Hasil untuk "Literature (General)"

S. Kimmel

This essay briefly examines the role of architectural history in María Rosa Menocal's effort to test the conventions of Romance philology. By drawing the familiar built environment into esoteric debates about language and literature, Menocal sought to render medieval Iberia more accessible to general readers and to create a scholarly space for interdisciplinary research that bridges peninsular religious and linguistic divisions. The result of this effort, particularly in the American academy, is today's medieval and early modern Iberian studies, where scholars enjoy greater flexibility in their research and teaching even while continuing to grapple with the theoretical and political risks implicit in Menocal's approach.

I. Croy, S. Nordin, T. Hummel

Bronwyn H Hall, J. Mairesse, P. Mohnen

We review the econometric literature on measuring the returns to R&D. The theoretical frameworks that have been used are outlined, followed by an extensive discussion of measurement and econometric issues that arise when estimating the models. We then provide a series of tables summarizing the major results that have been obtained and conclude with a presentation of R&D spillover returns measurement. In general, the private returns to R&D are strongly positive and somewhat higher than those for ordinary capital, while the social returns are even higher, although variable and imprecisely measured in many cases.

F. Lewis, Hongwei Zhang, Kristian Hengster-Movrić et al.

H. Selye

James H. Brown

N. Davies

A. Lasota, M. Mackey

J. Schumpeter

Patrick C Kyllonen, R. E. Christal

Dale T. Miller, M. Ross

S. Freud

Michael P. Johnson, Kathleen J. Ferraro

J. Agirre, Mihaela Atanasova, Haroldas Bagdonas et al.

This article describes the Collaborative Computational Project No. 4 (CCP4). It is intended as a general literature citation for the use of the CCP4 software suite in structure determination.

Joana Breidenbach

Colonized Australia presents a very grim picture of Aboriginal people in general. They are in a minority in their own land and many Aboriginal trib es and languages in fact, became extinct. Land, sacred to Aboriginals, has been lost to the c olonizers. It is only the Aboriginal consciousness that keeps them united in this predic ament in spite of their differences in language, culture, colour, region and religion. It is also this unity that makes them fight, at times with a martial spirit, against discrimination and m otivates them to assert their Aboriginal identity. In the past few decades Australia has pro duced a considerable amount of Aboriginal Literature reflecting Aboriginal struggle economic freedom, legal recognition and reforms for basic living conditions. Mudrooroo, Jack Davis, Ale xis Wright, Kim Scott, and other Aboriginal Writers represent these issues through different li terary genres of poetry, fiction and drama. In this paper I try to explain how Aboriginal form can be identified and how functions of Aboriginality can be recognized in the writings of Mudrooroo and Jack Davis writings that belong to the early phase of Aboriginal period.

Dimitry S. Davydow

Maxwell J. Jacobson, Daniel Xie, Jackson Shen et al.

Scientific discovery is slowed by fragmented literature that requires excessive human effort to gather, analyze, and understand. AI tools, including autonomous summarization and question answering, have been developed to aid in understanding scientific literature. However, these tools lack the structured, multi-step approach necessary for extracting deep insights from scientific literature. Large Language Models (LLMs) offer new possibilities for literature analysis, but remain unreliable due to hallucinations and incomplete extraction. We introduce Elhuyar, a multi-agent, human-in-the-loop system that integrates LLMs, structured AI, and human scientists to extract, analyze, and iteratively refine insights from scientific literature. The framework distributes tasks among specialized agents for filtering papers, extracting data, fitting models, and summarizing findings, with human oversight ensuring reliability. The system generates structured reports with extracted data, visualizations, model equations, and text summaries, enabling deeper inquiry through iterative refinement. Deployed in materials science, it analyzed literature on tungsten under helium-ion irradiation, showing experimentally correlated exponential helium bubble growth with irradiation dose and temperature, offering insight for plasma-facing materials (PFMs) in fusion reactors. This demonstrates how AI-assisted literature review can uncover scientific patterns and accelerate discovery.

Sabiya Banu Masthan Ali, Oussema Kirmani, Aroosa Hameed et al.

Large Language Models (LLMs) are widely used in software engineering to generate, complete, translate, and fix code, improving developer productivity. While most research focuses on the energy consumption and carbon emissions of model training and inference, far less attention has been given to the sustainability of the code these models produce. The efficiency of generated code affects the long-term environmental impact of software systems. Inefficient code can increase CPU usage, memory consumption, execution time, and overall energy use during deployment and operation. As LLM-generated code becomes more common in real-world projects, even small inefficiencies can lead to high environmental costs over time. This paper examines existing research on the sustainability of code generated by LLMs. We conduct a systematic literature review to analyze selected primary studies and investigate the extent to which LLMs are capable of producing sustainable code. In addition, we examine how sustainability is defined and measured in this context, including the metrics and evaluation strategies used to assess energy efficiency and resource usage. We also explore whether techniques such as fine-tuning and prompt engineering influence the sustainability of generated code. Through a structured analysis of the selected studies, we categorize research efforts based on their methodological approaches, evaluation practices, and experimental settings. The findings indicate that research in this area remains relatively limited and fragmented, with no widely accepted framework for measuring or benchmarking the sustainability of LLM-generated code. These observations highlight the need for clearer definitions, standardized evaluation methods, and systematic research to support environmentally friendly AI-assisted software engineering.

Michael Edward Stewart

The marriage of Germanus, nephew of Emperor Justin I (r. 518–527), to Matasuintha, former Gothic queen and granddaughter of Theoderic the Great (r. 475–526), in late 549 or early 550, was a significant yet often overlooked moment in the later stages of the Gothic War. Scholars generally interpret the marriage as a pragmatic alliance shaped by immediate strategic concerns – either a political manoeuvre by Justinian or a personal initiative by Germanus following his appointment as commander in Italy. This article revisits that assumption by exploring three related questions. First, did the marriage and military appointment signal a reconciliation between Justinian and Germanus, or a calculated attempt by the emperor to stabilize a deteriorating political situation? Second, how did their relationship evolve in the years leading up to the union, particularly after Theodora’s death in 548? Finally, more speculatively, was Germanus’ earlier decision to marry his daughter to the general John in 545 connected to his own dynastic ambitions?

Rosni Vasu, Chandrayee Basu, Bhavana Dalvi Mishra et al.

Large Language models have demonstrated promising performance in research ideation across scientific domains. Hypothesis development, the process of generating a highly specific declarative statement connecting a research idea with empirical validation, has received relatively less attention. Existing approaches trivially deploy retrieval augmentation and focus only on the quality of the final output ignoring the underlying reasoning process behind ideation. We present $\texttt{HypER}$ ($\textbf{Hyp}$othesis Generation with $\textbf{E}$xplanation and $\textbf{R}$easoning), a small language model (SLM) trained for literature-guided reasoning and evidence-based hypothesis generation. $\texttt{HypER}$ is trained in a multi-task setting to discriminate between valid and invalid scientific reasoning chains in presence of controlled distractions. We find that $\texttt{HypER}$ outperformes the base model, distinguishing valid from invalid reasoning chains (+22\% average absolute F1), generates better evidence-grounded hypotheses (0.327 vs. 0.305 base model) with high feasibility and impact as judged by human experts ($>$3.5 on 5-point Likert scale).