Hasil untuk "Human ecology. Anthropogeography"

Federico Morelli, Yanina Benedetti, Peter Mikula et al.

Abstract Flight initiation distance (FID) is a metric often used to study an individual's perceptions of risk when facing a predatory threat. Longer FID indicates lower risk‐taking, while shorter FID identifies bolder individuals who tolerate greater risk. Until now, no studies have tested the potential effect of the observer's sex on the escape behaviour of wild birds. Given observed differences in how laboratory animals may respond to the sex of humans interacting with them, the lack of reports in the field is surprising. In five European countries, we tested whether urban birds perceived the risk posed by approaching female versus male observers differently, using FID as a response variable. First, we matched the female and male observers according to their height and clothing. Then, we fitted Bayesian regression models, controlling for the phylogenetic relatedness of bird species, to test for the effect of human observer sex after controlling for a variety of other important factors known to explain variation in FID (starting distance, flock size, sex of the target bird, land use characteristics and vegetation cover). We found that male birds were more risk‐tolerant than females and – unexpectedly—birds in general escaped sooner when approached by women than by men. The escape difference associated with the observer's sex (~1 m longer when approached by women than by men) was consistent in populations across all five examined European countries. We discussed various hypotheses to explain birds' escape responses related to the observer's sex; however, further research is necessary to fully understand this phenomenon. Read the free Plain Language Summary for this article on the Journal blog.

Manish Kumar Shrivastava, Malancha Chakrabarty

This article discusses three major 2025 reports—the Global Multidimensional Poverty Index, the Climate Inequality Report, and the COP 30 decision on the Global Goal on Adaptation—and highlights a core policy conundrum: effective climate action depends on the eradication of poverty, achievement of developmental aspirations, and reducing inequality. This aligns with the United Nations Framework Convention on Climate Change principle of Common but Differentiated Responsibilities and Respective Capabilities, which recognizes poverty eradication as a primary goal of developing countries. Poverty and climate vulnerability reinforce each other, as the bulk of multidimensionally poor people are located in regions with high climate risks and lack adaptive capacity. Unless climate action is specifically targeted towards disadvantaged sections, it can worsen poverty, inequality, and vulnerability. A low-carbon transition can lead to further concentration of wealth, higher poverty rates, and greater climate vulnerability if inequality is left unaddressed.

Mohsen Papi, Mohammad Rahmani

The present study aims to investigate the regeneration process of the worn-out urban fabric in the Masour neighborhood of Khorramabad city, focusing on neighborhood-oriented planning and pedestrian-oriented approaches to improve residents’ quality of life. The statistical population consists of all residents of the Masour neighborhood, with a population of approximately 22,833 people in 2025 and an area of 222 hectares. Sampling was conducted using a mixed approach, including simple random, cluster random, and purposive non-random methods, and a total of 37 open-ended questionnaires were completed for data collection. This study is applied-developmental in nature, using a mixed-method (qualitative–quantitative) approach. The research tools included questionnaires, SWOT analysis, Delphi technique, and statistical analyses conducted with SPSS software, along with expert consultations. The main novelty of this research lies in the simultaneous application of neighborhood-based and pedestrian-oriented strategies in urban regeneration, which has been rarely examined in an integrated manner in the literature. The results revealed that regeneration strategies can be categorized into four groups: aggressive (SO), revision (WO), diversification (ST), and defensive (WT). Implementing these strategies can significantly improve physical, social, economic, environmental, and managerial indicators in the neighborhood. Overall, the findings demonstrate that regeneration of Masour with a pedestrian-oriented perspective not only addresses structural and infrastructural issues but also enhances quality of life and residents’ satisfaction.

Bahar İlgen, Georges Hattab

Text simplification is essential for making public health information accessible to diverse populations, including those with limited health literacy. However, commonly used evaluation metrics in Natural Language Processing (NLP), such as BLEU, FKGL, and SARI, mainly capture surface-level features and fail to account for human-centered qualities like clarity, trustworthiness, tone, cultural relevance, and actionability. This limitation is particularly critical in high-stakes health contexts, where communication must be not only simple but also usable, respectful, and trustworthy. To address this gap, we propose the Human-Centered Readability Score (HCRS), a five-dimensional evaluation framework grounded in Human-Computer Interaction (HCI) and health communication research. HCRS integrates automatic measures with structured human feedback to capture the relational and contextual aspects of readability. We outline the framework, discuss its integration into participatory evaluation workflows, and present a protocol for empirical validation. This work aims to advance the evaluation of health text simplification beyond surface metrics, enabling NLP systems that align more closely with diverse users' needs, expectations, and lived experiences.

Himabindu Thogaru, Saisubramaniam Gopalakrishnan, Zishan Ahmad et al.

Manufacturing planners face complex operational challenges that require seamless collaboration between human expertise and intelligent systems to achieve optimal performance in modern production environments. Traditional approaches to analyzing simulation-based manufacturing data often create barriers between human decision-makers and critical operational insights, limiting effective partnership in manufacturing planning. Our framework establishes a collaborative intelligence system integrating Knowledge Graphs and Large Language Model-based agents to bridge this gap, empowering manufacturing professionals through natural language interfaces for complex operational analysis. The system transforms simulation data into semantically rich representations, enabling planners to interact naturally with operational insights without specialized expertise. A collaborative LLM agent works alongside human decision-makers, employing iterative reasoning that mirrors human analytical thinking while generating precise queries for knowledge extraction and providing transparent validation. This partnership approach to manufacturing bottleneck identification, validated through operational scenarios, demonstrates enhanced performance while maintaining human oversight and decision authority. For operational inquiries, the system achieves near-perfect accuracy through natural language interaction. For investigative scenarios requiring collaborative analysis, we demonstrate the framework's effectiveness in supporting human experts to uncover interconnected operational issues that enhance understanding and decision-making. This work advances collaborative manufacturing by creating intuitive methods for actionable insights, reducing cognitive load while amplifying human analytical capabilities in evolving manufacturing ecosystems.

Yuchen Liu, Lino Lerch, Luigi Palmieri et al.

Predicting human behavior in shared environments is crucial for safe and efficient human-robot interaction. Traditional data-driven methods to that end are pre-trained on domain-specific datasets, activity types, and prediction horizons. In contrast, the recent breakthroughs in Large Language Models (LLMs) promise open-ended cross-domain generalization to describe various human activities and make predictions in any context. In particular, Multimodal LLMs (MLLMs) are able to integrate information from various sources, achieving more contextual awareness and improved scene understanding. The difficulty in applying general-purpose MLLMs directly for prediction stems from their limited capacity for processing large input sequences, sensitivity to prompt design, and expensive fine-tuning. In this paper, we present a systematic analysis of applying pre-trained MLLMs for context-aware human behavior prediction. To this end, we introduce a modular multimodal human activity prediction framework that allows us to benchmark various MLLMs, input variations, In-Context Learning (ICL), and autoregressive techniques. Our evaluation indicates that the best-performing framework configuration is able to reach 92.8% semantic similarity and 66.1% exact label accuracy in predicting human behaviors in the target frame.

Hajnal Gyeviki, Mihály Minkó, Mary Karyda et al.

Balaton Borders translates ecological data from Lake Balaton into ceramic tableware that represents human impact on the landscape, from reedbed reduction to shoreline modification and land erosion. Designed for performative dining, the pieces turn shared meals into multisensory encounters where food and data ceramics spark collective reflection on ecological disruption.

Chaerim Moon, Joohyung Kim

Supernumerary Robotic Limbs (SRLs) can enhance human capability within close proximity. However, as a wearable device, the generated moment from its operation acts on the human body as an external torque. When the moments increase, more muscle units are activated for balancing, and it can result in reduced muscular null space. Therefore, this paper suggests a concept of a motion planning layer that reduces the generated moment for enhanced Human-Robot Interaction. It modifies given trajectories with desirable angular acceleration and position deviation limits. Its performance to reduce the moment is demonstrated through the simulation, which uses simplified human and robotic system models.

Abeer Mohammed Galal El_Deen

ظهرت أهمية استخدام تكنولوجيا المعلومات والاتصالات (ICT) في الخدمات التي تقدمها المدن لساكنيها خلال العقد الماضي خاصة في الدول المتقدمة، وقد ساهم ذلك في تطور مجتمعاتها ورفع مستوى كفاءة معيشتهم. امتد هذا الاتجاه وهو تحويل المدن وخدماتها إلى مدن "ذكية" إلى جميع الدول بما فيها الدول النامية. أظهرت الدراسات أن تكلفة البنية الأساسية الذكية المرتفعة قد تعيق استخدامها في الدول النامية والفقيرة أو أنه سيقتصر استخدامها على فئات محددة من المجتمع. وحيث أن هدف المدن الذكية هو تحسين جودة حياة المواطنين فكان هناك حاجة لدراسة أساليب أو حلول لتطبيق التكنولوجيا في الدول النامية بشكل يناسب ويحسن جودة حياة مجتمعاتها. تتبع الدولة المصرية حاليا سياسات تتوافق مع اتجاه المدن الذكية على المستوى العلمي والتطبيقي، فقد تم تجهيز بنية أساسية ذكية ببعض مدن الجيل الرابع، وكذلك وضعت خطط لتقديم بعض الخدمات باستخدام تكنولوجيا المعلومات والاتصالات بالمدن القائمة. وحيث أن مصر تصنف من الدول النامية، فكان من الواجب عمل دراسة لإمكانية استفادة جميع فئات المجتمع الحضري المصري من هذه الخدمات بل ورفع مستوى معيشته من خلالها. يقترح البحث بعض الحلول الاستراتيجية والإجرائية التي تمكّن حصول جميع فئات مجتمع المدينة المصرية على الخدمات الذكية وسبل حل مشاكله باستخدام التكنولوجيا، من خلال استنباط ايجابيات وسلبيات المدينة المصرية الذكية ومجتمعاتها، وكذلك دراسة بعض التجارب الدولية المشابهة للحالة المصرية في هذا الصدد، وباستشارة المتخصصين في المجالات ذات الصلة، يضع البحث أنسب الحلول لمجتمع المدينة المصرية وأكثرها قابلية للتطبيق. The importance of using (ICT) has emerged over the past decade, this has contributed in developing the societies and raise their quality of life, especially in developed countries. The transformation of cities and their services into "smart" has spread all over the world, including developing countries. Studies have shown that the high cost of smart infrastructure may either hinder its use in developing and poor countries or limit its use to specific levels of society. Therefore, there was a need to come up with solutions to apply new technologies in developing countries that suit and improve the quality of life of their societies. Egypt currently is setting policies in order to be executed in smart cities. Smart infrastructure has been equipped in some new cities, and some services were transformed to be smart in existing cities. Since Egypt is classified as a developing country, it was necessary to study the possibility of benefiting all levels of the Egyptian urban society from these services and raising their quality of life. The research suggests some strategic and procedural solutions that enable all levels of Egyptian cities communities to acquire the smart services and solve their problems using technology. By extracting the pros and cons of the Egyptian city and its communities, as well as studying some international experiences similar to the Egyptian case, that used technology to solve their citizens problems, in addition of consulting specialists in related fields, the research sets the most appropriate and applicable solutions for the Egyptian smart city that suit their communities.

Huayi Zhou, Mukun Luo, Fei Jiang et al.

The 2D human pose estimation (HPE) is a basic visual problem. However, its supervised learning requires massive keypoint labels, which is labor-intensive to collect. Thus, we aim at boosting a pose estimator by excavating extra unlabeled data with semi-supervised learning (SSL). Most previous SSHPE methods are consistency-based and strive to maintain consistent outputs for differently augmented inputs. Under this genre, we find that SSHPE can be boosted from two cores: advanced data augmentations and concise consistency training ways. Specifically, for the first core, we discover the synergistic effects of existing augmentations, and reveal novel paradigms for conveniently producing new superior HPE-oriented augmentations which can more effectively add noise on unlabeled samples. We can therefore establish paired easy-hard augmentations with larger difficulty gaps. For the second core, we propose to repeatedly augment unlabeled images with diverse hard augmentations, and generate multi-path predictions sequentially for optimizing multi-losses in a single network. This simple and compact design is interpretable, and easily benefits from newly found augmentations. Comparing to state-of-the-art SSL approaches, our method brings substantial improvements on public datasets. And we extensively validate the superiority and versatility of our approach on conventional human body images, overhead fisheye images, and human hand images. The code is released in https://github.com/hnuzhy/MultiAugs.

Max Lamparth, Anthony Corso, Jacob Ganz et al.

To some, the advent of artificial intelligence (AI) promises better decision-making and increased military effectiveness while reducing the influence of human error and emotions. However, there is still debate about how AI systems, especially large language models (LLMs) that can be applied to many tasks, behave compared to humans in high-stakes military decision-making scenarios with the potential for increased risks towards escalation. To test this potential and scrutinize the use of LLMs for such purposes, we use a new wargame experiment with 214 national security experts designed to examine crisis escalation in a fictional U.S.-China scenario and compare the behavior of human player teams to LLM-simulated team responses in separate simulations. Here, we find that the LLM-simulated responses can be more aggressive and significantly affected by changes in the scenario. We show a considerable high-level agreement in the LLM and human responses and significant quantitative and qualitative differences in individual actions and strategic tendencies. These differences depend on intrinsic biases in LLMs regarding the appropriate level of violence following strategic instructions, the choice of LLM, and whether the LLMs are tasked to decide for a team of players directly or first to simulate dialog between a team of players. When simulating the dialog, the discussions lack quality and maintain a farcical harmony. The LLM simulations cannot account for human player characteristics, showing no significant difference even for extreme traits, such as "pacifist" or "aggressive sociopath." When probing behavioral consistency across individual moves of the simulation, the tested LLMs deviated from each other but generally showed somewhat consistent behavior. Our results motivate policymakers to be cautious before granting autonomy or following AI-based strategy recommendations.

Katie Seaborn

We humans are biased - and our robotic creations are biased, too. Bias is a natural phenomenon that drives our perceptions and behavior, including when it comes to socially expressive robots that have humanlike features. Recognizing that we embed bias, knowingly or not, within the design of such robots is crucial to studying its implications for people in modern societies. In this chapter, I consider the multifaceted question of bias in the context of humanoid, AI-enabled, and expressive social robots: Where does bias arise, what does it look like, and what can (or should) we do about it. I offer observations on human-robot interaction (HRI) along two parallel tracks: (1) robots designed in bias-conscious ways and (2) robots that may help us tackle bias in the human world. I outline a curated selection of cases for each track drawn from the latest HRI research and positioned against social, legal, and ethical factors. I also propose a set of critical next steps to tackle the challenges and opportunities on bias within HRI research and practice.

Chengyuan Xu, Kuo-Chin Lien, Tobias Höllerer

When designing an AI-assisted decision-making system, there is often a tradeoff between precision and recall in the AI's recommendations. We argue that careful exploitation of this tradeoff can harness the complementary strengths in the human-AI collaboration to significantly improve team performance. We investigate a real-world video anonymization task for which recall is paramount and more costly to improve. We analyze the performance of 78 professional annotators working with a) no AI assistance, b) a high-precision "restrained" AI, and c) a high-recall "zealous" AI in over 3,466 person-hours of annotation work. In comparison, the zealous AI helps human teammates achieve significantly shorter task completion time and higher recall. In a follow-up study, we remove AI assistance for everyone and find negative training effects on annotators trained with the restrained AI. These findings and our analysis point to important implications for the design of AI assistance in recall-demanding scenarios.

Shota Inoue, Hailong Liu, Takahiro Wada

Digital human models of motion sickness have been actively developed, among which models based on subjective vertical conflict (SVC) theory are the most actively studied. These models facilitate the prediction of motion sickness in various scenarios such as riding in a car. Most SVC theory models predict the motion sickness incidence (MSI), which is defined as the percentage of people who would vomit with the given specific motion stimulus. However, no model has been developed to describe milder forms of discomfort or specific symptoms of motion sickness, even though predicting milder symptoms is important for applications in automobiles and daily use vehicles. Therefore, the purpose of this study was to build a computational model of symptom progression of vestibular motion sickness based on SVC theory. We focused on a model of vestibular motion sickness with six degrees-of-freedom (6DoF) head motions. The model was developed by updating the output part of the state-of-the-art SVC model, termed the 6DoF-SVC (IN1) model, from MSI to the MIsery SCale (MISC), which is a subjective rating scale for symptom progression. We conducted an experiment to measure the progression of motion sickness during a straight fore-aft motion. It was demonstrated that our proposed method, with the parameters of the output parts optimized by the experimental results, fits well with the observed MISC.

Meredith C. VanAcker, Vickie L. Denicola, A. Denicola et al.

Although the role of host movement in shaping infectious disease dynamics is widely acknowledged, methodological separation between animal movement and disease ecology has prevented researchers from leveraging empirical insights from movement data to advance landscape scale understanding of infectious disease risk. To address this knowledge gap, we examine how movement behaviour and resource utilization by white-tailed deer (Odocoileus virginianus) determines blacklegged tick (Ixodes scapularis) distribution, which depend on deer for dispersal in a highly fragmented New York City borough. Multi-scale hierarchical resource selection analysis and movement modelling provide insight into how deer's movements contribute to the risk landscape for human exposure to the Lyme disease vector-I. scapularis. We find deer select highly vegetated and accessible residential properties which support blacklegged tick survival. We conclude the distribution of tick-borne disease risk results from the individual resource selection by deer across spatial scales in response to habitat fragmentation and anthropogenic disturbances.

Stefani A. Crabtree, J. Dunne

We propose defining a field of research called 'archaeoecology' that examines the past ~60 000 years of interactions between humans and ecosystems to better understand the human place within them. Archaeoecology explicitly integrates questions, data, and approaches from archaeology and ecology, and coalesces recent and future studies that demonstrate the usefulness of integrating archaeological, environmental, and ecological data for understanding the past. Defining a subfield of archaeoecology, much as the related fields of environmental archaeology and palaeoecology have emerged as distinct areas of research, provides a clear intellectual context for helping us to understand the trajectory of human-ecosystem interactions in the past, during the present, and into the future.

A. Hersperger, Simona R. Grădinaru, Ana Beatriz Pierri Daunt et al.

Landscape ecology as an interdisciplinary science has great potential to inform landscape planning, an integrated, collaborative practice on a regional scale. It is commonly assumed that landscape ecological concepts play a key role in this quest. The aim of the paper is to identify landscape ecological concepts that are currently receiving attention in the scientific literature, analyze the prevalence of these concepts and understand how these concepts can inform the steps of the planning processes, from goal establishment to monitoring. We analyzed all empirical and overview papers that have been published in four key academic journals in the field of landscape ecology and landscape planning in the years 2015–2019 (n = 1918). Title, abstract and keywords of all papers were read in order to identify landscape ecological concepts. A keyword search was applied to identify the use of these and previously mentioned concepts in common steps of the planning cycle. The concepts Structure, Function, Change, Scale, Landscape as human experience, Land use, Landscape and ecosystem services, Green infrastructure, and Landscape resilience were prominently represented in the analyzed literature. Landscape ecological concepts were most often mentioned in context of the landscape analysis steps and least in context of goal establishment and monitoring. The current literature spots landscape ecological concepts with great potential to support landscape planning. However, future studies need to address directly how these concepts can inform all steps in the planning process.

Jian Peng, Yan-xu Liu, R. Corstanje et al.

Kate Ritzel, T. Gallo

As cities expand to accommodate a growing human population, their impacts to natural ecosystems and the wildlife residing within them increase. Some animals that persist in urban environments demonstrate behaviors distinct from their non-urban counterparts. These potential behavioral changes are the subject of a growing body of research in the areas of wildlife ecology, biology, and conservation. In spite of increasing urban wildlife research, studies focused specifically on changing behavior in urban mammals is limited. We conducted a systematic literature review to synthesize current research on behavior changes in wild urban mammals. We included 83 papers published between 1987 and March 2020. Omnivores were the leading subject of study, closely followed by carnivores and the specific behaviors most widely studied were home range and vigilance. Among the reviewed studies, there were 166 observations of 44 distinct behaviors with 155 occurrences of behavior change relative to conspecifics in non-urban areas. The most commonly studied and observed type of behavior change was alert behavior. Results indicate urban environments drive adaptive responses in behavior including changes in home range and diet preference, shifts in activity budget and vigilance, decreased flight initiation distance, and increased nocturnal activity. Some urban mammal species even demonstrated the ability to modulate behaviors based on environmental cues. Our results highlight the need for long-term wildlife behavior studies across a variety of urban settings to promote successful urban wildlife management and conservation.

Elie Grinfeder, S. Haupert, Manon Ducrettet et al.

One mainstay of soundscape ecology is to understand acoustic pattern changes, in particular the relative balance between biophony (biotic sounds), geophony (abiotic sounds), and anthropophony (human-related sounds). However, little research has been pursued to automatically track these three components. Here, we introduce a 15-year program that aims at estimating soundscape dynamics in relation to possible land use and climate change. We address the relative prevalence patterns of these components during the first year of recording. Using four recorders, we monitored the soundscape of a large coniferous Alpine forest at the France-Switzerland border. We trained an artificial neural network (ANN) with mel frequency cepstral coefficients to systematically detect the occurrence of silence and sounds coming from birds, mammals, insects (biophony), rain (geophony), wind (geophony), and aircraft (anthropophony). The ANN satisfyingly classified each sound type. The soundscape was dominated by anthropophony (75% of all files), followed by geophony (57%), biophony (43%), and silence (14%). The classification revealed expected phenologies for biophony and geophony and a co-occurrence of biophony and anthropophony. Silence was rare and mostly limited to night time. It was possible to track the main soundscape components in order to empirically estimate their relative prevalence across seasons. This analysis reveals that anthropogenic noise is a major component of the soundscape of protected habitats, which can dramatically impact local animal behavior and ecology.