Abstract Noise pollution is a growing challenge for public health and livability in Greater Cairo. This study develops a calibrated framework for noise-sensitive land use planning by integrating field measurements,morphological analysis, and dual simulation tools (Predictor-LimA and Autodesk Forma). Case studies in Sheraton Heliopolis, New Cairo, and El Shorouk reveal noise hotspots, receptor vulnerabilities, and land-use conflicts. Unlike conventional noise mapping, the framework links empirical data with simulation outputs to produce quantitative conflict scores and receptor-sensitivity maps, enabling planners to embed acoustic resilience into zoning and design decisions. The contribution lies in bridging applied acoustics with urban planning and governance, positioning acoustic comfort as a principle of environmental justice and livability. Recommendations—such as façade orientation, vegetative buffers, and zoning amendments—are presented as evidence-based interventions derived from measured exceedance zones rather than prescriptive best practices. The results demonstrate how integrating acoustic criteria into land use policy can foster healthier, more equitable urban environments and provide a replicable model for rapidly urbanizing cities.
Cities. Urban geography, Urban groups. The city. Urban sociology
When LLM-based multi-agent systems disagree, current practice treats this as noise to be resolved through consensus. We propose it can be signal. We focus on hate speech moderation, a domain where judgments depend on cultural context and individual value weightings, producing high legitimate disagreement among human annotators. We hypothesize that convergent disagreement, where agents reason similarly but conclude differently, indicates genuine value pluralism that humans also struggle to resolve. Using the Measuring Hate Speech corpus, we embed reasoning traces from five perspective-differentiated agents and classify disagreement patterns using a four-category taxonomy based on reasoning similarity and conclusion agreement. We find that raw reasoning divergence weakly predicts human annotator conflict, but the structure of agent discord carries additional signal: cases where agents agree on a verdict show markedly lower human disagreement than cases where they do not, with large effect sizes (d>0.8) surviving correction for multiple comparisons. Our taxonomy-based ordering correlates with human disagreement patterns. These preliminary findings motivate a shift from consensus-seeking to uncertainty-surfacing multi-agent design, where disagreement structure - not magnitude - guides when human judgment is needed.
Empathy is central to human connection, yet people often struggle to express it effectively. In blinded evaluations, large language models (LLMs) generate responses that are often judged more empathic than human-written ones. Yet when a response is attributed to AI, recipients feel less heard and validated than when comparable responses are attributed to a human. To probe and address this gap in empathic communication skill, we built Lend an Ear, an experimental conversation platform in which participants are asked to offer empathic support to an LLM role-playing personal and workplace troubles. From 33,938 messages spanning 2,904 text-based conversations between 968 participants and their LLM conversational partners, we derive a data-driven taxonomy of idiomatic empathic expressions in naturalistic dialogue. Based on a pre-registered randomized experiment, we present evidence that a brief LLM coaching intervention offering personalized feedback on how to effectively communicate empathy significantly boosts alignment of participants' communication patterns with normative empathic communication patterns relative to both a control group and a group that received video-based but non-personalized feedback. Moreover, we find evidence for a silent empathy effect that people feel empathy but systematically fail to express it. Nonetheless, participants reliably identify responses aligned with normative empathic communication criteria as more expressive of empathy. Together, these results advance the scientific understanding of how empathy is expressed and valued and demonstrate a scalable, AI-based intervention for scaffolding and cultivating it.
First paragraphs:
Welcome to the winter 2024–2025 issue of JAFSCD! On our cover, we share a photo from the article Hāloa: The long breath of Hawaiian sovereignty, water rights, and Indigenous law, by Puanani Apoliona-Brown. The historical photo depicts a Protect Kaho‘olawe ‘Ohana (aka PKO or ‘Ohana) press conference at the Bishop Museum in Honolulu, Hawaii, on January 31, 1977. PKO was a small group of Native Hawaiian activists who organized to stop the bombing of a sacred island that the U.S. Navy had used for target practice since World War II. Featured in the foreground are Leimomi Apoliona (at the left) and Dr. Emmett Aluli (at the right). The article’s author, Ms. Apoliona-Brown, is the daughter of Leimomi Apoliona and one of the research fellows whose work is shared in this issue.
We are pleased to feature a special section of articles produced by the Tribal Food Systems Research Fellows of the First Nations Development Institute. These emerging Indigenous scholars include Danya Carroll, Lynn Mad Plume, Nicole Redvers, Puanani Apoliona-Brown, Daniel Hayden, Amber Hayden, Stafford Rotehrakwas Maracle, Jennifer Tewathahá:kwa Maracle, Stephen Lougheed, and Jasmine Jimerson.
A thematic analysis of this special collection of papers is provided in our first Indigenous Food Sovereignty column, authored by Mapuana Antonio, Joseph Brewer, Richard Elm-Hill, Michael Kotutwa Johnson, Tabitha Robin, A-Dae Romero-Briones, Lois Stevens, and Keith Williams. We see this column and the first special collection of papers as the beginning of an ongoing effort to solicit and publish the work of Indigenous food system researchers, and we are grateful for the support of First Nations Development Institute to make this happen. A special debt of gratitude goes to Keith Williams for his Herculean work serving as associate editor for this special section, including mentoring authors and editing their manuscripts. Keith is very generous with his time and values helping young and emerging Indigenous researchers. We are privileged to have him engaged with JAFSCD! . . .
Ambiguity in natural language instructions poses significant risks in safety-critical human-robot interaction, particularly in domains such as surgery. To address this, we propose a framework that uses Large Language Models (LLMs) for ambiguity detection specifically designed for collaborative surgical scenarios. Our method employs an ensemble of LLM evaluators, each configured with distinct prompting techniques to identify linguistic, contextual, procedural, and critical ambiguities. A chain-of-thought evaluator is included to systematically analyze instruction structure for potential issues. Individual evaluator assessments are synthesized through conformal prediction, which yields non-conformity scores based on comparison to a labeled calibration dataset. Evaluating Llama 3.2 11B and Gemma 3 12B, we observed classification accuracy exceeding 60% in differentiating ambiguous from unambiguous surgical instructions. Our approach improves the safety and reliability of human-robot collaboration in surgery by offering a mechanism to identify potentially ambiguous instructions before robot action.
Florian Mai, David Kaczér, Nicholas Kluge Corrêa
et al.
Two core challenges of alignment are 1) scalable oversight and 2) accounting for the dynamic nature of human values. While solutions like recursive reward modeling address 1), they do not simultaneously account for 2). We sketch a roadmap for a novel algorithmic framework that trains a superhuman reasoning model to decompose complex tasks into subtasks that are still amenable to human-level guidance. Our approach relies on what we call the part-to-complete generalization hypothesis, which states that the alignment of subtask solutions generalizes to the alignment of complete solutions. We advocate for the need to measure this generalization and propose ways to improve it in the future.
Social identity theory (SIT) and social categorization theory (SCT) are two facets of the social identity approach (SIA) to understanding social phenomena. SIT and SCT are models that describe and explain how people interact with one another socially, connecting the individual to the group through an understanding of underlying psychological mechanisms and intergroup behaviour. SIT, originally developed in the 1970s, and SCT, a later, more general offshoot, have been broadly applied to a range of social phenomena among people. The rise of increasingly social machines embedded in daily life has spurned efforts on understanding whether and how artificial agents can and do participate in SIA activities. As agents like social robots and chatbots powered by sophisticated large language models (LLMs) advance, understanding the real and potential roles of these technologies as social entities is crucial. Here, I provide a primer on SIA and extrapolate, through case studies and imagined examples, how SIT and SCT can apply to artificial social agents. I emphasize that not all human models and sub-theories will apply. I further argue that, given the emerging competence of these machines and our tendency to be taken in by them, we experts may need to don the hat of the uncanny killjoy, for our own good.
Nur Aini Mahmudah, Nur Agustin Mardiana, Aditya Wirawantoro Putra
et al.
Stunting remains a critical public health issue in Indonesia. The "Acceleration of Stunting Reduction" program is targeting adolescents (teenagers) as one of its key groups. This reflects the understanding that adolescents play a vital role in overcoming the problem of stunting, both as parents-to-be and as individuals who can experience the negative impact of malnutrition, which in turn contributes to the incidence of stunting. This study exposing the effectiveness of educational interventions in improving adolescent knowledge and awareness about stunting and the utilization of local foods as preventive measures. Conducted at State Senior High School of Kademangan (SMKN 1 Kademangan), Blitar, the study involved 101 eleventh-grade students who participated in a series of counselling (educational sessions) on topic stunting and local food utilization. A pretest-posttest design was used to assess changes in knowledge across seven key areas related to stunting and nutrition. The results indicated substantial improvements in understanding, with knowledge increases ranging from 37% to 58% across all areas. These findings underscore the importance of targeted nutritional education in enhancing adolescent health literacy. Ongoing educational sessions, practical activities, and further research are recommended to sustain and build upon these gains, ultimately contributing to the reduction of stunting prevalence in Indonesia.
Mubarik Mubarik, Ibnu Hadjar, Welli Meinarni
et al.
Abad ke-21 ditandai dengan teknologi yang terus berkembang dengan cepat hingga munculnya kecerdasan buatan atau artificial intelligence (AI) yang berfokus pada pembuatan sistem atau mesin yang dapat melakukan tugas-tugas yang biasanya memerlukan kecerdasan manusia. Hampir seluruh aspek kehidupan bergantung pada teknologi, termasuk bidang pendidikan. Kemajuan teknologi belum dimanfaatkan dengan optimal oleh guru. Sehingga diperlukan pelatihan untuk meningkatkan pengetahuan guru dalam menggunakan teknologi AI dalam pembelajaran. Kegiatan ini dilaksanakan dalam bentuk pelatihan yang diawali dengan tahap persiapan meliputi koordinasi, observasi dan wawancara. Selanjutnya tahap pelatihan dengan tahap pertama yaitu penyajian materi melalui presentasi dan diskusi, serta tahap kedua pendampingan pembuatan project berupa media pembelajaran berbasis AI. Kemudian dilakukan evaluasi untuk mengukur ketercapaian tujuan pelaksanaan pengabdian ini. Kegiatan ini diikuti 15 orang guru SMP Negeri 3 Sirenja. Keberhasilan kegiatan ini dilihat dari pemahaman peserta tentang teknologi AI dan kemampuan peserta menggunakan AI. Setelah mengikuti kegiatan ini peserta memperoleh pengetahuan tambahan tentang pemanfaatan AI dan mereka mampu menggunakan teknologi AI dalam merancang media pembelajran sesuai bidangnya masing-masing.
Effective human-robot collaboration requires robot to adopt their roles and levels of support based on human needs, task requirements, and complexity. Traditional human-robot teaming often relies on a pre-determined robot communication scheme, restricting teamwork adaptability in complex tasks. Leveraging strong communication capabilities of Large Language Models (LLMs), we propose a Human-Robot Teaming Framework with Multi-Modal Language feedback (HRT-ML), a framework designed to enhance human-robot interaction by adjusting the frequency and content of language-based feedback. HRT-ML framework includes two core modules: a Coordinator for high-level, low-frequency strategic guidance, and a Manager for subtask-specific, high-frequency instructions, enabling passive and active interactions with human teammates. To assess the impact of language feedback in collaborative scenarios, we conducted experiments in an enhanced Overcooked environment with varying levels of task complexity (easy, medium, hard) and feedback frequency (inactive, passive, active, superactive). Our results show that as task complexity increases relative to human capabilities, human teammates exhibited a stronger preference towards robotic agents that can offer frequent, proactive support. However, when task complexities exceed the LLM's capacity, noisy and inaccurate feedback from superactive robotic agents can instead hinder team performance, as it requires human teammates to increase their effort to interpret and respond to a large number of communications, with limited performance return. Our results offer a general principle for robotic agents to dynamically adjust their levels and frequencies of communications to work seamlessly with humans and achieve improved teaming performance.
This paper addresses the scope for action by municipalities in a climate emergency and places it in the framework of ecomodern (urban) policy. We analyse the way in which two German ‘climate emergency municipalities’ translate conflicts of post-fossil transformation into concrete political and planning strategies. Although more than 2,200 authorities around the world have already declared a climate emergency, research on the impact of these resolutions on the political orientation of municipalities is very limited. Our research focus is on the (potentially agonistic) treatment of conflicts in planning. We argue that in times of a socio-ecological crisis, success in conflict resolution cannot refer to appeasement and depoliticisation. Instead, we propose a framework of five criteria, based on critical theory on ecomodern strategies, planning processes and degrowth. Thus, this practice-related and explorative paper connects empirical insights from the German cities of Constance and Berlin with an innovative normative framework. The findings tell a complex story of an, at least partial, admission of the failure of previous climate mitigation strategies, a lack of social institutions of limits, an instrumental relation to nature and a disregard for social injustices. The paper discusses how municipalities, in the context of ongoing tensions over the post-fossil transformation in Germany, on the one hand hold on to business-as-usual approaches, but on the other hand also set political impulses for change.
Cities. Urban geography, Urbanization. City and country
Este artigo se desenvolve a partir das considerações de que a apropriações nos espaços públicos urbanos está relacionada com os usos e ocupações do solo urbano, e que, a leitura do aspecto edificado do tecido urbano é chave de interpretação para as relações que se estabelecem nos espaços públicos. O principal objetivo é apresentar a influência do uso e ocupação do solo na apropriação do entorno da praça Regina Frigeri Furno, em Vitória-ES. Para tanto, o estudo de caso divide-se em três partes, sendo que a primeira busca estabelecer e entender os períodos de consolidação do tecido urbano em questão; a segunda parte corresponde a análise tipo-morfológica do entorno imediato à praça; e, por fim, a terceira demonstra as relações de apropriação do espaço público com os edifícios do entorno da praça. Como resultado, foi possível demonstrar as considerações iniciais da pesquisa, sendo que, as atividades comerciais e de prestação de serviços, ao lado da própria constituição do tecido urbano, influenciaram nas dinâmicas observadas de apropriação dos espaços públicos.
Aesthetics of cities. City planning and beautifying, Urban groups. The city. Urban sociology
This article examines the extent to which Indigenous-led food systems and sovereignty goals, frameworks, and priorities are recognized, affirmed, and supported within the agri-food public sector. For this study, we focus on the Ontario Ministry of Agriculture, Food and Rural Affairs (OMAFRA), but the findings and analysis have implications for settler-Indigenous relations more broadly. First, we situate Indigenous food systems and sovereignties within the context of agri-food bureaucracies in Canada. We then present the research design, which involved 27 interviews with people working within or collaborating with OMAFRA on issues related to agricultural land use, programming, and development, and Indigenous relations and food systems. The findings are categorized into five themes: differing needs, visions, and priorities; land access, conversion, and health; representation; consultation and consent in agri-food programming; capacity building. The findings reveal major gaps in Indigenous representation, leadership, and control, and an absence of Indigenous-led planning and decision-making in the agri-food public sector. The findings further show that non-Indigenous people lack crucial knowledge concerning treaties and Indigenous relationships to land and stewardship, which creates ongoing and significant barriers to reconciliation. We close by discussing key barriers and opportunities for supporting Indigenous food system and sovereignty programming and ways forward for deepening settler knowledge of Indigenous issues and experiences. The perspectives shared in this study are intended to provide food system research, planning, policy, and practice with insights in order to begin to address structural injustices and better support Indigenous food sovereignty.
Humans have internal models of robots (like their physical capabilities), the world (like what will happen next), and their tasks (like a preferred goal). However, human internal models are not always perfect: for example, it is easy to underestimate a robot's inertia. Nevertheless, these models change and improve over time as humans gather more experience. Interestingly, robot actions influence what this experience is, and therefore influence how people's internal models change. In this work we take a step towards enabling robots to understand the influence they have, leverage it to better assist people, and help human models more quickly align with reality. Our key idea is to model the human's learning as a nonlinear dynamical system which evolves the human's internal model given new observations. We formulate a novel optimization problem to infer the human's learning dynamics from demonstrations that naturally exhibit human learning. We then formalize how robots can influence human learning by embedding the human's learning dynamics model into the robot planning problem. Although our formulations provide concrete problem statements, they are intractable to solve in full generality. We contribute an approximation that sacrifices the complexity of the human internal models we can represent, but enables robots to learn the nonlinear dynamics of these internal models. We evaluate our inference and planning methods in a suite of simulated environments and an in-person user study, where a 7DOF robotic arm teaches participants to be better teleoperators. While influencing human learning remains an open problem, our results demonstrate that this influence is possible and can be helpful in real human-robot interaction.
We describe the steps of developing the MDMT (Multi-Dimensional Measure of Trust), an intuitive self-report measure of perceived trustworthiness of various agents (human, robot, animal). We summarize the evidence that led to the original four-dimensional form (v1) and to the most recent five-dimensional form (v2). We examine the measure's strengths and limitations and point to further necessary validations.
Takahiro Yabe, Kota Tsubouchi, Toru Shimizu
et al.
Modeling and predicting human mobility trajectories in urban areas is an essential task for various applications. The recent availability of large-scale human movement data collected from mobile devices have enabled the development of complex human mobility prediction models. However, human mobility prediction methods are often trained and tested on different datasets, due to the lack of open-source large-scale human mobility datasets amid privacy concerns, posing a challenge towards conducting fair performance comparisons between methods. To this end, we created an open-source, anonymized, metropolitan scale, and longitudinal (90 days) dataset of 100,000 individuals' human mobility trajectories, using mobile phone location data. The location pings are spatially and temporally discretized, and the metropolitan area is undisclosed to protect users' privacy. The 90-day period is composed of 75 days of business-as-usual and 15 days during an emergency. To promote the use of the dataset, we will host a human mobility prediction data challenge (`HuMob Challenge 2023') using the human mobility dataset, which will be held in conjunction with ACM SIGSPATIAL 2023.
El objetivo general del estudio es elaborar los mapas de ruido del centro histórico de la ciudad de Matanzas, Cuba. Se aplicó una metodología de medición y modelación de datos a partir de la cual se obtuvieron cuatro mapas para diferentes horarios del día y uno promedio que muestran las zonas de mayor contaminación sonora. Se concluye que el ruido existente en la mayor parte del área analizada viola lo recomendado por la OMS y lo establecido en la NC 26 de 2012.
Human settlements. Communities, Demography. Population. Vital events