based services (LBS). Building Information Modeling (BIM) data contains multiple dimensional computer-aided design information. Some studies have utilized BIM data to automatically extract 3D indoor maps. A complete 3D indoor map consists of both floor-level maps and cross-floor paths. Currently, the floor-level indoor maps are mainly either grid-based maps or topological maps, and the cross-floor path generation schemes are not adaptive to building elements with irregular 3D shapes. To address these issues, this study proposes a novel scheme to extract an accurate 3D indoor map with any shape using BIM data. Firstly, this study extracts grid-based maps from BIM data and generates the topological maps directly through the grid-based maps using image thinning. A novel hybrid indoor map, termed Grid-Topological map, is then formed by the grid-based maps and topological maps jointly. Secondly, this study obtains the cross-floor paths from cross-floor building elements by a four-step process, namely X-Z projection, boundary extraction, X-Z topological path generation, and path-BIM intersection. Finally, experiments on eight typical types of cross-floor building elements and three multi-floor real-world buildings were conducted to prove the effectiveness of the proposed scheme, the average accuracy rates of the evaluated paths are higher than 88%. This study will advance the 3D indoor maps generation and inspire the application of indoor maps in indoor LBS, indoor robots, and 3D geographic information systems

Research on a Surface Defect Detection Algorithm Based on MobileNet-SSD

This paper aims to achieve real-time and accurate detection of surface defects by using a deep learning method. For this purpose, the Single Shot MultiBox Detector (SSD) network was adopted as the meta structure and combined with the base convolution neural network (CNN) MobileNet into the MobileNet-SSD. Then, a detection method for surface defects was proposed based on the MobileNet-SSD. Specifically, the structure of the SSD was optimized without sacrificing its accuracy, and the network structure and parameters were adjusted to streamline the detection model. The proposed method was applied to the detection of typical defects like breaches, dents, burrs and abrasions on the sealing surface of a container in the filling line. The results show that our method can automatically detect surface defects more accurately and rapidly than lightweight network methods and traditional machine learning methods. The research results shed new light on defect detection in actual industrial scenarios

Algorithms for Road Networks Matching Considering Scale Variation and Data Update

Road network matching is an important prerequisite for the change detection and data updating of spatial database, and the matching of road networks at different scales is very important. In this paper, the existing algorithms road networks matching are summarized and analyzed firstly, and according to the problems and difficulties in the road networks matching at different scales, an algorithm integrating multiple matching techniques was designed. Based on the characteristics of road networks at different scales, the method of evaluating the structure of spatial scene was improved. The limitations of the algorithm based on stroke matching were analyzed for road networks data at the different scales, and the algorithm named “partial stroke matching” was put forward. The experiments indicate that the algorithm given in this paper can be used in matching of road networks at different scales, the effect of matching is good, and the running efficiency is high as well

Robust Contextual Bandits via Bootstrapping

Upper confidence bound (UCB) based contextual bandit algorithms require one to know the tail property of the reward distribution. Unfortunately, such tail property is usually unknown or difficult to specify in real-world applications. Using a tail property heavier than the ground truth leads to a slow learning speed of the contextual bandit algorithm, while using a lighter one may cause the algorithm to diverge. To address this fundamental problem, we develop an estimator (evaluated from historical rewards) for the contextual bandit UCB based on the multiplier bootstrapping technique. We first establish sufficient conditions under which our estimator converges asymptotically to the ground truth of contextual bandit UCB. We further derive a second order correction for our estimator so as to obtain its confidence level with a finite number of rounds. To demonstrate the versatility of the estimator, we apply it to design a BootLinUCB algorithm for the contextual bandit. We prove that the BootLinUCB has a sub-linear regret upper bound and also conduct extensive experiments to validate its superior performance