Hasil untuk "Literature (General)"

A. Gawer, M. Cusumano

This paper brings together the recent literature on industry platforms and shows how it relates to managing innovation within and outside the firm as well as to dealing with technological and market disruptions and change over time. First, we identify distinct types of platforms. Our analysis of a wide range of industry examples suggests that there are two predominant types of platforms: internal or company-specific platforms, and external or industry-wide platforms. We define internal (company or product) platforms as a set of assets organized in a common structure from which a company can efficiently develop and produce a stream of derivative products. We define external (industry) platforms as products, services, or technologies that act as a foundation upon which external innovators, organized as an innovative business ecosystem, can develop their own complementary products, technologies, or services. Second, we summarize from the literature general propositions on the design, economics, and strategic management of platforms. Third, we review the case of Intel and other examples to illustrate the range of technological, strategic, and business challenges that platform leaders and their competitors face as markets and technologies evolve. Finally, we identify practices associated with effective platform leadership and avenues for future research to deepen our understanding of this important phenomenon and what firms can do to manage platform-related competition and innovation.

M. Rosenberg

J. Metz, R. Nisbet, S. Geritz

G. C. Pascoe

Kyongkuk Cho, D. Yoon

S. Tyas, L. Pederson

S. K. Dwivedy, P. Eberhard

J. Turner, R. Müller

S. Haustein, Isabella Peters, Cassidy R. Sugimoto et al.

Data collected by social media platforms have been introduced as new sources for indicators to help measure the impact of scholarly research in ways that are complementary to traditional citation analysis. Data generated from social media activities can be used to reflect broad types of impact. This article aims to provide systematic evidence about how often Twitter is used to disseminate information about journal articles in the biomedical sciences. The analysis is based on 1.4 million documents covered by both PubMed and Web of Science and published between 2010 and 2012. The number of tweets containing links to these documents was analyzed and compared to citations to evaluate the degree to which certain journals, disciplines, and specialties were represented on Twitter and how far tweets correlate with citation impact. With less than 10% of PubMed articles mentioned on Twitter, its uptake is low in general but differs between journals and specialties. Correlations between tweets and citations are low, implying that impact metrics based on tweets are different from those based on citations. A framework using the coverage of articles and the correlation between Twitter mentions and citations is proposed to facilitate the evaluation of novel social‐media‐based metrics.

Eri Ohta, Eri Ohta, Etsuko Okada et al.

Generalized pustular psoriasis (GPP) is a rare, potentially life-threatening inflammatory disease characterized by neutrophilic pustules and systemic inflammation. We report a case of severe GPP triggered by SARS-CoV-2 infection in a 46-year-old woman with a long history of psoriasis. Eleven days after recovery from COVID-19 pneumonia, she developed widespread pustules and fever. Histopathology revealed subcorneal spongiform pustules and dermal neutrophilic infiltration consistent with GPP. Systemic corticosteroids followed by etretinate and deucravacitinib achieved complete remission. A literature review identified 11 infection- and 10 vaccine-related GPP cases. Compared with vaccine-associated cases, infection-related flares showed longer latency and higher corticosteroid use. Mechanistically, both SARS-CoV-2 infection and vaccination may be associated with IL-36 axis activation, potentially via spike protein–driven, Toll-like receptor–mediated innate immune signaling. This case highlights that distinct immune kinetics may underlie infection- and vaccine-related GPP, while supporting a putative role of IL-36–driven inflammation in COVID-19–associated disease exacerbation.

S. Lal, C. Adair

Zhi Zhang, Yan Liu, Zhejing Hu et al.

Compounding errors pose a significant challenge in automatic literature review generation, as inaccuracies can cascade across multi-stage retrieval and generation workflows. Existing self-correction strategies often lack mechanisms to effectively track and consolidate verified information throughout the process, making it difficult to prevent error accumulation and propagation. In this paper, we propose Structure-Guided Memory Consolidation (SGMC), a novel framework that incrementally consolidates and verifies information using structured representations at each stage of the literature review pipeline. SGMC consists of three key modules: Tree-Guided Memory for hierarchical literature retrieval and outline generation, Hub-Guided Memory for evidence extraction and iterative content refinement, and Self-Loop Memory for proactive error correction via historical feedback. Extensive experiments on public benchmarks and a newly constructed large-scale dataset demonstrate that SGMC achieves state-of-the-art performance in citation accuracy and content quality, significantly mitigating compounding errors in long-form literature review generation.

Aarush Sinha, Viraj Virk, Dipshikha Chakraborty et al.

Language Models [LMs] are now playing an increasingly large role in information generation and synthesis; the representation of scientific knowledge in these systems needs to be highly accurate. A prime challenge is hallucination; that is, generating apparently plausible but actually false information, including invented citations and nonexistent research papers. This kind of inaccuracy is dangerous in all the domains that require high levels of factual correctness, such as academia and education. This work presents a pipeline for evaluating the frequency with which language models hallucinate in generating responses in the scientific literature. We propose ArxEval, an evaluation pipeline with two tasks using ArXiv as a repository: Jumbled Titles and Mixed Titles. Our evaluation includes fifteen widely used language models and provides comparative insights into their reliability in handling scientific literature.

Sebastian Holm, MD, Mario Zambrana, MD, Juan E. Berner, MD, PhD et al.

Summary:. Generative artificial intelligence (AI) large language models are an emerging technology, with ChatGPT and Gemini being 2 well-known examples. The current literature discusses clinical applications and limitations of AI, but its role in research has not yet been extensively evaluated. This study aimed to assess the role of ChatGPT and Gemini in developing novel and clinically relevant research ideas (RIs) for systematic reviews (SRs) in head and neck reconstruction. ChatGPT and Gemini were prompted to provide 10 novel and clinically relevant RIs for SRs in the following domains: head and neck reconstruction in general, microsurgery, and complications in reconstructive head and neck procedures. A comprehensive search was then performed for SRs in MEDLINE, Cochrane Library, and Embase to determine the novelty of the RIs generated. A total of 60 RIs were generated, with half created by ChatGPT and the other half by Gemini. Overall, 3613 entries were found through the literature search. After deduplication and screening, a total of 50 studies that partially addressed the AI-generated RIs were identified and were included in the present review. Out of the 60 AI-generated RIs, 42 had not been previously studied and were therefore considered novel. No statistically significant differences were found between the outputs generated by Gemini and ChatGPT. Both ChatGPT and Gemini were able to effectively generate novel and clinically relevant RIs for SRs, although their suggestions were generally broad. This study demonstrated that AI could potentially aid in the process of conducting novel SRs.

Mohey Eldeen H. H. Ali, Ahmed F. Tayel, Hossam M. Ezzat et al.

Energy plays a crucial role in national development, influencing critical sectors such as industry, agriculture, healthcare, and education. Accurate energy consumption prediction is essential for efficient energy management, helping prevent imbalances between supply and demand and potential energy shortages. This study aims to forecast the total primary energy supply (TPES), using Egypt as a case study for the first time in literature and utilizing several models (ordinary differential equations (ODEs), regression, and ANN models). Although ordinary differential equations (ODEs) offer flexibility and convenience, their application in energy forecasting remains limited. One of the main objectives of this research is to evaluate the effectiveness of ODEs in predicting energy consumption. Various ODE and regression models are employed to identify the most suitable model amongst each category for forecasting energy demand. Additionally, an artificial neural network (ANN) is developed, trained, validated, and tested for the same forecasting task. The study compares the performance of the selected ODE model (Mendelsohn), with the selected regression model (Polynomial), and an ANN model predicting Egypt’s TPES until 2035. By assessing multiple forecasting methods, this work improves the accuracy and reliability of energy consumption predictions, which is crucial for sustainable energy planning and policy development.

Susobhan Mandal, S. Shankaranarayanan

General relativity and quantum field theory are the cornerstones of our understanding of physical processes, from subatomic to cosmic scales. While both theories work remarkably well in their tested domains, they show minimal overlap. However, our research challenges this separation by revealing that non-perturbative effects bridge these distinct domains. We introduce a novel mechanism wherein, at linear order, spin-2 fields around an arbitrary background acquire \emph{effective mass} due to the spontaneous symmetry breaking (SSB) of either global or local symmetry of complex scalar field minimally coupled to gravity. The action of the spin-2 field is identical to the extended Fierz-Pauli (FP) action, corresponding to the mass deformation parameter $α= 1/2$. We show that this occurs due to the effect of SSB on the variation of the energy-momentum tensor of the matter field, which has a dominant effect during SSB. The extended FP action has a salient feature, compared to the standard FP action: the action has 6 degrees of freedom with no ghosts. For local $U(1)$ SSB, we establish that the effective mass of spin-2 fields is related to the mass of the gauge boson and the electric charge of the complex scalar field. Interestingly, our results indicate that the millicharged dark matter scalar fields, generating dark photons, can produce a mass of spin-2 fields of the same order as the Hubble constant $(H_0)$. Hence, we argue that the dark sector offers a natural explanation for the acceleration of the current Universe.

Xuemei Tang, Xufeng Duan, Zhenguang G. Cai

Large language models (LLMs) have emerged as a potential solution to automate the complex processes involved in writing literature reviews, such as literature collection, organization, and summarization. However, it is yet unclear how good LLMs are at automating comprehensive and reliable literature reviews. This study introduces a framework to automatically evaluate the performance of LLMs in three key tasks of literature writing: reference generation, literature summary, and literature review composition. We introduce multidimensional evaluation metrics that assess the hallucination rates in generated references and measure the semantic coverage and factual consistency of the literature summaries and compositions against human-written counterparts. The experimental results reveal that even the most advanced models still generate hallucinated references, despite recent progress. Moreover, we observe that the performance of different models varies across disciplines when it comes to writing literature reviews. These findings highlight the need for further research and development to improve the reliability of LLMs in automating academic literature reviews.

Jann Zosso

Guided by the Einstein equivalence principle that identifies the phenomenon of gravitation as a manifestation of the dynamics of spacetime in contrast to a localizable force, we review and explore its consequences on formulating a theory of gravity. The resulting space of metric theories of gravity may address open conceptual and observational puzzles through a wealth of effects beyond general relativity, whose traces can be searched for within today's and tomorrow's gravitational testing grounds. Above all, we offer a generic metric theory generalization of Isaacson's approach to the leading-order field equations of physical perturbations with a well-defined notion of energy-momentum carried by the gravitational waves. Within this framework, we identify the backreaction of the Isaacson energy-momentum flux onto the background spacetime with the displacement memory effect that induces a permanent distortion of space after the passage of a gravitational wave. This effect is a well-known prediction of GR whose dominant contribution captures its inherent non-linear nature, manifest in the ability of gravity to gravitate. However, the novel interpretation of memory as naturally arising within the Isaacson approach to gravitational waves comes with two main advantages. Firstly, it allows for a unified understanding of both the null and the ordinary memory effect, which are respectively sourced by unbound energy fluxes that do and do not reach asymptotic null infinity. Secondly, and most importantly, this approach allows for a consistent derivation of the memory formula for a large class of metric theories with considerable lessons to be learned for upcoming future measurements of the memory effect.

Arif Ali, Zakir Ullah, Rehman Ullah et al.

Background: Digestive issues are recognized as significant contributors to various chronic diseases, including obesity, diabetes, and cardiovascular disease. Barley, a traditional grain, offers considerable promise in addressing these health challenges due to unique nutritional and bioactive compounds. Objective: This review examines the therapeutic potential of various parts of barley, underutilized resource, for chronic disease prevention and management. Method: ology: A comprehensive literature search was conducted across multiple databases like Google Scholar, PubMed, and ISI Web of Science, to identify nutritional components and functional ingredients in barley that contribute to gut health and chronic disease mitigation. Results: The finding suggests that humans digest barley starch more slowly than wheat and rice, which benefits chronic disease management. Barley's high-molecular-weight β-glucan high content acts as a prebiotic, promotes gut health through microbiome modulation and short-chain fatty acid production, potentially preventing colon cancer and boosting immunity. Recent studies on exploring barley grass of high land showed functional ingredients such as flavonoids, saponarin lutonarin, superoxide dismutase, gamma-aminobutyric acid, polyphenols K, Ca, Se, tryptophan chlorophyll, and vitamins, suggesting potential for enhanced antioxidant activity and improved management of chronic conditions like diabetes, cholesterol, hypertension, cardiovascular health, liver protection, and even boosted immunity. Conclusion: This review underscores the therapeutic potential of barley and its components in chronic disease management, highlighting the need for well-designed clinical trials to translate these findings into effective interventions.

E. Tjeertes, S. Hoeks, Sabine B J C Beks et al.

BackgroundObesity is generally believed to be a risk factor for the development of postoperative complications. Although being obese is associated with medical hazards, recent literature shows no convincing data to support this assumption. Moreover a paradox between body mass index and survival is described. This study was designed to determine influence of body mass index on postoperative complications and long-term survival after surgery.MethodsA single-centre prospective analysis of postoperative complications in 4293 patients undergoing general surgery was conducted, with a median follow-up time of 6.3 years. We analyzed the impact of bodyweight on postoperative morbidity and mortality, using univariate and multivariate regression models.ResultsThe obese had more concomitant diseases, increased risk of wound infection, greater intraoperative blood loss and a longer operation time. Being underweight was associated with a higher risk of complications, although not significant in adjusted analysis. Multivariate regression analysis demonstrated that underweight patients had worse outcome (HR 2.1; 95 % CI 1.4-3.0), whereas being overweight (HR 0.6; 95 % CI 0.5–0.8) or obese (HR 0.7; 95 % CI 0.6–0.9) was associated with improved survival.ConclusionObesity alone is a significant risk factor for wound infection, more surgical blood loss and a longer operation time. Being obese is associated with improved long-term survival, validating the obesity paradox. We also found that complication and mortality rates are significantly worse for underweight patients. Our findings suggest that a tendency to regard obesity as a major risk factor in general surgery is not justified. It is the underweight patient who is most at risk of major postoperative complications, including long-term mortality.