Community endowment service localization and globalization

to solve the pension problem brought by the aging and aging, community endowment service as a new endowment mode has become hot spot. Britain, the United States, Japan and other developed countries have formed a distinctive pattern of community endowment service. Community endowment service pattern in China is also actively explore, in the process, we accumulated a lot of valuable experience, but also there are quite a lot of problems to be solved. We should be in combination with the actual situation in our country, on the basis of draw lessons from foreign advanced community endowment development practices at the same time, set the global vision, local consciousness, people＇s standard all organic unification, perfecting endowment service system in our country, to have a more substantial development.

Cross-Cultural Identity and Media Globalization, Localization

Abstract:Recent years witness the great concerns and anxieties from an increasing number of researchers upon the issue of media globalization and culture identity,or rather homogenization and heterogeneity.There,however,exists a harmonious relationship between media globalization and culture identity,rather than a contradictive one.Media globalization can effectively represent audience’s culture identity in its process of localization.In sense of culture,reasons to media localization and measures to achieve localization will be discussed from the commercial,social and practical perspectives.

Globalization and Glocalization in the History of Chinese Medicine

The globalization of Chinese medicine, forged through successive waves of migration, cultural exchanges, and economic imperatives, constitutes a nuanced and intricate process with historical roots extending over millennia. It stands as the culmination of interconnected historical events that reverberated beyond the confines of China, emerging as a phenomenon characterized by the adjustment of Chinese medical theories, clinical practices, and materia medica to indigenous customs and healthcare traditions prevalent in both proximate and distant regions. In these glocalized processes, the global and the local intersect and mix. The frameworks of globalization and glocalization allow a critical interpretation of the many hybridizations that have shaped overseas Chinese medicine's history and present.

Manufacturing Localization,Technology Backfire,and Economic De-globalization

Since the global financial crisis,global value chain(GVC)have been contracting amid the trend toward economic de-globalization.With GVC participation rate in mind as the core indicator of economic de-globalization,in this paper we create a multi-country general equilibrium model to characterize the mechanism by which manufacturing localization affects GVC participation rate.Our theoretical derivation shows that changes in the local manufacturing status of final products in various countries directly influence the GVC participation rate of those countries.When the local proportion of a country’s final products reaches a certain level,rising local proportion of intermediate inputs,economic growth below the world average level,and technology progress all cause the country’s GVC participation rate to decline,giving rise to de-globalization at the manufacturing and trade levels.We further provide a comprehensive interpretation based on an empirical test of the deep-seated causes of economic de-globalization in relation to such economic phenomena as increasing trade concentration,the“technology backlash”effect of the new industrial revolution,and economic growth driven by the combined forces of trade protectionism and quantitative easing.

基于Local-Global-VIT细粒度分类算法的蝴蝶识别

【目的】准确鉴别蝴蝶种类,动态观测蝴蝶群落多样性变化对生境质量评估、生态环境恢复等方面具有重要意义。针对现有蝴蝶识别方法仅依靠整体特征,忽略了局部特征导致识别生态图像能力不足的问题,本研究旨在开发一种Local-Global-VIT细粒度分类算法的蝴蝶识别方法。【方法】本研究以5科200种共计25 279张蝴蝶图像为识别对象,采用多种数据增强方法扩充图像数据;通过视觉Transformer(vision transformer, VIT)层级结构及自注意力机制逐层选择局部令牌并保留至最后一层学习蝴蝶局部判别部位信息;聚合高层全局令牌消除复杂背景干扰;通过对比损失拉大类间距提高区分度。除此之外,使用合理的学习率调整策略和迁移学习方法,优化了模型收敛过程,在不增加参数量的情况下提高了性能。【结果】Local-Global-VIT算法在大规模细粒度公开数据集Butterfly-200上识别准确率达91.20%,较改进前提升了1.15%,比最优的一般害虫识别算法EfficientNet_b0和细粒度分类算法TransFG准确率分别高了1.83%和0.64%,F1分值分别提高了1.89%和0.88%。【结论】Local-Global-VIT算法以细粒度识别方式有效解决了蝴蝶类内差异大、类间差异小的分类难题,能准确地识别蝴蝶种类,有助于高效评估生境质量。

Scheme Based on Multi-Level Patch Attention and Lesion Localization for Diabetic Retinopathy Grading

Early screening of diabetes retinopathy(DR)plays an important role in preventing irreversible blindness.Existing research has failed to fully explore effective DR lesion information in fundus maps.Besides,traditional attention schemes have not considered the impact of lesion type differences on grading,resulting in unreasonable extraction of important lesion features.Therefore,this paper proposes a DR diagnosis scheme that integrates a multi-level patch attention generator(MPAG)and a lesion localization module(LLM).Firstly,MPAGis used to predict patches of different sizes and generate a weighted attention map based on the prediction score and the types of lesions contained in the patches,fully considering the impact of lesion type differences on grading,solving the problem that the attention maps of lesions cannot be further refined and then adapted to the final DR diagnosis task.Secondly,the LLM generates a global attention map based on localization.Finally,the weighted attention map and global attention map are weighted with the fundus map to fully explore effective DR lesion information and increase the attention of the classification network to lesion details.This paper demonstrates the effectiveness of the proposed method through extensive experiments on the public DDR dataset,obtaining an accuracy of 0.8064.

Deep brain implantable microelectrode arrays for detection and functional localization of the subthalamic nucleus in rats with Parkinson’s disease

The subthalamic nucleus(STN)is considered the best target for deep brain stimulation treatments of Parkinson’s disease(PD).It is difficult to localize the STN due to its small size and deep location.Multichannel microelectrode arrays(MEAs)can rapidly and precisely locate the STN,which is important for precise stimulation.In this paper,16-channel MEAs modified with multiwalled carbon nanotube/poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)(MWCNT/PEDOT:PSS)nanocomposites were designed and fabricated,and the accurate and rapid identification of the STN in PD rats was performed using detection sites distributed at different brain depths.These results showed that nuclei in 6-hydroxydopamine hydrobromide(6-OHDA)-lesioned brains discharged more intensely than those in unlesioned brains.In addition,the MEA simultaneously acquired neural signals from both the STN and the upper or lower boundary nuclei of the STN.Moreover,higher values of spike firing rate,spike amplitude,local field potential(LFP)power,and beta oscillations were detected in the STN of the 6-OHDA-lesioned brain,and may therefore be biomarkers of STN localization.Compared with the STNs of unlesioned brains,the power spectral density of spikes and LFPs synchronously decreased in the delta band and increased in the beta band of 6-OHDA-lesioned brains.This may be a cause of sleep and motor disorders associated with PD.Overall,this work describes a new cellular-level localization and detection method and provides a tool for future studies of deep brain nuclei.

Studying the co-evolution of information diffusion,vaccination behavior and disease transmission in multilayer networks with local and global effects

Today,with the rapid development of the internet,a large amount of information often accompanies the rapid transmission of disease outbreaks,and increasing numbers of scholars are studying the relationship between information and the disease transmission process using complex networks.In fact,the disease transmission process is very complex.Besides this information,there will often be individual behavioral measures and other factors to consider.Most of the previous research has aimed to establish a two-layer network model to consider the impact of information on the transmission process of disease,rarely divided into information and behavior,respectively.To carry out a more in-depth analysis of the disease transmission process and the intrinsic influencing mechanism,this paper divides information and behavior into two layers and proposes the establishment of a complex network to study the dynamic co-evolution of information diffusion,vaccination behavior,and disease transmission.This is achieved by considering four influential relationships between adjacent layers in multilayer networks.In the information layer,the diffusion process of negative information is described,and the feedback effects of local and global vaccination are considered.In the behavioral layer,an individual's vaccination behavior is described,and the probability of an individual receiving a vaccination is influenced by two factors:the influence of negative information,and the influence of local and global disease severity.In the disease layer,individual susceptibility is considered to be influenced by vaccination behavior.The state transition equations are derived using the micro Markov chain approach(MMCA),and disease prevalence thresholds are obtained.It is demonstrated through simulation experiments that the negative information diffusion is less influenced by local vaccination behavior,and is mainly influenced by global vaccination behavior;vaccination behavior is mainly influenced by local disease conditions,and is less influenced by global disease conditions;the disease transmission threshold increases with the increasing vaccination rate;and the scale of disease transmission increases with the increasing negative information diffusion rate and decreases with the increasing vaccination rate.Finally,it is found that when individual vaccination behavior considers both the influence of negative information and disease,it can increase the disease transmission threshold and reduce the scale of disease transmission.Therefore,we should resist the diffusion of negative information,increase vaccination proportions,and take appropriate protective measures in time.

Feature extension and matching for mobile robot global localization

This paper introduces an indoor global localization method by extending and matching features. In the proposed method, the environment is partitioned into convex subdivisions. Local extended maps of the subdivisions are then built by exten- ding features to constitute the local extended map set. While the robot is moving in the environment, the local extended map of the current local environment is established and then matched with the local extended map set. Therefore, global localization in an indoor environment can be achieved by integrating the position and ori- entation matching rates. Both theoretical analysis and comparison experimental result are provided to verify the effectiveness of the proposed method for global localization.

Global localization of in-pipe robot based on ultra-long wave antenna array and global position system

A global localization system of in-pipe robot is introduced in this paper.Global position system(GPS)is applied to monitor the motion of robot along the whole pipeline which is equally divided intomany segments by tracking stations.The definite segment in which robot existing can be detected and thisis long-range localization.Ultra-long wave(ULW)is adopted to solve the problem of metallic shieldingand realize effective communication between inside and outside of pipeline.ULW emitter is carried byrobot.When the plant is broken or defects on pipe-wall are inspected,the robot will stop moving.Anten-na array is presented and disposed upon the definite segment to search the accurate location of robot,andthis is short-range localization.In this paper,five-antenna array is adopted and an effective linear signalfusion algorithm is presented.The localization precision reaches R < 25cm.By tests in Shengli oil field,the whole system is verified with robust solutions.

Deep Global Multiple-Scale and Local Patches Attention Dual-Branch Network for Pose-Invariant Facial Expression Recognition

Pose-invariant facial expression recognition(FER)is an active but challenging research topic in computer vision.Especially with the involvement of diverse observation angles,FER makes the training parameter models inconsistent from one view to another.This study develops a deep global multiple-scale and local patches attention(GMS-LPA)dual-branch network for pose-invariant FER to weaken the influence of pose variation and selfocclusion on recognition accuracy.In this research,the designed GMS-LPA network contains four main parts,i.e.,the feature extraction module,the global multiple-scale(GMS)module,the local patches attention(LPA)module,and the model-level fusion model.The feature extraction module is designed to extract and normalize texture information to the same size.The GMS model can extract deep global features with different receptive fields,releasing the sensitivity of deeper convolution layers to pose-variant and self-occlusion.The LPA module is built to force the network to focus on local salient features,which can lower the effect of pose variation and self-occlusion on recognition results.Subsequently,the extracted features are fused with a model-level strategy to improve recognition accuracy.Extensive experimentswere conducted on four public databases,and the recognition results demonstrated the feasibility and validity of the proposed methods.

Integrated multi-scale approach combining global homogenization and local refinement for multi-field analysis of high-temperature superconducting composite magnets

Second-generation high-temperature superconducting(HTS)conductors,specifically rare earth-barium-copper-oxide(REBCO)coated conductor(CC)tapes,are promising candidates for high-energy and high-field superconducting applications.With respect to epoxy-impregnated REBCO composite magnets that comprise multilayer components,the thermomechanical characteristics of each component differ considerably under extremely low temperatures and strong electromagnetic fields.Traditional numerical models include homogenized orthotropic models,which simplify overall field calculation but miss detailed multi-physics aspects,and full refinement(FR)ones that are thorough but computationally demanding.Herein,we propose an extended multi-scale approach for analyzing the multi-field characteristics of an epoxy-impregnated composite magnet assembled by HTS pancake coils.This approach combines a global homogenization(GH)scheme based on the homogenized electromagnetic T-A model,a method for solving Maxwell's equations for superconducting materials based on the current vector potential T and the magnetic field vector potential A,and a homogenized orthotropic thermoelastic model to assess the electromagnetic and thermoelastic properties at the macroscopic scale.We then identify“dangerous regions”at the macroscopic scale and obtain finer details using a local refinement(LR)scheme to capture the responses of each component material in the HTS composite tapes at the mesoscopic scale.The results of the present GH-LR multi-scale approach agree well with those of the FR scheme and the experimental data in the literature,indicating that the present approach is accurate and efficient.The proposed GH-LR multi-scale approach can serve as a valuable tool for evaluating the risk of failure in large-scale HTS composite magnets.

SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach:Multistory Round Building Scenario over LoRa Network

In situations when the precise position of a machine is unknown,localization becomes crucial.This research focuses on improving the position prediction accuracy over long-range(LoRa)network using an optimized machine learning-based technique.In order to increase the prediction accuracy of the reference point position on the data collected using the fingerprinting method over LoRa technology,this study proposed an optimized machine learning(ML)based algorithm.Received signal strength indicator(RSSI)data from the sensors at different positions was first gathered via an experiment through the LoRa network in a multistory round layout building.The noise factor is also taken into account,and the signal-to-noise ratio(SNR)value is recorded for every RSSI measurement.This study concludes the examination of reference point accuracy with the modified KNN method(MKNN).MKNN was created to more precisely anticipate the position of the reference point.The findings showed that MKNN outperformed other algorithms in terms of accuracy and complexity.

Survey of Indoor Localization Based on Deep Learning

This study comprehensively examines the current state of deep learning (DL) usage in indoor positioning.It emphasizes the significance and efficiency of convolutional neural networks (CNNs) and recurrent neuralnetworks (RNNs). Unlike prior studies focused on single sensor modalities like Wi-Fi or Bluetooth, this researchexplores the integration of multiple sensor modalities (e.g.,Wi-Fi, Bluetooth, Ultra-Wideband, ZigBee) to expandindoor localization methods, particularly in obstructed environments. It addresses the challenge of precise objectlocalization, introducing a novel hybrid DL approach using received signal information (RSI), Received SignalStrength (RSS), and Channel State Information (CSI) data to enhance accuracy and stability. Moreover, thestudy introduces a device-free indoor localization algorithm, offering a significant advancement with potentialobject or individual tracking applications. It recognizes the increasing importance of indoor positioning forlocation-based services. It anticipates future developments while acknowledging challenges such as multipathinterference, noise, data standardization, and scarcity of labeled data. This research contributes significantly toindoor localization technology, offering adaptability, device independence, and multifaceted DL-based solutionsfor real-world challenges and future advancements. Thus, the proposed work addresses challenges in objectlocalization precision and introduces a novel hybrid deep learning approach, contributing to advancing locationcentricservices.While deep learning-based indoor localization techniques have improved accuracy, challenges likedata noise, standardization, and availability of training data persist. However, ongoing developments are expectedto enhance indoor positioning systems to meet real-world demands.

Improved PSO-Extreme Learning Machine Algorithm for Indoor Localization

Wi Fi and fingerprinting localization method have been a hot topic in indoor positioning because of their universality and location-related features.The basic assumption of fingerprinting localization is that the received signal strength indication(RSSI)distance is accord with the location distance.Therefore,how to efficiently match the current RSSI of the user with the RSSI in the fingerprint database is the key to achieve high-accuracy localization.In this paper,a particle swarm optimization-extreme learning machine(PSO-ELM)algorithm is proposed on the basis of the original fingerprinting localization.Firstly,we collect the RSSI of the experimental area to construct the fingerprint database,and the ELM algorithm is applied to the online stages to determine the corresponding relation between the location of the terminal and the RSSI it receives.Secondly,PSO algorithm is used to improve the bias and weight of ELM neural network,and the global optimal results are obtained.Finally,extensive simulation results are presented.It is shown that the proposed algorithm can effectively reduce mean error of localization and improve positioning accuracy when compared with K-Nearest Neighbor(KNN),Kmeans and Back-propagation(BP)algorithms.

Phase induced localization transition

Localization phenomenon is an important research field in condensed matter physics.However,due to the complexity and subtlety of disordered systems,new localization phenomena always emerge unexpectedly.For example,it is generally believed that the phase of the hopping term does not affect the localization properties of the system,so the calculation of the phase is often ignored in the study of localization.Here,we introduce a quasiperiodic model and demonstrate that the phase change of the hopping term can significantly alter the localization properties of the system through detailed numerical simulations,such as the inverse participation ratio and multifractal analysis.This phase-induced localization transition provides valuable information for the study of localization physics.

Interplay between topology and localization on superconducting circuits

Topological insulators occupy a prominent position in the realm of condensed matter physics. Nevertheless, the presence of strong disorder has the potential to disrupt the integrity of topological states, leading to the localization of all states.This study delves into the intricate interplay between topology and localization within the one-dimensional Su–Schrieffer–Heeger(SSH) model, which incorporates controllable off-diagonal quasi-periodic modulations on superconducting circuits.Through the application of external alternating current(ac) magnetic fluxes, each transmon undergoes controlled driving,enabling independent tuning of all coupling strengths. Within a framework of this model, we construct comprehensive phase diagrams delineating regions characterized by extended topologically nontrivial states, critical localization, and coexisting topological and critical localization phases. The paper also addresses the dynamics of qubit excitations, elucidating distinct quantum state transfers resulting from the intricate interplay between topology and localization. Additionally, we propose a method for detecting diverse quantum phases utilizing existing experimental setups.

Dynamical localization in a non-Hermitian Floquet synthetic system

We investigate the non-Hermitian effects on quantum diffusion in a kicked rotor model where the complex kicking potential is quasi-periodically modulated in the time domain.The synthetic space with arbitrary dimension can be created by incorporating incommensurate frequencies in the quasi-periodical modulation.In the Hermitian case,strong kicking induces the chaotic diffusion in the four-dimension momentum space characterized by linear growth of mean energy.We find that the quantum coherence in deep non-Hermitian regime can effectively suppress the chaotic diffusion and hence result in the emergence of dynamical localization.Moreover,the extent of dynamical localization is dramatically enhanced by increasing the non-Hermitian parameter.Interestingly,the quasi-energies become complex when the non-Hermitian parameter exceeds a certain threshold value.The quantum state will finally evolve to a quasi-eigenstate for which the imaginary part of its quasi-energy is large most.The exponential localization length decreases with the increase of the non-Hermitian parameter,unveiling the underlying mechanism of the enhancement of the dynamical localization by nonHermiticity.

A Universally Beneficial and Inclusive Economic Globalization:Connotations,Logical Basis and Contemporary Relevance

The world is undergoing seismic upheavals unseen in a century.Globalization faces complex challenges and attacks from the political forces advocating reverse globalization and deglobalization.Pessimistic arguments such as“the end of globalization”1 and“global trade fragmentation”2 keep rising.The open world economy is at a crossroads.

Scenario Modeling-Aided AP Placement Optimization Method for Indoor Localization and Network Access

Owing to the ubiquity of wireless networks and the popularity of WiFi infrastructures,received signal strength(RSS)-based indoor localization systems have received much attention.The placement of access points(APs)significantly influences localization accuracy and network access.However,the indoor scenario and network access are not fully considered in previous AP placement optimization methods.This study proposes a practical scenario modelingaided AP placement optimization method for improving localization accuracy and network access.In order to reduce the gap between simulation-based and field measurement-based AP placement optimization methods,we introduce an indoor scenario modeling and Gaussian process-based RSS prediction method.After that,the localization and network access metrics are implemented in the multiple objective particle swarm optimization(MOPSO)solution,Pareto front criterion and virtual repulsion force are applied to determine the optimal AP placement.Finally,field experiments demonstrate the effectiveness of the proposed indoor scenario modeling method and RSS prediction model.A thorough comparison confirms the localization and network access improvement attributed to the proposed anchor placement method.