Tind Technologies (Norway)
Hes-so: ArODES Open Archive (University of Applied Sciences and Arts Western Switzerland / Haute école spécialisée de Suisse occidentale / FH Westschweiz)Not a member yet
15764 research outputs found
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Gender and geographical bias in the editorial decision-making process of biomedical journals ::a case-control study
No full textObjectives: To assess whether the gender (primary) and geographical affiliation (post-hoc) of the first and/or last authors are associated with publication decisions after peer review.
Design: Case-control study.
Setting: Biomedical journals.
Participants: Original peer-reviewed manuscripts submitted between 1 January 2012 and 31 December 2019.
Main outcome measure: Manuscripts accepted (cases) and rejected for publication (controls).
Results: Of 6213 included manuscripts, 5294 (85.2%) first and 5479 (88.1%) last authors' gender were identified; 2511 (47.4%) and 1793 (32.7%) were women, respectively. The proportion of women first and last authors was 48.4% (n=1314) and 32.2% (n=885) among cases and 46.4% (n=1197) and 33.2% (n=908) among controls. After adjustment, the association between the first author's gender and acceptance for publication remained non-significant 1.04 (0.92 to 1.17). Acceptance for publication was lower for first authors affiliated to Asia 0.58 (0.46 to 0.73), Africa 0.75 (0.41 to 1.36) and South America 0.68 (0.40 to 1.16) compared with Europe, and for first author affiliated to upper-middle country-income 0.66 (0.47 to 0.95) and lower-middle/low 0.69 (0.46 to 1.03) compared with high country-income group. It was significantly higher when both first and last authors were affiliated to different countries from same geographical and income groups 1.35 (1.03 to 1.77), different countries and geographical but same income groups 1.50 (1.14 to 1.96) or different countries, geographical and income groups 1.78 (1.27 to 2.50) compared with authors from similar countries. The study funding was independently associated with the acceptance for publication (when compared with no funding, 1.40; 1.04 to 1.89 for funding by association & foundations, 2.76; 1.87 to 4.10 for international organisations, 1.30; 1.04 to 1.62 for non-profit & associations & foundations). The reviewers' recommendations of the original submitted version were significantly associated with the outcome (unadjusted 5.36; 4.98 to 5.78 for acceptance compared with rejection). Gender of the first author was not associated with reviewers' recommendations (adjusted 0.96, 0.87 to 1.06).
Conclusions: We did not identify evidence of gender bias during the editorial decision-making process for papers sent out to peer review. However, the under-representation in manuscripts accepted for publication of first authors affiliated to Asia, Africa or South America and those affiliated to upper/lower-middle and low country-income group, indicates poor representation of global scientists' opinion and supports growing demands for improving equity, diversity and inclusion in biomedical research. The more diverse the countries and incomes of the first and last authors, the greater the chances of the publication being accepted
A stochastic geometry approach to performance modeling of SWIPT vehicular networks
No full textWith the increasing number of devices and the advent of 5G and 6G networks, ensuring reliable power and data connectivity remains a significant challenge, particularly in rural or remote areas. Simultaneous Wireless Information and Power Transfer (SWIPT) networks have emerged as a promising solution to power devices without batteries. However, their deployment in real-world scenarios is hindered by complex channel conditions and spatial dynamics. This research introduces a two-tier analytical model grounded in stochastic geometry, where base stations (BSs) are arranged along roads following a Poisson Line Cox Process (PLCP), while user equipment (UEs) is distributed using a Poisson Point Process (PPP). A comparative evaluation against planar PPP-based models demonstrates the performance advantages of this novel approach. Additionally, a Genetic Algorithm (GA) is applied to explore real-world scenario parameters, enhancing the model's adaptability and performance in practical applications
Industry 4.0 adoption challenges in lean-agile-resilient-green agri-food supply chain
No full textPurpose
By incorporating I4.0 technologies, the agri-food supply chain (AFSC) can become leaner, faster, more robust and greener. However, many challenges must be overcome to fully realise I4.0 in this context. Therefore, this paper aims to identify the challenges that hinder the adoption of I4.0 technologies on the development of the Lean, Agile, Resilient and Green (LARG) AFSC.
Design/methodology/approach
The approach adopted was to identify challenges addressed in the literature with expert opinion and Total Interpretive Structural Modelling (TISM) for adaptation. In addition, a Weighted Influence Non-linear Gauge Systems (WINGS) methodology has been developed that uses expert opinion to generate a power and influence matrix.
Findings
The results show that lack of commitment and understanding of top management (X12), lack of long term vision (X17) and lack of incentives and government support (15) are the most important challenges.
Research limitations/implications
This study does not explore the effectiveness of the concluded challenges of I4.0 and their strategy to overcome them. Also, the authors relied on a limited sample size for this study, which might not cover the detailed challenges within LARG AFSC. Finally, this study lacks in future advancement of I4.0, which may further affect the challenges.
Practical implications
By mentioning the key challenges, this study empowers LARG AFSC organisations to build a targeted strategy for smoother I4.0 implementation.
Originality/value
Industry 4.0 challenges remain unexplored in LARG AFSC. This improved awareness equips managers to navigate better the potential issues and complexity that may arise when adopting I4.0 in the LARG AFSC
Improving quality control of whole slide images by explicit artifact augmentation
No full textThe problem of artifacts in whole slide image acquisition, prevalent in both clinical workflows and research-oriented settings, necessitates human intervention and re-scanning. Overcoming this challenge requires developing quality control algorithms, that are hindered by the limited availability of relevant annotated data in histopathology. The manual annotation of ground-truth for artifact detection methods is expensive and time-consuming. This work addresses the issue by proposing a method dedicated to augmenting whole slide images with artifacts. The tool seamlessly generates and blends artifacts from an external library to a given histopathology dataset. The augmented datasets are then utilized to train artifact classification methods. The evaluation shows their usefulness in classification of the artifacts, where they show an improvement from 0.10 to 0.01 AUROC depending on the artifact type. The framework, model, weights, and ground-truth annotations are freely released to facilitate open science and reproducible research
Jeux de cartes performatifs ::interviews croisées de DD Dorvillier et Douglas E. Stanley
No full textUne pièce participative où la frontière entre spectateur et interprète est remise en question. Chaque personne porte un casque et reçoit des consignes. En fonction de ses choix se dessine peu à peu une danse collective
Expression manuelle anténatale de colostrum ::un tremplin pour l’allaitement en cas de diabète
No full textL’allaitement maternel représente un défi chez les femmes atteintes de diabète. Dans cet article, Julie Flohic, sage-femme consultante en lactation IBCLC, expose les bénéfices de l'expression manuelle anténatale de colostrum pour le démarrage de l'allaitement maternel chez les femmes concernées, et propose un protocole de mise en application
Overview of LifeCLEF 2024 ::challenges on species distribution prediction and identification
No full textBiodiversity monitoring using machine learning and AI-based approaches is becoming increasingly popular. It allows for providing detailed information on species distribution and ecosystem health at a large scale and contributes to informed decision-making on environmental protection. Species identification based on images and sounds, in particular, is invaluable for facilitating biodiversity monitoring efforts and enabling prompt conservation actions to protect threatened and endangered species. The multiplicity of methods developed, however, makes it important to evaluate their performance on realistic datasets and using standardized evaluation protocols. The LifeCLEF lab has been setting up such evaluations since 2011, encouraging machine learning researchers to work on this topic and promoting the adoption of the technologies developed by stakeholders. The 2024 edition proposes five data-oriented challenges related to the identification and prediction of biodiversity: (i) BirdCLEF: bird call identification in soundscapes, (ii) FungiCLEF: revisiting fungi species recognition beyond 0-1 cost, (iii) GeoLifeCLEF: remote sensing based prediction of species, (iv) PlantCLEF: Multi-species identification in vegetation plot images, and (v) SnakeCLEF: revisiting snake species identification in medically important scenarios. This paper overviews the motivation, methodology, and main outcomes of those five challenges
