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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Maroc ::de grandes divergences territoriales à l’aune d’une décélération démographique
No full textL’analyse du dernier recensement effectué au Maroc permet de répondre à des questions sur la dynamique géodémographique de ce pays : ce pays n’arrive-t-il pas au terme de sa transition démographique ? Quelles sont les diversités de son processus de littoralisation ? Comment se recompose son peuplement et celui de ses grandes villes
Effectiveness of interventions to improve digital health literacy in forced migrant populations ::mixed methods systematic review
No full textBackground: Digital health literacy (DHL), recognized as a key determinant of health, can influence health and well-being, improve health equity, and reduce health disparities. However, DHL is often limited among forced migrant populations, who usually lack the skills to understand and evaluate health information or to access and use digital health resources appropriately. Objective: We aimed to (1) identify effective interventions designed to improve DHL among forced migrant populations and (2) categorize and describe the characteristics of interventions that aim to improve the abilities of forced migrants or adapt digital health services to meet the needs and expectations of forced migrant populations limited by low levels of DHL. Methods: We conducted a mixed methods systematic review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, involving an iterative process among the authors. A medical information specialist assisted in developing a search strategy for the 6 most relevant databases (MEDLINE, Embase, CINAHL, Web of Science, Academic Search Premier, and PsycINFO) and the Google Scholar search engine, covering studies published between 2000 and 2022. Pairs of reviewers selected, individually and independently, titles, abstracts, and then full texts. Data extraction and quality assessment were performed by 2 reviewers and validated by a senior researcher. We used narrative synthesis to provide a comprehensive overview of effective DHL interventions for forced migrant populations, highlighting their success factors. Results: We identified 1845 studies, of which only 6 (0.33%) were finally selected for narrative synthesis. Studies were excluded due to irrelevance, lack of primary data, or low methodological quality. The analysis revealed a diverse methodological landscape with a predominance of qualitative approaches aimed at understanding the challenges and needs of forced migrants concerning DHL. The main challenges were associated with cultural, linguistic, and practical contexts. Interventions targeted various groups, including older adults, individuals with low literacy or education, and those with limited digital experience. We identified 4 effective educational intervention categories to enhance DHL among forced migrants: education and training; education and social support; enabling and education; and social, educational, technological, and infrastructural support. Overall, most of the studies (5/6, 83%) reported positive results in terms of improving DHL among forced migrants. Conclusions: This systematic review highlights the importance of improving DHL among forced migrant populations to promote their health and well-being. In addition, it provides comprehensive knowledge about effective interventions conducted with these groups. These findings can inform stakeholders, particularly policy makers, of the need to address low DHL among forced migrant populations. Going forward, these stakeholders need to develop innovative initiatives that rely on holistic approaches and are based on the specific needs of forced migrants to improve equity and health outcomes
Mettre à contribution les usager·es ::comment les collectivités publiques et destinations touristiques s’inspirent des pratiques de communication numérique du secteur privé
No full textPassive sampling in brackish waters ::monitoring metals and organic pollutants in the Camargue and its application to phytoremediation
No full textThe Camargue region in southern France, characterized by its unique brackish water ecosystems, faces significant contamination from agricultural runoff, introducing organic pollutants and metals into the environment. This study presents a four-seasonal monitoring program comprising nine sampling campaigns, using Polar Organic Chemical Integrative Samplers (POCIS) and Diffusive Gradients in Thin Films (DGT) to explore the temporal and spatial dynamics of contaminants. A comprehensive analysis was conducted on 483 organic pollutants, encompassing a broad spectrum of herbicides, fungicides, their degradation products, and a comprehensive list of heavy metals. By providing time-weighted average concentrations, these techniques allowed to track seasonal variations driven by agricultural cycles and hydrological processes. The results revealed persistent organic pollutants, such as 2-methyl-4-chlorophenoxyacetic acid (MCPA), atrazine metabolites, and metolachlor ethanesulfonic acid (ESA), exhibiting clear seasonal peaks aligned with crop applications. Heavy metals, including copper and barium, displayed temporal fluctuations influenced by both anthropogenic activities and natural geochemical processes. To demonstrate the applicability of this monitoring approach, passive samplers were also deployed within an existing phytoremediation system based on Phragmites australis, allowing to assess its effectiveness in contaminant removal. While organic pollutants were reduced by up to 95 %, metal removal remained variable. This study underscores the high suitability of passive sampling for long-term environmental monitoring in brackish water ecosystems, offering a robust methodology to track pollutant dynamics, support regulatory assessments, and evaluate remediation strategies. The findings provide critical insights for future monitoring programs aimed at balancing agricultural productivity with the protection of fragile aquatic environments
Automated identification of eye motion in raw MRI data using machine learning
No full textMagnetic resonance imaging (MRI) is invaluable for the detailed visualization of soft tissues. However, its susceptibility to motion artifacts presents a challenge in ophthalmology due to the continuous eye movement. A recently developed technique effectively resolves eye motion in MRI, but it strongly relies on an eye tracker (ET), which, being a resource-intensive system, limits its broader adoption. The present work introduces a novel approach that, based on machine learning techniques, enables automated identification of eye motion in raw MRI data collected using a fast-sampling acquisition strategy. Such MRI data were acquired from nine healthy subjects while visual stimuli directed their gaze. A synchronized ET signal was also recorded to label the data. A broad spectrum of features representing the raw MRI acquisitions were extracted, and the classification models were built based on the most discriminative ones. Motion identification was primarily driven by phase-related features, while the models achieved accuracy and recall values exceeding 98%. This study represents an important first step towards obtaining a high-quality MRI of the eye without depending on supplementary hardware
Policies and guidelines supporting the sustainability of human milk donation to milk banks in Switzerland ::a document analysis
No full textBackground: Donor milk from a human milk bank is recommended for vulnerable infants when the mother’s own milk is scarce. Globally, despite an increasing number of human milk banks, the demand for donor milk exceeds the supply, and access remains inequitable and insufficient. The objective of this document analysis was to investigate how global (universal), European, and national policies and guidelines support the sustainability of human milk donation in Switzerland. Methods: Database searches of Medline (via PubMed) and CINAHL were completed in June 2024. Eight documents were included: three guidelines, two position papers/consensus statements, two toolkits and one document with policy recommendations. The analysis was constructed around the micro (individual), meso (institutional) and macro (healthcare system and policies) level framework of structures and systems. Results: Global and European documents offered general recommendations that can be flexibly adapted for each location. Global documents contained explicit recommendations related to sustainability for donations mainly at the macro and meso levels, whereas European documents recommendations related to factors influencing sustainability of human milk donation at all three levels. Swiss guidelines primarily addressed the meso level through specific recommendations adapted to the national context. Regarding sustainability and its three pillars, the majority of the identified recommendations focused on the social pillar. The economic pillar was moderately addressed, whereas the environmental pillar—encompassing issues such as milk wastage, contamination, pollution associated with single-used plastics and broader environmental impacts—was largely overlooked. Conclusions: The sustainability of human milk donation is addressed inconsistently in current global, European and national guidelines on human milk banking. Some documents provide multiple explicit recommendations across different levels, while others refer to the sustainability of donation only implicitly. Future work and research should consider providing a coherent framework across policy, organisational and behavioural levels to enhance the sustainability of human milk donation
TRAFFIC ::Testbed foR Assessing energy eFFiciency In throughput Computing
No full textInformation and Communication Technologies (ICT) represent a significant share of global resource usage. Notably, the energy consumption of data centres has emerged as a critical concern, escalating rapidly, especially with the rise of generative Artificial Intelligence (AI) models. This surge in energy demand calls for efforts focused on measuring and reducing the energy and carbon footprints of components used in ICT service delivery. However, the variety and multitude of devices involved in these services make it challenging to accurately measure and evaluate these footprints.
In this paper, we propose an alternative approach to assess energy impacts by considering data centres for what they are: throughput computing systems offering ICT services. We define a simple experimental testbed and evaluate several machines with different hardware capacities. These machines serve two ICT services: a loop incrementing a counter until a defined limit, and an AI inference predicting the next token based on an input.
Our experiment highlights three main take-aways: (a) the CPU usage is a poor predictor of the power consumption, (b) the energy or CO2 quantity associated with a service visit highly depends on the total load the service is facing, and (c) modern machines do not yield better energy figures compared to older ones in all circumstances
