Considering activity level propositions in the evaluation of forensic biology findings is becoming more common place. There are increasing numbers of publications demonstrating different transfer mechanisms that can occur under a variety of circumstances. Some of these publications have shown the possibility of DNA transfer from site to site on an exhibit, for instance as a result of packaging and transport. If such a possibility exists, and the case circumstances are such that the area on an exhibit where DNA is present or absent is an observation that is an important diagnostic characteristic given the propositions, then site to site transfer should be taken into account during the evaluation of observations. In this work we demonstrate the ways in which site to site transfer can be built into Bayesian networks when carrying out activity level evaluations of forensic biology findings. We explore the effects of considering qualitative vs quantitative categorisation of DNA results. We also show the importance of taking into account multiple individual’s DNA being transferred (such as unknown or wearer DNA), even if the main focus of the evaluation is the activity of one individual.
This paper proposes an amendment of the classification of safety events based on their controllability and contemplates the potential of an event to escalate into higher severity classes. It considers (1) whether the end-user had the opportunity to intervene into the course of an event, (2) the level of end-user familiarity with the situation, and (3) the positive or negative effects of end-user intervention against expected outcomes. To examine its potential, we applied the refined classification to 296 aviation safety investigation reports. The results suggested that pilots controlled only three-quarters of the occurrences, more than three-thirds of the controlled cases regarded fairly unfamiliar situations, and the flight crews succeeded to mitigate the possible negative consequences of events in about 71% of the cases. Further statistical tests showed that the controllability-related characteristics of events had not significantly changed over time, and they varied across regions, aircraft, operational and event characteristics, as well as when fatigue had contributed to the occurrences. Overall, the findings demonstrated the value of using the controllability classification before considering the actual outcomes of events as means to support the identification of system resilience and successes. The classification can also be embedded in voluntary reporting systems to allow end-users to express the degree of each of the controllability characteristics so that management can monitor them over time and perform internal and external benchmarking. The mandatory reports concerned, the classification could function as a decision-making parameter for prioritising incident investigations.
In this policy evaluation report, the results of the first 2 years of the Interreg funded ABCitiEs project are presented. In total 16 entrepreneurship collectives have been studied in 5 partner regions, i.e. Athens, Vilnius, Varazdin-Cakovec, Manchester and Amsterdam. The report contains an analysis of the cases and gives an overview of the most important opportunities and challenges faced by these cases. On the basis of these result, 4 policy directions have been selected in which improvement are considered most successful, i.e. access to funding, intermediaries, monitoring and experimental learning environments. Also, the report presents the action plans that have been formulated on the basis of these policy directions for the cities involved in this project. In the last 2 years of the project, project partners will implement these action plans in their respective cities.
MULTIFILE
In the last decade, the automotive industry has seen significant advancements in technology (Advanced Driver Assistance Systems (ADAS) and autonomous vehicles) that presents the opportunity to improve traffic safety, efficiency, and comfort. However, the lack of drivers’ knowledge (such as risks, benefits, capabilities, limitations, and components) and confusion (i.e., multiple systems that have similar but not identical functions with different names) concerning the vehicle technology still prevails and thus, limiting the safety potential. The usual sources (such as the owner’s manual, instructions from a sales representative, online forums, and post-purchase training) do not provide adequate and sustainable knowledge to drivers concerning ADAS. Additionally, existing driving training and examinations focus mainly on unassisted driving and are practically unchanged for 30 years. Therefore, where and how drivers should obtain the necessary skills and knowledge for safely and effectively using ADAS? The proposed KIEM project AMIGO aims to create a training framework for learner drivers by combining classroom, online/virtual, and on-the-road training modules for imparting adequate knowledge and skills (such as risk assessment, handling in safety-critical and take-over transitions, and self-evaluation). AMIGO will also develop an assessment procedure to evaluate the impact of ADAS training on drivers’ skills and knowledge by defining key performance indicators (KPIs) using in-vehicle data, eye-tracking data, and subjective measures. For practical reasons, AMIGO will focus on either lane-keeping assistance (LKA) or adaptive cruise control (ACC) for framework development and testing, depending on the system availability. The insights obtained from this project will serve as a foundation for a subsequent research project, which will expand the AMIGO framework to other ADAS systems (e.g., mandatory ADAS systems in new cars from 2020 onwards) and specific driver target groups, such as the elderly and novice.
266 woorden Op school kan de situatie zich voordoen dat de leerkracht onvoldoende tegemoet kan komen aan de extra ondersteuning die leerlingen met autisme nodig hebben. De klas kan te groot zijn, de leerkracht kan handelingsverlegen zijn, etc.. In dit projectplan wordt onderbouwd wat de relevantie is voor de dagelijkse praktijk van de leerkracht en de leerling met autisme en daaraan gerelateerde problemen. Tevens wordt onderbouwd waarom beeldende therapie theoretisch en empirisch kan bijdragen als creatieve oplossing voor kinderen met aan autisme gerelateerde problemen die in de klas extra aandacht vragen. Deze kinderen hebben een andere manier van informatie verwerken, kunnen zich vaak verbaal moeilijk uiten en hebben vaak sociale problemen. Deze kinderen lopen risico op verslavingsproblematiek (33%) en eenzaamheid, angst en depressie op volwassen leeftijd (80%). Kunstvormen in een leeromgeving bieden andere mogelijkheden voor kinderen om zich te uiten en om samen te werken. In dit projectplan wordt beschreven waarom het zinvol is te onderzoeken wat de effectiviteit is van beeldende therapie voor kinderen met autisme in primair (speciaal) onderwijs, ter preventie van risicogedrag. Het behandelprogramma ‘Zelf in beeld, beeldende therapie voor kinderen met autisme (bijlage 1) lijkt veelbelovende resultaten op te leveren (Schweizer, 2020). Om een indruk van de resultaten van praktijkgericht onderzoek naar ‘Zelf in beeld’ te krijgen kunt u de korte animatie bekijken (3 min): https://youtu.be/cVAAzRHZnb0 In dit vervolgproject wordt verkend in hoeverre ‘Zelf in beeld’ van toegevoegde waarde van kan zijn voor kind, leerkracht en ouders, binnen de setting van Speciaal Onderwijs. Dit project heeft een innovatief karakter omdat er een nieuwe vorm van (preventief) werken binnen passend onderwijs wordt toegepast en onderzocht.
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.
