Urban livability in socially disadvantaged neighborhoods: The experience of the German program “socially integrative city”

This paper asks how the livability of socially disadvantaged urban neighborhoods can be improved with the help of publicly funded area-based urban regeneration. It builds on the history of area-based regeneration policies in Germany aiming at upgrading and resolving urban problems at the neighborhood level. Its main argument is as follows. First, the fate of conventional physical upgrading policies focusing on the livability of deprived urban areas depends on the development environment. While successful upgrading sometimes makes inner-city neighborhoods so attractive that they run into a trend towards gentrification and displacement of the urban poor, the stabilization of less privileged areas cannot always be guaranteed. Second, alternative approaches are needed, linking limited physical upgrading with socially oriented policies, building on strategies like neighborhood management and empowerment. Third, they can make a substantial contribution to stabilizing deprived neighborhoods, thereby improving the general living conditions and the opportunities of the urban poor. However, they require at least some permanent intervention. Thus, they transcend the logic of area-based regeneration normally limited to restoring faith into the private real estate market and thereby directing inward investment into them that improves the quality of the physical environment. Fourth, they are hardly able to overcome significant negative stigmatization in cities that are severely hit by economic downturn and population decline. This is especially true when they act as arrival areas for consecutive waves of migrants, making it necessary to redefine the role of those areas in cities and accepting their high concentration of urban problems as a starting point for different area-based policies dealing with them. The key empirical background of the paper is the German system of urban development grants and an evaluation of the so-called program of “socially integrative city”.     

Simulation of interaction between high-temperature process and heat emission from electricity system in summer

During the hot summer season, using electricity systems increases the local anthropogenic heat emission, further increasing the temperature. Regarding anthropogenic heat sources, electric energy consumption, heat generation, indoor and outdoor heat transfer, and exchange in buildings play a critical role in the change in the urban thermal environment. Therefore, the Weather Research and Forecasting (WRF) Model was applied in this study to investigate the heat generation from an indoor electricity system and its influence on the outdoor thermal environment. Through the building effect parameterization (BEP) of a multistorey urban canopy scheme, a building energy model (BEM) to increase the influence of indoor air conditioning on the electricity consumption system was proposed. In other words, the BEP+BEM urban canopy parameterization scheme was set. High temperatures and a summer heat wave were simulated as the background weather. The results show that using the BEP+BEM parameterization scheme of indoor and outdoor energy exchange in the WRF model can better simulate the air temperature near the surface layer on a sunny summer. During the day, the turning on the air conditioning and other electrical systems have no obvious effect on the air temperature near the surface layer in the city, whereas at night, the air temperature generally increases by 0.6 ℃, especially in densely populated areas, with a maximum temperature rise of approximately 1.2 ℃ from 22:00 to 23:00. When the indoor air conditioning target temperature is adjusted to 25–27 ℃, the total energy release of the air conditioning system is reduced by 12.66%, and the temperature drops the most from 13:00 to 16:00, with an average of approximately 1 ℃. Further, the denser the building is, the greater the temperature drop.     

一种融合社交信任的矩阵分解推荐算法

为了解决数据稀疏性对推荐算法预测准确度的影响,本文提出了一种融合社交信任的矩阵分解推荐算法。在相关数据集上实验结果表明,预测的平均绝对误差和均方根误差都得到了明显的改善。     

Controllable preparation of porous ZrB2–SiC ceramics via using KCl space holders

In this work, a novel and facile technique based on using KCl as space holders, along with partial sintering (at 1900 °C for 30 min), was explored to prepare porous ZrB₂–SiC ceramics with controllable pore structure, tunable compressive strength and thermal conductivity. The as-prepared porous ZrB₂–SiC samples possess high porosity of 45–67%, low average pore size of 3–7 μm, high compressive strength of 32–106 MPa, and low room temperature thermal conductivity of 13–34 W m⁻¹ K⁻¹. The porosity, pore structure, compressive strength and thermal conductivity of porous ZrB₂–SiC ceramics can be tuned simply by changing KCl content and its particle size. The effect of porosity and pore structure on the thermal conductivity of as-prepared porous ZrB₂–SiC ceramics was examined and found to be consistent with the classical model for porous materials. The poring mechanism of porous ZrB₂–SiC samples via adding pore-forming agent combined with partial sintering was also preliminary illustrated.     

A novel fabrication procedure for producing high strength hydroxyapatite ceramic scaffolds with high porosity

Hydroxyapatite (HA) scaffolds were fabricated using the space holder method with a pressureless sintering process in a systematically developed manner at different fabrication stages to increase the strength of the scaffold at high porosity. Polyvinyl alcohol (PVA) and Polymethyl methacrylate (PMMA) were used as binders and space holder agents, respectively. The physical properties of the HA scaffolds were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), linear shrinkage test, and porosity measurements. The mechanical properties of the HA scaffolds were analyzed using compressive strength measurements. The results revealed that the HA scaffold met the expected quality requirements with a compressive strength of 2.2 MPa at a porosity of 65.6% with pore sizes distributed in the range of 126–385 μm. The shrinkage of the scaffold diameter occurred by 20.27%, this diameter shrinkage predominantly to the shrinkage of the HA scaffold caused by sintering. Besides, suspect that a higher PMMA concentration causes pore size shrinkage upon sintering. The formation of pore interconnections was evidenced by SEM observations and the ‘translucent light method’ developed in this study. The results of the scaffold phase test using XRD showed that the final scaffold consisted only of the HA phase, as the PVA and PMMA phases burned out during the sintering process.     

基于控制变量参数化方法的自由漂浮空间机器人路径规划研究

针对自由漂浮空间机器人传统路径规划方法对基座卫星扰动较大的问题，提出了一种基于控制变量参数化的路径规划方法。该方法将路径规划问题转化成以基座姿态扰动最小为目标函数并满足一系列约束条件的最优控制问题，并采用控制变量参数化方法进行离散化处理，将最优控制问题转化成求解非线性规划问题，并给出了完整的理论收敛性证明，从而准确地估计出自由漂浮空间机器人末端执行器的最优路径。仿真结果表明，与传统的分解加速度方法相比，该方法得到的运动路径所引起的基座卫星姿态扰动为0.104rad，相比传统方法降低了17.53%，验证了所提路径规划方法的有效性与最优性。     

Prediction of risk factors of cyberbullying-related words in Korea: Application of data mining using social big data

The study examined a decision tree analysis using social big data to conduct the prediction model on types of risk factors related to cyberbullying in Korea. The study conducted an analysis of 103,212 buzzes that had noted causes of cyberbullying and data were collected from 227 online channels, such as news websites, blogs, online groups, social network services, and online bulletin boards. Using opinion-mining method and decision tree analysis, the types of cyberbullying were sorted using SPSS 25.0. The results indicated that the total rate of types of cyberbullying in Korea was 44%, which consisted of 32.3% victims, 6.4% perpetrators, and 5.3% bystanders. According to the results, the impulse factor was also the greatest influence on the prediction of the risk factors and the propensity for dominance factor was the second greatest factor predicting the types of risk factors. In particular, the impulse factor had the most significant effect on bystanders, and the propensity for dominance factor was also significant in influencing online perpetrators. It is necessary to develop a program to diminish the impulses that were initiated by bystanders as well as victims and perpetrators because many of those bystanders have tended to aggravate impulsive cyberbullying behaviors.     

It doesn’t take a village to fall for misinformation: Social media use,discussion heterogeneity preference,worry of the virus,faith in scientists,and COVID-19-related misinformation beliefs

With the circulation of misinformation about the COVID-19 pandemic, the World Health Organization has raised concerns about an “infodemic,” which exacerbates people’s misperceptions and deters preventive measures. Against this backdrop, this study examined the conditional indirect effect of social media use and discussion heterogeneity preference on COVID-19-related misinformation beliefs in the United States, using a national survey. Findings suggested that social media use was positively associated with misinformation beliefs, while discussion heterogeneity preference was negatively associated with misinformation beliefs. Furthermore, worry of COVID-19 was found to be a significant mediator as both associations became more significant when mediated through worry. In addition, faith in scientists served as a moderator that mitigated the indirect effect of discussion heterogeneity preference on misinformation beliefs. That is, among those who had stronger faiths in scientists, the indirect effect of discussion heterogeneity preference on misinformation belief became more negative. The findings revealed communication and psychological factors associated with COVID-19-related misinformation beliefs and provided insights into coping strategies during the pandemic.     

Modeling online user behaviors with competitive interactions

Online user behaviors are increasingly modulated by social media. Extant literature mainly focuses on investigating how network structures affect user behaviors. However, recent empirical results demonstrate that user behaviors and network structures usually coevolve dynamically, and topological patterns turn out to be inadequate for characterizing real-world user behaviors. In this paper, we present a dynamic model to deal with this challenge. This proposed model is mainly governed by two competing principles: homophily and homeostasis. Empirical evaluations of three online real-world datasets suggest that the proposed dynamic model can well predict long-range online user behaviors.