Hasil untuk "Human ecology. Anthropogeography"

R. Lawrence

A large body of research highlights the positive effects of physical exposure to and feeling psychologically connected to nature on human well-being. Such research, however, is limited by a lack of racially diverse samples, raising concerns about generalizability. In this study, I addressed this gap in the literature by drawing upon an online sample of Americans stratified by race to examine how time spent outdoors and a sense of connectedness to nature affects individual subjective well-being, a broad concept which refers to how people evaluate the quality of their own lives. I found that time spent outdoors was positively associated with respondents’ life satisfaction and positive affect, while nature connectedness was positively associated with life satisfaction, positive affect and, interestingly, negative affect. Black Americans reported higher life satisfaction and positive affect as well as lower negative affect than their White counterparts. I conclude with a discussion of the findings and avenues for future research.

Fatima Shane D.S. Flavier, M. R. Dy

The COVID-19 pandemic greatly affected the education sector and the students’ learning outcomes. Studies have found that adolescents’ reading habits, whether from leisure or academic reading, significantly influence their academic performance. Thus, this study determined the relationship between academic and leisure reading habits, particularly the frequency, duration, and preferred and actual reading materials, with the academic performance of Filipino college students in the context of flexible learning during the pandemic. The 153 respondents completed an online survey through Google Forms. Using descriptive statistics, the findings revealed varying amounts of time spent reading for academic and leisure purposes, but students are more inclined to read for educational purposes. Using correlational analysis, it was found that the more time students spend reading academic materials, the higher their general weighted average (GWA) is. Further, the more time students spend reading academic journals, the higher their GWA is. No significant relationship was found between their leisure reading habits and academic performance. It is recommended that students read their academic materials daily to achieve good academic performance.

Chantale Lauer, Peter Pfeiffer, Nijat Mehdiyev

Integrating Large Language Models (LLMs) into business process management tools promises to democratize Business Process Model and Notation (BPMN) modeling for non-experts. While automated frameworks assess syntactic and semantic quality, they miss human factors like trust, usability, and professional alignment. We conducted a mixed-methods evaluation of our proposed solution, an LLM-powered BPMN copilot, with five process modeling experts using focus groups and standardized questionnaires. Our findings reveal a critical tension between acceptable perceived usability (mean CUQ score: 67.2/100) and notably lower trust (mean score: 48.8\%), with reliability rated as the most critical concern (M=1.8/5). Furthermore, we identified output-quality issues, prompting difficulties, and a need for the LLM to ask more in-depth clarifying questions about the process. We envision five use cases ranging from domain-expert support to enterprise quality assurance. We demonstrate the necessity of human-centered evaluation complementing automated benchmarking for LLM modeling agents.

Xuechen Li, Shuai Zhang, Nan Cao et al.

While the proliferation of foundation models has significantly boosted individual productivity, it also introduces a potential challenge: the homogenization of creative content. In response, we revisit Design-by-Analogy (DbA), a cognitively grounded approach that fosters novel solutions by mapping inspiration across domains. However, prevailing perspectives often restrict DbA to early ideation or specific data modalities, while reducing AI-driven design to simplified input-output pipelines. Such conceptual limitations inadvertently foster widespread design fixation. To address this, we expand the understanding of DbA by embedding it into the entire creative process, thereby demonstrating its capacity to mitigate such fixation. Through a systematic review of 85 studies, we identify six forms of representation and classify techniques across seven stages of the creative process. We further discuss three major application domains: creative industries, intelligent manufacturing, and education and services, demonstrating DbA's practical relevance. Building on this synthesis, we frame DbA as a mediating technology for human-AI collaboration and outline the potential opportunities and inherent risks for advancing creativity support in HCI and design research.

Gerda Claeskens, Céline Cunen, Nils Lid Hjort

Datasets encountered when examining deeper issues in ecology and evolution are often complex. This calls for careful strategies for both model building, model selection, and model averaging. Our paper aims at motivating, exhibiting, and further developing focused model selection criteria. In contexts involving precisely formulated interest parameters, these versions of FIC, the focused information criterion, typically lead to better final precision for the most salient estimates, confidence intervals, etc. as compared to estimators obtained from other selection methods. Our methods are illustrated with real case studies in ecology; one related to bird species abundance and another to the decline in body condition for the Antarctic minke whale.

E. Fernández-Corredor, Alba Fuster‐Alonso, Francisco Ramírez et al.

Abstract Integrative approaches that investigate trophic ecology drivers provide knowledge to explore and predict changes in food‐web dynamics under contrasting scenarios of global change. However, there are few studies that analyse the relationship between environmental factors and trophic interactions and that additionally consider other human stressors such as fisheries. Here, we use Bayesian Stable isotope mixing models to study the trophic ecology of a widespread pelagic predatory fish, the swordfish (Xiphias gladius), in the western Mediterranean Sea and the adjacent Atlantic waters. We explore the relationships between dietary estimates and biological, environmental and anthropogenic drivers using generalized additive models (GAMs). GAMs are used to develop spatial predictions of present prey consumption and, as a prospective exercise, to project changes in prey consumption under different future climate change scenarios. Overall, we found that swordfish diet varied as a response to changing environmental conditions, particularly to varying sea surface temperature (SST), mixed layer depth (MLD) and chlorophyll‐a concentration (Chl); and to fishing pressure. Fish consumption was related to SST and MLD. Squid consumption was related to SST, with the greatest contributions observed in swordfish of intermediate lengths. Squid had a higher contribution to swordfish diet around the Canary Islands and the western Mediterranean Sea, while gelatinous organisms were more consumed around the Gulf of Cádiz. The consumption of gelatinous organisms was higher in smaller swordfish and in areas with lower productivity. Our prospective exercise suggested different diet alterations under contrasting future global change scenarios. For the first time, we provide quantitative evidence of how large‐scale, spatial–temporal patterns in fishing pressure and environmental conditions can shape the diet of swordfish. Our study presents useful results to assess the diet of this predator and highlight how incorporating trophic interactions into projections can improve our understanding of future distributions.

Françoise Okah Efogo, Paul Awoa Awoa

This article focuses on the challenges that uncertainty poses to countries in global and regional value chains. In this perspective, it focuses specifically on African countries and enriches the results with a comparative approach. Indeed, using a gravity model for 49 African countries and all their trading partners from 1990 to 2019, the paper proposes a comparative analysis of the effects of uncertainty on global trade in value chains and on trade in value chains within Africa. The robustness of the results shows that domestic uncertainty can drive the expansion of intra-African trade in value chains, while uncertainty in the partner country hinders the flourishing of trade relationships within a value chain.

Katelyn Xiaoying Mei, Nic Weber

The recent rapid advancement of LLM-based AI systems has accelerated our search and production of information. While the advantages brought by these systems seemingly improve the performance or efficiency of human activities, they do not necessarily enhance human capabilities. Recent research has started to examine the impact of generative AI on individuals' cognitive abilities, especially critical thinking. Based on definitions of critical thinking across psychology and education, this position paper proposes the distinction between demonstrated and performed critical thinking in the era of generative AI and discusses the implication of this distinction in research and development of AI systems that aim to augment human critical thinking.

Chaeyeon Lim

While generative artificial intelligence (Gen AI) increasingly transforms academic environments, a critical gap exists in understanding and mitigating human biases in AI interactions, such as anchoring and confirmation bias. This position paper advocates for metacognitive AI literacy interventions to help university students critically engage with AI and address biases across the Human-AI interaction workflows. The paper presents the importance of considering (1) metacognitive support with deliberate friction focusing on human bias; (2) bi-directional Human-AI interaction intervention addressing both input formulation and output interpretation; and (3) adaptive scaffolding that responds to diverse user engagement patterns. These frameworks are illustrated through ongoing work on "DeBiasMe," AIED (AI in Education) interventions designed to enhance awareness of cognitive biases while empowering user agency in AI interactions. The paper invites multiple stakeholders to engage in discussions on design and evaluation methods for scaffolding mechanisms, bias visualization, and analysis frameworks. This position contributes to the emerging field of AI-augmented learning by emphasizing the critical role of metacognition in helping students navigate the complex interaction between human, statistical, and systemic biases in AI use while highlighting how cognitive adaptation to AI systems must be explicitly integrated into comprehensive AI literacy frameworks.

Jacinto Colan, Ana Davila, Yutaro Yamada et al.

Human-robot collaboration in surgery represents a significant area of research, driven by the increasing capability of autonomous robotic systems to assist surgeons in complex procedures. This systematic review examines the advancements and persistent challenges in the development of autonomous surgical robotic assistants (ASARs), focusing specifically on scenarios where robots provide meaningful and active support to human surgeons. Adhering to the PRISMA guidelines, a comprehensive literature search was conducted across the IEEE Xplore, Scopus, and Web of Science databases, resulting in the selection of 32 studies for detailed analysis. Two primary collaborative setups were identified: teleoperation-based assistance and direct hands-on interaction. The findings reveal a growing research emphasis on ASARs, with predominant applications currently in endoscope guidance, alongside emerging progress in autonomous tool manipulation. Several key challenges hinder wider adoption, including the alignment of robotic actions with human surgeon preferences, the necessity for procedural awareness within autonomous systems, the establishment of seamless human-robot information exchange, and the complexities of skill acquisition in shared workspaces. This review synthesizes current trends, identifies critical limitations, and outlines future research directions essential to improve the reliability, safety, and effectiveness of human-robot collaboration in surgical environments.

Lorenzo Marini, P. Batáry, R. Carmenta et al.

Much research and media attention has revolved around the environmental impacts of growing global human populations. While the conclusions remain contested, these assessments have largely neglected the ecological and conservation impacts of other key regional processes such as declining populations, ageing demographics and rural‐to‐urban migration. These demographic shifts are increasingly prevalent across many regions of the world, and will have significant direct effects on natural resource management and biodiversity conservation by altering individual consumption patterns, land use, land stewardship and natural disturbances. Given that the scientific foundation around this topic is still developing, we first present an initial examination of some of the key environmental impacts, aiming to elevate awareness and encourage further research in these areas. Beyond the ecological implications, declining populations, ageing demographics and rural‐to‐urban migration carry intricate social and cultural consequences that can affect people and nature interactions. Ecological studies that focus on single dimensions of biodiversity or ecosystem responses often overlook these complexities. Demographic changes are likely to be accompanied by shifts in environmental attitudes and connections with nature, all of which will influence our capacity to adapt to or mitigate environmental changes. Finally, environmental policy and practice frameworks are potentially unprepared and their success could be sensitive to these socio‐cultural and demographic shifts. Synthesis and applications: This brief overview demonstrates that population decline, ageing and rural‐to‐urban migration can have extensive implications for biodiversity and the socio‐cultural relationships between people and nature. However, the significance, dynamics and consequences of these processes are still largely overlooked. We believe that these changes warrant specific attention from the research, policy and practice communities, as understanding the outcomes and feedbacks associated with depopulation, ageing populations, loss of culture and tradition and ecological change could aid in designing landscapes and informing management that enhances both human well‐being and biodiversity conservation.

V. Van Eetvelde, Andreas Aagaard Christensen, A. Hersperger

Chenlin He, Xiaojun Wang, Jiyu Jiao et al.

Abstract The combination of fingerprint positioning and 5G (the 5th Generation Mobile Communication Technology) offers broader application prospects for indoor positioning technology, but also brings challenges in real-time performance. In this paper, we propose a fingerprint positioning method based on a deep convolutional neural network (DCNN) using a classification approach in a single-base station scenario for massive multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems. We introduce an angle-delay domain fingerprint matrix that simplifies the computation process and increases the location differentiation. The cosine distance is chosen as the fingerprint similarity criterion due to its sensitivity to angular differences. First, the DCNN model is used to determine the sub-area to which the mobile terminal belongs, and then the weighted K-nearest neighbor (WKNN) matching algorithm is used to estimate the position within the sub-area. The positioning performance is simulated in a DeepMIMO indoor environment, showing that the classification DCNN method reduces the positioning time by 77.05% compared to the non-classification method, with only a 1.08% increase in average positioning error.

Fredua Agyemang

Sustainable Development Goal 11 (SDG 11) focuses on making cities and human settlements inclusive, safe, resilient, and sustainable. Although the goal primarily addresses urban development, its principles also extend to rural areas, but the extent to which South Africa’s legal and policy frameworks empower traditional authorities to contribute to the development of their communities, particularly towards achieving SDG11, remains insufficiently explored. This study investigates how South Africa’s national legislative frameworks on traditional leadership have been applied to support the advancement of SDG 11. It examines the legal provisions within the 1996 Constitution of the Republic of South Africa, and relevant legislation to determine whether these frameworks provide a strong legal basis for promoting SDG 11 through the empowerment of traditional leadership. This study employs a desktop research methodology involving a comprehensive review of relevant laws, policies, and case law. Secondary data were gathered from case studies, journal articles, books, case laws, and credible internet sources. The findings suggest that the traditional authority system is deeply embedded within the South African Constitution, as well as legislative and policy frameworks, and has been effectively leveraged to advance SDG 11. Key insights emphasise the constitutional and legal recognition of traditional authorities and highlight the enforcement of traditional leadership roles and functions through various legal cases, and SDG 11-aligned programmes in South Africa. The areas where the role and functions of traditional leadership intersect with SDG 11 and rural development include security and safety, community participation, land management and sustainable settlements, cultural heritage and community identity, disaster management, and environmental stewardship. The empowerment of traditional leadership in South Africa has significant implications for achieving SDG 11 and rural development. These implications include enhanced local governance and service delivery, increased accountability and transparency, balanced rural-urban linkages, promotion of environmental stewardship, and the fostering of inclusive development. It also strengthens rural resilience, preserves cultural heritage, promotes sustainable resource management, and improves community engagement. However, challenges related to power dynamics, equity, and the need for policy integration and cohesion must be addressed to ensure that traditional leadership empowerment contributes effectively to sustainable development in South Africa.

Casper Boongaling Agaton, Eunice del Rosario, Marie Faye Orca et al.

Research in human ecology and sustainability holds significant importance in addressing global challenges related to the environment, society, and the well-being of the current and future generations. There is an urgent need for a platform to inform new knowledge, practices, policies, and behaviors that contribute toward a more sustainable, resilient, and harmonious coexistence between humans and their environment. The Journal of Human Ecology and Sustainability (JHES) aims to publish interdisciplinary, multidisciplinary, and transdisciplinary research on all aspects of human-environment interactions, community development, and other fields of social science that link with the people, organizations, and government to achieve human-ecological security. This note, which summarizes the contributions in the first volume of the journal, provides a brief background of the transformation of the Journal of Human Ecology to JHES, the official academic publication of the College of Human Ecology, University of the Philippines Los Baños.

Florian Hartig, Nerea Abrego, Alex Bush et al.

New technologies for acquiring biological information such as eDNA, acoustic or optical sensors, make it possible to generate spatial community observations at unprecedented scales. The potential of these novel community data to standardize community observations at high spatial, temporal, and taxonomic resolution and at large spatial scale ('many rows and many columns') has been widely discussed, but so far, there has been little integration of these data with ecological models and theory. Here, we review these developments and highlight emerging solutions, focusing on statistical methods for analyzing novel community data, in particular joint species distribution models; the new ecological questions that can be answered with these data; and the potential implications of these developments for policy and conservation.

Evan Cleave, Cailin Wark, Emmanuel Kyeremeh

For cities, immigration is now considered a vital part of local economic and community development. Over the past half-century, many cities have experienced a series challenges caused by the impacts of late-stage demographic transition; the slow bleeding of skilled domestic workers to larger metropolitan areas; and the decline of traditional economic sectors. As a result, there has been a prioritization of attracting and retaining high-skilled and well-educated immigrants by local governments through locally-focused, place-based policies. Within this context, this paper examines the ways that cities in the Province of Ontario, Canada are constructing and implementing immigrant attraction, integration, and retention strategies. To achieve this goal, we identified and examined the local immigration policies of the 52 cities in Ontario, 36 of which have a formal immigration policy document. A comprehensive content analysis was conducted on these available to identify the ways that immigration is conceptualized, and the specific policies and approaches that local governments are implementing. Statistical analysis was used to determine if there was variation in policy across different types of cities. Based on this analysis, local governments are generally developing holistic, place-based policies – however, there is variation in approaches across cities of different sizes and geographies. These place-specific policies draw on local assets and advantages (i.e. existing migrant communities; local amenities and attractions; economic and education opportunities) while also work to enhance enhancing local capacity (i.e. building networks and immigration partnerships; training employers and city workers).

Harshith Bachimanchi, Matthew I. M. Pinder, Chloé Robert et al.

The implementation of deep learning algorithms has brought new perspectives to plankton ecology. Emerging as an alternative approach to established methods, deep learning offers objective schemes to investigate plankton organisms in diverse environments. We provide an overview of deep-learning-based methods including detection and classification of phyto- and zooplankton images, foraging and swimming behaviour analysis, and finally ecological modelling. Deep learning has the potential to speed up the analysis and reduce the human experimental bias, thus enabling data acquisition at relevant temporal and spatial scales with improved reproducibility. We also discuss shortcomings and show how deep learning architectures have evolved to mitigate imprecise readouts. Finally, we suggest opportunities where deep learning is particularly likely to catalyze plankton research. The examples are accompanied by detailed tutorials and code samples that allow readers to apply the methods described in this review to their own data.

Wieslaw Bartkowski, Andrzej Nowak, Filip Ignacy Czajkowski et al.

When we go for a walk with friends, we can observe an interesting effect: From step lengths to arm movements - our movements unconsciously align; they synchronize. Prior research found that this synchronization is a crucial aspect of human relations that strengthens social cohesion and trust. Generalizing from these findings in synchronization theory, we propose a dynamical approach that can be applied in the design of non-humanoid robots to increase trust. We contribute the results of a controlled experiment with 51 participants exploring our concept in a between-subjects design. For this, we built a prototype of a simple non-humanoid robot that can bend to follow human movements and vary the movement synchronization patterns. We found that synchronized movements lead to significantly higher ratings in an established questionnaire on trust between people and automation but did not influence the willingness to spend money in a trust game.

Vanessa Taveras Dalmau, A. Coghlan

The inherent complexity of social–ecological systems (SES) can make them difficult to understand and manage, particularly with regards to human–wildlife issues such as poaching. This study uses an approach to human ecology that uses conceptual models and templates within feedback-guided analysis to analyze the complex relationship between ecotourism and poaching in a marine protected area in the Dominican Republic. Our findings show that the application of these templates through a sequenced process is advantageous as it: (i) enables a comprehensive understanding of SES; (ii) helps manage complexity as a system; and (iii) allows researchers to identify the feedback structures driving human–environment interactions that may perpetuate ecosystem decline. These benefits represent a valuable starting point for more comprehensive policy-making and management