Hasil untuk "Early Christian literature. Fathers of the Church, etc."

Andrea Simongini, Maria Kherlakian, Alicia López-Oramas et al.

In this Letter, we present early emission data analysis of the tidal disruption event TDE2025aarm, including optical, UV and X-ray data. At a redshift of z = 0.01368, TDE2025aarm is the second closest TDE ever discovered, offering an unprecedented opportunity to study such phenomena in great details. We observed TDE2025aarm in optical with the Liverpool Telescope for a total of three epochs, and complemented our dataset with ancillary spectroscopic and photometric data. The early optical spectra are characterized by a blue-continuum and helium, hydrogen and possibly Bowen lines typical of H+He events. The optical light curves peak at M_g ~ -18.63 mag and are well described by fallback of a M_star ~ 0.16 M_sun star onto a M_BH ~ 2x10^{7} M_sun black hole. We report Swift-XRT detection in the 0.3-10 keV range, with a total flux of F_X ~ 1.42x10^{-14} erg s-1 cm-2, fitted by a black-body with kT ~ 0.39 keV. This makes TDE2025aarm a new event among optical/UV bright TDEs detected in soft X-rays. Our analysis suggests that the early emission from TDE2025aarm is powered by circularization shocks, and that the delayed accretion scenario best describes the observed features.

Xiangting Wu, Linhui Wu, Michael Park et al.

A growing literature has examined whether innovation is becoming less disruptive, spanning diverse domains and data sources and using a range of methodologies. This paper provides an inventory of 105 studies exploring this question. The evidence is largely consistent in direction. Studies spanning scientific papers, patents, products, legal cases, music, and visual art consistently report evidence of a decline. This pattern holds not only for citation-based measures, but also for text-based approaches, firm displacement rates, product similarity networks, and audio and visual embeddings. The literature has also identified notable exceptions, including rebounds in specific domains and predictable variation across field lifecycles. We catalog each study's data, methods, and findings to provide a resource for researchers and policymakers seeking to understand the current state of the evidence.

Mingyue Kong, Yinglong Zhang, Likun Sheng et al.

As academic research becomes increasingly diverse, traditional literature evaluation methods face significant limitations,particularly in capturing the complexity of academic dissemination and the multidimensional impacts of literature. To address these challenges, this paper introduces a novel literature evaluation model of citation structural diversity, with a focus on assessing its feasibility as an evaluation metric. By refining citation network and incorporating both ciation structural features and semantic information, the study examines the influence of the proposed model of citation structural diversity on citation volume and long-term academic impact. The findings reveal that literature with higher citation structural diversity demonstrates notable advantages in both citation frequency and sustained academic influence. Through data grouping and a decade-long citation trend analysis, the potential application of this model in literature evaluation is further validated. This research offers a fresh perspective on optimizing literature evaluation methods and emphasizes the distinct advantages of citation structural diversity in measuring interdisciplinarity.

Janne Rotter, William Bailkoski

AI has the potential to significantly improve how NGOs utilize their limited resources for societal benefits, but evidence about how NGOs adopt AI remains scattered. In this study, we systematically investigate the types of AI adoption use cases in NGOs and identify common challenges and solutions, contextualized by organizational size and geographic context. We review the existing primary literature, including studies that investigate AI adoption in NGOs related to social impact between 2020 and 2025 in English. Following the PRISMA protocol, two independent reviewers conduct study selection, with regular cross-checking to ensure methodological rigour, resulting in a final literature body of 65 studies. Leveraging a thematic and narrative approach, we identify six AI use case categories in NGOs - Engagement, Creativity, Decision-Making, Prediction, Management, and Optimization - and extract common challenges and solutions within the Technology-Organization-Environment (TOE) framework. By integrating our findings, this review provides a novel understanding of AI adoption in NGOs, linking specific use cases and challenges to organizational and environmental factors. Our results demonstrate that while AI is promising, adoption among NGOs remains uneven and biased towards larger organizations. Nevertheless, following a roadmap grounded in literature can help NGOs overcome initial barriers to AI adoption, ultimately improving effectiveness, engagement, and social impact.

Jorryt Matthee

Studies of the distant Universe are providing key insights into our understanding of the formation of galaxies. The advent of the James Webb Space Telescope (JWST) has significantly enhanced our observational capabilities, leading to an expanded redshift frontier, providing unprecedented detail in the characterization of early galaxies and enabling the discovery of new populations of accreting black holes. This review aims to provide an introduction to the basic processes and components that shape the observed spectra of galaxies, with a focus on their relevance to techniques with which high-redshift galaxies are selected. The review further introduces specific topics that have attracted significant attention in recent literature, including the discovery of highly efficient galaxy formation in the early Universe, the relation between galaxies and the process of reionization, new insights into the formation of the first stars and the enrichment of interstellar gas with heavy elements, and breakthroughs in our understanding of the origins of supermassive black holes.

Jorge del Reguero González, Alberto Romero Molero

La necrópolis del Gallo de Carteia (San Roque, Cádiz) constituye uno de los espacios funerarios extramuros más relevantes de esta antigua ciudad romana de origen fenicio-púnico emplazada en la bahía de Algeciras. Pese a su desaparición en los años sesenta del pasado siglo XX, en esta contribución exponemos, por un lado, una puesta al día de los datos conocidos sobre los estudios de este contexto funerario, realizando para ello un recorrido diacrónico sobre la historia de sus excavaciones. Por otro, aportamos documentación inédita procedente de los archivos de la Fundación Casa de Alba, del Museo de San Isidro y del Archivo-Biblioteca de la Real Academia de Bellas Artes de San Fernando. Gracias a ella podemos defender algunas hipótesis en relación con las primeras intervenciones en la necrópolis, la probable existencia de una basílica cristiana en la misma o las motivaciones de Julio Martínez Santa-Olalla para emprender nuevas excavaciones en la Colonia Libertinorum Carteia.

Nicole Neis, Juergen Beyerer

The increasing complexity of automated driving functions and their growing operational design domains imply more demanding requirements on their validation. Classical methods such as field tests or formal analyses are not sufficient anymore and need to be complemented by simulations. For simulations, the standard approach is scenario-based testing, as opposed to distance-based testing primarily performed in field tests. Currently, the time evolution of specific scenarios is mainly described using trajectories, which limit or at least hamper generalizations towards variations. As an alternative, maneuver-based approaches have been proposed. We shed light on the state of the art and available foundations for this new method through a literature review of early and recent works related to maneuver-based scenario description. It includes related modeling approaches originally developed for other applications. Current limitations and research gaps are identified.

Stacy S. McGaugh, James M. Schombert, Federico Lelli et al.

Galaxies in the early universe appear to have grown too big too fast, assembling into massive, monolithic objects more rapidly than anticipated in the hierarchical $Λ$CDM structure formation paradigm. The available photometric data are consistent with there being a population of massive galaxies that form early ($z \gtrsim 10$) and quench rapidly over a short ($\lesssim 1$ Gyr) timescale, consistent with the traditional picture for the evolution of giant elliptical galaxies. Similarly, kinematic observations as a function of redshift show that massive spirals and their scaling relations were in place at early times. Explaining the early emergence of massive galaxies requires either an extremely efficient conversion of baryons into stars at $z>10$ or a more rapid assembly of baryons than anticipated in $Λ$CDM. The latter possibility was explicitly predicted in advance by MOND. We discuss some further predictions of MOND, such as the early emergence of clusters of galaxies and early reionization.

Szymon Drzyżdżyk, Marek Gilski

Artykuł prezentuje polemikę Epifaniusza z Salaminy z herezjami angelologicznymi. Jego celem nie jest więc tylko prezentowanie błędów, ale i apologia poprawnej wiary. Artykuł składa się z czterech części: pierwsza dotyczy istnienia aniołów, druga koncentruje się na ich pochodzeniu, trzecia podejmuje kwestię ich działania, a czwarta analizuje rolę aniołów w pośmiertnym życiu człowieka. Do najważniejszych wniosków płynących z analiz należą. 1. Angelologia była przedmiotem zainteresowania nie tylko herezji przedchrześcijańskich (wywodzących się z judaizmu), ale i herezji, które wyłoniły się z chrześcijaństwa. Ta tematyka nie pojawia się natomiast przy okazji omawiania greckich szkół filozoficznych. 2. Autor Panarionu nie zawsze jasno prezentuje błędy związane z angelologią. Sam bowiem zaznacza, że niejednokrotnie nie było mu łatwo precyzyjnie wskazać naturę danej herezji. 3. Epifaniusz poddaje herezje angelologiczne krytyce w oparciu o argumenty biblijne, rozumowe, podważając ich źródła, sprowadzając niektóre tezy do absurdu. Odwołuje się także do argumentu z autorytetu, powołując się na ojców (Klemens, Ireneusz, Hipolit).

Grzegorz Mariusz Baran

Tim Schubert, Richard W Peck, Alexander Gimson et al.

Early diagnosis of diseases holds the potential for deep transformation in healthcare by enabling better treatment options, improving long-term survival and quality of life, and reducing overall cost. With the advent of medical big data, advances in diagnostic tests as well as in machine learning and statistics, early or timely diagnosis seems within reach. Early diagnosis research often neglects the potential for optimizing individual diagnostic paths. To enable personalized early diagnosis, a foundational framework is needed that delineates the diagnosis process and systematically identifies the time-dependent value of various diagnostic tests for an individual patient given their unique characteristics. Here, we propose the first foundational framework for early and timely diagnosis. It builds on decision-theoretic approaches to outline the diagnosis process and integrates machine learning and statistical methodology for estimating the optimal personalized diagnostic path. To describe the proposed framework as well as possibly other frameworks, we provide essential definitions. The development of a foundational framework is necessary for several reasons: 1) formalism provides clarity for the development of decision support tools; 2) observed information can be complemented with estimates of the future patient trajectory; 3) the net benefit of counterfactual diagnostic paths and associated uncertainties can be modeled for individuals 4) 'early' and 'timely' diagnosis can be clearly defined; 5) a mechanism emerges for assessing the value of technologies in terms of their impact on personalized early diagnosis, resulting health outcomes and incurred costs. Finally, we hope that this foundational framework will unlock the long-awaited potential of timely diagnosis and intervention, leading to improved outcomes for patients and higher cost-effectiveness for healthcare systems.

Christian Mancas

Homogeneous binary function products are often encountered in the sub-universes modeled by databases, from genealogical trees to sports, from education to healthcare, etc. Their properties must be discovered and enforced by the software applications managing such data to guarantee plausibility. The (Elementary) Mathematical Data Model provides 18 dyadic-type homogeneous binary function product constraint types. MatBase, an intelligent data and knowledge base management system prototype, allows database designers to simply declare them by only clicking corresponding checkboxes and automatically generates code for enforcing them. This paper describes the algorithms that MatBase uses for enforcing all these 18 homogeneous binary function product constraint types, which may also be used by developers not having access to MatBase.

Megan Delamer, Shubham Kanodia, Caleb I. Cañas et al.

We confirm TOI-4201 b as a transiting Jovian mass planet orbiting an early M dwarf discovered by the Transiting Exoplanet Survey Satellite. Using ground based photometry and precise radial velocities from NEID and the Planet Finder Spectrograph, we measure a planet mass of 2.59$^{+0.07}_{-0.06}$ M$_{J}$, making this one of the most massive planets transiting an M-dwarf. The planet is $\sim$0.4\% the mass of its 0.63 M$_{\odot}$ host and may have a heavy element mass comparable to the total dust mass contained in a typical Class II disk. TOI-4201 b stretches our understanding of core-accretion during the protoplanetary phase, and the disk mass budget, necessitating giant planet formation to either take place much earlier in the disk lifetime, or perhaps through alternative mechanisms like gravitational instability.

Amna Batool, Didar Zowghi, Muneera Bano

As artificial intelligence transforms a wide range of sectors and drives innovation, it also introduces complex challenges concerning ethics, transparency, bias, and fairness. The imperative for integrating Responsible AI (RAI) principles within governance frameworks is paramount to mitigate these emerging risks. While there are many solutions for AI governance, significant questions remain about their effectiveness in practice. Addressing this knowledge gap, this paper aims to examine the existing literature on AI Governance. The focus of this study is to analyse the literature to answer key questions: WHO is accountable for AI systems' governance, WHAT elements are being governed, WHEN governance occurs within the AI development life cycle, and HOW it is executed through various mechanisms like frameworks, tools, standards, policies, or models. Employing a systematic literature review methodology, a rigorous search and selection process has been employed. This effort resulted in the identification of 61 relevant articles on the subject of AI Governance. Out of the 61 studies analysed, only 5 provided complete responses to all questions. The findings from this review aid research in formulating more holistic and comprehensive Responsible AI (RAI) governance frameworks. This study highlights important role of AI governance on various levels specially organisational in establishing effective and responsible AI practices. The findings of this study provides a foundational basis for future research and development of comprehensive governance models that align with RAI principles.

Łukasz Libowski

Tłumaczenie na język polski dzieła Piotra z Cluny "Kazanie o świętym Marcelim, papieżu i męczenniku"

Jeremiah Mutie

Debates on whether early Christians relied solely on exorcism and other miraculous healing under the assumption that all diseases are a result of demonic activity, continue. On the one end of this scholarly continuum are those who hold that early Christians only approached disease and healing as purely spiritual phenomena (hence, focusing on exorcism and other kinds of miraculous healing), while, on the other end, others have argued that early Christians accepted a naturalistic view of the causes for diseases and, consequently, sought naturalistic solutions to diseases. However, like in many other areas of life and thought in early Christianity, there is truth in both of these contentions. Rather than choose sides in this debate, this paper will argue that, just like in other areas, early Christians chose and modified existing approaches to sickness and death based on their understanding of the scriptural teachings on these subjects. As such, their approaches provide some key lessons to the current Covid-19 stricken Church.

Alessandra Mastrobuono-Battisti, Ross P. Church, Melvyn B. Davies

We model the effects of collisions and close encounters on the stellar populations observed in the Milky Way nuclear stellar cluster (NSC). Our analysis is based on $N$-body simulations in which the NSC forms by accretion of massive stellar clusters around a supermassive black hole. We attach stellar populations to our $N$-body particles and follow the evolution of their stars, and the rate of collisions and close encounters. The most common encounters are collisions between pairs of main-sequence stars, which lead to mergers: destructive collisions between main-sequence stars and compact objects are rare. We find that the effects of collisions on the stellar populations are small for three reasons. First, our models possess a core which limits the maximum stellar density. Secondly, the velocity dispersion in the NSC is similar to the surface escape velocities of the stars, which minimises the collision rate. Finally, whilst collisions between main-sequence stars destroy bright giants by accelerating their evolution, they also create them by accelerating the evolution of lower-mass stars. These two effects approximately cancel out. We also investigate whether the G2 cloud could be a fuzzball: a compact stellar core which has accreted a tenuous envelope in a close encounter with a red giant. We conclude that fuzzballs with cores below $2\,M_\odot$ have thermal times-scales too short to reproduce G2. A fuzzball with a black-hole core could reproduce the surface properties of G2 but the production rate of such objects in our model is low.

Arpit Narechania, Alireza Karduni, Ryan Wesslen et al.

There are a few prominent practices for conducting reviews of academic literature, including searching for specific keywords on Google Scholar or checking citations from some initial seed paper(s). These approaches serve a critical purpose for academic literature reviews, yet there remain challenges in identifying relevant literature when similar work may utilize different terminology (e.g., mixed-initiative visual analytics papers may not use the same terminology as papers on model-steering, yet the two topics are relevant to one another). In this paper, we introduce a system, VitaLITy, intended to complement existing practices. In particular, VitaLITy promotes serendipitous discovery of relevant literature using transformer language models, allowing users to find semantically similar papers in a word embedding space given (1) a list of input paper(s) or (2) a working abstract. VitaLITy visualizes this document-level embedding space in an interactive 2-D scatterplot using dimension reduction. VitaLITy also summarizes meta information about the document corpus or search query, including keywords and co-authors, and allows users to save and export papers for use in a literature review. We present qualitative findings from an evaluation of VitaLITy, suggesting it can be a promising complementary technique for conducting academic literature reviews. Furthermore, we contribute data from 38 popular data visualization publication venues in VitaLITy, and we provide scrapers for the open-source community to continue to grow the list of supported venues.

Gonzalo Fernández, Javier Velaza, José Vilella Masana

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Asket Kaur, Navya Rao, Tanya Joon

The literature review presented below on Action Recognition in the wild is the in-depth study of Research Papers. Action Recognition problem in the untrimmed videos is a challenging task and most of the papers have tackled this problem using hand-crafted features with shallow learning techniques and sophisticated end-to-end deep learning techniques.