The Consequentialist Scale: Translation and empirical investigation in a Greek sample

The Consequentialist Scale (Robinson, 2012) [89] assesses the endorsement of consequentialist and deontological moral beliefs. This study empirically investigated the application of the Greek translation of the Consequentialist Scale in a sample of native Greek speakers. Specifically, 415 native Greek speakers completed the questionnaire. To uncover the underlying structure of the 10 items in the Consequentialist Scale, an Exploratory Factor Analysis (EFA) was conducted. The results revealed a three-factor solution, where the deontology factor exhibited the same structure as the original work by Robinson (2012) [89], while the original consequentialism factor split into two separate factors. Significant Pearson's r correlations were observed between age and responses to the Consequentialist Scale. Separate EFAs were conducted for two age groups based on a medial split: younger (36 years old or less) and older (more than 36 years old). Interestingly, the younger group exhibited a two-factor solution with the same structure as the original work, while the older group showed a three-factor solution. A hierarchical k-means cluster analysis revealed that the cluster of participants who scored higher in deontology compared to consequentialism primarily consisted of older participants, whereas the two other clusters comprised of younger participants exhibited the reverse pattern. Neither gender nor previous experience with philosophy significantly affected scores on the Consequentialist Scale. Overall, our study provides evidence that the Consequentialist Scale is suitable for use in the Greek population

Variable structure robot control systems: The RAPP approach

International audienceThis paper presents a method of designing variable structure control systems for robots. As the on-board robot computational resources are limited, but in some cases the demands imposed on the robot by the user are virtually limitless, the solution is to produce a variable structure system. The task dependent part has to be exchanged, however the task governs the activities of the robot. Thus not only exchange of some task-dependent modules is required, but also supervisory responsibilities have to be switched. Such control systems are necessary in the case of robot companions, where the owner of the robot may demand from it to provide many services.

Familial hypercholesterolaemia in children and adolescents from 48 countries: a cross-sectional study

Background: Approximately 450 000 children are born with familial hypercholesterolaemia worldwide every year, yet only 2·1% of adults with familial hypercholesterolaemia were diagnosed before age 18 years via current diagnostic approaches, which are derived from observations in adults. We aimed to characterise children and adolescents with heterozygous familial hypercholesterolaemia (HeFH) and understand current approaches to the identification and management of familial hypercholesterolaemia to inform future public health strategies. Methods: For this cross-sectional study, we assessed children and adolescents younger than 18 years with a clinical or genetic diagnosis of HeFH at the time of entry into the Familial Hypercholesterolaemia Studies Collaboration (FHSC) registry between Oct 1, 2015, and Jan 31, 2021. Data in the registry were collected from 55 regional or national registries in 48 countries. Diagnoses relying on self-reported history of familial hypercholesterolaemia and suspected secondary hypercholesterolaemia were excluded from the registry; people with untreated LDL cholesterol (LDL-C) of at least 13·0 mmol/L were excluded from this study. Data were assessed overall and by WHO region, World Bank country income status, age, diagnostic criteria, and index-case status. The main outcome of this study was to assess current identification and management of children and adolescents with familial hypercholesterolaemia. Findings: Of 63 093 individuals in the FHSC registry, 11 848 (18·8%) were children or adolescents younger than 18 years with HeFH and were included in this study; 5756 (50·2%) of 11 476 included individuals were female and 5720 (49·8%) were male. Sex data were missing for 372 (3·1%) of 11 848 individuals. Median age at registry entry was 9·6 years (IQR 5·8-13·2). 10 099 (89·9%) of 11 235 included individuals had a final genetically confirmed diagnosis of familial hypercholesterolaemia and 1136 (10·1%) had a clinical diagnosis. Genetically confirmed diagnosis data or clinical diagnosis data were missing for 613 (5·2%) of 11 848 individuals. Genetic diagnosis was more common in children and adolescents from high-income countries (9427 [92·4%] of 10 202) than in children and adolescents from non-high-income countries (199 [48·0%] of 415). 3414 (31·6%) of 10 804 children or adolescents were index cases. Familial-hypercholesterolaemia-related physical signs, cardiovascular risk factors, and cardiovascular disease were uncommon, but were more common in non-high-income countries. 7557 (72·4%) of 10 428 included children or adolescents were not taking lipid-lowering medication (LLM) and had a median LDL-C of 5·00 mmol/L (IQR 4·05-6·08). Compared with genetic diagnosis, the use of unadapted clinical criteria intended for use in adults and reliant on more extreme phenotypes could result in 50-75% of children and adolescents with familial hypercholesterolaemia not being identified. Interpretation: Clinical characteristics observed in adults with familial hypercholesterolaemia are uncommon in children and adolescents with familial hypercholesterolaemia, hence detection in this age group relies on measurement of LDL-C and genetic confirmation. Where genetic testing is unavailable, increased availability and use of LDL-C measurements in the first few years of life could help reduce the current gap between prevalence and detection, enabling increased use of combination LLM to reach recommended LDL-C targets early in life

A Framework for Rapid Robotic Application Development for Citizen Developers

It is common knowledge among computer scientists and software engineers that ”building robotics systems is hard”: it includes applied and specialized knowledge from various scientific fields, such as mechanical, electrical and computer engineering, computer science and physics, among others. To expedite the development of robots, a significant number of robotics-oriented middleware solutions and frameworks exist that provide high-level functionality for the implementation of the in-robot software stack, such as ready-to-use algorithms and sensor/actuator drivers. While the aforementioned focus is on the implementation of the core functionalities and control layer of robots, these specialized tools still require extensive training, while not providing the envisaged freedom in design choices. In this paper, we discuss most of the robotics software development methodologies and frameworks, analyze the way robotics applications are built and propose a new resource-oriented architecture towards the rapid development of robot-agnostic applications. The contribution of our work is a methodology and a model-based middleware that can be used to provide remote robot-agnostic interfaces. Such interfaces may support robotics application development from citizen developers by reducing hand-coding and technical knowledge requirements. This way, non-robotics experts will be able to integrate and use robotics in a wide range of application domains, such as healthcare, home assistance, home automation and cyber–physical systems in general

A Data-Driven Bandwidth Allocation Framework with QoS Considerations for EONs

This paper proposes a data-driven bandwidth allocation (BA) framework for periodically and dynamically reconfiguring an elastic optical network according to predictive BA (PBA) models. The proposed framework is scalable to the number of network connections and also adaptive to the increasing traffic of each network connection separately and to the overall network load as well. This is achieved by formulating the BA problem as a partially observable Markov decision process (POMDP), which constitutes a reinforcement learning (RL) algorithm. Specifically, RL is performed continuously and independently (locally) for each network connection according to the most recent data that describe the traffic demand behavior of each network connection. A central controller monitors the network performance that is jointly achieved from all the PBA models and is capable of dynamically modifying the reward function of the POMDP, ensuring that the quality-of-service (QoS) requirements are met. A reward function R(C) is examined with a clear impact on the network performance when C is modified. For evaluating the network performance, for each R(C), the routing and spectrum allocation (RSA) problem is solved according to an integer linear programming (ILP) algorithm and an RSA heuristic alternative, with both the ILP and the heuristic RSA taking as inputs the outputs of the inferred PBA models. Results indicate that, with the appropriate settings of C, bandwidth is efficiently allocated, while ensuring that the QoS requirements are met

On learning bandwidth allocation models for time-varying traffic in flexible optical networks

We examine the problem of bandwidth allocation (BA) on flexible optical networks in the presence of traffic demand uncertainty. We assume that the daily traffic demand is given in the form of distributions describing the traffic demand fluctuations within given time intervals. We wish to find a predictive BA (PBA) model that infers from these distributions the bandwidth that best fits the future traffic demand fluctuations. The problem is formulated as a Partially Observable Markov Decision Process and is solved by means of Dynamic Programming. The PBA model is compared to a number of benchmark BA models that naturally arise after the assumption of traffic demand uncertainty. For comparing all the BA models developed, a conventional routing and spectrum allocation heuristic is used adhering each time to the BA model followed. We show that for a network operating at its capacity crunch, the PBA model significantly outperforms the rest on the number of blocked connections and unserved bandwidth. Most importantly, the PBA model can be autonomously adapted upon significant traffic demand variations by continuously training the model as real-time traffic information arrives into the network

A Data-Driven Bandwidth Allocation Framework with QoS Considerations for EONs

This paper proposes a data-driven bandwidth allocation (BA) framework for periodically and dynamically reconfiguring an elastic optical network according to predictive BA (PBA) models. The proposed framework is scalable to the number of network connections and also adaptive to the increasing traffic of each network connection separately and to the overall network load as well. This is achieved by formulating the BA problem as a partially observable Markov decision process (POMDP), which constitutes a reinforcement learning (RL) algorithm. Specifically, RL is performed continuously and independently (locally) for each network connection according to the most recent data that describe the traffic demand behavior of each network connection. A central controller monitors the network performance that is jointly achieved from all the PBA models and is capable of dynamically modifying the reward function of the POMDP, ensuring that the quality-of-service (QoS) requirements are met. A reward function R(C) is examined with a clear impact on the network performance when C is modified. For evaluating the network performance, for each R(C), the routing and spectrum allocation (RSA) problem is solved according to an integer linear programming (ILP) algorithm and an RSA heuristic alternative, with both the ILP and the heuristic RSA taking as inputs the outputs of the inferred PBA models. Results indicate that, with the appropriate settings of C, bandwidth is efficiently allocated, while ensuring that the QoS requirements are met