Search Results (212)

This article offers a critical reading of the fishing neighbourhood of Paramos, located on the northern coast of Portugal, through a methodological approach that combines documentary photography and cognitive cartography. The study investigates the relationships between identity, landscape, and power within a territory marked by spatial fragmentation, symbolic exclusion, and functional indeterminacy. By means of a structured visual essay supported by field observation and interpretive maps, Paramos is examined as a liminal urban enclave whose ambiguities reveal tensions between memory, informal appropriation, and control devices. Drawing on authors such as Lefebvre, Augé, Hayden, Domingues, Foucault, and Latour, the article argues that the photographic image, used as a critical tool, can unveil hidden territorial logics and contribute to a more inclusive and situated spatial discourse. Full article

Background: Oncocytoma and angiomyolipoma (AML) are benign renal tumors that may mimic malignant lesions on imaging. With the increasing use of partial nephrectomy (PN) for renal masses, accurate preoperative characterization of these lesions is essential. This study highlights the role of partial nephrectomy as a valuable diagnostic tool in situations where imaging is inconclusive or raises concern for malignancy without definitive confirmation. In the absence of a reliable preoperative diagnosis, partial nephrectomy provides direct histologic verification with minimal perioperative morbidity. Moreover, it offers curative potential when malignancy is present. By achieving both diagnostic certainty and renal preservation, this approach is well-suited for clinical scenarios in which imaging ambiguity might otherwise result in overtreatment through radical surgery or undertreatment Material and methods: in this retrospective study, we reviewed our 5-year experience (2019–2024), 188 partial nephrectomies—including bilateral procedures and operations on solitary kidneys—using robotic and open approaches. All of these 30 tumors were solid renal masses with indeterminate imaging features or suspicious characteristics suggestive of malignancy, further underscoring the limitations of current preoperative diagnostic modalities. Results: Histopathological evaluation confirmed benign renal tumors in 30 cases, with oncocytoma diagnosed in 18 cases (16 robotic, 2 open) and AML in 12 cases (9 robotic, 3 open). Conclusions: Even when imaging raises suspicion of malignancy or remains inconclusive, many small renal masses are ultimately confirmed as benign upon histopathological examination. This study underscores the diagnostic uncertainty associated with small renal tumors and highlights the value of partial nephrectomy as a decisive diagnostic intervention. In situations where non-invasive modalities fail to provide definitive answers, partial nephrectomy offers tissue confirmation with minimal morbidity. Furthermore, when malignancy is present, this approach ensures appropriate oncologic management while preserving renal function. Our findings support the integration of this strategy into routine clinical practice, particularly when diagnostic clarity is essential for guiding safe and effective treatment. Full article

Multi-dimensional classification (MDC), in which the training data are concurrently associated with numerous label variables across many dimensions, has garnered significant interest recently. Most of the current MDC methods are based on the framework of supervised learning, which induces a predictive model from a large amount of precisely labeled data. So, they are challenged to obtain satisfactory learning results in the situation where the training data are not annotated with precise labels but assigned with ambiguous labels. Besides, the current MDC algorithms only consider the scenario of centralized learning, where all training data are handled at a single node for the purpose of classifier induction. However, in some real applications, the training data are not consolidated at a single fusion center, but rather are dispersedly distributed among multiple nodes. In this study, we focus on the problem of decentralized classification involving partial multi-dimensional data that have partially accessible candidate labels, and develop a distributed method called dPL-MDC for learning with these partial labels. In this algorithm, we conduct one-vs.-one decomposition on the originally heterogeneous multi-dimensional output space, such that the problem of partial MDC can be transformed into the issue of distributed partial multi-label learning. Then, by using several shared anchor data to characterize the global distribution of label variables, we propose a novel distributed approach to learn the label confidence of the training data. Under the supervision of recovered credible labels, the classifier can be induced by exploiting the high-order label dependencies from a common low-dimensional subspace. Experiments performed on various datasets indicate that our proposed method is capable of achieving learning performance in distributed partial MDC. Full article

In the recent K-12 educational literature, arts-based data visualization has been positioned as a compelling means of rendering data science and statistical learning accessible, motivating, and empowering for youth, as data users and producers. However, the only research to attend carefully to youth’s data-based, artistic storytelling practices has been limited in scope to specific storytelling mechanisms, like youth’s metaphor usage. Engaging in design-based research, we sought to understand the art and design decisions that youth make and the data-based arguments and stories that youth tell through their arts-based data visualizations. We drew upon embodied theory to acknowledge the holistic, synergistic, and situated nature of student learning and making. Corresponding with emerging accounts of youth arts-based data visualization practices, we saw regular evidence of art, storytelling, and personal subjectivities intertwining. Contributing to this literature, we found that these intersections surfaced in a number of domains, including youth’s pictorial symbolism, visual encoding strategies, and data decisions like manifold pictorial symbols arranged to support complex, multilayered, ambiguous narratives; qualitative data melding community and personal lived experience; and singular statements making persuasive appeals. This integration of art, story, agency, and embodiment often manifested in ways that seemed to jostle against traditional notions of and norms surrounding data science. Full article

This article presents an in-depth examination of the technical and cultural dimensions of singing practices within the traditional music of sub-Saharan Africa. Utilizing an extensive body of theoretical and ethnomusicological research, comparative transcription, and culturally situated observation, it presents a comprehensive framework for understanding the significance of the human voice in various performance contexts. The study revolves around a tripartite model—auditory clarity, ambiguous auditory clarity, and occlusion—that delineates the varying levels of audibility of vocal lines amidst intricate instrumental arrangements. The article examines case studies from West, East, and Southern Africa, highlighting essential vocal techniques such as straight tone, nasal resonance, ululation, and controlled (or delayed) vibrato. It underscores the complex interplay between language, melody, and rhythm in tonal languages. The analysis delves into the influence of sound reinforcement technologies on vocal presence and cultural authenticity, positing that PA systems have the capacity to either enhance or disrupt the equilibrium between traditional aesthetics and modern requirements. This research is firmly rooted in a blend of African and Western theoretical frameworks, drawing upon the contributions of Nketia, Agawu, Chernoff, and Kubik. It proposes a nuanced methodology that integrates technical analysis with cultural significance. It posits that singing in African traditional music transcends mere expression, serving as a vessel for collective memory, identity, and the socio-musical framework. The article concludes by emphasizing the enduring strength and flexibility of African vocal traditions, illustrating their capacity for evolution while preserving fundamental communicative and artistic values. Full article

Despite the existence of standardized collision regulations such as the International Regulations for Preventing Collisions at Sea (COLREGs), ship collisions continue to occur, indicating persistent gaps in how navigators interpret and apply these rules. The COLREGs are globally adopted rules that govern vessel conduct to avoid collisions. Borderline encounter situations—such as those between head-on and crossing, or overtaking and crossing—pose particular challenges, often resulting in inconsistent or ambiguous interpretations. This study models navigator awareness as a probabilistic function of encounter angle, aiming to identify interpretive transition zones and cognitive uncertainty in rule application. A structured survey was conducted with 101 licensed navigators, each evaluating simulated ship encounter scenarios with varying relative bearings. Responses were collected using a Likert scale and analyzed in angular sectors known for interpretational ambiguity: 006–012° for head on to crossing (HC) and 100–160° for overtaking to crossing (OC). Gaussian curve fitting was applied to the response distributions, with the awareness center (μ) and standard deviation (σ) serving as indicators of consensus and ambiguity. The results reveal sharp shifts in awareness near 008° and 160°, suggesting cognitively unstable zones. Risk-averse interpretation patterns were also observed, where navigators tended to classify borderline situations more conservatively under uncertainty. These findings suggest that navigator awareness is not deterministic but probabilistically structured and context sensitive. The proposed awareness modeling framework helps bridge the gap between regulatory prescriptions and real world navigator behavior, offering practical implications for MASS algorithm design and COLREGs refinement. Full article

Swearing, a highly emotive form of expression, has recently attracted growing interest from researchers. Yet, swearing is still largely unexamined as a form of self-talk among professional athletes, coaches, and casual exercisers. This study aims to fill that gap by creating and validating the Use of Swear Words in Sport and Exercise Questionnaire (USWSEQ), an innovative instrument intended to assess how often athletes, coaches, and recreational exercisers utilize common swear words. The questionnaire was administered to 513 participants and underwent exploratory (n = 333) and confirmatory (n = 180) factor analyses to evaluate its content structure, reliability, and validity. Results confirmed a robust two-factor model: (1) Self/Other Degradation, and (2) Situational Swearing. Notably, the study emphasizes the complexity involved in researching language, because swear words can be semantically ambiguous, pragmatically flexible, and deeply embedded in culture. These attributes present challenges for conventional psychometric methods and highlight the importance of function-based and context-aware frameworks. Full article

This study examines the role of creative resistance, or “artivism”, in Nigeria’s #EndSARS movement, a youth-led campaign against police brutality that peaked in October 2020. Drawing on Robert Entman’s Framing Theory, it analyzes how different art forms reframed public perceptions of the Special Anti-Robbery Squad (SARS) and countered government efforts to delegitimize the protests. Using a qualitative approach, the research employs purposive sampling of Twitter-sourced art forms to explore how these pieces exposed systemic injustice, amplified protester voices, and mobilized local and global support. Findings reveal that artivists personalized SARS brutality, dismantled narratives portraying protesters as criminals, and invoked moral urgency through evocative symbolism, leveraging social media’s virality to sustain the movement’s momentum. The study highlights SARS’ paradoxical role as a state-sanctioned yet reviled entity, demonstrating how creative expressions clarified this ambiguity into a clarion call for reform. By situating #EndSARS within Nigeria’s protest legacy, this analysis underscores art’s transformative power in digital-age activism, offering a blueprint for resistance against oppression. It contributes to scholarship on social movements by illustrating how art and technology intersect to challenge power, preserve collective memory, and demand accountability, with implications for future struggles in Nigeria and beyond. Full article

Theoretical and practical ambiguities concerning the impact of Party organization governance on corporate carbon emissions in China are addressed in this study. Data from A-share-listed state-owned enterprises in Shanghai and Shenzhen (2016–2021) are analyzed to determine how Party organization governance influences corporate carbon emissions. Fixed-effects regression results indicate that Party organization governance significantly reduces emissions. Robustness is established via alternative variable measures, instrumental variable estimation, and the inclusion of supplementary controls. Notably, the effect weakens as privatization intensifies. Conversely, SOEs embedding high-quality Party building into articles of association or situated in regions rich in red cultural resources exhibit amplified emission reductions. The findings enhance comprehension of Party organization governance’s environmental role in SOEs and inform the design of low-carbon energy governance frameworks. Full article

Relative to other renewable energy technologies, concentrated solar power (CSP) is only in the beginning phases of large-scale deployment. Its incorporation into national grids is steadily growing, with anticipation of its substantial contribution to the energy mix. A number of emerging economies are situated in areas that receive abundant amounts of direct normal irradiance (DNI), which translates into expectations of significant effectiveness for CSP. However, any assessment related to the planning of CSP facilities is challenging because of the complexity of the associated criteria and the number of stakeholders. Additional complications are the differing concepts and configurations for CSP plants available, a dearth of related experience, and inadequate amounts of data in some developing countries. The goal of the work presented in this paper was to evaluate the practical CSP implementation options for such parts of the world. Ambiguity and imprecision issues were addressed through the application of multi-criteria decision-making (MCDM) in a fuzzy environment. Six technology combinations, involving dry cooling and varied installed capacity levels, were examined: three parabolic trough collectors with and without thermal storage, two solar towers with differing storage levels, and a linear Fresnel with direct steam generation. The in-depth performance analysis was based on 4 main criteria and 29 sub-criteria. Quantitative and qualitative data, plus input from 44 stakeholders, were incorporated into the proposed fuzzy analytic hierarchy process (AHP) model. In addition to demonstrating the advantages and drawbacks of each scenario relative to the local energy sector requirements, the model’s results also provide accurate recommendation guidelines for integrating CSP technology into national grids while respecting stakeholders’ priorities. Full article

Human pose comparison involves measuring the similarities in body postures between individuals to understand movement patterns and interactions, yet existing methods are often insufficiently robust and flexible. In this paper, we propose a RotJoint-based pipeline for pose similarity estimation that is both fine-grained and generalizable, as well as robust. Firstly, we developed a comprehensive benchmark for action ambiguity that intuitively and effectively evaluates the robustness of pose comparison methods against challenges such as body shape variations, viewpoint variations, and torsional poses. To address these challenges, we define a feature representation called RotJoints, which is strongly correlated with both the semantic and spatial characteristics of the pose. This parameter emphasizes the description of limb rotations across multiple dimensions, rather than merely describing orientation. Finally, we propose TemporalRotNet, a Transformer-based network, trained via supervised contrastive learning to capture spatial–temporal motion features. It achieves 93.7% accuracy on NTU-RGB+D close set action classification and 88% on the open set, demonstrating its effectiveness for dynamic motion analysis. Extensive experiments demonstrate that our RotJoint-based pipeline produces results more aligned with human understanding across a wide range of common pose comparison tasks and achieves superior performance in situations prone to ambiguity. Full article

This study examines the experiences of pre-service teachers (PSTs) participating in Australian state-and-territory-specific programs that address teacher workforce shortages in Australia. Using a multi-methods approach, data from surveys and interviews are used to explore the impact of these programs on PSTs’ academic outcomes, professional learning, and well-being. Situated learning theory underpins the analysis, emphasising communities of practice, legitimate peripheral participation, and self-identity development. The findings reveal significant benefits such as accelerated career growth, enhanced confidence, and the integration of theory and practice. Enablers include school mentorship, university flexibility, and financial compensation inclusive of paid teaching programs. However, challenges persist, including emotional and workload pressures, inconsistent mentorship, and ambiguous application processes. This study recommends improving policy implementation and support structures, advocating for streamlined application processes, strategic workload management, and enhanced mentorship. These findings could contribute to understanding the competing demands of PSTs and inform policy improvements for future educators. Full article

Research about the lived experiences of asylum-seeking and refugee youth can evoke powerful emotions for those involved. Young people who escaped perilous situations often bear strong emotions linked to their experiences of migration and displacement, as well as their encounters with disorientation, insecurity, isolation, discrimination and racism in unfamiliar contexts in the host society. Such emotions and emotionally charged places can be challenging to work with as researchers and require reflexive and situated methodological and ethical judgements. This paper investigates the emotional complexities of fieldwork with vulnerable young people by reflecting on (dis)comfort and discusses how to negotiate these issues with care and consideration. It draws from qualitative participatory and creative fieldwork experiences using story mapping, photovoice, walk-along and community theatre approaches in Amsterdam, Brussels, Leipzig and Newcastle-Upon-Tyne. It reports on a range of critical ethical and methodological issues that arose in our work that address meaningful relationships, reciprocity and trust, understanding the field, positionality and reflexivity, and challenges around the co-production of knowledge and leaving the field. Throughout, the paper flags various complex and, at times, ambiguous ethical and methodological issues that emerged throughout the research process and argues for research approaches that are sensitive to the contextual and multi-faceted nature of investigating young refugees and asylum seekers in European cities. Full article

Weakly supervised video anomaly detection aims to identify abnormal events in video sequences without requiring frame-level supervision, which is a challenging task in computer vision. Traditional methods typically rely on low-level visual features with weak supervision from a single backbone branch, which often struggles to capture the distinctive characteristics of different categories. This limitation reduces their adaptability to real-world scenarios. In real-world situations, the boundary between normal and abnormal events is often unclear and context-dependent. For example, running on a track may be considered normal, but running on a busy road could be deemed abnormal. To address these challenges, RelVid is introduced as a novel framework that improves anomaly detection by expanding the relative feature gap between classes extracted from a single backbone branch. The key innovation of RelVid lies in the integration of auxiliary tasks, which guide the model to learn more discriminative features, significantly boosting the model’s performance. These auxiliary tasks—including text-based anomaly detection and feature reconstruction learning—act as additional supervision, helping the model capture subtle differences and anomalies that are often difficult to detect in weakly supervised settings. In addition, RelVid incorporates two other components, which include class activation feature learning for improved feature discrimination and a temporal attention module for capturing sequential dependencies. This approach enhances the model’s robustness and accuracy, enabling it to better handle complex and ambiguous scenarios. Evaluations on two widely used benchmark datasets, UCF-Crime and XD-Violence, demonstrate the effectiveness of RelVid. Compared to state-of-the-art methods, RelVid achieves superior performance in both detection accuracy and robustness. Full article

Traditional transferable utility (TU) games assume precise real-valued utilities for coalition outcomes, but real-world situations often involve uncertainty or imprecision. Interval TU games extend the classical framework by representing utilities and payoffs as closed intervals, leveraging interval arithmetic to address inherent ambiguities in data. This paper reviews the theoretical foundations of interval TU games and explores allocating solutions under uncertainty. Central to this study is the adaptation of consistency, a fundamental property in game-theoretical resolutions, to the interval framework. Drawing on concepts such as the pseudo equal allocations of non-separable costs and the pseudo weighted allocations of non-separable costs, we characterize these allocation resolutions through a specific reduction and related consistency. By bridging classical TU games with interval generalizations, this study offers a robust foundation for analyzing allocations under uncertainty and outlines avenues for future research in theoretical and applied game theory. Full article