Hasil untuk "Literature (General)"

R. Agha, Ginimol Mathew, Rasha Rashid et al.

The use of AI in research and the literature is increasing. The need for transparency is clear. Here we present a guideline to transparently reporting the use of AI in any manuscript in general. The guideline items cover; declaration, purpose and scope, AI tools and configuration, data inputs and safeguards, human oversight and verification, bias, ethics and regulatory compliance and reproducibility and transparency. This guide will evolve over time as technology, systems and behaviour evolve.

L. Prokopy, Kristin M. Floress, D. Klotthor-Weinkauf et al.

E. Murphy, R. Dingwall, D. Greatbatch et al.

Roy F. Baumeister, Mark R. Leary

Narrative literature reviews serve a vital scientific function, but few resources help people learn to write them. As compared with empirical reports, literature reviews can tackle broader and more abstract questions, can engage in more post hoc theorizing without the danger of capitalizing on chance, can make a stronger case for a null-hypothesis conclusion, and can appreciate and use methodological diversity better. Also, literature reviews can draw any of 4 conclusions: The hypothesis is correct, it has not been conclusively established but is the currently best guess, it is false, or the evidence permits no conclusion. Common mistakes of authors of literature review manuscripts are described.

Nizar Becheikh, Réjean Landry, Nabil Amara

Iain R. Murray, J. Arnott

Tugba Cayirli, Emre A. Veral

J. Somers, E. Goldner, P. Waraich et al.

L. Järup, M. Berglund, C. Elinder et al.

W. R. Knechel, G. V. Krishnan, Mikhail Pevzner et al.

J. Roehrich, M. Lewis, G. George

Governments around the world, but especially in Europe, have increasingly used private sector involvement in developing, financing and providing public health infrastructure and service delivery through public-private partnerships (PPPs). Reasons for this uptake are manifold ranging from rising expenditures for refurbishing, maintaining and operating public assets, and increasing constraints on government budgets stifle, seeking innovation through private sector acumen and aiming for better risk management. Although PPPs have attracted practitioner and academic interest over the last two decades, there has been no attempt to integrate the general and health management literature to provide a holistic view of PPPs in healthcare delivery. This study analyzes over 1400 publications from a wide range of disciplines over a 20-year time period. We find that despite the scale and significance of the phenomenon, there is relatively limited conceptualization and in-depth empirical investigation. Based on bibliographic and content analyses, we synthesize formerly dispersed research perspectives into a comprehensive multi-dimensional framework of public-private partnerships. In so doing, we provide new directions for further research and practice.

Tehseen Hussain, Fraz Ahmad, Dr. Zia Ur Rehman

The rapid integration of the Internet of Things (IoT) into healthcare ecosystems has revolutionized patient monitoring and data accessibility; however, it has simultaneously expanded the cyber-attack surface, leaving sensitive medical data vulnerable to sophisticated breaches. This systematic literature review (SLR) addresses the critical challenge of balancing high-level security with the severe resource constraints of medical sensors and edge devices. By synthesizing evidence from 80 high-impact studies including 18 primary research articles published between 2022 and 2025 this paper evaluates the quality and efficacy of emerging cryptographic frameworks. The methodology utilizes a rigorous quality assessment framework to categorize research into "Strong," "Moderate," and "Weak" tiers. Key findings reveal a significant paradigm shift toward lightweight symmetric ciphers, such as GIFT and PRESENT, and certificateless authentication protocols like ELWSCAS, which reduce communication overhead in narrow-band environments. The analysis further explores the role of blockchain-assisted decentralization and DNA-based encryption in mitigating Single Point of Failure risks and providing high entropy. While decentralized models significantly enhance data integrity, they frequently encounter a scalability wall regarding transaction latency. Furthermore, the review assesses quantum readiness, noting that while lattice-based standards are being ported to microcontrollers, memory footprints remain a barrier for simpler sensors. Ultimately, this SLR maps the current technical frontiers and provides a strategic roadmap for future research, emphasizing the transition toward lightweight, quantum-resistant architectures as the next essential step in securing the global healthcare IoT infrastructure. Conflict of Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding The research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data Fabrication/Falsification Statement The author(s) declare that no data has been fabricated, falsified, or manipulated in this study. Participant Consent The authors confirm that Informed consent was obtained from all participants, and confidentiality was duly maintained. Copyright and Licensing For all articles published in the NIJEC journal, Copyright (c) of this study is with author(s).

Peter Jansen, Peter Clark, Doug Downey et al.

Contemporary automated scientific discovery has focused on agents for generating scientific experiments, while systems that perform higher-level scientific activities such as theory building remain underexplored. In this work, we formulate the problem of synthesizing theories consisting of qualitative and quantitative laws from large corpora of scientific literature. We study theory generation at scale, using 13.7k source papers to synthesize 2.9k theories, examining how generation using literature-grounding versus parametric knowledge, and accuracy-focused versus novelty-focused generation objectives change theory properties. Our experiments show that, compared to using parametric LLM memory for generation, our literature-supported method creates theories that are significantly better at both matching existing evidence and at predicting future results from 4.6k subsequently-written papers

Laihong Zhou, Hong Jin, Ping Chen et al.

Abstract Aiming at the problem of actuator failure of quadrotor unmanned aerial vehicle (UAV), a novel fault detection method based on dual channel inertial sensors is proposed. Firstly, the actuator partial failure fault model is introduced into the UAV dynamic model. Then, a double channel observer is designed with the angular velocity of the rolling channel and the pitch channel as the state variable. The angular velocity estimation errors of the two channels are calculated and compared with the estimation error threshold. Based on this result, the fault channels are initially located while the actuator faults are identified. Finally, according to the angular velocity control characteristics of the two channels, an accurate fault location algorithm is designed to determine the location of the faulty rotor. Two groups of simulation flight experiments were carried out in MATLAB/SIMULINK environment. During the simulation process, the algorithm proposed by this paper was compared and analyzed with the H-/L∞ algorithm proposed in the recent literature. The results show that the algorithm in this paper can not only achieve precise fault location, but also detect faults quickly. Compared with the H-/L∞ algorithm, the detection speed of this algorithm has increased by more than 80%. The real flight experiment results of the quadrotor UAV prototype verify the effectiveness of the proposed algorithm for actuator fault detection in actual flight scenarios.

Gal Puris, Angela Chetrit, Eldad Katorza

As medical imaging continues to expand, concerns about the potential risks of ionizing radiation to the developing fetus have led to a preference for non-radiation-based alternatives such as ultrasonography and fetal MRI. This review examines the current evidence on the safety of MRI during pregnancy, with a focus on 3 T MRI and contrast agents, aiming to provide a comprehensive synthesis that informs clinical decision-making, ensures fetal safety and supports the safe use of all available modalities that could impact management. We conducted a comprehensive review of studies from 2000 to 2024 on MRI safety during pregnancy, focusing on 3 T MRI and gadolinium use. The review included peer-reviewed articles and large database studies, summarizing key findings and identifying areas for further research. Fetal MRI, used alongside ultrasound, enhances diagnostic accuracy for fetal anomalies, particularly in the brain, thorax, gastrointestinal and genitourinary systems, with no conclusive evidence of adverse effects on fetal development. While theoretical risks such as tissue heating and acoustic damage exist, studies show no significant harm at 1.5 T or 3 T, though caution is still advised in the first trimester. Regarding gadolinium-based contrast agents, the evidence is conflicting: while some studies suggest risks such as stillbirth and rheumatological conditions, animal studies show minimal fetal retention and no significant toxicity, and later clinical research has not substantiated these risks. The existing literature on fetal MRI is encouraging, suggesting minimal risks; however, further investigation through larger, prospective and long-term follow-up studies is essential to comprehensively determine its safety and late effects.

Данило Крюков

Об’єктом дослідження є цифрова трансформація у зовнішніх корпоративних комунікаціях – процес, що змінює способи взаємодії організацій зі стейкхолдерами та формування довіри в цифрову добу. Актуальність теми зумовлена тим, що цифрові інструменти відкривають нові канали взаємодії, але водночас вимагають переосмислення теоретичних основ, оцінки рівня цифрової зрілості та створення інтегрованих моделей для стратегічного управління комунікаціями, особливо в умовах швидких соціально-технологічних змін. Метою дослідження є синтез класичних і сучасних підходів у сфері комунікацій та розробка на основі отриманих висновків моделі, що дозволяє оцінювати цифрову зрілість і виявляти міждисциплінарні зв’язки трансформацій. Методи дослідження включають аналіз і синтез наукових джерел, порівняння класичних і новітніх підходів, а також моделювання та абстрагування для побудови концептуальних рамок. Основні висновки полягають у тому, що сформульовано дві моделі цифрової трансформації у зовнішніх комунікаціях. Перша – External Corporate Communication Maturity Model (ECCMM), п’ятирівнева модель цифрової зрілості, яка описує поступовий розвиток від базових до інноваційних практик. Друга – TOC-модель (Technical, Organizational, Communicational), що пояснює взаємодію технологічних, організаційних і комунікаційних чинників та пропонує набір індикаторів для подальших емпіричних досліджень. Запропоновані підходи створюють підґрунтя як для стандартизації наукових досліджень цифрової трансформації, так і для практичного оцінювання рівня цифрової зрілості корпоративних комунікацій у різних контекстах, зокрема в українському

Mansi Garg, Lee-Chi Wang, Bhavesh Ghanchi et al.

This work presents a Biomedical Literature Question Answering (Q&A) system based on a Retrieval-Augmented Generation (RAG) architecture, designed to improve access to accurate, evidence-based medical information. Addressing the shortcomings of conventional health search engines and the lag in public access to biomedical research, the system integrates diverse sources, including PubMed articles, curated Q&A datasets, and medical encyclopedias ,to retrieve relevant information and generate concise, context-aware responses. The retrieval pipeline uses MiniLM-based semantic embeddings and FAISS vector search, while answer generation is performed by a fine-tuned Mistral-7B-v0.3 language model optimized using QLoRA for efficient, low-resource training. The system supports both general medical queries and domain-specific tasks, with a focused evaluation on breast cancer literature demonstrating the value of domain-aligned retrieval. Empirical results, measured using BERTScore (F1), show substantial improvements in factual consistency and semantic relevance compared to baseline models. The findings underscore the potential of RAG-enhanced language models to bridge the gap between complex biomedical literature and accessible public health knowledge, paving the way for future work on multilingual adaptation, privacy-preserving inference, and personalized medical AI systems.

L. Filipe O. Costa, Francisco Frutos-Alfaro, José Natário et al.

In a recent paper we discussed when it is possible to define reference frames nonrotating with respect to distant inertial reference objects (extension of the IAU reference systems to exact general relativity), and how to construct them. We briefly review the construction, illustrating it with further examples, and caution against the recent misuse of zero angular momentum observers (ZAMOs).

Haokun Liu, Yangqiaoyu Zhou, Mingxuan Li et al.

AI holds promise for transforming scientific processes, including hypothesis generation. Prior work on hypothesis generation can be broadly categorized into theory-driven and data-driven approaches. While both have proven effective in generating novel and plausible hypotheses, it remains an open question whether they can complement each other. To address this, we develop the first method that combines literature-based insights with data to perform LLM-powered hypothesis generation. We apply our method on five different datasets and demonstrate that integrating literature and data outperforms other baselines (8.97\% over few-shot, 15.75\% over literature-based alone, and 3.37\% over data-driven alone). Additionally, we conduct the first human evaluation to assess the utility of LLM-generated hypotheses in assisting human decision-making on two challenging tasks: deception detection and AI generated content detection. Our results show that human accuracy improves significantly by 7.44\% and 14.19\% on these tasks, respectively. These findings suggest that integrating literature-based and data-driven approaches provides a comprehensive and nuanced framework for hypothesis generation and could open new avenues for scientific inquiry.

Yuxuan Lai, Yupeng Wu, Yidan Wang et al.

Abstract. Automatically generating scientific literature surveys is a valuable task that can significantly enhance research efficiency. However, the diverse and complex nature of information within a literature survey poses substantial challenges for generative models. In this paper, we design a series of prompts to systematically leverage large language models (LLMs), enabling the creation of comprehensive literature surveys through a step-by-step approach. Specifically, we design prompts to guide LLMs to sequentially generate the title, abstract, hierarchical headings, and the main content of the literature survey. We argue that this design enables the generation of the headings from a high-level perspective. During the content generation process, this design effectively harnesses relevant information while minimizing costs by restricting the length of both input and output content in LLM queries. Our implementation with Qwen-long achieved third place in the NLPCC 2024 Scientific Literature Survey Generation evaluation task, with an overall score only 0.03% lower than the second-place team. Additionally, our soft heading recall is 95.84%, the second best among the submissions. Thanks to the efficient prompt design and the low cost of the Qwen-long API, our method reduces the expense for generating each literature survey to 0.1 RMB, enhancing the practical value of our method.