Search Results (22)

This paper presents a meta-analysis of social research on water, offering a novel methodological contribution to the study of emerging interdisciplinary research fields. We propose and implement a mixed methods framework that integrates quantitative network analysis with qualitative research, aiming to enhance both to give access to new emerging empirical fields and enhance the analytical depth of empirical social research. Drawing on a dataset of publications from the Web of Science over four distinct time intervals, we identify thematic clusters through keyword co-occurrence networks that reveal the evolving structure and internal dynamics of the field. Our findings show a clear trend toward increasing interdisciplinarity, responsiveness to global events, and contemporary challenges such as the emergence of COVID-19 and the continued centrality of topics related to water management and evaluation. By uncovering latent structures, our approach not only maps the field’s development but also lays the foundation for targeted qualitative analysis of articles representative of identified clusters. This methodological design contributes to the broader discourse on mixed methods research in the social sciences by demonstrating how computational tools can enhance the transparency and reliability of qualitative inquiry without sacrificing its interpretive richness. Furthermore, this study opens new avenues for critically reflecting on the epistemic culture of social water research, particularly in relation to its proximity to applied science and governance-oriented perspectives. The proposed method holds potential relevance for both academic researchers and decision makers in the water sector, offering a means to systematically access dispersed knowledge and identify underrepresented subfields. Overall, the study showcases the potential of mixed methods designs for navigating and structuring complex interdisciplinary research landscapes. Full article

Carbon capture and storage (CCS) plays a role in mitigating climate change, but effective implementation requires accurate prediction of CO₂ behavior in geological formations. This study introduces a novel machine learning framework for quantifying uncertainty across 2D and 3D carbon storage models. We develop a dimension-adaptive Bayesian neural network architecture that enables efficient knowledge transfer between dimensional representations while maintaining physical consistency. The framework incorporates aleatoric uncertainty from inherent geological variability and epistemic uncertainty from model limitations. Trained on over 5000 high-fidelity simulations across multiple geological scenarios, our approach demonstrates superior computational efficiency, reducing analysis time for 3D models by 87% while maintaining prediction accuracy within 5% of full simulations. The framework effectively captures complex uncertainty patterns in spatiotemporal CO₂ plume evolution. It identifies previously unrecognized parameter interdependencies, particularly between vertical permeability anisotropy and capillary entry pressure, which significantly impact plume migration in 3D models but are often overlooked in 2D representations. Compared with traditional Monte Carlo methods, our approach provides more accurate uncertainty bounds and enhanced identification of high-risk scenarios. This multidimensional framework enables rapid assessment of storage capacity and leakage risk under uncertainty, providing a practical tool for CCS site selection and operational decision-making across dimensional scales. Full article

This study investigates the effects of embodied (breathing exercises) and cognitive interventions on adolescents’ flow experience and cognition patterns. Using a mixed-methods design, 303 vocational high school students were assigned to three groups: Embodied Task Group (N = 108), Cognitive Task Group (N = 100), and Mental Health Course Group (N = 95). Experiment 1 employed a 3×2 Multivariate Analysis of Covariance (MANCOVA) design to compare flow experience dimensions, while Experiment 2 used Epistemic Network Analysis (ENA) to analyze diary entries. Results showed that the Embodied Task Group outperformed the Cognitive Task Group in “Unambiguous Feedback” (η_p² = 0.01, a small effect) and had higher “Transformation of Time” (η_p² = 0.01, a small effect) than the Mental Health Course Group. ENA revealed that the Embodied Group developed stronger body-environment interaction patterns, shifting cognition pattern from psychological evaluations to dynamic bodily processes over time. Conversely, the Cognitive Task Group maintained event-focused cognition with weaker mind–body integration. Findings highlight breathing exercises’ potential to enhance flow experience through embodied awareness and multisensory processing, offering practical implications for mental health education by promoting embodied learning tasks to foster flow experience. Full article

Masonry-lined tunnels form a vital part of the world’s operational railway networks. However, in many cases their structural condition is deteriorating, so it is vital to undertake regular condition assessments to ensure their safety. In order to reduce costs and improve the repeatability of these assessments, automated deep learning-based tunnel analysis workflows have been proposed. However, for such methods to be applied in practice to a safety-critical situation, it is necessary to validate their conclusions. This study analysed how uncertainty quantification methods can be used to assess the test time performance of neural networks trained for masonry joint segmentation without the laborious labelling of additional ground truths. It applies test-time augmentation (TTA) and Monte Carlo dropout (MCD) to evaluate both the aleatoric and epistemic uncertainties of a selection of trained models. It then shows how these can be used to generate uncertainty maps to aid an engineer’s interpretation of the neural network output. Full article

Scientists often disagree on the best theory to describe a scientific event. While such debates are a natural part of healthy scientific discourse, the timeframe for scientists to converge on an ideal method may not always align with real-life knowledge dynamics. In this article, I use an event from the history of chemistry as inspiration to develop Agent-Based Models of epistemic networks, exploring method selection within a scientific community. These models reveal several situations where incorrect, simpler methods can persist, even when substantial evidence supports a more complex method. This becomes particularly evident when different evidence-sharing timeframes are analyzed. The network structure connecting the scientists plays a crucial role in determining how and when convergence on the correct method is achieved, guided by real-world evidence. This framework provides a foundation for further exploration of scientists’ behavior in past and future discoveries, as well as how agents internalize scientific information. Full article

This article investigates conceptions of morality within the framework of ressentimentful victimhood in the manosphere, while also exploring avenues for resistance among young individuals encountering the “hatred pipeline”. In Study 1, we use the emotional mechanism of ressentiment to examine how incels construct narratives of victimhood rooted in the notion of sexual entitlement that remains owed and unfulfilled, alongside its “black pill” variant emphasising moral and epistemic superiority. Through a linguistic corpus analysis and content examination of 4chan and Incel.is blog posts, we find evidence of ressentiment morality permeating the language and communication within the incel community, characterised by blame directed at women, and the pervasive themes of victimhood, powerlessness, and injustice. In Study 2, we delve into young individuals’ reflections on incel morality and victimhood narratives as they engage with online networks of toxic masculinity in the manosphere. Drawing from semi-structured interviews with young participants who have accessed the manosphere, we explore their perceptions of risks, attribution of blame, and experiences of empathy towards individuals navigating the “hatred pipeline”. Our analysis underscores the significance of ressentiment in elucidating alternative conceptions of morality and victimhood, while shedding light on the potential for acceptance or resistance within online environments characterised by hatred. Full article

Remote sensing stands as a fundamental technique in contemporary environmental monitoring, facilitating extensive data collection and offering invaluable insights into the dynamic nature of the Earth’s surface. The advent of deep learning, particularly convolutional neural networks (CNNs), has further revolutionized this domain by enhancing scene understanding. However, despite the advancements, traditional CNN methodologies face challenges such as overfitting in imbalanced datasets and a lack of precise uncertainty quantification, crucial for extracting meaningful insights and enhancing the precision of remote sensing techniques. Addressing these critical issues, this study introduces BayesNet, a Bayesian neural network (BNN)-driven CNN model designed to normalize and estimate uncertainties, particularly aleatoric and epistemic, in remote sensing datasets. BayesNet integrates a novel channel–spatial attention module to refine feature extraction processes in remote sensing imagery, thereby ensuring a robust analysis of complex scenes. BayesNet was trained on four widely recognized unmanned aerial vehicle (UAV)-based remote sensing datasets, UCM21, RSSCN7, AID, and NWPU, and demonstrated good performance, achieving accuracies of 99.99%, 97.30%, 97.57%, and 95.44%, respectively. Notably, it has showcased superior performance over existing models in the AID, NWPU, and UCM21 datasets, with enhancements of 0.03%, 0.54%, and 0.23%, respectively. This improvement is significant in the context of complex scene classification of remote sensing images, where even slight improvements mark substantial progress against complex and highly optimized benchmarks. Moreover, a self-prepared remote sensing testing dataset is also introduced to test BayesNet against unseen data, and it achieved an accuracy of 96.39%, which showcases the effectiveness of the BayesNet in scene classification tasks. Full article

Misinformation has posed significant threats to all aspects of people’s lives. One of the most active areas of research in misinformation examines how individuals are misinformed. In this paper, we study how and to what extent agents are misinformed in an extended bounded confidence model, which consists of three parts: (i) online selective neighbors whose opinions differ from their own but not by more than a certain confidence level; (ii) offline neighbors, in a Watts–Strogatz small-world network, whom an agent has to communicate with even though their opinions are far different from their own; and (iii) a Bayesian analysis. Furthermore, we introduce two types of epistemically irresponsible agents: agents who hide their honest opinions and focus on disseminating misinformation and agents who ignore the messages received and follow the crowd mindlessly. Simulations show that, in an environment with only online selective neighbors, the misinforming is more successful with broader confidence intervals. Having offline neighbors contributes to being cautious of misinformation, while employing a Bayesian analysis helps in discovering the truth. Moreover, the agents who are only willing to listen to the majority, regardless of the truth, unwittingly help to bring about the success of misinformation attempts, and they themselves are, of course, misled to a greater extent. Full article

Peer assessment (PA) is a formative assessment tool that can effectively monitor the development process of knowledge construction. In comment-based PA, comments contain the evidence of how the assessors construct knowledge to conduct professional assessments, which initiates a research perspective to explore the dynamic knowledge construction of the assessors. Quantitative ethnography is both a method for the quantitative analysis of qualitative data and a technique for the network modelling of professional competencies, providing a new way of thinking about the analysis and evaluation of knowledge construction processes. In this paper, quantitative ethnography was used to mine the comments generated from comment-based PA activities to reveal the characteristics of student teachers’ knowledge construction and the developmental trajectories of knowledge structure at different learning stages. The experimental results show that the student teachers’ knowledge structures and knowledge levels evolve in the PA environment, and the cognitive network gradually tends to become more complex and balanced. The student teachers showed stage and gender differences in the level of knowledge progression during the learning process. The second PA was a turning point in knowledge progression. The knowledge structures of the male and female groups are biased towards different kinds of knowledge elements. Full article

Current state-of-the-art engine condition monitoring is based on a minimum of one steady-state data point per flight. Due to the scarcity of available data points, there are difficulties distinguishing between random scatter and an underlying fault introducing a detection latency of several flights. Today’s increased availability of data acquisition hardware in modern aircraft provides continuously sampled in-flight measurements, so-called full-flight data. These full-flight data give access to sufficient data points to detect faults within a single flight, significantly improving the availability and safety of aircraft. Artificial neural networks are considered well suited for the timely analysis of an extensive amount of incoming data. This article proposes uncertainty quantification for artificial neural networks, leading to more reliable and robust fault detection. An existing approach for approximating the aleatoric uncertainty was extended by an Out-of-Distribution Detection in order to take the epistemic uncertainty into account. The method was statistically evaluated, and a grid search was performed to evaluate optimal parameter combinations maximizing the true positive detection rates. All test cases were derived based on in-flight measurements of a commercially operated regional jet. Especially when requiring low false positive detection rates, the true positive detections could be improved 2.8 times while improving response times by approximately 6.9 compared to methods only accounting for the aleatoric uncertainty. Full article

The goal of this research is to develop a novel second-generation-based biogas supply chain network design (BG-SCND) model that takes into account the triple bottom line approach. Biogas is a promising renewable energy source that can be obtained from a variety of easily accessible second-generation wastes, including animal manure, municipal waste, and agricultural leftovers. Integrated optimization of the biogas generation system is essential for a speedy and environmentally friendly transition to sustainable biodiesel production. The dynamic environment of the energy market significantly impairs the decisions of the BG-SCND model; therefore, a hybrid solution approach using flexible programming and possibilistic programming is suggested. To verify the suggested model and approach for solving the problem, a thorough computational analysis of a case study is conducted. The case study findings demonstrate that considerable investment is necessary to attain social and environmental well-being goals and safeguard decisions against epistemic uncertainty. Policymakers involved in the planning of biogas production and distribution projects may find the proposed approach useful. Full article

Climate change causes global effects on multiple levels. The anthropogenic input of greenhouse gases increases the atmospheric mean temperature. It furthermore leads to a higher probability of extreme weather events (e.g., heat waves, floods) and thus strongly impacts the habitats of humans, animals, and plants. Against this background, research and innovation activities are increasingly focusing on potential health-related aspects and feasible adaptation and mitigation strategies. Progressing urbanization and demographic change paired with the climate change-induced heat island effect exposes humans living in urban habitats to increasing health risks. By employing scientometric methods, this scoping study provides a systematic bird’s eye view on the epistemic landscapes of climate change, its health-related effects, and possible technological and nature-based interventions and strategies in order to make urban areas climate proof. Based on a literature corpus consisting of 2614 research articles collected in SCOPUS, we applied network-based analysis and visualization techniques to map the different scientific communities, discourses and their interrelations. From a public health perspective, the results demonstrate the range of either direct or indirect health effects of climate change. Furthermore, the results indicate that a public health-related scientific discourse is converging with an urban planning and building science driven discourse oriented towards urban blue and green infrastructure. We conclude that this development might mirror the socio-political demand to tackle emerging climate change-induced challenges by transgressing disciplinary boundaries. Full article

With the availability of data and computational technologies in the modern world, machine learning (ML) has emerged as a preferred methodology for data analysis and prediction. While ML holds great promise, the results from such models are not fully unreliable due to the challenges introduced by uncertainty. An ML model generates an optimal solution based on its training data. However, if the uncertainty in the data and the model parameters are not considered, such optimal solutions have a high risk of failure in actual world deployment. This paper surveys the different approaches used in ML to quantify uncertainty. The paper also exhibits the implications of quantifying uncertainty when using ML by performing two case studies with space physics in focus. The first case study consists of the classification of auroral images in predefined labels. In the second case study, the horizontal component of the perturbed magnetic field measured at the Earth’s surface was predicted for the study of Geomagnetically Induced Currents (GICs) by training the model using time series data. In both cases, a Bayesian Neural Network (BNN) was trained to generate predictions, along with epistemic and aleatoric uncertainties. Finally, the pros and cons of both Gaussian Process Regression (GPR) models and Bayesian Deep Learning (DL) are weighed. The paper also provides recommendations for the models that need exploration, focusing on space weather prediction. Full article

In this paper, a chance-constrained (CC) framework is developed to manage the voltage control problem of medium-voltage (MV) distribution systems subject to model uncertainty. Such epistemic uncertainties are inherent in distribution system analyses given that an exact model of the network components is not available. In this context, relying on the simplified deterministic models can lead to insufficient control decisions. The CC-based voltage control framework is proposed to tackle this issue while being able to control the desired protection level against model uncertainties. The voltage control task disregarding the model uncertainties is firstly formulated as a linear optimization problem. Then, model uncertainty impacts on the above linear optimization problem are evaluated. This analysis defines that the voltage control problem subject to model uncertainties should be modelled with a joint CC formulation. The latter is accordingly relaxed to individual CC optimizations using the proposed methods. The performance of proposed CC voltage control methods is finally tested in comparison with that of the robust optimization. Simulation results confirm the accuracy of confidence level expected from the proposed CC voltage control formulations. The proposed technique allows the system operators to tune the confidence level parameter such that a tradeoff between operation costs and conservatism level is attained. Full article

This pilot case study sought to investigate patterns of interactions between learners and their instructor in a teacher education course called “Computer Science for Teachers”. This course was constructed to leverage aspects of open world game design elements in order to investigate the effects of degrees of autonomy in gameplay/learning. This course was conducted in a specially built social learning platform based on Elgg software. Student interactions with the instructor and other students in this course were analyzed to determine the learning networks students constructed during each key learning activity as well as the epistemic spaces defined by these interactions. Descriptive statistics along with social network analysis (SNA) and epistemic network analysis (ENA) were used to investigate these data. The findings indicate that more traditional/less open world gaming type learning activities were associated with learning networks and epistemic spaces that were teacher-centered and narrower, while more open world gaming/high levels of autonomy (student-centric) learning activities were associated with learning networks that were highly decentralized and epistemic spaces that featured students asking and answering questions of/for one another. These findings were consistent with existing research into player behavior in open world type games and learner behavior in settings with high levels of autonomy support. Implications for further research are discussed. Full article