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Verification and validation (V&V) are complex processes combining different approaches and incorporating many different methods including many activities. System engineers regularly face the question if their V&V activities lead to better products, and having appropriate criteria at hand for evaluation of safety and cybersecurity of the systems would help to answer such a question. Additionally, when there is a demand to improve the quality of an already managed V&V process, there is a struggle over what criteria to use in order to measure the improvement. This paper presents an extensive set of criteria suitable for safety and cybersecurity evaluation of cyberphysical systems. The evaluation criteria are agreed upon by 60 researchers from 32 academic and industrial organizations jointly working in a large-scale European research project on 13 real-world use cases from the domains of automotive, railway, aerospace, agriculture, healthcare, and industrial robotics.

Many organizations are moving their systems to the cloud, where providers consolidate multiple clients using virtualization, which creates challenges to business-critical applications. Research has shown that hypervisors fail, often causing common-mode failures that may abruptly disrupt dozens of virtual machines simultaneously. We hypothesize and empirically show that a significant percentage of virtual machines affected by a hypervisor failure are capable of continuing execution on a new hypervisor. Supported by this observation, we design a technique for recovering from hypervisor failures through efficient virtual machine migration to a co-located hypervisor, which allows virtual machines to continue executing with minimal downtime and which can be transparently applied to existing applications. We evaluate a proof-of-concept implementation using fault injection of hardware and software faults and show that it can recover, on average, 41-46% of all virtual machines, as well as having a mean virtual machine downtime of 3 seconds.

Ultra-short-term HRV features assess minor autonomous nervous system variations such as variations resulting from cognitive stress peaks during demanding tasks. Several studies compare ultra-short-term and short-term HRV measurements to investigate their reliability. However, existing experiments are conducted in low cognitively demanding environments. In this paper, we propose to evaluate these measurements’ reliability under cognitively demanding tasks using a near real-life setting. For this purpose, we selected 31 HRV features, extracted from data collected from 21 programmers performing code comprehension, and compared them across 18 different time frames, ranging from 3 min to 10 s. Statistical significance and correlation tests were performed between the features extracted using the larger window (3 min) and the same features extracted with the other 17 time frames. We paired these analyses with Bland–Altman plots to inspect how the extraction window size affects the HRV features. The main results show 13 features that presented at least 50% correlation when using 60-second windows. The HF and mNN features achieved around 50% correlation using a 30-second window. The 30-second window was the smallest time frame considered to have reliable measurements. Furthermore, the mNN feature proved to be quite robust to the shortening of the time resolution.

In the present research project, a novel portable battery-powered handheld device able to produce micron and submicron fibers using centrifugal forces is proposed. The design includes spinnerets with a clamshell configuration with multiple chambers or reservoirs (2, 4, and 8) and different exit orifice diameters (400, 500, 600, and 800 µm). The rotational speed is controlled via an Arduino microcontroller. To validate the design, a series of experiments were conducted and the effect of the orifice diameter, number of chambers, and velocity on the resulting fibers’ diameter and yield was studied. For the experiments, a polymeric solution of Polyvinyl Alcohol (PVA) was prepared. The fiber yield was gravimetrically quantified, and the fiber morphology and diameter were analyzed by means of scanning electron microscopy (SEM). The experimental results showed that spinnerets with an orifice diameter of 500 microns yielded the greatest amount of fibers (0.0777 g). In addition, the number of chambers also affected the amount of fibers produced, and it was determined that the fiber diameter size is dependent on the spinneret speed. Fibers 80 nm in diameter were observed at 6500 rpm.