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Downsizing does not extend to dwarf galaxies: identifying the stellar mass regimes shaped by supernova and AGN feedback
Full text linkWe explore how the fraction of red (quenched) galaxies varies in the dwarf galaxy regime (10 M < < 10 M), using a mass-complete sample of 5900 dwarfs at , constructed using deep multi-wavelength data in the COSMOS field. The red fraction decreases steadily until 10 M and then increases again towards lower stellar masses. This ‘U’ shape demonstrates that the traditional notion of ‘downsizing’ (i.e. that progressively lower-mass galaxies maintain star formation until later epochs) is incorrect-downsizing does not continue uninterrupted into the dwarf regime. The U shape persists regardless of environment, indicating that it is driven by internal processes rather than external environment-driven mechanisms. Our results suggest that, at 10 M, the quenching of star formation is dominated by supernova (SN) feedback and becomes more effective with decreasing stellar mass, as the potential well becomes shallower. At 10 M, the quenching is driven by a mix of SN feedback and active galactic nucleus (AGN) feedback (which becomes more effective with increasing stellar mass, as central black holes become more massive). The processes that quench star formation are least effective in the range 10 M < < 10 M, likely because the potential well is deep enough to weaken the impact of SN feedback, while the effect of AGN feedback is still insignificant. The cosmological simulations tested here do not match the details of how the red fraction varies as a function of stellar mass. We propose that the red fraction versus stellar mass relation (particularly in the dwarf regime) is a powerful calibrator for the processes that regulate star formation in galaxy formation models
A Novel Analytical Beam Formulation and Its Application on Composite Wind Turbine Blades
Full text linkThis paper presents a novel analytical formulation for modelling the mechanics of non-uniform and asymmetrical straight beams made of functionally graded materials (FGMs) and composites. This approach addresses the complexities caused by the asymmetry of the cross-section and those arising from the variations in geometry and material properties along the beam's axis by approximating these variations as stepped changes. It is assumed that each segment of the beam has constant properties, which are determined through the averaging of functions representing the actual property variations. This method enables efficient and accurate modelling/representation of beam structures such as wind turbine blades. The accuracy and reliability of the analytical model are verified through a comparison with the Technical University of Denmark (DTU) 10 MW reference wind turbine blade, considering two representative load cases (bending, BLC1 and torsional, BLC2) and confirming its ability to accurately predict the structural response. Furthermore, the study assesses the computational performance of the model, demonstrating its efficiency. This study contributes to the literature by providing a robust and computationally efficient approach for the analysis of wind turbine blades
Multi-Attribute Physical-Layer Authentication Against Jamming and Battery-Depletion Attacks in LoRaWAN
Full text linkLoRaWAN is widely used for IoT environmental monitoring, but its lightweight security mechanisms leave the physical layer vulnerable to availability attacks such as jamming and battery-depletion. These risks are particularly critical in mission-critical environmental monitoring systems. This paper proposes a multi-attribute physical-layer authentication (PLA) framework that supports uplink legitimacy assessment by jointly exploiting radio, energy, and temporal attributes, specifically RSSI, altitude, battery_level, battery_drop_speed, event_step, and time_rank. Using publicly available Brno LoRaWAN traces, we construct a device-aware semi-synthetic dataset comprising 230,296 records from 1921 devices over 13.68 days, augmented with energy, spatial, and temporal attributes and injected with controlled jamming and battery-depletion anomalies. Five classifiers (Random Forest, Multi-Layer Perceptron, XGBoost, Logistic Regression, and K-Nearest Neighbours) are evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. The Multi-Layer Perceptron achieves the strongest detection performance (F1-score = 0.8260, AUC-ROC = 0.8953), with Random Forest performing comparably. Deployment-oriented computational profiling shows that lightweight models such as Logistic Regression and the MLP achieve near-instantaneous prediction latency (below 2 μs per sample) with minimal CPU overhead, while tree-based models incur higher training and storage costs but remain feasible for Network Server-side deployment
Personal Construct Psychology - still going strong at 70?
Full text linkThis paper considers the status of personal construct psychology (PCP) 70 years after George Kelly published his magnum opus presenting this approach. A description is provided of the basic tenets of personal construct theory, and the applications of the theory in a range of different settings is reviewed. Limitations of, and challenges that have faced, PCP are considered, including those relating to international growth, relationships with other approaches, methodology, and institutionalization. Possible ways forward are outlined, and an illustration of the potential of personal construct theory and its methodology to address global issues of topical and interdisciplinary concern is provide by considering a research program on perpetrators and victims of extreme violence. It is concluded that personal construct psychology remains healthy and of considerable contemporary relevance, but that it has unfulfilled potential in relation to explicitly addressing political issues
Doing hope in troubled times
Full text linkIn this paper, hope is critically considered within the context of the persistent impact of colonisation, neoliberalism and rising fascism. The following question is asked: In these troubling times, can we, and should we, hope? The urgent case for hope is then made, before a more systemic, relational, decolonised hope is proposed as a viable way of doing hope together
Two late-T dwarfs at kiloparsec distances revealed by JWST UNCOVER survey
Full text linkWe conducted a search for brown dwarf candidates in a James Webb Space Telescope deep field around A2744 to investigate the space density of these objects at kiloparsec distances. Our methodology employed an initial selection based on photometric colours, followed by spectral energy distribution fitting to both stellar atmospheric models and high-redshift galaxy templates. This approach yielded two robust T dwarf candidates and one possible L subdwarf candidate. The T dwarfs have estimated Galactic heights of 0.43 and 0.86 kpc, likely residing near the outer edges of the Galactic thin and thick discs, respectively. We measure a T dwarf surface number density of 0.094 per squared arcmin in the UNCOVER field, lower than previous predictions but consistent at the order-of-magnitude level. We also provide space number density estimates for T5-T8.9 dwarfs across different effective temperature and spectral type bins, finding that T5-T7 dwarfs out to 2 kpc have significantly lower densities than their solar neighbourhood counterparts, whilst T8 dwarfs within the thick disc exhibit densities comparable to local values. Our analysis demonstrates that broad-band near- to mid-infrared photometry provides high sensitivity to late-T dwarfs but is relatively less sensitive to L and early-T dwarfs. Spectroscopy is typically required to distinguish photometric candidates of L dwarfs, early-T subdwarfs, and high-redshift galaxies in JWST deep fields. This study demonstrates the potential for expanding our understanding of brown dwarf distributions and characteristics at unprecedented distances, offering new insights into substellar populations beyond the solar neighbourhood
"It's a relationship just like any other relationship” - Systemic therapists' perspectives on Friend Therapy"
Full text linkMany people place friendships at the centre of their lives, yet systemic therapists often focus exclusively on familial and romantic relationships. In this study, we explored how systemic therapists understand and approach working relationally with friends in therapy. We interviewed seventeen UK-based systemic practitioners and analysed the data using reflexive thematic analysis. Although most participants were unfamiliar with friend therapy, they expressed curiosity, ambivalence, and thoughtful reflection on their personal and professional values. Participants recognised that practitioners could apply systemic skills to friendships, especially given social and cultural shifts that elevate the role of friends. At the same time, they identified barriers, including institutional norms, a lack of evidence, and concerns about applying therapeutic frameworks to these relationships. Our findings suggest that systemic therapists may be well-placed to work therapeutically with friends, but doing so could require shifts in training, tools, and ideology. Friend therapy challenges the field to reconsider which relationships deserve therapeutic attention
Causality aware explainable deep reinforcement learning with adaptive attention mechanisms for scalable resource orchestration in 6G wireless networks
No full textThe emerging sixth generation (6G) wireless networks are expected to enable ultra dense, heterogeneous and AI native environments, supporting mission critical applications with stringent requirements on latency, scalability and interpretability. However, conventional Deep Reinforcement Learning (DRL) based resource management frameworks lack transparency, struggle with high-dimensional state spaces and fail to adapt effectively under dynamic network conditions. To address these challenges, this article proposed a novel Causality Aware Explainable Deep Reinforcement Learning (CE-DRL) framework that integrates causal inference with adaptive attention mechanisms for scalable and interpretable resource orchestration in 6G environments. Besides that the proposed framework also construct dynamic causal graphs to identify and prune inactive features to reduce the complexity and enhance convergence ratio. Additionally, a dual layer adaptive attention mechanism is added to refine both the temporal and spatial features under varying network loads. The framework further embeds explainable AI (XAI) components such as saliency maps and layer wise relevance propagation to offer transparent decision traces. Extensive simulations were performed on a virtualized 6G testbed to demonstrate the performance of proposed framework. The obtained results signify that CE-DRL outperforms other baseline DRL algorithms to reduce the latency upto 35%, energy efficiency upto 28% and increase the throughput by 14% and explainability factor upto 170% as a percentage ratio and ensure a reliable and scalable AI driven resource orchestration for real-time 6G wireless infrastructures
Toward an automated cross-multimodal verification of mobile app bug fixes integrating user feedback, developer responses, changelogs, and UI visual analysis
No full textContext: Verifying claimed bug fixes in mobile applications is crucial, yet the "fixed but not resolved" phenomenon remains a persistent challenge. Existing bug analysis tools focus on pre-fix tasks like detection and reproduction, but lack mechanisms to holistically verify a fix post-deployment by cross-referencing developer claims, visual UI changes, and subsequent user feedback. This gap leads to persistent bugs, wasted developer effort, and user dissatisfaction. Objective: This paper introduces BUGFixChecker, the first framework for automated, multimodal cross-verification of mobile app bug fixes. Our primary goal is to determine if a claimed fix has truly resolved a user-reported issue. Methods: BUGFixChecker integrates five data sources: the original user bug report, the developer's fix claim, "before" and "after" UI screenshots, and post-fix user reviews. The core methodology employs a Multimodal Large Language Model (MLLM) guided by a Chain-of-Thought prompt to perform a comparative reasoning task. We evaluated the framework on a curated dataset of 53 real-world bug fix cases from Android applications. Results: BUGFixChecker achieved a high overall accuracy of 83.0 % and a macro F1-score of 0.805 in correctly verifying the status of bug fixes. It proved particularly effective at identifying discrepancies with strong evidentiary signals, such as "Unresolved Visual Mismatch" (F1-score = 0.865). Most significantly, a rigorouss ablation study demonstrated the critical contribution of the visual modality: the full multimodal framework outperformed a text-only baseline by over 19 % points in F1-score (0.805 vs. 0.610), proving that visual evidence is indispensable for this task. Conclusion: BUGFixChecker offers a novel and pragmatic approach to automated bug fix verification. By moving beyond pre-fix analysis to the critical post-fix verification stage, our multimodal framework provides a scalable solution to enhance the integrity of bug tracking systems, reduce developer workload, and ensure higher software quality in rapidly evolving mobile ecosystems
Injury patterns and cumulative injury burden among U.S. competitive fencers: a survey
Full text linkBackground: Fencing is a highly asymmetrical sport that combines both repetitive upper-extremity and lower-extremity actions. Although fencing related injuries have been described in clinical- and competition-based cohorts, population level data capturing both training and competition exposures and cumulative injury burden remains limited. Objective: To characterize injury patterns, mechanisms, and anatomical distribution among adult competitive fencers and to examine associations between training related exposures and reported injury burden. Methods: Adult competitive fencers registered with USA Fencing were invited to complete an anonymous web-based survey capturing demographics, training and competition exposures, and self-reported fencing related injuries. Injury burden was defined as experiencing three or more lifetime fencing-related injuries among injured respondents. Multivariable logistic regression was used to examine associations between training exposures and injury burden with continuous predictors modeled using restricted (natural) cubic splines to allow for non-linear relationships. Descriptive analyses, correlation analyses, and Poisson regression were performed as sensitivity analyses. Results: Among 303 respondents, 270 (89.1%) reported at least one fencing related injury, accounting for 571 total injuries. Overuse injuries predominated and most frequently involved the knee, ankle and dominant upper extremity with gradual-onset, non-contact mechanisms accounting for the majority of the injuries. Upper-extremity injuries were significantly more likely to occur on the dominant side. In multivariable analyses, years of fencing experience demonstrated a significant non-linear association with higher injury burden, while weekly training volume showed a non-linear association that approached statistical significance. Age at starting fencing, competition frequency, and sex were not independently associated with injury burden. Sensitivity analyses using Poisson regression yielded qualitatively similar findings. Conclusions: Among adult competitive fencers, higher injury burden is most strongly associated with cumulative training exposure, particularly years of fencing experience, with additional contribution from weekly training volume. Injury patterns are characterized by overuse and pronounced dominant-side upper-extremity involvement, consistent with the sport’s asymmetrical biomechanical demands. These findings underscore the importance of monitoring cumulative exposure and addressing asymmetrical loading to mitigate recurrent injury burden in fencing