New immune regulators of sciatic nerve regeneration?Lessons from the neighborhood

For decades,the immune system has been associated with host protection against infectious pathogens or tumors,while also contributing to autoimmunity.Notwithstanding,this paradigm is now changing,with recent studies highlighting novel roles for immune mediators in the maintenance of steady-state tissue homeostasis.In this perspective,we review some of the latest findings featuring immune modulators of the nervous system pathophysiology,with a special focus on interleukin(IL)-17.

Integrating Neighborhood Geographic Distribution and Social Structure Influence for Social Media User Geolocation

Geolocating social media users aims to discover the real geographical locations of users from their publicly available data,which can support online location-based applications such as disaster alerts and local content recommen-dations.Social relationship-based methods represent a classical approach for geolocating social media.However,geographically proximate relationships are sparse and challenging to discern within social networks,thereby affecting the accuracy of user geolocation.To address this challenge,we propose user geolocation methods that integrate neighborhood geographical distribution and social structure influence(NGSI)to improve geolocation accuracy.Firstly,we propose a method for evaluating the homophily of locations based on the k-order neighbor-hood geographic distribution(k-NGD)similarity among users.There are notable differences in the distribution of k-NGD similarity between location-proximate and non-location-proximate users.Exploiting this distinction,we filter out non-location-proximate social relationships to enhance location homophily in the social network.To better utilize the location-proximate relationships in social networks,we propose a graph neural network algorithm based on the social structure influence.The algorithm enables us to perform a weighted aggregation of the information of users’multi-hop neighborhood,thereby mitigating the over-smoothing problem of user features and improving user geolocation performance.Experimental results on real social media dataset demonstrate that the neighborhood geographical distribution similarity metric can effectively filter out non-location-proximate social relationships.Moreover,compared with 7 existing social relationship-based user positioning methods,our proposed method can achieve multi-granularity user geolocation and improve the accuracy by 4.84%to 13.28%.

Which Neighborhoods Have Easier Access to Online Home Delivery Services?A Spatiotemporal Accessibility Analysis in Nanjing,China

The rise in online home delivery services(OHDS)has had a significant impact on how urban services are supplied and used in recent years.Studies on the spatial accessibility of OHDS are emerging,but few is known about the temporal dimension of OHDS accessibility as well as the geographic and socioeconomic differences in the spatiotemporal accessibility of OHDS.This study measures the spatiotemporal accessibility of four types of OHDS,namely leisure,fresh and convenient,medical,and catering services.The geographic and socioeconomic disparities in the spatiotemporal accessibility of these four types of OHDS are then identified using spatial statistical methods and the Kruskal-Wallis test(K-W test).The case study in Nanjing,China,suggests that:1)spatiotemporal accessibility better reflects the temporal variation of OHDS accessibility and avoids overestimation of OHDS accessibility when only considering its spatial dimension.2)The spatiotemporal accessibility of OHDS varies geographically and socioeconomically.Neighborhoods located in the main city or neighborhoods with higher housing prices,higher population density,and higher point of interest(POI)mix have better OHDS spatiotemporal accessibility.Our study contributes to the understanding of OHDS accessibility from a spatiotemporal perspective,and the empirical insights can assist policymakers in creating intervention plans that take into account variations in OHDS spatiotemporal accessibility.

The GSI and Building a Neighborhood Community with a Shared Future

Against the backdrop of the international political and economic configuration featuring that“the East is rising and the West is declining”,relations between China and its neighboring countries enjoy steady development in general.However,the return of the Cold War mentality,rampant protectionism and prominent security governance issues have seriously threatened peace and stability in China’s neighboring region.

Relations with Neighboring Countries, Neighboring Region and China’s Neighborhood Environment

The Chinese government released the Outlook on China’s Foreign Policy on Its Neighborhood In the New Era in October 2023,which explicitly stated that“the neighborhood is where China survives and thrives and the foundation of its development and prosperity”.Such a high positioning of the neighborhood indicates that China attaches great importance to its neighborhood and is resolutely determined to deal well with it.The neighborhood relationship features a dual structure:one is the state-to-state relations,meaning the bilateral relations between China and its neighboring countries;the other is regional relations,meaning relationship between China and its neighboring countries as a coexisting region,which collectively make up the overall neighborhood environment.Fostering a sound neighborhood environment is imperative to China’s security and development.

New Developments in the Security Situation in China’s Neighborhood and the Building of a Community with a Shared Future for Neighbors

The world is undergoing a new round of turbulence and changes,with severe conflicts in multiple regions.This has far-reaching implications for the evolution of the international situation.In this context,China’s neighboring regions have been impacted in many ways,and the security situation in the neighborhood is undergoing complex changes.This inevitably affects the building of a community with a shared future in the region.

GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization

With the increasing demand for electrical services,wind farm layout optimization has been one of the biggest challenges that we have to deal with.Despite the promising performance of the heuristic algorithm on the route network design problem,the expressive capability and search performance of the algorithm on multi-objective problems remain unexplored.In this paper,the wind farm layout optimization problem is defined.Then,a multi-objective algorithm based on Graph Neural Network(GNN)and Variable Neighborhood Search(VNS)algorithm is proposed.GNN provides the basis representations for the following search algorithm so that the expressiveness and search accuracy of the algorithm can be improved.The multi-objective VNS algorithm is put forward by combining it with the multi-objective optimization algorithm to solve the problem with multiple objectives.The proposed algorithm is applied to the 18-node simulation example to evaluate the feasibility and practicality of the developed optimization strategy.The experiment on the simulation example shows that the proposed algorithm yields a reduction of 6.1% in Point of Common Coupling(PCC)over the current state-of-the-art algorithm,which means that the proposed algorithm designs a layout that improves the quality of the power supply by 6.1%at the same cost.The ablation experiments show that the proposed algorithm improves the power quality by more than 8.6% and 7.8% compared to both the original VNS algorithm and the multi-objective VNS algorithm.

Multi-Granularity Neighborhood Fuzzy Rough Set Model on Two Universes

The two universes multi-granularity fuzzy rough set model is an effective tool for handling uncertainty problems between two domains with the help of binary fuzzy relations. This article applies the idea of neighborhood rough sets to two universes multi-granularity fuzzy rough sets, and discusses the two-universes multi-granularity neighborhood fuzzy rough set model. Firstly, the upper and lower approximation operators are defined in the two universes multi-granularity neighborhood fuzzy rough set model. Secondly, the properties of the upper and lower approximation operators are discussed. Finally, the properties of the two universes multi-granularity neighborhood fuzzy rough set model are verified through case studies.

My Neighborhood

I live in Haihe Neighborhood.It'sa beautifu1l community with 30 tall buildings.I have some neighbors.They are kind and friendly.On the one hand,they are always ready to help others.For example,they help others by taking care of their pets and offering umbrellas on rainy days.On the other hand.

Research on the Index System of Old Neighborhood Renovation Based on Green Building Evaluation Standard

With the advancement of the transformation,the contradiction between the residents’demand for a better living environment and the convenience of living in the settlements comes to the fore.Effective identification,organic integration,timely adoption,and correct decision-making for the transformation of old neighborhoods are pressing issues in the transformation of old neighborhoods.Therefore,this paper takes the green building evaluation standards of various countries as the research basis and support for the construction of the transformation strategy of old neighborhoods.Through the collection and comparative analysis of the indicators of green building evaluation standards,the index system of transformation is formed,and it also provides a certain foundation for the subsequent related research.

Attribute Reduction of Neighborhood Rough Set Based on Discernment

For neighborhood rough set attribute reduction algorithms based on dependency degree,a neighborhood computation method incorporating attribute weight values and a neighborhood rough set attribute reduction algorithm using discernment as the heuristic information was proposed.The reduction algorithm comprehensively considers the dependency degree and neighborhood granulation degree of attributes,allowing for a more accurate measurement of the importance degrees of attributes.Example analyses and experimental results demonstrate the feasibility and effectiveness of the algorithm.

Living in a Condominium Regime:Neighborhood Conflicts in Low-Income Housing,Tijuana,Baja California-A Secondary Publication

This paper analyzes neighborhood conflicts arising from the condominium property regime,under which social housing promoted by Mexican government institutions and private developers is acquired.This regime has facilitated access to housing for the salaried population,but it is far from contributing to the attainment of the right to housing.The research takes as case studies housing complexes located in the city of Tijuana,Baja California,Mexico.The analysis included a mixed methodology.Firstly,similar case studies were analyzed;secondly,a review of the Condominium Property Regime Law was carried out in order to understand its implications.In view of the pandemic situation,a virtual survey was applied to the inhabitants of these areas,as well as interviews with presidents of neighborhood committees of these complexes.From the above,it was found that this form of ownership generates conflicts,reflected in the dissatisfaction of the inhabitants with their housing,disagreements and controversies in their organization and coexistence.The inhabitants have to collectively solve the problems they face,related to the maintenance and use of common areas,insecurity,and cleanliness,among others.In addition,there is a lack of support from local authorities.With this,it is concluded that although the inhabitants have a space that solves their housing problem,it does not manage to be a space that adequately guarantees their right to housing.

A Robust Collaborative Recommendation Algorithm Based on k-distance and Tukey M-estimator

The existing collaborative recommendation algorithms have lower robustness against shilling attacks.With this problem in mind,in this paper we propose a robust collaborative recommendation algorithm based on k-distance and Tukey M-estimator.Firstly,we propose a k-distancebased method to compute user suspicion degree(USD).The reliable neighbor model can be constructed through incorporating the user suspicion degree into user neighbor model.The influence of attack profiles on the recommendation results is reduced through adjusting similarities among users.Then,Tukey M-estimator is introduced to construct robust matrix factorization model,which can realize the robust estimation of user feature matrix and item feature matrix and reduce the influence of attack profiles on item feature matrix.Finally,a robust collaborative recommendation algorithm is devised by combining the reliable neighbor model and robust matrix factorization model.Experimental results show that the proposed algorithm outperforms the existing methods in terms of both recommendation accuracy and robustness.

Vibration reduction technology and the mechanisms of surrounding rock damage from blasting in neighborhood tunnels with small clearance

Interlaid rock is an important component in the construction of neighborhood tunnels that supports and reinforces the area between two tunnels.However,the blasting load during excavation can sometimes damage the interlaid rock and threaten the stability of a tunnel’s structure.This paper presents a case study of the small clearance section of the Liantang highway tunnel project in Shenzhen,China,where the minimum distance between the two tunnels involved is only 0.5 m.To reduce the damage to the interlaid rock caused by blasting loads,we proposed a four-part excavation method with a vibrationcushioning rock layer in the following tunnel of neighborhood tunnels.Numerical simulation was used to model the damage prevention mechanism of the vibration-cushioning rock layer and to better understand the propagation of cracks in the interlaid rock.Furthermore,based on the simulation results,combined microseismic controlled-blasting technology was implemented,using innovative blasting patterns combined with different charge structures and blasting equipment designed according to the varying thickness of the interlaid rock.Finally,this implementation succeeded in protecting interlaid rock during blasting operations.

A Time Neighborhood Method for the Verification of Landfalling Typhoon Track Forecast

Landfalling typhoons can cause disasters over large regions.The government and emergency responders need to take measures to mitigate disasters according to the forecast of landfall position,while slight timing error can be ignored.The reliability of operational model forecasts of typhoon landfall position needs to be evaluated beforehand,according to the forecasts and observation of historical cases.In the evaluation of landfalling typhoon track,the traditional method based on point-to-point matching methods could be influenced by the predicted typhoon translation speed.Consequently,the traditional track evaluation method may result in a large track error even if the predicted landfall position is close to observation.The purpose of this paper is to address the above issue using a simple evaluation method of landfalling typhoon track forecast based on the time neighborhood approach.In this new method,the timing error was lessened to highlight the importance of the position error during the landfall of typhoon.The properties of the time neighborhood method are compared with the traditional method based on numerical forecast results of 12 landfalling typhoon cases.Results demonstrated that the new method is not sensitive to the sampling frequency,and that the difference between the time neighborhood and traditional method will be more obvious when the moving speed of typhoon is moderate(between 15−30 km h^(−1)).The time neighborhood concept can be easily extended to a broader context when one attempts to examine the position error more than the timing error.

Discussion on neighborhood optimal trajectory online correction algorithm and its application range

This paper presents a neighborhood optimal trajectory online correction algorithm considering terminal time variation,and investigates its application range.Firstly,the motion model of midcourse guidance is established,and the online trajectory correction-regenerating strategy is introduced.Secondly,based on the neighborhood optimal control theory,a neighborhood optimal trajectory online correction algorithm considering the terminal time variation is proposed by adding the consideration of terminal time variation to the traditional neighborhood optimal trajectory correction method.Thirdly,the Monte Carlo simulation method is used to analyze the application range of the algorithm,which provides a basis for the division of application domain of the online correction algorithm and the online regeneration algorithm of midcourse guidance trajectory.Finally,the simulation results show that the algorithm has high real-time performance,and the online correction trajectory can meet the requirements of terminal constraint change.The application range of the algorithm is obtained through Monte Carlo simulation.

China’s Efforts to Build a Community with a Shared Future in the Neighborhood from the Perspective of Major-country Competition

Since the 18th National Congress of the Communist Party of China,China has proposed and actively acted on the important vision of building a community with a shared future for mankind.In October 2013,President Xi Jinping put forward that“the building of a community with a shared future should be advanced in China’s neighboring countries”.

Two-Layer Information Granulation:Mapping-Equivalence Neighborhood Rough Set and Its Attribute Reduction

Attribute reduction,as one of the essential applications of the rough set,has attracted extensive attention from scholars.Information granulation is a key step of attribute reduction,and its efficiency has a significant impact on the overall efficiency of attribute reduction.The information granulation of the existing neighborhood rough set models is usually a single layer,and the construction of each information granule needs to search all the samples in the universe,which is inefficient.To fill such gap,a new neighborhood rough set model is proposed,which aims to improve the efficiency of attribute reduction by means of two-layer information granulation.The first layer of information granulation constructs a mapping-equivalence relation that divides the universe into multiple mutually independent mapping-equivalence classes.The second layer of information granulation views each mapping-equivalence class as a sub-universe and then performs neighborhood informa-tion granulation.A model named mapping-equivalence neighborhood rough set model is derived from the strategy of two-layer information granulation.Experimental results show that compared with other neighborhood rough set models,this model can effectively improve the efficiency of attribute reduction and reduce the uncertainty of the system.The strategy provides a new thinking for the exploration of neighborhood rough set models and the study of attribute reduction acceleration problems.

Image defocus deblurring method based on gradient difference of boundary neighborhood

Background For static scenes with multiple depth layers,existing defocused image deblurring methods have the problems of edge-ringing artifacts or insufficient deblurring owing to inaccurate estimation of the blur amount,and prior knowledge in nonblind deconvolution is not strong,which leads to image detail recovery challenges.Methods To this end,this study proposes a blur map estimation method for defocused images based on the gradient difference of the boundary neighborhood,which uses the gradient difference of the boundary neighborhood to accurately obtain the amount of blurring,thereby preventing boundary ringing artifacts.The obtained blur map is then used for blur detection to determine whether the image needs to be deblurred,thereby improving the efficiency of deblurring without manual intervention and judgment.Finally,a nonblind deconvolution algorithm was designed to achieve image deblurring based on the blur amount selection strategy and sparse prior.Results Experimental results showed that our method improves PSNR(Peak Signal-to-Noise Ratio)and SSIM(Structural Similarity Index)by an average of 4.6%and 7.3%,respectively,compared to existing methods.Conclusions Experimental results showed that the proposed method outperforms existing methods.Compared to existing methods,our method can better solve the problems of boundary ringing artifacts and detail information preservation in defocused image deblurring.

CIIS Organizes International Symposium to Commemorate China’s Principle in Neighborhood Diplomacy

On October 24,the International Symposium to Commemorate the10th Anniversary of China's Principle of Amity,Sincerity,Mutual Benefit and Inclusiveness in Neighborhood Diplomacy was held in Beijing.Co-hosted by the Office of the Central Commission for Foreign Affairs and the Ministry of Foreign Affairs,and organized by China Institute of International Studies(CIIS),the Symposium was attended by more than200 representatives including political figures from neighboring countries,heads of international and regional organizations,diplomatic envoys of neighboring countries to China and renowned experts and scholars.