The ITk detector, the new ATLAS silicon tracking system for the High Luminosity LHC (HL-LHC), will be equipped with 3D pixel sensor modules in the innermost layer (L0). The pixel cell dimensions will be 25 × 100 µ m 2 in the barrel and 50 × 50 µ m 2 in the end-caps, with one readout electrode at the centre of each pixel and four bias electrodes at the corners. Sensors from pre-production wafers (50 × 50 µ m 2 ) produced by FBK have been bump-bonded to ITkPixV1.1 chips at IZM. Bare modules have been assembled in Genoa on Single Chip Cards (SCCs) and characterized in laboratory measurements and in test beam campaigns. Some of these modules have been irradiated in Bonn and at the CERN IRRAD facility. Preliminary results of their characterization after irradiation

Comparing portfolio performance is complex due to the fact that each model is dominant in its own risk space. Since there is no single dominant performance measure, the research problem is how to incorporate several different measures into a performance evaluation model that allows portfolios to be ranked. In this regard, the objective of this study was to develop a new comprehensive method for comparing portfolio performance based on multiple-criteria decision-making (MCDM). This paper proposes an integrated approach for stock market decision making that combines the Analytic Hierarchy Process (AHP) and the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), which allow hierarchical evaluation of a finite number of alternatives according to different criteria. This hybrid approach is especially advantageous, utilizing the strengths of both individual methods. AHP enables the decomposition of a complex problem into its constituent parts and the determination of weights for criteria, while the PROMETHEE method allows the investor to determine the preference function, complete ranking, and analysis of the robustness of the results. For the MCDM model in this study, different dimensions of performance measures are considered criteria: return measures, risk measures, stability measures, and predictability measures. The methodology has been applied in comparing real portfolios selected on the basis of different risk measures. For this purpose, weekly return data were used for a sample of stocks that are components of the STOXX Europe 600 Index for the period 2000–2020. In addition, a sensitivity analysis is performed to investigate the strength of the results of this method. It suggests that the simultaneous consideration of different performance measures and the investor’s attitude towards the importance of these measures are notably important in the portfolio efficiency estimation process.

Various methods are nowadays available to design observers for broad classes of systems, where the primary focus is on establishing the convergence of the estimated states. Nevertheless, the question of the tuning of the observer to achieve satisfactory estimation performance remains largely open. In this context, we present a general design framework for the online tuning of the observer gains. Our starting point is a robust nominal observer designed for a general nonlinear system, for which an input-to-state stability property can be established. Our goal is then to improve the performance of this nominal observer. We present for this purpose a new hybrid multi-observer scheme, whose flexibility can be exploited to enforce various desirable properties, e.g., fast convergence and good sensitivity to measurement noise. We prove that an input-to-state stability property also holds for the proposed scheme and, importantly, we ensure that the estimation performance in terms of a quadratic cost is (strictly) improved. We illustrate the efficiency of the approach in improving the performance of given nominal observers in two numerical examples (Van der Pol oscillator and lithium-ion battery model).

Robotic Telepresence (RT) is a promising medium for students who are unable to attend in-person classes. It enables remote students to be present in the classroom and interact with their classmates and instructors. However, it can be limiting to their identity self-perception and projection, which may have repercussions on the social dynamics and inclusion within the classroom. We present preliminary findings of a qualitative analysis of 12 observations and interviews with RT attendees. We examine RT design and use aspects that either supported identity self-perception and projection or limited it. Finally, we present telepresence robots design and use recommendations for the classroom context.

Citizens and policy institutions increasingly express their concerns regarding the emerging challenges in the context of Artificial Intelligence (AI) and have concrete demands for the protection of human rights. In parallel, studies in the field of AI and Human-Robot Interaction (HRI) indicate the impact of social robots on children's development. We conducted a systematic review based on UNICEF's AI Policy Guidance to map the landscape of research on social robots and children's rights. We used the PRISMA method and identified N=37 papers that address one of the rights, which we then annotated to indicate tendencies and areas of alignment and misalignment with the UNICEF guidance. Our findings reveal that although the field of HRI is looking at specific rights, with a focus on inclusion, some of the rights have been under-researched. Furthermore, we observed a misalignment between HRI and UNICEF regarding the terminology. With this paper, we hope to bring awareness to the field of HRI regarding children's rights and to highlight directions for alignment among research, societal needs, and policy.

Robots could support older adults' well-being by engaging them in meaningful conversations, specifically to reflect on, support, and improve different aspects of their well-being. We implemented a system on a QT social robot to conduct short autonomous conversations with older adults, to help understand what brings them feelings of joy and meaning in life. We evaluated the system with written surveys and observations of 12 participants including older adults, caregivers, and dementia care staff. From this, we saw the need to improve user experience through personalized interaction that better support older adults as they talk about well-being. Improving the interactions will involve improving the conversation flow, detecting emotions and nonverbal cues, and natural language processing to extract topics around well-being.

We present an ethnographic observational study of six robot cafes in Japan to understand how service robots are performatively staged and presented to the public. We particularly attend to the diverse ways in which the physical setting and ambience of the cafes, the verbal characterization of and staff behaviors toward robots, explicit and implicit instructions on appropriate interactions with robots, and handling of robot malfunctions constitute robots as socially acceptable and useful in daily life. Such scaffolding enables robots to provide material and affective services to cafe visitors, and visitors to explore various interaction possibilities with robots. Our work contributes to the critical study of the ongoing construction of "robot cultures" in Japan, and calls attention to public interactions with robots and the importance of contextual staging beyond individual robot features in human-robot interaction design.

Evaluation of robots commonly occurs using various stimuli, including photos, videos, and live interaction. However, a better understanding of how and why chosen stimuli affect perceptions, and how evaluations using lower fidelity media (e.g. photos) compare to evaluations using higher context stimuli (e.g., videos), is needed. Through a survey of 599 M-Turk participants, we compare robot evaluations based on exposure to three types of media - photos, GIFs, and promotional videos. We analyze nine perception and behavioral intention measures of three home robots with varying levels of anthropomorphism (Olly, Jibo, and Liku): overall liking, liking of appearance, liking of intended use, eeriness, human-likeness, performance expectations, privacy concerns, information seeking intention, and purchase intention. We find that photos consistently differ from ratings of videos for all measures, except for liking of robots' intended use. Use of GIFs led to measurements in line with videos for seven of the nine measurements, due to the importance of movement in perceptual assessments and character judgments (e.g., friendly, creepy). Except for the most human-like robot, neither photos nor GIFs captured human-likeness to a similar degree as videos, due to the importance of speech in assessments. Though GIFs captured informational and overall privacy concerns well, they did not adequately capture physical privacy concerns.

As a step towards designing a home robot to support older adults' ikigai (meaning in life), we interviewed the family members who provide care for them. After conducting interviews with ten family caregivers in Japan, we found that older adults' physical health is a major concern to both caregivers and older adults. However, concerns over loneliness were not prioritized by caregivers, though they did perceive older adults' worries around this issue. Additionally, caregivers saw a number of ways a social robot could be designed to address this, as well as its ability to go beyond loneliness in promoting more fulfilling lives among older adults. Finally, we conclude that an ikigai robot may be designed to support both the ikigai of older adults and (indirectly) their family caregivers.

The use of socially assistive robots is able to alleviate some depression symptoms, according to existing research. However, due to comorbidities that often accompany depression and the unique experiences of each individual, it is necessary to get a better understanding of how SARs should be personalized. Through 10 hourlong workshops with 10 individuals living with depression, we explored the customization of a zoomorphic SAR for adults with depression. By using the SAR Therabot? as a base platform, participants designed their own unique covering for the robot, and discussed desired robot behaviors and privacy concerns around data collection. Though the physical designs of the robots varied greatly, participants expressed common themes regarding their preference for a soft touchable exterior, comfort with sharing data with their therapists, and interest in the robot producing more realistic sounds and movements, among other design features.

Malassezia is a lipophilic yeast that is a part of the human mycobiome. Malassezia folliculitis appears when the benign colonization of the hair follicles, by the Malassezia yeasts, becomes symptomatic with pruritic papules and pustules. Although Malassezia folliculitis is common in hospital departments, diagnosing and treating it varies among dermatologists and countries. The European Academy of Dermatology and Venereology Mycology Task Force Malassezia folliculitis working group has, therefore, sought to develop these recommendations for the diagnosis and management of Malassezia folliculitis. Recommendations comprise methods for diagnosing Malassezia folliculitis, required positive findings before starting therapies and specific treatment algorithms for individuals who are immunocompetent, immunocompromised or who have compromised liver function. In conclusion, this study provides a clinical strategy for diagnosing and managing Malassezia folliculitis.

With the rise in the number of robots in our daily lives, human-robot encounters will become more frequent. To improve human-robot interaction (HRI), people will require explanations of robots' actions, especially if they do something unexpected. Our focus is on robot navigation, where we explain why robots make specific navigational choices. Building on methods from the area of Explainable Artificial Intelligence (XAI), we employ a semantic map and techniques from the area of Qualitative Spatial Reasoning (QSR) to enrich visual explanations with knowledge-level spatial information. We outline how a robot can generate visual and textual explanations simultaneously and test our approach in simulation.

Selenium (Se) is a nonmetal that is essential for humans and other animals, and is considered beneficial for plants. The bioavailability of Se strongly influences its content in the food chain. Soils are the main source of Se, and their Se content primarily influences its availability, along with other soil properties. A field survey was conducted on soils of Southeast Europe, specifically in Croatia (Osijek), Bosnia and Herzegovina (Sarajevo, Banja Luka, Mostar, and Prud), and Serbia (Novi Sad). Soil samples were taken from the arable soil layer (0–30 cm depth), and two types of Se availability were measured: Se extracted using pure HNO3 (SeTot) and Se readily extracted in water (SeH2O). Only soils from the Mostar area had Se concentrations above deficit levels (0.5 mg kg−1), with the highest values of cation exchange capacity (CEC), soil organic matter (SOM) measured as loss of ignition (LOI), total C, total N, ZnTot and CdTot. The connections between the chemical characteristics of the soil and SeH2O were investigated. Principal component analysis (PCA) explained 73.7% of the variance in the data set in the first three principal components (PCs). Using the provided data, we developed a partial least squares (PLS) regression model that predicted the amount of SeH2O in the soil, with an accuracy ranging from 77% to 90%, depending on the input data. The highest loadings in the model were observed for LOI, CEC, total C, total N, and SeTot. Our results indicate the need for biofortification in these key agricultural areas to supplement the essential dietary requirements of humans and livestock. To efficiently and economically implement biofortification measures, we recommend utilizing regression models to accurately predict the availability of Se.

This study aimed to assess the efficiency of ketamine and medetomidine by two different doses and routes on anesthesia depth and cardiac stability in red-eared sliders. Each turtle was anesthetized two times, with seven days wash-out period. Induction of anesthesia consisted of a bolus combination of ketamine (10 mg/kg) and medetomidine (0.2 mg/kg) administered in the left brachial biceps in the intramuscular protocol, or a bolus combination of ketamine (20 mg/kg) and medetomidine (0.2 mg/kg) administered in subcarapacial sinus after clear blood presence confirmation in the intravenous protocol. Vital signs, reaction on the skin palpation, manual mouth opening for endotracheal intubation, palpebral and cloacal reflex, and the withdrawal reflex of the front and hind limbs were measured and recorded every 5 minutes for 60 minutes after anesthesia injection. Atipamezole (1 mg/kg) was administered in the right brachial biceps one hour after ketamine and medetomidine administration. Needle insertion and possible painful reactions to drug administration were also evaluated and recorded. Obtained data were analyzed for normality and paired t-tests, Wilcoxon, or McNamar tests were performed where appropriate. The values of P ≤ 0.05 were considered significant. A significantly less pronounced decrease in heart rate was observed with intravenous anesthesia protocol. Both protocols recorded complete anesthesia recovery 60 minutes after intramuscular atipamezole administration. A ketamine-medetomidine dose combination administered intravenously provides a more stable and consistent anesthetic plane in red-eared sliders than ketamine-medetomidine administered intramuscularly.