This review evaluates the methodological quality of current front-of-pack labeling research and discusses future research challenges. Peer-reviewed articles were identified using a computerized search of the databases PubMed andWeb of Science (ISI) from1990 to February 2011; reference lists fromkey published articleswere used as well. The quality of the 31 included studies was assessed. The results showed that the methodological quality of published front-of-pack labeling research is generally low to mediocre; objective observational data-based consumer studies were of higher quality than consumer studies relying on self-reports. Experimental studies that included a control group were lacking. The review further revealed a lack of a validated methodology to measure the use of front-of-pack labels and the effects of these labels in real-life settings. In conclusion, few methodologically sound front-of-pack labeling studies are presently available. The highest methodological quality and the greatest public health relevance are achieved by measuring the health effects of front-of-pack labels using biomarkers in a longitudinal, randomized, controlled design in a real-life setting.
LINK
Abstract: Existing frailty models have enhanced research and practice; however, none of the models accounts for the perspective of older adults upon defining and operationalizing frailty. We aim to propose a mixed conceptual model that builds on the integral model while accounting for older adults’ perceptions and lived experiences of frailty. We conducted a traditional literature review to address frailty attributes, risk factors, consequences, perceptions, and lived experiences of older adults with frailty. Frailty attributes are vulnerability/susceptibility, aging, dynamic, complex, physical, psychological, and social. Frailty perceptions and lived experience themes/subthemes are refusing frailty labeling, being labeled “by others” as compared to “self-labeling”, from the perception of being frail towards acting as being frail, positive self-image, skepticism about frailty screening, communicating the term “frail”, and negative and positive impacts and experiences of frailty. Frailty risk factors are classified into socio-demographic, biological, physical, psychological/cognitive, behavioral, and situational/environmental factors. The consequences of frailty affect the individual, the caregiver/family, the healthcare sector, and society. The mixed conceptual model of frailty consists of interacting risk factors, interacting attributes surrounded by the older adult’s perception and lived experience, and interacting consequences at multiple levels. The mixed conceptual model provides a lens to qualify frailty in addition to quantifying it.
DOCUMENT
Deploying robots from indoor to outdoor environments (vise versa) with stable and accurate localization is very important for companies to secure the utilization in industrial applications such as delivering harvested fruits from plantations, deploying/docking, navigating under solar panels, passing through tunnels/underpasses and parking in garages. This is because of the sudden changes in operational conditions such as receiving high/low-quality satellite signals, changing field of view, dealing with lighting conditions and addressing different velocities. We observed these limitations especially in indoor-outdoor transitions after conducting different projects with companies and obtaining inaccurate localization using individual Robotics Operating Systems (ROS2) modules. As there are rare commercial solutions for IO-transitions, AlFusIOn is a ROS2-based framework aims to fuse different sensing and data-interpretation techniques (LiDAR, Camera, IMU, GNSS-RTK, Wheel Odometry, Visual Odometry) to guarantee the redundancy and accuracy of the localization system. Moreover, maps will be integrated to robustify the performance and ensure safety by providing geometrical information about the transitioning structures. Furthermore, deep learning will be utilized to understand the operational conditions by labeling indoor and outdoor areas. This information will be encoded in maps to provide robots with expected operational conditions in advance and beyond the current sensing state. Accordingly, this self-awareness capability will be incorporated into the fusion process to control and switch between the localization techniques to achieve accurate and smooth IO-transitions, e.g., GNSS-RTK will be deactivated during the transition. As an urgent and unique demand to have an accurate and continuous IO-transition towards fully autonomous navigation/transportation, Saxion University and the proposal’s partners are determined to design a commercial and modular industrial-based localization system with robust performance, self-awareness about the localization capabilities and less human interference. Furthermore, AlFusIOn will intensively collaborate with MAPS (a RAAKPRO proposed by HAN University) to achieve accurate localization in outdoor environments.
Artificiële Intelligentie (AI) speelt een steeds belangrijkere rol in mediaorganisaties bij de automatische creatie, personalisatie, distributie en archivering van mediacontent. Dit gaat gepaard met vragen en bezorgdheid in de maatschappij en de mediasector zelf over verantwoord gebruik van AI. Zo zijn er zorgen over discriminatie van bepaalde groepen door bias in algoritmes, over toenemende polarisatie door de verspreiding van radicale content en desinformatie door algoritmes en over schending van privacy bij een niet transparante omgang met data. Veel mediaorganisaties worstelen met de vraag hoe ze verantwoord met AI-toepassingen om moeten gaan. Mediaorganisaties geven aan dat bestaande ethische instrumenten voor verantwoorde AI, zoals de EU “Ethics Guidelines for trustworthy AI” (European Commission, 2019) en de “AI Impact Assessment” (ECP, 2018) onvoldoende houvast bieden voor het ontwerp en de inzet van verantwoorde AI, omdat deze instrumenten niet specifiek zijn toegespitst op het mediadomein. Hierdoor worden deze ethische instrumenten nog nauwelijks toegepast in de mediasector, terwijl mediaorganisaties aangeven dat daar wel behoefte aan is. Het doel van dit project is om mediaorganisaties te ondersteunen en begeleiden bij het inbedden van verantwoorde AI in hun organisaties en bij het ontwerpen, ontwikkelen en inzetten van verantwoorde AI-toepassingen, door domeinspecifieke ethische instrumenten te ontwikkelen. Dit gebeurt aan de hand van drie praktijkcasussen die zijn aangedragen door mediaorganisaties: pluriforme aanbevelingssystemen, inclusieve spraakherkenningssystemen voor de Nederlandse taal en collaboratieve productie-ondersteuningssystemen. De ontwikkeling van de ethische instrumenten wordt uitgevoerd met een Research-through-Design aanpak met meerdere iteraties van informatie verzamelen, analyseren prototypen en testen. De beoogde resultaten van dit praktijkgerichte onderzoek zijn: 1) nieuwe kennis over het ontwerpen van verantwoorde AI in mediatoepassingen, 2) op media toegespitste ethische instrumenten, en 3) verandering in de deelnemende mediaorganisaties ten aanzien van verantwoorde AI door nauwe samenwerking met praktijkpartners in het onderzoek.
