Research on the experience of building new districts in ecotourism cities – For Guanshanhu District, Guiyang City

In recent years, many cities have upgraded their local infrastructure through the development of tourism. Tourism development aims to create urban tourism neighborhoods with newly renovated infrastructure, allowing tourists to understand the local history and culture while promoting local economic development. This paper focuses on the Guanshanhu District of Guiyang City as the research subject. By analyzing the district’s natural resources, cultural heritage, and tourism facilities, it discusses the strategies and measures for constructing new ecotourism districts. integrating ecotourism elements into the construction of new districts, and promoting the harmonious development of the economy, society, and the environment, which is aimed at providing a reference for the development of new ecotourism districts. It can adopt the methods of investigation and research, data review, experience analysis, summarization, and learning so that it can learn from the experience in the planning of new urban areas in the future, and achieve the goal of promoting the construction of new urban areas by tourism development and creating an “eco-tourism” environment to foster the city

Business Policy Experiments using Fractional Factorial Designs: Consumer Retention on DoorDash

This paper investigates an approach to both speed up business decision-making and lower the cost of learning through experimentation by factorizing business policies and employing fractional factorial experimental designs for their evaluation. We illustrate how this method integrates with advances in the estimation of heterogeneous treatment effects, elaborating on its advantages and foundational assumptions. We empirically demonstrate the implementation and benefits of our approach and assess its validity in evaluating consumer promotion policies at DoorDash, which is one of the largest delivery platforms in the US. Our approach discovers a policy with 5% incremental profit at 67% lower implementation cost.Comment: 14 page

Dynamic capillary pressure analysis of tight sandstone based on digital rock model

In recent studies, dynamic capillary pressure has shown significant impacts on the flow behaviors in porous media under transient flow condition. However, the effect of dynamic capillary pressure effect on tight sandstone is still not very clear. Since lattice Boltzmann method (LBM) is a very promising and widely used method in analyzing flow behaviors, therefore, a two-phase D3Q27 LBM model is adopted in this paper to simulate the flow behaviors and analyze the dynamic capillary pressure effect in tight sandstone. Moreover, a new pore segmentation method for tight sandstone base on U-net deep learning model is implemented in this study to improve the pore boundary qualities of pore space, which is crucial for two-phase LBM simulation of tight sandstone. A total of 3800 3D sub-volume data sets extracted from computed tomography data of 19 tight sandstone samples are selected as ground truth data to train the network and segment the pore space afterward. The simulation results based on the segmented digital rock model, show that nonwetting phase fluid prefer the path with lower dynamic capillary pressure in the seepage process before breaking through the porous model. Furthermore, the increase of injection rate causes the saturation changes more quickly, injection rate also shows apparent positive correlation relationship with capillary pressure, which implies that dynamic capillary pressure effect also exists in tight sandstone, and LBM based two-phase flow simulation could be used to quantitatively analyze such effect in tight sandstone.Cited as: Cao, Y., Tang, M., Zhang, Q., Tang, J., Lu, S. Dynamic capillary pressure analysis of tight sandstone based on digital rock model. Capillarity, 2020, 3(2): 28-35, doi: 10.46690/capi.2020.02.0

Flux Balance Analysis of Dynamic Metabolism in Shewanella oneidensis MR-1 Using a Static Optimization Approach

Shewanella bacteria are facultative anaerobes isolated from aquatic and sedimentary environments (Hau and Gralnick 2007) with a broad capacity for reduction of multiple electron receptors (Pinchuk et al. 2009; Serres and Riley 2006), including Fe(III), Mn(IV), sulfur, nitrate, and fumarate. With the accomplishment of complete genome sequencing of several Shewanella bacteria, the general pictures of the carbon metabolism have been revealed (Serres and Riley 2006). metabolism. One of the most physiological methods to decipher the time-variant metabolic regulation is to determine the dynamic distribution of intracellular metabolic fluxes since it reveals the final response of cellular metabolism to genomic, transcriptional and post-transcriptional regulations (Sauer 2006; Tang et al. 2009). In order to track the dynamic intracellular metabolic regulation, dynamic flux balance analysis (DFBA) was developed (Mahadevan et al. 2002), in which cell growth phase was divided into numerous stages, assuming that at each stage a new metabolic steady state was maintained. All the metabolic fluxes were then searched to satisfy the objective functions set for each stage. By solving this nonlinear optimization model using a cutting-edge nonlinear optimization solver (IPOPT), we confirmed the changing of carbon sources for the growth of Shewanella oneidensis MR-1 and deciphered the dynamic regulation of intracellular metabolism

Privacy protection for e-health systems by means of dynamic authentication and three-factor key agreement

During the past decade, the electronic healthcare (e-health) system has been evolved into a more patient-oriented service with smaller and smarter wireless devices. However, these convenient smart devices have limited computing capacity and memory size, which makes it harder to protect the user’s massive private data in the e-health system. Although some works have established a secure session key between the user and the medical server, the weaknesses still exist in preserving the anonymity with low energy consumption. Moreover, the misuse of biometric information in key agreement process may lead to privacy disclosure, which is irreparable. In this study, we design a dynamic privacy protection mechanism offering the biometric authentication at the server side whereas the exact value of the biometric template remains unknown to the server. And the user anonymity can be fully preserved during the authentication and key negotiation process because the messages transmitted with the proposed scheme are untraceable. Furthermore, the proposed scheme is proved to be semantic secure under the Real-or-Random Model. The performance analysis shows that the proposed scheme suits the e-health environment at the aspect of security and resource occupation

A Collaborative Jamming Algorithm Based on Multi-UAV Scheduling

In this paper, we consider the problem of multi-unmanned aerial vehicles' scheduling for cooperative jamming, where UAVs equipped with directional antennas perform collaborative jamming tasks against several targets of interest. To ensure effective jamming towards the targets, we formulate it as an non-convex optimization problem, aiming to minimize the communication performance of the targets by jointly optimizing UAVs' deployment and directional antenna orientations. Due to the unique structure of the problem, we derive an equivalent transformation by introducing a set of auxiliary matrices. Subsequently, we propose an efficient iterative algorithm based on the alternating direction method of multipliers, which decomposes the problem into multiple tractable subproblems solved in closed-form or by gradient projection method. Extensive simulations validate the efficacy of the proposed algorithm

Temporal trends of ischemic stroke attributable to high fasting plasma glucose in China from the global burden of disease study 2019

BackgroundCurrently ischemic stroke poses a serious disease burden globally, and high fasting plasma glucose is one of the important risk factors. The aim of this study was to investigate the disease burden of ischemic stroke due to fasting glucose during 1990-2019 in China, to estimate the effect of age, period, and cohort on the trend of ischemic stroke disease burden, and to predict the disease burden of ischemic stroke in 2020-2030.MethodsIschemic stroke burden data were obtained by screening from the Global Burden of Disease Study 2019 (GBD 2019) database for high-risk populations in China. Annual average percentage change (AAPC) was calculated using the Joinpoint regression model to assess the trend of ischemic stroke burden between 1990 and 2019. Age-period-cohort models were introduced to estimate the independent effects of age, period, and cohort on ischemic stroke burden, and to predict the ischemic stroke burden in 2020-2030 based on Bayesian age-period-cohort models.ResultsFrom 1990 to 2019, the number of ischemic stroke deaths due to high fasting plasma glucose in China continued to increase with an AAPC of 3.61. Trends in age-standardized incidence rates did not show statistical significance. In the age-period-cohort analysis, the age effect of ischemic stroke burden showed a continuously increasing trend over the study period. The period effect showed an overall favorable trend over the study period. The overall and cohort effects for males showed an overall increasing trend, whereas the cohort effect for females showed a decreasing trend after a decreasing trend for the 1945 birth cohort.ConclusionsThis study found that ischemic stroke due to high fasting plasma glucose in China has generally fluctuated between 1990 and 2019, with a decreasing trend in recent years, and projections also suggest that it will continue to show a decreasing trend in the future. Age and period of birth were the main elements influencing the burden of disease, especially among the elderly and men. Policies should be used to promote the prevention of known risk factors and to strengthen health management for key populations

KMT2A promotes melanoma cell growth by targeting hTERT signaling pathway.

Melanoma is an aggressive cutaneous malignancy, illuminating the exact mechanisms and finding novel therapeutic targets are urgently needed. In this study, we identified KMT2A as a potential target, which promoted the growth of human melanoma cells. KMT2A knockdown significantly inhibited cell viability and cell migration and induced apoptosis, whereas KMT2A overexpression effectively promoted cell proliferation in various melanoma cell lines. Further study showed that KMT2A regulated melanoma cell growth by targeting the hTERT-dependent signal pathway. Knockdown of KMT2A markedly inhibited the promoter activity and expression of hTERT, and hTERT overexpression rescued the viability inhibition caused by KMT2A knockdown. Moreover, KMT2A knockdown suppressed tumorsphere formation and the expression of cancer stem cell markers, which was also reversed by hTERT overexpression. In addition, the results from a xenograft mouse model confirmed that KMT2A promoted melanoma growth via hTERT signaling. Finally, analyses of clinical samples demonstrated that the expression of KMT2A and hTERT were positively correlated in melanoma tumor tissues, and KMT2A high expression predicted poor prognosis in melanoma patients. Collectively, our results indicate that KMT2A promotes melanoma growth by activating the hTERT signaling, suggesting that the KMT2A/hTERT signaling pathway may be a potential therapeutic target for melanoma

Improving Heterogeneous Model Reuse by Density Estimation

This paper studies multiparty learning, aiming to learn a model using the private data of different participants. Model reuse is a promising solution for multiparty learning, assuming that a local model has been trained for each party. Considering the potential sample selection bias among different parties, some heterogeneous model reuse approaches have been developed. However, although pre-trained local classifiers are utilized in these approaches, the characteristics of the local data are not well exploited. This motivates us to estimate the density of local data and design an auxiliary model together with the local classifiers for reuse. To address the scenarios where some local models are not well pre-trained, we further design a multiparty cross-entropy loss for calibration. Upon existing works, we address a challenging problem of heterogeneous model reuse from a decision theory perspective and take advantage of recent advances in density estimation. Experimental results on both synthetic and benchmark data demonstrate the superiority of the proposed method.Comment: 9 pages, 5 figues. Accepted by IJCAI 202