Abstract: Electronic and electrical waste (e-waste) is growing fast. The purpose of this study is to examine young consumers’ purchase intention of refurbished electronic devices (REDs) such as laptop, tablet, mobile phone and game console. From literature review the factors that influence young consumers’ purchase intention were identified as ‘environmental awareness’, ‘social acceptance’, ‘seller/brand reputation and availability’, and ‘affordability and value’. For each factor a few statements were developed and used as independent variables in a questionnaire. One statement was added about purchase intention as dependent variable. A Pearson correlation coefficient test us showed a clear positive correlation of ‘environmental awareness’ and ‘affordability and value’ with the intention to purchase REDs, but not for the other two factors. This analysis contributes to knowledge on young consumers’ perceptions of refurbished electronic devices and can inform the design of innovative value propositions and new business models for REDs that contribute to a circular economy
MULTIFILE
This study addresses the burgeoning global shortage of healthcare workers and the consequential overburdening of medical professionals, a challenge that is anticipated to intensify by 2030 [1]. It explores the adoption and perceptions of AI-powered mobile medical applications (MMAs) by physicians in the Netherlands, investigating whether doctors discuss or recommend these applications to patients and the frequency of their use in clinical practice. The research reveals a cautious but growing acceptance of MMAs among healthcare providers. Medical mobile applications, with a substantial part of IA-driven applications, are being recognized for their potential to alleviate workload. The findings suggest an emergent trust in AI-driven health technologies, underscored by recommendations from peers, yet tempered by concerns over data security and patient mental health, indicating a need for ongoing assessment and validation of these applications
Als relatief nieuw begrip in de context van e-learning krijgt ‘mobile learning’ steeds meer aandacht, wat ten dele kan worden verklaard door de ontwikkeling en verspreiding van mobiele technologie. Als we de pleitbezorgers van ‘mobile learning’ moeten geloven, dan wordt deze vorm van leren belangrijker en is het denkbaar dat sommige leerprocessen in de toekomst volledig op die wijze vormgegeven zullen worden. Probleem is dat een eenduidige definitie van ‘mobile learning’ nog altijd ontbreekt, dat er meningsverschillen zijn over de technologie die tot het domein van ‘mobile learning’ behoort, en dat er betrekkelijk weinig resultaten zijn van succesvolle inzet van mobiele technologie in leerprocessen. Daarbij wordt onder succesvol verstaan dat het heeft bijgedragen aan de effectiviteit van het leren, en daarmee aan een beter leerresultaat en een efficiënter leerproces, waarbij onder het laatste verstaan wordt dat het maximale leereffect wordt bereikt met een beperkte inzet van mensen en middelen. Deze notitie beoogt enige duidelijkheid te scheppen in de definitiekwestie en in de visies op leren die een rol spelen bij ‘mobile learning’. Vanuit dat perspectief wordt vervolgens ingegaan op kenmerken van mobiele technologie en ontwikkelingen die daarin verwacht worden. Aansluitend wordt er dieper ingegaan op leerprocessen en de rol die mobiele technologie daarin zou kunnen vervullen, waarna de notitie wordt afgesloten met een kijkkader om de mogelijke inzet en betekenis van ‘mobile learning’ in onderwijssituaties te kunnen duiden en beoordelen.
Today, embedded devices such as banking/transportation cards, car keys, and mobile phones use cryptographic techniques to protect personal information and communication. Such devices are increasingly becoming the targets of attacks trying to capture the underlying secret information, e.g., cryptographic keys. Attacks not targeting the cryptographic algorithm but its implementation are especially devastating and the best-known examples are so-called side-channel and fault injection attacks. Such attacks, often jointly coined as physical (implementation) attacks, are difficult to preclude and if the key (or other data) is recovered the device is useless. To mitigate such attacks, security evaluators use the same techniques as attackers and look for possible weaknesses in order to “fix” them before deployment. Unfortunately, the attackers’ resourcefulness on the one hand and usually a short amount of time the security evaluators have (and human errors factor) on the other hand, makes this not a fair race. Consequently, researchers are looking into possible ways of making security evaluations more reliable and faster. To that end, machine learning techniques showed to be a viable candidate although the challenge is far from solved. Our project aims at the development of automatic frameworks able to assess various potential side-channel and fault injection threats coming from diverse sources. Such systems will enable security evaluators, and above all companies producing chips for security applications, an option to find the potential weaknesses early and to assess the trade-off between making the product more secure versus making the product more implementation-friendly. To this end, we plan to use machine learning techniques coupled with novel techniques not explored before for side-channel and fault analysis. In addition, we will design new techniques specially tailored to improve the performance of this evaluation process. Our research fills the gap between what is known in academia on physical attacks and what is needed in the industry to prevent such attacks. In the end, once our frameworks become operational, they could be also a useful tool for mitigating other types of threats like ransomware or rootkits.
A world where technology is ubiquitous and embedded in our daily lives is becoming increasingly likely. To prepare our students to live and work in such a future, we propose to turn Saxion’s Epy-Drost building into a living lab environment. This will entail setting up and drafting the proper infrastructure and agreements to collect people’s location and building data (e.g. temperature, humidity) in Epy-Drost, and making the data appropriately available to student and research projects within Saxion. With regards to this project’s effect on education, we envision the proposal of several derived student projects which will provide students the opportunity to work with huge amounts of data and state-of-the-art natural interaction interfaces. Through these projects, students will acquire skills and knowledge that are necessary in the current and future labor-market, as well as get experience in working with topics of great importance now and in the near future. This is not only aligned with the Creative Media and Game Technologies (CMGT) study program’s new vision and focus on interactive technology, but also with many other education programs within Saxion. In terms of research, the candidate Postdoc will study if and how the data, together with the building’s infrastructure, can be leveraged to promote healthy behavior through playful strategies. In other words, whether we can persuade people in the building to be more physically active and engage more in social interactions through data-based gamification and building actuation. This fits very well with the Ambient Intelligence (AmI) research group’s agenda in Augmented Interaction, and CMGT’s User Experience line. Overall, this project will help spark and solidify lasting collaboration links between AmI and CMGT, give body to AmI’s new Augmented Interaction line, and increase Saxion’s level of education through the dissemination of knowledge between researchers, teachers and students.
The demand for mobile agents in industrial environments to perform various tasks is growing tremendously in recent years. However, changing environments, security considerations and robustness against failure are major persistent challenges autonomous agents have to face when operating alongside other mobile agents. Currently, such problems remain largely unsolved. Collaborative multi-platform Cyber- Physical-Systems (CPSs) in which different agents flexibly contribute with their relative equipment and capabilities forming a symbiotic network solving multiple objectives simultaneously are highly desirable. Our proposed SMART-AGENTS platform will enable flexibility and modularity providing multi-objective solutions, demonstrated in two industrial domains: logistics (cycle-counting in warehouses) and agriculture (pest and disease identification in greenhouses). Aerial vehicles are limited in their computational power due to weight limitations but offer large mobility to provide access to otherwise unreachable places and an “eagle eye” to inform about terrain, obstacles by taking pictures and videos. Specialized autonomous agents carrying optical sensors will enable disease classification and product recognition improving green- and warehouse productivity. Newly developed micro-electromechanical systems (MEMS) sensor arrays will create 3D flow-based images of surroundings even in dark and hazy conditions contributing to the multi-sensor system, including cameras, wireless signatures and magnetic field information shared among the symbiotic fleet. Integration of mobile systems, such as smart phones, which are not explicitly controlled, will provide valuable information about human as well as equipment movement in the environment by generating data from relative positioning sensors, such as wireless and magnetic signatures. Newly developed algorithms will enable robust autonomous navigation and control of the fleet in dynamic environments incorporating the multi-sensor data generated by the variety of mobile actors. The proposed SMART-AGENTS platform will use real-time 5G communication and edge computing providing new organizational structures to cope with scalability and integration of multiple devices/agents. It will enable a symbiosis of the complementary CPSs using a combination of equipment yielding efficiency and versatility of operation.
