Hasil untuk "Communities. Classes. Races"

Deividson Sá Fernandes de Souza, Simone Sehnem, Patricia Guarnieri et al.

Purpose – This paper aims to provide a comprehensive literature review on the practices and challenges in managing waste electrical and electronic equipment (WEEE) in smart cities. Design/methodology/approach – A systematic literature review was conducted using the Methodi Ordinatio. Articles published between 2012 and 2022 were analyzed, totaling 149 references, of which 30 were included in the final review. Findings – Emerging technologies such as the Internet of Things (IoT), big data and artificial intelligence (AI) are frequently highlighted as promising solutions for efficient e-waste management. Governance models and public policies are widely recognized as crucial for the successful implementation of WEEE management practices in smart cities. Originality/value – This study underscores the role of advanced technologies, such as IoT and AI, in enhancing urban mobility and WEEE management. Key challenges include information security, privacy, interoperability, costs and sustainability. The findings reveal a convergence between smart cities and WEEE management, fostering the circular economy and the recovery of valuable materials.

Elze de Vink, Frank W. Takes, Akrati Saxena

Understanding community structures is crucial for analyzing networks, as nodes join communities that collectively shape large-scale networks. In real-world settings, the formation of communities is often impacted by several social factors, such as ethnicity, gender, wealth, or other attributes. These factors may introduce structural inequalities; for instance, real-world networks can have a few majority groups and many minority groups. Community detection algorithms, which identify communities based on network topology, may generate unfair outcomes if they fail to account for existing structural inequalities, particularly affecting underrepresented groups. In this work, we propose a set of novel group fairness metrics to assess the fairness of community detection methods. Additionally, we conduct a comparative evaluation of the most common community detection methods, analyzing the trade-off between performance and fairness. Experiments are performed on synthetic networks generated using LFR, ABCD, and HICH-BA benchmark models, as well as on real-world networks. Our results demonstrate that the fairness-performance trade-off varies widely across methods, with no single class of approaches consistently excelling in both aspects. We observe that Infomap and Significance methods are high-performing and fair with respect to different types of communities across most networks. The proposed metrics and findings provide valuable insights for designing fair and effective community detection algorithms.

Alireza Nourani-Dargiri

The United States cash bail system unconstitutionally hinders protest rights enshrined in the First Amendment. Protesting on controversial issues, while protected activity, often risks arrests and other interactions with police. Unfortunately, studies show that protesters of color are arrested at higher rates than white protesters. Cash bail, in turn, increases the cost associated with the arrests related to protests, further disincentivizing protesters from engaging in lawful activity. Although the overwhelming majority of these protests and demonstrations are peaceful, and many of the charges in these arrests are eventually dropped, arrested protesters are still required to put up hundreds––sometimes even thousands––of dollars to be released pretrial. If they cannot, they must remain in jail until their trial, until the charges are dropped, or until they are able to raise enough money to be released. This pretrial detention, even if it only lasts a few days, has significant consequences. Furthermore, these consequences are not shared evenly: the cash bail system disparately impacts people of color, who are imposed bail at higher rates and at higher amounts, meaning they will also experience negative consequences at a disproportionate rate. Because states are criminalizing more conduct, elevating charges from misdemeanors to felonies, and continuing to impose bail amounts on protesters, the intersection between cash bail and protests is unavoidable. In turn, many people could be afraid to protest because they do not have enough money to afford their bail if they are arrested at the protest, and because they cannot afford the negative consequences of awaiting their trial in jail. This Article discusses how cash bail dissuades First Amendment expression by compounding existing consequences created by government action that also curtails lawful protests. Furthermore, the disparate rates at which protesters of color are arrested and later imposed bail raises an equal protection concern, deterring people of color from expressing constitutional rights. Removing cash bail in limited circumstances associated with otherwise lawful protesting, measured reform may help alleviate some of the disparate risks involved with protected activity. While eliminating bail altogether is the ultimate goal, this measured reform would be an incremental step towards broader change, building public support for holistic reform.

Joseph Llewellyn, Cecilia Katzeff, Daniel Pargman et al.

The United Nations Environment Programme (UNEP) released design indexes for sustainable neighbourhoods, including self-sufficiency, community plot ratio and civic generosity. However, they are developed from an urban planning perspective and not researched in relation to: a) the citizen perspective and b) how citizen interactions can contribute to the environment. Therefore, this research tests a novel set of indexes with an underused method of ethnographic video interviews with 14 citizens of a known sustainable neighbourhood in Stockholm, Sweden. A thematic analysis conducted on 28 h of interview data collected over a 4-week period yielded 5 main themes, from outdoor public spaces. Self-sufficiency findings suggest that citizens 1) perceive small scale self-sufficiency to be challenged by large scale structural efficiency and 2) circular actions with food waste to biogas can develop the self-sufficiency index further. Community plot ratio findings suggest that citizens 3) perceive community spaces to be accessible for all but not used by all. Civic generosity findings suggest citizens 4) perceive an imbalance between self-interests of the individual versus collective interests of the community, while 5) experienced citizens feel personally responsible to pioneer civic generosity interactions. UNEP indexes for designing neighbourhoods can define local sustainability, however, our findings support this, only if they can be acted upon by the citizens who live there.

Miriam Neßler

Current research underlines the important role of arrival infrastructures in urban spaces in enabling and shaping migrants’ arrival. These include arrival brokers, individuals who help newcomers access resources. As yet, we have little knowledge on brokers’ informal and commercial practices in the context of arrival, especially in European cities, whereby brokers unsettle common “distinctions between ‘state’ and ‘market,’ as well as ‘formal’ and ‘informal’” (Lindquist, 2012, p. 75). This article aims to contribute to our understanding of arrival brokers by shedding light on commercial brokering in an arrival area in Dortmund, Germany, looking at the relations between brokers and newcomer clients. The study is based on ethnographic research, including one year of participant observation in a broker’s shop, and interviews with both brokers and newcomers. Covering both perspectives, this article analyses how commercial arrival brokering shapes newcomers’ access to resources. The findings offer new insights into arrival brokers’ multiple facets of in/formal and commercial infrastructuring. The article shows how brokers’ accessibility depends on spatial, social, financial, and temporal factors. It is relational both within the local context of service provision and through setting the conditions governing resource access. Arrival brokers can influence newcomers’ arrival processes by enabling, channelling (and sometimes blocking) resource access while also offering opportunities for newcomers to circumvent and compensate for other—more formal—forms of support. Commercial brokering evolves as a practice between brokers and newcomers within, parallel to, and beyond the support provided by more formal institutions.

Ria Bhargava, Ruolin Zhang, Eleftheria Kontou

Electric vehicles (EVs) have a growing market share in the US and several policies promote their ownership and use. EV drivers face significantly longer charging times at public charging stations, compared to internal combustion engine vehicle drivers. When charging stations are colocated with other land uses, such as shopping and recreational ones, EV drivers can plan their trips and turn their charging down time to a productive or enjoyable experience. This study examines the variety of businesses and premises located near public charging stations in Chicago, Illinois, and explores their pricing levels, which may influence the accessibility of charging stations for EV users from diverse socio-economic backgrounds. Our findings reveal that the businesses closest to charging stations predominantly include food establishments, shops, and tourism-related venues, with the majority falling into affordable pricing categories. These results suggest that public charging stations in Chicago are situated in areas with amenities that cater to a wide range of income levels, promoting equitable access to EV infrastructure. On the other hand, chargers are overwhelmingly placed in majority white neighborhoods. We highlight the importance of considering both amenities proximity and affordability in the assessment of public charging infrastructure to support a more inclusive and equitable transition to electric vehicles.

Andrew Saba, Aderotimi Adetunji, Adam Johnson et al.

Autonomous motorsports aim to replicate the human racecar driver with software and sensors. As in traditional motorsports, Autonomous Racing Vehicles (ARVs) are pushed to their handling limits in multi-agent scenarios at extremely high ($\geq 150mph$) speeds. This Operational Design Domain (ODD) presents unique challenges across the autonomy stack. The Indy Autonomous Challenge (IAC) is an international competition aiming to advance autonomous vehicle development through ARV competitions. While far from challenging what a human racecar driver can do, the IAC is pushing the state of the art by facilitating full-sized ARV competitions. This paper details the MIT-Pitt-RW Team's approach to autonomous racing in the IAC. In this work, we present our modular and fast approach to agent detection, motion planning and controls to create an autonomy stack. We also provide analysis of the performance of the software stack in single and multi-agent scenarios for rapid deployment in a fast-paced competition environment. We also cover what did and did not work when deployed on a physical system the Dallara AV-21 platform and potential improvements to address these shortcomings. Finally, we convey lessons learned and discuss limitations and future directions for improvement.

Kevin L. McKinney, John M. Abowd

We use place of birth information from the Social Security Administration linked to earnings data from the Longitudinal Employer-Household Dynamics Program and detailed race and ethnicity data from the 2010 Census to study how long-term earnings differentials vary by place of birth for different self-identified race and ethnicity categories. We focus on foreign-born persons from countries that are heavily Hispanic and from countries in the Middle East and North Africa (MENA). We find substantial heterogeneity of long-term earnings differentials within country of birth, some of which will be difficult to detect when the reporting format changes from the current two-question version to the new single-question version because they depend on self-identifications that place the individual in two distinct categories within the single-question format, specifically, Hispanic and White or Black, and MENA and White or Black. We also study the USA-born children of these same immigrants. Long-term earnings differences for the 2nd generation also vary as a function of self-identified ethnicity and race in ways that changing to the single-question format could affect.

Jun Cao, Tanhua Jin, Tao Shou et al.

Car-dominated daily travel has caused many severe and urgent urban problems across the world, and such travel patterns have been found to be related to the built environment. However, few existing studies have uncovered the nonlinear relationship between the built environment and car dependency using a machine learning method, thus failing to provide policymakers with nuanced evidence-based guidance on reducing car dependency. Using data from Puget Sound regional household travel surveys, this study analyzes the complicated relationship between car dependency and the built environment using the gradient boost decision tree method. The results show that people living in high-density areas are less likely to rely on private cars than those living in low-density neighborhoods. Both threshold and nonlinear effects are observed in the relationships between the built environment and car dependency. Increasing road density promotes car usage when the road density is below 6 km/km2. However, the positive association between road density and car use is not observed in areas with high road density. Increasing pedestrian-oriented road density decreases the likelihood of using cars as the main mode. Such a negative effect is most effective when the pedestrian-oriented road density is over 14.5 km/km2. More diverse land use also discourages people’s car use, probably because those areas are more likely to promote active modes. Destination accessibility has an overall negative effect and a significant threshold effect on car dependency. These findings can help urban planners formulate tailored land-use interventions to reduce car dependency.

Lorenzo Bartali, Marco Gabiccini, Eugeniu Grabovic et al.

In this paper we derive the dynamic equations of a race-car model via Lie-group methods. Lie-group methods are nowadays quite familiar to computational dynamicists and roboticists, but their diffusion within the vehicle dynamics community is still limited. We try to bridge this gap by showing that this framework merges gracefully with the Articulated Body Algorithm (ABA) and enables a fresh and systematic formulation of the vehicle dynamics. A significant contribution is represented by a rigorous reconciliation of the ABA steps with the salient features of vehicle dynamics, such as road-tire interactions, aerodynamic forces and load transfers. The proposed approach lends itself both to the definition of direct simulation models and to the systematic assembly of vehicle dynamics equations required, in the form of equality constraints, in numerical optimal control problems. We put our approach on a test in the latter context which involves the solution of minimum lap-time problem (MLTP). More specifically, a MLTP for a race car on the Nürburgring circuit is systematically set up with our approach. The equations are then discretized with the direct collocation method and solved within the CasADi optimization suite. Both the quality of the solution and the computational efficiency demonstrate the validity of the presented approach.

Kyle Beadle, Marie Vasek

Online communities are not safe spaces for user privacy. Even though existing research focuses on creating and improving various content moderation strategies and privacy preserving technologies, platforms hosting online communities support features allowing users to surveil one another--leading to harassment, personal data breaches, and offline harm. To tackle this problem, we introduce a new, work-in-progress framework for analyzing data privacy within vulnerable, identity-based online communities. Where current SOUPS papers study surveillance and longitudinal user data as two distinct challenges to user privacy, more work needs to be done in exploring the sites where surveillance and historical user data assemble. By synthesizing over 40 years of developments in the analysis of surveillance, we derive properties of online communities that enable the abuse of user data by fellow community members and suggest key steps to improving security for vulnerable users. Deploying this new framework on new and existing platforms will ensure that online communities are privacy-conscious and designed more inclusively.

Lucas Federico Tebes

En este artículo plantearemos una aproximación a los sistemas de enseñanza en Arquitectura, comprendiendo que históricamente se desarrollaron desde un dispositivo pensado como Maestro-Discípulo, a partir del cual, el maestro dentro de un proceso formativo no necesariamente académico, introduce al discípulo en el conocimiento y oficio de, en este caso, la Arquitectura. Analizaremos cómo operan los sistemas de transposición didáctica, practicum y aprender-haciendo, comprendiendo que estos modos de aproximación desde la experiencia hacia la construcción del conocimiento, definidos por el maestro, condicionan el proceso proyectual del discípulo, manifestando una consolidación y reproducción del dispositivo Maestro-Discípulo.

Huan Qing

Community detection in weighted networks has been a popular topic in recent years. However, while there exist several flexible methods for estimating communities in weighted networks, these methods usually assume that the number of communities is known. It is usually unclear how to determine the exact number of communities one should use. Here, to estimate the number of communities for weighted networks generated from arbitrary distribution under the degree-corrected distribution-free model, we propose one approach that combines weighted modularity with spectral clustering. This approach allows a weighted network to have negative edge weights and it also works for signed networks. We compare the proposed method to several existing methods and show that our method is more accurate for estimating the number of communities both numerically and empirically.

A. Ofemile

In the English language teaching context in Nigeria, teacher training-institutions often use theoretical and outdated curriculum with little emphasis on the acquisition of communicative language teaching (CLT) skills or content mastery. This does not adequately prepare teachers for work in basic and secondary schools. This chapter focuses on the potential of using professional community of learning to train pre-service teachers of English in CLT approaches. Two groups of teachers with similar pre-service training were evaluated during teaching practice, with one group being exposed to CLT approaches. Results suggest that pre-service teachers that were exposed to CLT approaches performed better in class than those who were not. Following these results, the potential for replication and integration into the national teacher-training curriculum in Nigeria is explored.

Madhuri Sharma

This article establishes relationships between racial/ethnic diversity, segregation, and employment-by-industry-types in the counties of Tennessee. Using the American Community Survey and NAICS data, diversity scores, entropy indices, and location quotients for major-employment are computed for Tennessee's 95 counties. Cartographic analysis, followed by correlations, principal components and regression analyses help establish the above relationships. The north-east and west-central regions of Tennessee have concentration in primary-sectors of economy whereas counties with concentration in creative-class economy (e.g., Williamson, Davidson) have higher presence of Asians, and with greater human capital (education). Simultaneously, these are also the most segregated despite being diverse. Counties with higher diversity and higher share of African-Americans are segregated, despite having employment concentration in diverse set of industries. Enormous growth potentials exist in the sectors of education and health-care which can help Tennessee revitalize its economy.

Özlem Şenyiğit, Nur Yılmaz

Purpose In architectural education, the most important outcome aimed for architect candidates to acquire is the ability to show empathy. This study focuses on designing for users who have special physical needs while addressing the concept of establishing empathy together with universal designing principles in architectural education. Design/Methodology/Approach The empathic design method has been selected as the method of this study. As part of the study, it is aimed for students to offer a design solution in which they would fictionalize a universal design concept together with the empathic design which grounds on universal design principles. Findings As a result of the study, it was observed that when given a key method during the design process, students could approach the design with different points of view and interpret the datum with different approaches. Research Limitations/Implications This study, as part of the Çukurova University Department of Architecture Interior Design class, students were asked to implement interior design projects using the empathic design method with user groups of their choice in terms of characteristic and age range following the empathic design process. Social/Practical Implications It is to develop the awareness of architecture students in the creation process and such studies with different approaches and models to develop their perspectives. Originality/Value To increase the prevalence of accessibility and the participation of the disabled in society, the empathic design method can be foreseen to be a key method in other studies in architectural education.

Шенне Вадимовна Дамбаа, Надежда Дарыевна Сувандии

В статье представлены результаты сравнительного анализа лексики охоты в ряде тюркских языков народов Саяно-Алтая: тувинского, алтайского, хакасского и тофаларского. Анализ выполнен для определения общих и отличительных особенностей. Охота — один из древних видов деятельности народов этого региона, который потерял былую значимость. Вслед за этим уходит и лексика, фиксация которой важна для задач сохранения культуры. В ходе исследования выявлено четыре основных лексико-семантических групп: названия парнокопытных диких животных, хищников, мелких зверей и птиц. В выделенных лексико-семантических группах имеются общие лексемы, как общетюркское название еŋ//aŋ (еŋ mеŋ) — ‘добыча’ для обозначения общего названия дикого зверя; наименования птицы куш тув., алт., хус хак. В лексико-семантической группе «Названия хищных животных» встречаются большое количество эвфемизмов, заменяющих слова-табу, которые по этикету народов Саяно-Алтая являются священными запретами.

Olivier Sykes

This issue (5.2) of Transactions of AESOP brings together a selection of papers which address current themes and issues in planning education. Two of the papers reflect on the experience of teaching modules submitted to recent rounds of the AESOP Excellence in Teaching Award (ETA), one reports on an experience of internationalisation in planning education, and one is an invited paper by Andrea Frank the present Chair of the AESOP ETA Committee. They all provide original and insightful contributions addressing key themes in contemporary planning education including, the impacts of the COVID-19 pandemic, new technologies and modes of teaching delivery, the teaching of landscape in planning programmes, and, the internationalisation of planning cohorts and curricula.

Peide Cai, Hengli Wang, Huaiyang Huang et al.

Autonomous car racing is a challenging task in the robotic control area. Traditional modular methods require accurate mapping, localization and planning, which makes them computationally inefficient and sensitive to environmental changes. Recently, deep-learning-based end-to-end systems have shown promising results for autonomous driving/racing. However, they are commonly implemented by supervised imitation learning (IL), which suffers from the distribution mismatch problem, or by reinforcement learning (RL), which requires a huge amount of risky interaction data. In this work, we present a general deep imitative reinforcement learning approach (DIRL), which successfully achieves agile autonomous racing using visual inputs. The driving knowledge is acquired from both IL and model-based RL, where the agent can learn from human teachers as well as perform self-improvement by safely interacting with an offline world model. We validate our algorithm both in a high-fidelity driving simulation and on a real-world 1/20-scale RC-car with limited onboard computation. The evaluation results demonstrate that our method outperforms previous IL and RL methods in terms of sample efficiency and task performance. Demonstration videos are available at https://caipeide.github.io/autorace-dirl/

Xian Li

Procedural fairness has been a public concern, which leads to controversy when making decisions with respect to protected classes, such as race, social status, and disability. Some protected classes can be inferred according to some safe proxies like surname and geolocation for the race. Hence, implicitly utilizing the predicted protected classes based on the related proxies when making decisions is an efficient approach to circumvent this issue and seek just decisions. In this article, we propose a hierarchical random forest model for prediction without explicitly involving protected classes. Simulation experiments are conducted to show the performance of the hierarchical random forest model. An example is analyzed from Boston police interview records to illustrate the usefulness of the proposed model.