Practical Deep Reinforcement Learning Approach for Stock Trading

Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return. 30 stocks are selected as our trading stocks and their daily prices are used as the training and trading market environment. We train a deep reinforcement learning agent and obtain an adaptive trading strategy. The agent's performance is evaluated and compared with Dow Jones Industrial Average and the traditional min-variance portfolio allocation strategy. The proposed deep reinforcement learning approach is shown to outperform the two baselines in terms of both the Sharpe ratio and cumulative returns

Exploring the Relationship between the Clustering Degree of Children’s Business Formats and the Attractiveness of Commercial Centers in Wuhan by Modifying the Classic Retail Model

In recent years, the continued popularity of children’s consumption has made it a new factor that affects the attractiveness of Wuhan’s commercial centers. This study analyzes the characteristics of children’s business format agglomeration in Wuhan commercial centers based on the results of an on-the-spot investigation and estimates the passenger attraction of 66 commercial centers in the main urban area with the support of LBS data. In addition, we set up a control experiment group of commercial centers of various levels and explore the influence mechanism of the density of various types of children’s stores on the attraction of commercial centers by improving the classic retail model. The results indicate the following. (1) Children’s business formats in Wuhan’s commercial centers are active as a whole, and different types of children’s businesses have an unbalanced layout at the different levels of business centers. (2) There are both level ladder and internal level differences in the attractiveness of Wuhan commercial centers. (3) The direction and intensity of the influence of children’s business types on commercial centers of different levels differ. In city-level commercial centers, children’s education and entertainment formats play a role in promotion. In county-level commercial centers, the children’s education format is the most important, and overinvestment in the children’s department store format may not meet expectations. In community-level commercial centers, investment in children’s department stores yielded the best results. (4) Traffic impedance has a stable inhibitory effect at all levels of Wuhan commercial centers, which is in line with the classic retail gravity theory. Further, based on the above results, this paper puts forward suggestions on several types of adaptations that can be applied to children’s consumption stores at different levels of commercial centers to provide support for rationally utilizing the potential of the children’s consumption market

Is Compact Urban Form Good for Air Quality? A Case Study from China Based on Hourly Smartphone Data

In previous studies, planners have debated extensively whether compact development can improve air quality in urban areas. Most of them estimated pollution exposure with stationary census data that linked exposures solely to residential locations, therefore overlooking residents’ space–time inhalation of air pollutants. In this study, we conducted an air pollution exposure assessment by scrutinizing one-hour resolution population distribution maps derived from hourly smartphone data and air pollutant concentrations derived from inverse distance weighted interpolation. We selected Wuhan as the study area and used Pearson correlation analysis to explore the effect of compactness on population-weighted concentrations. The results showed that even if a compact urban form helps to reduce pollution concentrations by decreasing vehicle traveling miles and tailpipe emissions, higher levels of building density and floor area ratios may increase population-weighted exposure. With regard to downtown areas with high population density, compact development may locate more people in areas with excessive air pollution. In all, reducing density in urban public centers and developing a polycentric urban structure may aid in the improvement of air quality in cities with compact urban forms

Exploring the Influence Mechanism of Attractiveness on Wuhan’s Urban Commercial Centers by Modifying the Classic Retail Model

The attractiveness of commercial centers is one of the core issues in urban and rural planning research. To deepen the theoretical understanding of attractiveness and optimize modeling, we empirically analyzed the factors and mechanisms influencing the attractiveness of Wuhan’s commercial centers by improving the classic retail model and testing the age differentiation of mechanisms. The results indicate the following: (1) there is an obvious attractiveness gap in the commercial centers examined, and six have not met their planning expectations; (2) intensive and abundant shopping services, domestic services, sports and leisure services, and medical care services all promote attractiveness, but their impact on customers of different ages varies greatly. For young consumers, shopping services have the greatest effect on attractiveness, whereas for middle-aged and elderly consumers, sports and leisure services have the greatest effect; (3) the accumulation of length of development increases the likelihood of young people’s patronage, but the effect is weak; (4) traffic resistance shows a stable inhibitory effect, and middle-aged and elderly people are more sensitive to travel time than youth; (5) improving the retail model is effective, and the model is more powerful in explaining young consumers. This research also puts forward policy recommendations for the commercial centers’ industry configuration, new and old combinations, and traffic accessibility, and then proposes planning countermeasures for Wuhan’s city- and-county-level commercial center layout, local commercial land morphology organization, and the construction optimization of commercial centers that have not met expectations

High-Temperature Disaster Risk Assessment for Urban Communities: A Case Study in Wuhan, China

High-temperature risk disaster, a common meteorological disaster, seriously affects people’s productivity, life, and health. However, insufficient attention has been paid to this disaster in urban communities. To assess the risk of high-temperature disasters, this study, using remote sensing data and geographic information data, analyzes 973 communities in downtown Wuhan with the geography-weighted regression method. First, the study evaluates the distribution characteristics of high temperatures in communities and explores the spatial differences of risks. Second, a metrics and weight system is constructed, from which the main factors are determined. Third, a risk assessment model of high-temperature disasters is established from disaster-causing danger, disaster-generating sensitivity, and disaster-bearing vulnerability. The results show that: (a) the significance of the impact of the built environment on high-temperature disasters is obviously different from its coefficient space differentiation; (b) the risk in the old city is high, whereas that in the area around the river is low; and (c) different risk areas should design built environment optimization strategies aimed specifically at the area. The significance of this study is that it develops a high-temperature disaster assessment framework for risk identification, impact differentiation, and difference optimization, and provides theoretical support for urban high-temperature disaster prevention and mitigation

Spatial Differences in the Effect of Communities’ Built Environment on Residents’ Health: A Case Study in Wuhan, China

After 40 years of reform and opening-up policies, urbanization in China has significantly improved residents’ living standards; however, simultaneously, it has caused a series of health problems among Chinese citizens. Communities’ built environment is closely related to their residents’ health. However, few studies have examined the spatial differences in the health effects of community-built environments. Based on a 2013 health survey of residents in 20 communities in Wuhan, this study uses multilevel linear models to explore the effects of the built environment on residents’ health, analyzing the differences in its health-effect within different types of communities. The results showed that there were significant differences in the self-rated health status of residents in different communities, with those in high-end communities reporting a higher self-rated health status. The effect of the built environment on the health of residents in different communities was found to be inconsistent. For instance, the effect of the built environment on low-end community residents was very significant, but it was not obvious for residents in high-end communities. There are significant community-specific differences in the health- effect of the built environment: in high-end communities, residents’ health status was mainly restricted by travel accessibility, while in low-end communities, residents’ health status was mainly restricted by the accessibility of health facilities. Therefore, this paper proposes a built-environment optimization strategy for different types of communities to provide valuable insights for healthy community planning from a policy perspective

Development and evaluation of a live birth prediction model for evaluating human blastocysts from a retrospective study

Background: In infertility treatment, blastocyst morphological grading is commonly used in clinical practice for blastocyst evaluation and selection, but has shown limited predictive power on live birth outcomes of blastocysts. To improve live birth prediction, a number of artificial intelligence (AI) models have been established. Most existing AI models for blastocyst evaluation only used images for live birth prediction, and the area under the receiver operating characteristic (ROC) curve (AUC) achieved by these models has plateaued at ~0.65. Methods: This study proposed a multimodal blastocyst evaluation method using both blastocyst images and patient couple’s clinical features (e.g., maternal age, hormone profiles, endometrium thickness, and semen quality) to predict live birth outcomes of human blastocysts. To utilize the multimodal data, we developed a new AI model consisting of a convolutional neural network (CNN) to process blastocyst images and a multilayer perceptron to process patient couple’s clinical features. The data set used in this study consists of 17,580 blastocysts with known live birth outcomes, blastocyst images, and patient couple’s clinical features. Results: This study achieved an AUC of 0.77 for live birth prediction, which significantly outperforms related works in the literature. Sixteen out of 103 clinical features were identified to be predictors of live birth outcomes and helped improve live birth prediction. Among these features, maternal age, the day of blastocyst transfer, antral follicle count, retrieved oocyte number, and endometrium thickness measured before transfer are the top five features contributing to live birth prediction. Heatmaps showed that the CNN in the AI model mainly focuses on image regions of inner cell mass and trophectoderm (TE) for live birth prediction, and the contribution of TE-related features was greater in the CNN trained with the inclusion of patient couple's clinical features compared with the CNN trained with blastocyst images alone. Conclusions: The results suggest that the inclusion of patient couple’s clinical features along with blastocyst images increases live birth prediction accuracy. Funding: Natural Sciences and Engineering Research Council of Canada and the Canada Research Chairs Program

Rox-DNA Functionalized Silicon Nanodots for Ratiometric Detection of Mercury Ions in Live Cells

A ratiometric fluorescent sensor for mercury ions (Hg²⁺) has been constructed via covalent functionalization of silicon nanodot (SiND) with Hg²⁺-specific 6-carboxy-X-rhodamine (Rox)-tagged DNA. For the Rox-DNA functionalized SiND, the red fluorescence of Rox can be quenched by the blue-emitting SiND in the presence of Hg²⁺ due to structural change in DNA, which serves as the response signal. Meawhile, the fluorescence of SiND is insensitive to Hg²⁺ and acts as the reference signal. The wavelength difference in the optimal emission peak is as large as 190 nm between SiND (422 nm) and Rox (612 nm), which can efficaciously exclude the interference of the two emission peaks, and facilitates dual-color visualization of Hg²⁺ ions. The biofunctionalization of SiND improves the acid–base stability of SiND significantly, which is favorable for its application in the intracellular environment. Accordingly, a sensitive, simple, precise and rapid method for tracing Hg²⁺ was proposed. The limit of detection and precision of this method for Hg²⁺ was 9.2 nM and 8.8% (50 nM, <i>n</i> = 7), respectively. The increase of Hg²⁺ concentration in the range of 10–1500 nM was in accordance with linearly increase of the <i>I</i>₄₂₂/<i>I</i>₆₁₂ ratio. As for practical application, the recoveries in spiked human urine and serum samples were in the range of 81–107%. Moreover, this fluorescent nanosensor was utilized to the ratiometric detection of Hg²⁺ in HeLa cells

Testing the generalizability and effectiveness of deep learning models among clinics: sperm detection as a pilot study

Abstract Background Deep learning has been increasingly investigated for assisting clinical in vitro fertilization (IVF). The first technical step in many tasks is to visually detect and locate sperm, oocytes, and embryos in images. For clinical deployment of such deep learning models, different clinics use different image acquisition hardware and different sample preprocessing protocols, raising the concern over whether the reported accuracy of a deep learning model by one clinic could be reproduced in another clinic. Here we aim to investigate the effect of each imaging factor on the generalizability of object detection models, using sperm analysis as a pilot example. Methods Ablation studies were performed using state-of-the-art models for detecting human sperm to quantitatively assess how model precision (false-positive detection) and recall (missed detection) were affected by imaging magnification, imaging mode, and sample preprocessing protocols. The results led to the hypothesis that the richness of image acquisition conditions in a training dataset deterministically affects model generalizability. The hypothesis was tested by first enriching the training dataset with a wide range of imaging conditions, then validated through internal blind tests on new samples and external multi-center clinical validations. Results Ablation experiments revealed that removing subsets of data from the training dataset significantly reduced model precision. Removing raw sample images from the training dataset caused the largest drop in model precision, whereas removing 20x images caused the largest drop in model recall. by incorporating different imaging and sample preprocessing conditions into a rich training dataset, the model achieved an intraclass correlation coefficient (ICC) of 0.97 (95% CI: 0.94-0.99) for precision, and an ICC of 0.97 (95% CI: 0.93-0.99) for recall. Multi-center clinical validation showed no significant differences in model precision or recall across different clinics and applications. Conclusions The results validated the hypothesis that the richness of data in the training dataset is a key factor impacting model generalizability. These findings highlight the importance of diversity in a training dataset for model evaluation and suggest that future deep learning models in andrology and reproductive medicine should incorporate comprehensive feature sets for enhanced generalizability across clinics