A Visual Indoor Localization Method Based on Efficient Image Retrieval

The task of indoor visual localization, utilizing camera visual information for user pose calculation, was a core component of Augmented Reality (AR) and Simultaneous Localization and Mapping (SLAM). Existing indoor localization technologies generally used scene-specific 3D representations or were trained on specific datasets, making it challenging to balance accuracy and cost when applied to new scenes. Addressing this issue, this paper proposed a universal indoor visual localization method based on efficient image retrieval. Initially, a Multi-Layer Perceptron (MLP) was employed to aggregate features from intermediate layers of a convolutional neural network, obtaining a global representation of the image. This approach ensured accurate and rapid retrieval of reference images. Subsequently, a new mechanism using Random Sample Consensus (RANSAC) was designed to resolve relative pose ambiguity caused by the essential matrix decomposition based on the five-point method. Finally, the absolute pose of the queried user image was computed, thereby achieving indoor user pose estimation. The proposed indoor localization method was characterized by its simplicity, flexibility, and excellent cross-scene generalization. Experimental results demonstrated a positioning error of 0.09 m and 2.14° on the 7Scenes dataset, and 0.15 m and 6.37° on the 12Scenes dataset. These results convincingly illustrated the outstanding performance of the proposed indoor localization method.

Estimating High-Order Functional Connectivity Networks for Mild Cognitive Impairment Identification Based on Topological Structure

Functional connectivity networks (FCNs) are important in the diagnosis of neurological diseases and the understanding of brain tissue patterns. Recently, many methods, such as Pearson’s correlation (PC), Sparse representation (SR), and Sparse low-rank representation have been proposed to estimate FCNs. Despite their popularity, they only capture the low-order connections of the brain regions, failing to encode more complex relationships (i.e. , high-order relationships). Although researchers have proposed high-order methods, like PC + PC and SR + SR, aiming to build FCNs that can reflect more real state of the brain. However, such methods only consider the relationships between brain regions during the FCN construction process, neglecting the potential shared topological structure information between FCNs of different subjects. In addition, the low-order relationships are always neglected during the construction of high-order FCNs. To address these issues, in this paper we proposed a novel method, namely Ho-FCNTops, towards estimating high-order FCNs based on brain topological structure. Specifically, inspired by the Group-constrained sparse representation (GSR), we first introduced a prior assumption that all subjects share the same topological structure in the construction of the low-order FCNs. Subsequently, we employed the Correlation-reserved embedding (COPE) to eliminate noise and redundancy from the low-order FCNs. Meanwhile, we retained the original low-order relationships during the embedding process to obtain new node representations. Finally, we utilized the SR method on the obtained new node representations to construct the Ho-FCNTops required for disease identification. To validate the effectiveness of the proposed method, experiments were conducted on 137 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database to identify Mild Cognitive Impairment (MCI) patients from the normal controls. The experimental results demonstrate superior performance compared to baseline methods.

Semi-Supervised Graph Learning for Brain Disease Identification

Using resting-state functional magnetic resonance imaging (fMRI) technology to assist in identifying brain diseases has great potential. In the identification of brain diseases, graph-based models have been widely used, where graph represents the similarity between patients or brain regions of interest. In these models, constructing high-quality graphs is of paramount importance. Researchers have proposed various methods for constructing graphs from different perspectives, among which the simplest and most popular one is Pearson Correlation (PC). Although existing methods have achieved significant results, these graphs are usually fixed once they are constructed, and are generally operated separately from downstream task. Such a separation may result in neither the constructed graph nor the extracted features being ideal. To solve this problem, we use the graph-optimized locality preserving projection algorithm to extract features and the population graph simultaneously, aiming in higher identification accuracy through a task-dependent automatic optimization of the graph. At the same time, we incorporate supervised information to enable more flexible modelling. Specifically, the proposed method first uses PC to construct graph as the initial feature for each subject. Then, the projection matrix and graph are iteratively optimized through graph-optimization locality preserving projections based on semi-supervised learning, which fully employs the knowledge in various transformation spaces. Finally, the obtained projection matrix is applied to construct the subject-level graph and perform classification using support vector machines. To verify the effectiveness of the proposed method, we conduct experiments to identify subjects with mild cognitive impairment (MCI) and Autism spectrum disorder (ASD) from normal controls (NCs), and the results showed that the classification performance of our method is better than that of the baseline method.

Inhibitory Effect of 16 Fungicides on Persimmon Anthracnose Pathogen Colletotrichum horii

In this research,the inhibitory effect of 16 fungicides on Colletotrichum horri causing persimmon anthracnose was investigated using mycelial growth method and spore germination method. The results showed that among the 16 tested fungicides,10% of Difenoconazole WG,33. 5% of Copper quinolate SC,25%of Bromothalonil EC,70% of Mancozeb WP,430 g/L of Tebuconazole SC,50% of Prochloraz-manganese chloride and 400 g/L of Flusilazole EC achieved the best inhibitory effect on mycelial growth of C. horri,with the inhibition rate of 100%; 70% of Polyram WG,33. 5% of Copper quinolate SC,25% of Bromothalonil EC,70% of Mancozeb WP,50% of Chlorobromoisocyanuric acid AF,50% of Triram WP and 400 g/L of Flusilazole achieved the best inhibitory effect on spore germination of C. horri,with the germination rate of 0. In conclusion,33. 5% of Copper quinolate SC,25% of Bromothalonil EC,70% of Mancozeb WP and 400 g/L of Flusilazole EC achieved the best inhibitory effect both in mycelial growth and spore germination,which could be used as the preference fungicides for the control of persimmon anthracnose,and 70% of Polyram WG and 50% of Triram WP achieved the secondly best inhibitory effect,which could be used as alternative fungicides. The results of this research could provide scientific evidence for the effective control of persimmon anthracnose,and more optional pesticides for utilization in the production practice of persimmon industry.

Understanding the Correlations between Social Attention and Topic Trends of Scientific Publications

Purpose： We propose and apply a simplified nowcasting model to understand the correlations between social attention and topic trends of scientific publications. Design/methodology/approach： First, topics are generated from the obesity corpus by using the latent Dirichlet allocation （LDA） algorithm and time series of keyword search trends in Google Trends are obtained. We then establish the structural time series model using data from January 2004 to December 2012, and evaluate the model using data from January 2013. We employ a state-space model to separate different non-regression components in an observational time series （i.e. the tendency and the seasonality） and apply the ＂spike and slab prior＂ and stepwise regression to analyze the correlations between the regression component and the social media attention. The two parts are combined using Markov-chain Monte Carlo sampling techniques to obtain our results. Findings： The results of our study show that （1） the number of publications on child obesity increases at a lower rate than that of diabetes publications; （2） the number of publication on a given topic may exhibit a relationship with the season or time of year; and （3） there exists a correlation between the number of publications on a given topic and its social media attention, i.e. the search frequency related to that topic as identified by Google Trends. We found that our model is also able to predict the number of publications related to a given topic.

基于问卷调查的互联网自行车满意度Logistic回归分析:老年人视角分析

通过深圳新闻网、腾讯大粤网以及微信公众号等网络渠道,我们开展了关于互联网自行车用户满意度的网络问卷调查。基于问卷调查的结果,互联网自行车用户满意度相对于年收入、出行目的、年龄、骑行时间、骑行次数和原出行方式等进行了Logistic回归分析。结果显示,年收入、出行目的、年龄、骑行时间、骑行次数与互联网自行车的满意度之间的关系显著,原出行方式与互联网自行车的满意度之间的关系不显著。年收入增加、出行目的从通勤变到休闲健身等非高峰时段出行、年龄增加,用户对互联网自行车的满意度降低。骑行时间增加、骑行次数增加,用户对互联网自行车的满意度提高。以上分析可知,老年人对互联网自行车的满意度普遍较低。

Site Preference of Se and Te in Bi2Se3-xTex Thin Films

The ternary topological insulators Bi2Se3-xTex have attracted a great deal of attention due to their exotic physical and chemical properties.While most of the studies focus on the properties of these ternary TIs,limited research was performed to investigate the dynamic atomic stack of its crystal structure.We prepared highquality Bi2Se3-xTex thin films on Ga As(111)B substrates using molecular beam epitaxy,characterized with Raman spectroscopy,x-ray diffraction and photoelectron spectroscopy.It is found that when Se is replaced by Te,the preferred substituting sites are the middle layer at 0<x<1,and this is also valid for Se substituting Te at 2<x<3.In the middle region,the substituting atoms prefer to go to the first and the fifth layer.

Biological Characteristics of Persimmon Anthracnose Pathogen Colletotrichum horii and Screening of Inhibitory Fungicides

This experiment studied the biological characteristics of Colletotrichum horii causing persimmon anthracnose using the crossing method and blood cell counting plate method,and screened inhibitory fungicides via assessing the effects of 16 common fungicides on the mycelial growth and spore germination. The results showed that the most suitable temperature for mycelial growth of C. horri is 25℃,the most suitable temperature for spore germination is 28℃; the suitable p H for mycelial growth of C. horri is 4. 0-6. 0,the most suitable p H for spore germination is 4. 0; the optimal carbon source is glucose and maltose,and the optimal source of nitrogen is beef extract. Among the 16 common fungicides,33. 5% copper quinolate SC,25% bromothalonil EC and 70% Mancozeb WP have the optimal inhibitory effects on the mycelial growth and spore germination of C. horri,and can be used as preferred agent for prevention and control of persimmon anthracnose,followed by70% Polyram WG,400 g/L Flusilazole EC and 50% Thiram WP,which can be used as alternative agents. The results are expected to provide experimental basis for effective control of persimmon anthracnose.

CAV driving safety monitoring and warning via V2X-based edge computing system

Driving safety and accident prevention are attracting increasing global interest.Current safety monitoring systems often face challenges such as limited spatiotemporal coverage and accuracy,leading to delays in alerting drivers about potential hazards.This study explores the use of edge computing for monitoring vehicle motion and issuing accident warnings,such as lane departures and vehicle collisions.Unlike traditional systems that depend on data from single vehicles,the cooperative vehicle-infrastructure system collects data directly from connected and automated vehicles(CAVs)via vehicle-to-everything communication.This approach facilitates a comprehensive assessment of each vehicle’s risk.We propose algorithms and specific data structures for evaluating accident risks associated with different CAVs.Furthermore,we examine the prerequisites for data accuracy and transmission delay to enhance the safety of CAV driving.The efficacy of this framework is validated through both simulated and real-world road tests,proving its utility in diverse driving conditions.

One-year status of hepatic venous pressure gradient measurement from 85 hospitals in China

Aims:Surveys and research on the applications of the hepatic venous pressure gradient(HVPG)are important for understanding the current status and future development of this technology in China.This article aimed to investigate the status of hepatic venous pressure gradient measurement in China in 2022.Methods:We investigated the overall status of HVPG technology in China-including hospital distribution,hospital level,annual number of cases,catheters used,average cost,indications,and current challenges by using online questionnaire.By counting the number and percentages of cases of these results,we hope to clarify the current status of HVPG measurements in China.Results:According to the survey,85 hospitals in China used HVPG technology in 2022 distributed across 29 provinces.A total of 4989 HVPG measurements were performed in all of the surveyed hospitals in 2022,of which 2813 cases(56.4%)were measured alone.The average cost of HVPG measurement was 5646.8±2327.9 CNY.Of the clinical teams who performed the measurements(sometimes multiple per hospital),94.3%(82/87)used the balloon method,and the majority of the teams(72.4%,63/87)used embolectomy catheters.Conclusions:This survey clarified the clinical application status of HVPG in China and confirmed that some medical institutions in China have established a foundation for this technology.It is still necessary to continue promoting and popularizing this technology in the future.

静电驱动下指数型变截面梁式陀螺仪的非线性特性与分析

本文设计一类新型的指数型变截面梁式微陀螺,变截面指数梁的宽度与长度的关系为指数函数变化的形式,且指数梁的截面变化因子小于等于零,梁的厚度随梁长仍保持不变.考虑了曲率非线性和惯性非线性对系统的影响,利用扩展哈密顿原理、单模近似法和拉格朗日微分方程,建立了指数型变截面梁式非线性陀螺仪的振动控制方程、边界条件和非线性离散化模型,且考虑了陀螺仪驱动和检测两个方向上直流电压和交流电压对系统响应的影响,采用ADM法求解陀螺仪系统在不同截面变化因子下的静态响应,利用多尺度法对系统的非线性离散化模型进行摄动求解,分析各个参数对陀螺仪动态响应的影响规律.结果表明,随着截面变化因子的增大,指数梁陀螺仪的吸合电压和一阶固有频率逐渐增加,并呈现近似线性的变化规律.通过调节指数梁截面变化因子,可以控制科氏力响应峰值频率和检测峰值频率的差值.利用指数梁陀螺仪系统的硬化非线性特征,当交流电压施加在指数梁厚度方向时,选择合适的指数截面变化因子、阻尼比和交流电压,可以使指数梁式陀螺仪系统获得更好的带宽性能及线性可测范围;当交流电压施加在指数梁宽度方向,选取合适的截面变化因子,不仅可以获得较高的系统灵敏度性能,同时增加陀螺仪对外部输入角速度的线性可测范围.

A multi-task deep learning model for short-term taxi demand forecasting considering spatiotemporal dependences

Short-term taxi demand forecasting is of great importance to incentivize vacant cars moving from over-supply regions to over-demand regions,which can minimize the wait time for passengers and drivers.With the consideration of spatiotemporal dependences,this study proposes a multi-task deep learning(MTDL)model to predict short-term taxi demand in multi-zone level.The nonlinear Granger causality test is applied to explore the causality relationships among various traffic zones,and long short-term memory(LSTM)is used as the core neural unit to construct the framework of the multi-task deep learning model.In addition,several hyperparameter optimization methods(e.g.,grid search,random search,Bayesian optimization,hyperopt)are used to tune the model.Using the taxi trip data in New York City for validation,the multi-task deep learning model considering spatiotemporal dependences(MTDL*)is compared with the single-task deep learning model(STDL),the full-connected multi-task deep learning model(MTDL#)and other benchmark algorithms(such as LSTM,support vector machine(SVM)and k-nearest neighbors(k-NN)).The experiment results show that the proposed MTDL model is promising to predict short-term taxi demand in multi-zone level,the nonlinear Granger causality analysis is able to capture the spatiotemporal correlations among various traffic zones,and the Bayesian optimization is superior to the other three methods,which verified the feasibility and adaptability of the proposed method.

ENO1 promotes antitumor immunity by destabilizing PD-L1 in NSCLC

PD-L1 plays an important role in inhibiting T-cell activity and driving tumor cell escape from immune surveillance by binding its ligand,PD-1,on T cells.1 Several intracellular and extracellular factors,such as interferon-γ(IFN-γ),MYC,transforming growth factorβ,and miR-200,may modulate PD-L1 expression by transcriptional and posttranscriptional mechanisms.

A microfluidically controlled concave–convex membrane lens using an addressing operation system

Electrical control toolkits for microlens arrays are available to some extent,but for applications in environments with strong electromagnetic fields,radiation,or deep water,non-electrical actuation and control strategies are more appropriate.An integrated digital microfluidic zoom actuating unit with a logic addressing unit for a built-in membrane lens array,e.g.,a flexible bionic compound eye,is developed and studied in this article.A concave–convex membrane fluidic microvalve,which is the component element of the logic gate,actuator,and microlens,is proposed to replace the traditional solenoid valve.The functions of pressure regulation and decoding can be obtained by incorporating microvalves into fluidic networks according to equivalent circuit designs.The zoom actuating unit contains a pressure regulator to adjust the focal length of lenses with three levels,and the logic addressing unit contains a decoder to choose a typical lens from a hexagonal lens array.The microfluidic chip control system is connected flexibly to the actuating part,a membrane lens array.It is shown from a simulation and experimental demonstration that the designed and fabricated system,which is composed of a whole microfluidic zoom unit,addressing technology,and a microlens array,works well.Because these components are constructed in the same fabrication process and operate with the same work media and driving source,the system can be made highly compatible and lightweight for applications such as human-machine interfaces and soft robots.