Hasil untuk "French literature - Italian literature - Spanish literature - Portuguese literature"

Malik Sallam

Utility of vaccine campaigns to control coronavirus 2019 disease (COVID-19) is not merely dependent on vaccine efficacy and safety. Vaccine acceptance among the general public and healthcare workers appears to have a decisive role in the successful control of the pandemic. The aim of this review was to provide an up-to-date assessment of COVID-19 vaccination acceptance rates worldwide. A systematic search of the peer-reviewed English survey literature indexed in PubMed was done on 25 December 2020. Results from 31 peer-reviewed published studies met the inclusion criteria and formed the basis for the final COVID-19 vaccine acceptance estimates. Survey studies on COVID-19 vaccine acceptance rates were found from 33 different countries. Among adults representing the general public, the highest COVID-19 vaccine acceptance rates were found in Ecuador (97.0%), Malaysia (94.3%), Indonesia (93.3%) and China (91.3%). However, the lowest COVID-19 vaccine acceptance rates were found in Kuwait (23.6%), Jordan (28.4%), Italy (53.7), Russia (54.9%), Poland (56.3%), US (56.9%), and France (58.9%). Only eight surveys among healthcare workers (doctors and nurses) were found, with vaccine acceptance rates ranging from 27.7% in the Democratic Republic of the Congo to 78.1% in Israel. In the majority of survey studies among the general public stratified per country (29/47, 62%), the acceptance of COVID-19 vaccination showed a level of ≥70%. Low rates of COVID-19 vaccine acceptance were reported in the Middle East, Russia, Africa and several European countries. This could represent a major problem in the global efforts to control the current COVID-19 pandemic. More studies are recommended to address the scope of COVID-19 vaccine hesitancy. Such studies are particularly needed in the Middle East and North Africa, Sub-Saharan Africa, Eastern Europe, Central Asia, Middle and South America. Addressing the scope of COVID-19 vaccine hesitancy in various countries is recommended as an initial step for building trust in COVID-19 vaccination efforts.

Jiaqi Xiong, Orly Lipsitz, F. Nasri et al.

Background As a major virus outbreak in the 21st century, the Coronavirus disease 2019 (COVID-19) pandemic has led to unprecedented hazards to mental health globally. While psychological support is being provided to patients and healthcare workers, the general public's mental health requires significant attention as well. This systematic review aims to synthesize extant literature that reports on the effects of COVID-19 on psychological outcomes of the general population and its associated risk factors. Methods A systematic search was conducted on PubMed, Embase, Medline, Web of Science, and Scopus from inception to 17 May 2020 following the PRISMA guidelines. A manual search on Google Scholar was performed to identify additional relevant studies. Articles were selected based on the predetermined eligibility criteria. Results: Relatively high rates of symptoms of anxiety (6.33% to 50.9%), depression (14.6% to 48.3%), post-traumatic stress disorder (7% to 53.8%), psychological distress (34.43% to 38%), and stress (8.1% to 81.9%) are reported in the general population during the COVID-19 pandemic in China, Spain, Italy, Iran, the US, Turkey, Nepal, and Denmark. Risk factors associated with distress measures include female gender, younger age group (≤40 years), presence of chronic/psychiatric illnesses, unemployment, student status, and frequent exposure to social media/news concerning COVID-19. Limitations A significant degree of heterogeneity was noted across studies. Conclusions The COVID-19 pandemic is associated with highly significant levels of psychological distress that, in many cases, would meet the threshold for clinical relevance. Mitigating the hazardous effects of COVID-19 on mental health is an international public health priority.

Gabin Taibi, Joerg Osterrieder

This paper introduces an algorithmic framework for conducting systematic literature reviews (SLRs), designed to improve efficiency, reproducibility, and selection quality assessment in the literature review process. The proposed method integrates Natural Language Processing (NLP) techniques, clustering algorithms, and interpretability tools to automate and structure the selection and analysis of academic publications. The framework is applied to a case study focused on financial narratives, an emerging area in financial economics that examines how structured accounts of economic events, formed by the convergence of individual interpretations, influence market dynamics and asset prices. Drawing from the Scopus database of peer-reviewed literature, the review highlights research efforts to model financial narratives using various NLP techniques. Results reveal that while advances have been made, the conceptualization of financial narratives remains fragmented, often reduced to sentiment analysis, topic modeling, or their combination, without a unified theoretical framework. The findings underscore the value of more rigorous and dynamic narrative modeling approaches and demonstrate the effectiveness of the proposed algorithmic SLR methodology.

L. Bravi, B. Francioni, F. Murmura et al.

Abstract Food waste is a critical issue with multiple ethical, environmental and economic consequences. The aim of this study is to investigate which factors most affect food waste and determine what actions are undertaken to prevent it at the household level. The study, while privileging a behavioural perspective, focuses on the overall consumption process, from purchasing through final food consumption, thus assuming a broad perspective. The data for this study were collected among Italian, Spanish and English populations using three public online questionnaires administered from January to September 2017. This resulted in a total of 3323 usable questionnaires referring to a sample population aged between 18 and 35 years. As for the main motivation to waste food, the research findings provide strong evidence of the importance of in-store behaviour and food management at home in reducing the frequency of food waste in all the three countries examined. As for the actions preventing food waste, the consumption of leftovers appears as a relevant determinant in minimising food waste. The findings confirm that food waste is a complex issue that requires a broad approach of analysis considering several factors simultaneously. The study also provides further insights regarding the relationship between eating outside the home and food waste, which is a topic of debate in the extant literature. Finally, the study improves the overall knowledge about actions that prevent food waste, which have previously been poorly investigated.

Rosalia Tufano, Gabriele Bavota

Code Review consists in assessing the code written by teammates with the goal of increasing code quality. Empirical studies documented the benefits brought by such a practice that, however, has its cost to pay in terms of developers' time. For this reason, researchers have proposed techniques and tools to automate code review tasks such as the reviewers selection (i.e., identifying suitable reviewers for a given code change) or the actual review of a given change (i.e., recommending improvements to the contributor as a human reviewer would do). Given the substantial amount of papers recently published on the topic, it may be challenging for researchers and practitioners to get a complete overview of the state-of-the-art. We present a systematic literature review (SLR) featuring 119 papers concerning the automation of code review tasks. We provide: (i) a categorization of the code review tasks automated in the literature; (ii) an overview of the under-the-hood techniques used for the automation, including the datasets used for training data-driven techniques; (iii) publicly available techniques and datasets used for their evaluation, with a description of the evaluation metrics usually adopted for each task. The SLR is concluded by a discussion of the current limitations of the state-of-the-art, with insights for future research directions.

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.

Ana Petrovska, Guan Erjiage, Stefan Kugele

In the last two decades, the popularity of self-adaptive systems in the field of software and systems engineering has drastically increased. However, despite the extensive work on self-adaptive systems, the literature still lacks a common agreement on the definition of these systems. To this day, the notion of self-adaptive systems is mainly used intuitively without a precise understanding of the terminology. Using terminology only by intuition does not suffice, especially in engineering and science, where a more rigorous definition is necessary. In this paper, we investigate the existing formal definitions of self-adaptive systems and how these systems are characterised across the literature. Additionally, we analyse and summarise the limitations of the existing formal definitions in order to understand why none of the existing formal definitions is used more broadly by the community. To achieve this, we have conducted a systematic literature review in which we have analysed over 1400 papers related to self-adaptive systems. Concretely, from an initial pool of 1493 papers, we have selected 314 relevant papers, which resulted in nine primary studies whose primary objective was to define self-adaptive systems formally. Our systematic review reveals that although there has been an increasing interest in self-adaptive systems over the years, there is a scarcity of efforts to define these systems formally. Finally, as part of this paper, based on the analysed primary studies, we also elicit requirements and set a foundation for a potential (formal) definition in the future that is accepted by the community on a broader range.

Erin Argo, Tanim Ahmed, Sarah Gable et al.

During the past decade, there has been a significant increase in research focused on integrating AR User Interfaces into public safety applications, particularly for first responders in the domains of Emergency Medical Services, Firefighting, and Law Enforcement. This paper presents the results of a scoping review involving the application of AR user interfaces in the public safety domain and applies an established systematic review methodology to provide a comprehensive analysis of the current research landscape, identifying key trends, challenges, and gaps in the literature. This review includes peer-reviewed publications indexed by the major scientific databases up to April 2025. A basic keyword search retrieved 1,751 papers, of which 90 were deemed relevant for this review. An in-depth analysis of the literature allowed the development of a faceted taxonomy that categorizes AR user interfaces for public safety. This classification lays a solid foundation for future research, while also highlighting key design considerations, challenges, and gaps in the literature. This review serves as a valuable resource for researchers and developers, offering insights that can drive further advances in the field.

Lonni Besançon, Guillaume Cabanac, Cyril Labbé et al.

We report evidence of a new set of sneaked references discovered in the scientific literature. Sneaked references are references registered in the metadata of publications without being listed in reference section or in the full text of the actual publications where they ought to be found. We document here 80,205 references sneaked in metadata of the International Journal of Innovative Science and Research Technology (IJISRT). These sneaked references are registered with Crossref and all cite -- thus benefit -- this same journal. Using this dataset, we evaluate three different methods to automatically identify sneaked references. These methods compare reference lists registered with Crossref against the full text or the reference lists extracted from PDF files. In addition, we report attempts to scale the search for sneaked references to the scholarly literature.

M. M. Gomes, C. Mathias, A. Nardi

This paper investigates the historical evolution of psychiatry and its interrelationship with neurology from the Enlightenment to the early 20th century. It examines the transnational development of these disciplines primarily across Europe, with particular emphasis on key regions such as France, Germany, Britain, Italy, Spain and Portugal, while also including some related remarks about Brazil. The objective is to elucidate how advancements in scientific knowledge, medical practices, and societal perceptions of mental illness have influenced the trajectories of psychiatry and neurology. This analysis is grounded in a diverse array of historical sources, including scholarly literature. It critically examines prominent figures, institutional dynamics, and shifts in societal norms to uncover the intricate interplay of cultural, political, and intellectual forces that have shaped the evolution of these fields. The study reveals a complex tapestry of influences - such as the humanization of treatment, significant scientific breakthroughs, and interdisciplinary collaboration - that have contributed to the development of psychiatry and neurology. Our research underscores the necessity of contextualizing the historical framework within which these disciplines emerged and evolved, thereby offering insights into contemporary practices and policies in mental health care and neurological understanding. Through this historical analysis, we illuminate the nuanced narrative of psychiatry and neurology, highlighting their transnational development and the various factors propelling their evolution. By comprehending the historical foundations of these disciplines, we can glean valuable insights into the challenges and opportunities confronting contemporary mental health care and neurological research, thereby informing future directions in the field.

Jorge Magaz-molina, Ángeles Layuno Rosas, Julia Faria

Skania L. Geldres-Weiss, I. Küster, N. Vila

Purpose The purpose of this paper is, first, to predict eWOM volume based on emoji presence in a tweet, amount of emojis in a tweet and time frame (posting date ex ante COVID-19 or posting date ex post COVID-19) influences. And second, to identify whether there are differences between the samples and a moderation effect of country on the relationship studied. All in a B2B context, particularly in international trade shows (ITSs). Design/methodology/approach The data was collected from X (formerly and still commonly known as Twitter), from 10 ITSs in five countries (France, Spain, the UK, Mexico and the USA), considering two ITSs per country. In total, 9,329 tweets were analyzed and content analysis was used: 3,566 tweets from Period 1, posting date ex ante COVID-19 and 5,763 tweets from Period 2, posting date ex post COVID-19. Findings The results show, first, in a B2B context, that tweets with emoji presence, more emojis and tweets posted before the pandemic have the highest volume of eWOM. Second, that culture moderates the volume of eWOM. Specifically, in the US sample, all predictors significantly drive eWOM volume, even though the USA is the country that uses the least amount of emojis on Twitter. Originality/value This research answers a gap in the literature, contributing to empirical research on the adoption, use, measurement and effect of emoji usage in real-world communication in different countries.

S. Rivas‐Martínez, F. Fernández‐González, J. Loidi et al.

Jorge Díaz-Cintas, Lydia Hayes

This article depicts the current state of the art of English-language audiovisual translation (AVT) and sheds light on the recent changes impacting media localisation practices, viewing patterns, and viewer agency. The motivations that have catapulted practices like subtitling and dubbing into English on video-on-demand (VOD) platforms are considered, and special emphasis is placed on the move from English-language content to the production and distribution of originals created in a multitude of languages and showcased on streaming platforms with subs and dubs in English. Some of the latest technological advancements are discussed as they have influenced the viewing experience and audience selection of AVT modes, leading to a significant change in viewing patterns and preferences of anglophone viewers. Challenges posed to studying and training in English AVT are identified and the importance of inverting one’s gaze and perceiving English as the target language of translation, rather than the source language, is stressed in this new paradigm. Concrete suggestions are made for potential avenues of research in this flourishing field, which will hopefully contribute to painting a more detailed picture of English AVT.

C. Liddy, Isabella Moroz, Ariana Mihan et al.

Background: Electronic consultation (eConsult) is an asynchronous electronic communication tool allowing primary care providers to obtain a specialist consultant's expert opinion in a timely manner, thereby offering a potential solution to excessive wait times for specialist care, which remain a serious concern in many countries. Introduction: Our 2014 review of eConsult services demonstrated feasibility and high acceptability among patients and providers. However, gaps remain in knowledge regarding eConsult's impact on system costs and patient outcomes. Materials and Methods: Following the PRISMA guidelines, we conducted a systematic review in May 2017 of English and French literature on OVID Medline, EMBASE, ERIC, and CINAHL databases, examining all studies on eConsult services published since our previous review. The Quadruple Aim Framework was used to synthesize outcomes. Articles reporting on the impact of eConsult on access, patient safety and satisfaction, utilization rates, clinical workflow, and continuing medical education were analyzed using a narrative synthesis approach. Results: The initial search yielded 1,021 results, 50 of which were included on abstract and received a quality assessment and full text review. Of these, 43 were included in our final analysis. Results demonstrated the worldwide presence of eConsult services in North America and countries beyond, including Brazil, Australia, Spain, and The Netherlands. The breadth of specialty services offered has greatly expanded beyond dermatology and includes cardiology, nephrology, and hematology among others. Overall impact on access measures, acceptability, cost, and provider satisfaction remain positive. There is limited research on population health outcomes of morbidity and mortality. Conclusions: The availability of eConsult services has spread both geographically and in terms of specialty services offered. By allowing for a greater population to be served, access to care is being improved; however, long-term impact should continue to be assessed with a focus on patient safety, morbidity, mortality, and cost effectiveness metrics.

Filip Zamfirov

Today, software systems have a significant role in various domains among which are healthcare, entertainment, transport and logistics, and many more. It is only natural that with this increasing dependency on software, the number of software systems increases. Additionally, these systems become more and more complex. All this leads to a rise in the number of software faults also known as bugs. As a result, the ability to locate the source of a bug (e.g. a file or a commit) is vital for the development and maintenance of efficient software solutions. Bug localization refers to the automated process of discovering files that contain bugs, based on a bug report. This research project aims to make a literature review on different techniques for bug localization. This study distinguishes itself from other surveys and literature reviews [1] in one significant way. The focus of the work is on identifying, categorizing and analyzing existing bug localization methods and tools which were evaluated in an industrial setting. To the best of my knowledge, there are no other works that prioritise this aspect. Unfortunately, such literature is scarce, therefore, bug localization techniques evaluated on open source software are also included.

Afsana Khan, Marijn ten Thij, Anna Wilbik

Federated Learning (FL) has emerged as a promising distributed learning paradigm with an added advantage of data privacy. With the growing interest in having collaboration among data owners, FL has gained significant attention of organizations. The idea of FL is to enable collaborating participants train machine learning (ML) models on decentralized data without breaching privacy. In simpler words, federated learning is the approach of ``bringing the model to the data, instead of bringing the data to the mode''. Federated learning, when applied to data which is partitioned vertically across participants, is able to build a complete ML model by combining local models trained only using the data with distinct features at the local sites. This architecture of FL is referred to as vertical federated learning (VFL), which differs from the conventional FL on horizontally partitioned data. As VFL is different from conventional FL, it comes with its own issues and challenges. In this paper, we present a structured literature review discussing the state-of-the-art approaches in VFL. Additionally, the literature review highlights the existing solutions to challenges in VFL and provides potential research directions in this domain.

Yudong Li, Yuqing Zhang, Zhe Zhao et al.

Scientific literature serves as a high-quality corpus, supporting a lot of Natural Language Processing (NLP) research. However, existing datasets are centered around the English language, which restricts the development of Chinese scientific NLP. In this work, we present CSL, a large-scale Chinese Scientific Literature dataset, which contains the titles, abstracts, keywords and academic fields of 396k papers. To our knowledge, CSL is the first scientific document dataset in Chinese. The CSL can serve as a Chinese corpus. Also, this semi-structured data is a natural annotation that can constitute many supervised NLP tasks. Based on CSL, we present a benchmark to evaluate the performance of models across scientific domain tasks, i.e., summarization, keyword generation and text classification. We analyze the behavior of existing text-to-text models on the evaluation tasks and reveal the challenges for Chinese scientific NLP tasks, which provides a valuable reference for future research. Data and code are available at https://github.com/ydli-ai/CSL

Bianca Minetto Napoleão, Fabio Petrillo, Sylvain Hallé et al.

Context: New scientific evidence continuously arises with advances in Software Engineering (SE) research. Conventionally, Systematic Literature Reviews (SLRs) are not updated or updated intermittently, leaving gaps between updates, during which time the SLR may be missing crucial new evidence. Goal: We propose and evaluate a concept and process called Continuous Systematic Literature Review (CSLR) in SE. Method: To elaborate on the CSLR concept and process, we performed a synthesis of evidence by conducting a meta-ethnography, addressing knowledge from varied research areas. Furthermore, we conducted a case study to evaluate the CSLR process. Results: We describe the resulting CSLR process in BPMN format. The case study results provide indications on the importance and feasibility of applying CSLR in practice to continuously update SLR evidence in SE. Conclusion: The CSLR concept and process provide a feasible and systematic way to continuously incorporate new evidence into SLRs, supporting trustworthy and up-to-date evidence for SLRs in SE.

Lisa Apini-Welcland, Dr. Marina A. S. Daniele, Dr. Lucia Rocca-Ihenacho et al.

The aim of this scoping review is to map the available evidence on pre-service education and continuous professional development (CPD) for maternal health professionals providing services to pregnant women with Female Genital Mutilation/Cutting (FGM/C) in OECD countries. FGM/C is a form of gender-based violence and has become a global phenomenon due to changing patterns in migration flows. Pre-service education curricula and CPD for maternal health professionals need to ensure FGM/C inclusion in order to provide quality services in high-prevalence countries or those serving as home for diaspora communities. Inclusion criteria are studies, training curricula for the education of midwives, doctors or other health professionals providing maternity services, protocols and guidelines on FGM/C training, other online FGM/C training resources from OECD countries. Documents from 2010 onwards will be included. Studies in English, Spanish, French, Italian, Portuguese and German are eligible for inclusion. The search will be carried out using keywords derived from the Population-Concept-Context (PCC) framework and entered into three databases. A grey literature search will be conducted to identify additional material, policy documents, training resources and PhD theses. The search strategy will be supplemented by focused searching for guidelines and other online resources on FGM/C and CPD activities as well as study curricula. Key personnel from education institutions, professional associations, regulatory bodies and FGM/C experts will be contacted to contribute to the review based on their knowledge and experience. All eligible material will be objectively summarized and transferred into a standardized data extraction form. Findings will be collated by type, search studies or educational curricula to draw overall conclusions about the status of professional training opportunities in OECD countries.