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Resilience of mega-satellite constellations: how node failures impact inter-satellite networking over time?
No full textMega-satellite constellations have the potential to leverage inter-satellite links to deliver low-latency end-to-end communication services globally, thereby extending connectivity to underserved regions. However, harsh space environments make satellites vulnerable to failures, leading to node removals that disrupt inter-satellite networking. With the high risk of satellite node failures, understanding their impact on end-to-end services is essential. This study investigates the importance of individual nodes on inter-satellite networking and the resilience of mega satellite constellations against node failures. We represent the mega-satellite constellation as discrete temporal graphs and model node failure events accordingly. To quantify node importance for targeted services over time, we propose a service-aware temporal betweenness metric. Leveraging this metric, we develop an analytical framework to identify critical nodes and assess the impact of node failures. The framework takes node failure events as input and efficiently evaluates their impacts across current and subsequent time windows. Simulations on the Starlink constellation setting reveal that satellite networks inherently exhibit resilience to node failures, as their dynamic topology partially restore connectivity and mitigate the long-term impact. Furthermore, we find that the integration of rerouting mechanisms is crucial for unleashing the full resilience potential to ensure rapid recovery of inter-satellite networking
Lung cancer burden attributable to ambient particulate matter: a nationally representative population-based case-control study
No full textBackgroundParticulate matter with a diameter of 2.5 micrometers or less (PM2.5) is a known lung carcinogen, but its impact in low-pollution settings is less understood. We assessed the association between long-term PM2.5 exposure and lung cancer risk in Northern Ireland (NI), a region with relatively low air pollution levels.MethodsWe conducted a population-based case-control study using data from the Northern Ireland Cancer Registry and the Northern Ireland Cohort for the Longitudinal Study of Ageing. The study included 917 lung cancer cases diagnosed in 2014 and 8,088 controls without lung cancer. Eight-year average PM2.5 exposure was estimated by linking residential postcodes to 1 km² resolution pollution maps. Fully adjusted logistic regression models were used, controlling for key confounders including smoking status and deprivation index to estimate odds ratios (ORs) and their 95% confidence intervals (95% CI), and population attributable fractions (PAFs).ResultsIndividuals in the highest PM2.5 tertile (>9.6 µg/m³) had a 37% increased lung cancer risk (OR: 1.37; 95% CI: 1.12–1.68) compared to the lowest tertile (<7.4 µg/m³). The association was stronger in women (OR: 1.79; 95% CI: 1.32–2.44) and not detected in men. Exposure above 10 µg/m³ accounted for 10% of cases, approximately 137 preventable lung cancers annually.DiscussionEven in low-pollution regions, PM2.5 contributes to lung cancer risk, especially in women. Strengthened air quality measures are needed to reduce preventable disease.<br/
Numerical validation of an oscillating surge wave energy converter with a frictional power take-off system using SPH
No full textSemantic-assisted object clustering for multi-modal referring video segmentation
No full textThis paper concentrates on Multi-modal Referring Video Segmentation task, where a well optimized model is able to recognize and segment the target objects referred by the given guidance signals, e.g., language description. Early approaches model this task as a sequence prediction problem. The lack of a global view of video content leads to difficulties in effectively utilizing inter-frame relationships. Some recent works propose to perform temporal modeling with vanilla attention mechanism. However, the condensed visual representation tends to be messy about target information due to occlusion or motion blur. Unlimited non-local operation would spread such noise to all the sequences and interfere with the extraction of global representations. To address the above issue, we present Semantic-assisted Object Cluster network (SOC) and the improved SOC++ in this paper. Our method unifies temporally selective interaction and cross-modal alignment to achieve video-level understanding. In SOC++, a proxy-assisted multi-modal fusion module is introduced to perform preliminary bidirectional activation. Then a semantic integration module with progressive frame-to-video structure facilitates joint space learning across modalities and time steps. Considering that potential noisy visual embeddings would impair the overall representation of target objects in unconstrained inter-frame interactions, we propose to perform tendentious video aggregation through emphasizing the indicative role of the informative frames with lower entropy in this part. A multi-modal query contrastive supervision is also utilized to help construct well-aligned joint space at the video level. Moreover, to integrate the advantage of high-level video information and the low-level details of each frame, we introduce a dynamic query fusion module that performs joint updating of these embeddings. We conduct extensive experiments on popular referring video segmentation benchmarks, and our method outperforms state-of-the-art competitors on all benchmarks by a remarkable margin. Besides, the emphasis on temporal coherence enhances the segmentation stability and adaptability of our method in processing text expressions with temporal variations.</p
Exploring group creativity in face‐to‐face versus virtual settings
No full textRecent research has shown that virtual settings can negatively impact interactions within groups. However, few empirical studies have looked at group creative processes in virtual teams, with most research to date focusing on individuals. To address this gap, an experimental study was carried out to compare the creative performance of groups in face-to-face versus virtual environments. 54 groups, each comprised of four individuals, completed two creativity tests interposed with an activity that was designed to familiarize members of the group with each other. The groups were split equally between face-to-face and virtual video-based settings. It was seen in all groups that creative fluency decreased, whilst the originality of ideas generated/selected increased after groups completed a familiarization task. It was further found that the creative fluency of groups was significantly lower in virtual compared to face-to-face environments. By negatively impacting the fluency of the creative process in groups, it is therefore argued that virtual interactions have negative consequences for the number of ideas generated within groups
Optimisation of biopsy practice to facilitate timely diagnosis and management of kidney disease
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Dataset for "Canúint na Gaeilge i nDeisceart Laighean: an fhianaise ainmeolaíochta" [The dialect of Irish in South Leinster: the onomastic evidence]
No full textThis corpus contains a complete list of transliterated forms of townland names, and their recommended Irish language forms, from Counties Kilkenny, Wexford and Carlow for the purpose of gleaning dialectal evidence. This toponymic dataset is a collection of Irish townland names from Counties Kilkenny, Carlow, and Wexford (South Leinster). The corpus consists of official Irish language forms (Baile fearainn), their anglicized/transliterated forms (Townland), and the associated Parish (Paróiste) from each county. Townlands which were coined in English, and were then later translated to Irish by An Brainse Logainmneacha, are not included in the dataset. Although fundamentally qualitative, the data is highly suited for quantitative analysis to dissect and analyze the historical linguistic features of the Irish language preserved in the names. The dataset was compiled during the latter part of 2020 by consulting the official Placenames Database of Ireland (logainm.ie). This dataset is directly associated with the doctoral thesis: ‘Canúint na Gaeilge i nDeisceart Laighean: an fhianaise ainmeolaíochta (‘The dialect of Irish in South Leinster: the onomastic evidence’) by AM Jowett, published in December 2025. As the original data is publicly available on logainm.ie, this derived dataset is considered open for public use and modification. Researchers are strongly encouraged to provide proper attribution to both the source (logainm.ie) and the compiler (A M Jowett) when utilizing the data. This resource would be of interest to Linguists and/or Celtic Scholars (for etymology and phonetics), historians and genealogists (for local history research), and possibly to GIS Professionals or Digital Humanities Researchers who require authoritative mappings of Irish place names for cartography and large-scale textual analysis