What leads to loneliness?An integrative model of Social, Motivational and Emotional approaches in adolescence

Loneliness has been linked to many physical and mental health problems, especially during adolescence. From evolutionary, social needs, and cognitive approaches, this study examined whether emotional repair, relatedness need, and peer-rated indicators of relations behave in predicting loneliness, considering all approaches together. The sample consisted of 373 adolescents measured longitudinally at three time points. Results of a cross-lagged panel design found that, considering all the influences together, relatedness need showed the highest strength to predict loneliness. Furthermore, adolescents who were accepted by their peers and whose relatedness need was satisfied activated emotional regulation which additionally produced a decrease in prospective feelings of loneliness. In addition, loneliness has been shown to be a consequence of these variables

A framework concept for data visualization and structuring in a complex production process

This paper provides a concept study for a visual interface framework together with the software Sequence Planner for implementation on a complex industrial process for extracting process information in an efficient way and how to make use of a lot of data to visualize it in a standardized human machine interface for different user perspectives. The concept is tested and validated on a smaller simulation of a paint booth with several interconnected and supporting control systems to prove the functionality and usefulness in this kind of production system.The paper presents the resulting five abstraction levels in the framework concept, from a production top view down to the signal exchange between the different resources in one production cell, together with additional features. The simulation proves the setup with Sequence Planner and the visual interface to work by extract and present process data from a running sequence

Sense and sensitivity of double beta decay experiments

The search for neutrinoless double beta decay is a very active field in which the number of proposals for next-generation experiments has proliferated. In this paper we attempt to address both the sense and the sensitivity of such proposals. Sensitivity comes first, by means of proposing a simple and unambiguous statistical recipe to derive the sensitivity to a putative Majorana neutrino mass, m_bb. In order to make sense of how the different experimental approaches compare, we apply this recipe to a selection of proposals, comparing the resulting sensitivities. We also propose a "physics-motivated range" (PMR) of the nuclear matrix elements as a unifying criterium between the different nuclear models. The expected performance of the proposals is parametrized in terms of only four numbers: energy resolution, background rate (per unit time, isotope mass and energy), detection efficiency, and bb isotope mass. For each proposal, both a reference and an optimistic scenario for the experimental performance are studied. In the reference scenario we find that all the proposals will be able to partially explore the degenerate spectrum, without fully covering it, although four of them (KamLAND-Zen, CUORE, NEXT and EXO) will approach the 50 meV boundary. In the optimistic scenario, we find that CUORE and the xenon-based proposals (KamLAND-Zen, EXO and NEXT) will explore a significant fraction of the inverse hierarchy, with NEXT covering it almost fully. For the long term future, we argue that Xe-based experiments may provide the best case for a 1-ton scale experiment, given the potentially very low backgrounds achievable and the expected scalability to large isotope masses.Comment: 30 pages, 12 figures, 6 table

A semantic memory bank assisted by an embodied conversational agents for mobile devices

Alzheimer’s disease is a type of dementia that causes memory loss and interferes with intellectual abilities seriously. It has no current cure and therapeutic efficiency of current medication is limited. However, there is evidence that non-pharmacological treatments could be useful to stimulate cognitive abilities. In the last few year, several studies have focused on describing and under- standing how Virtual Coaches (VC) could be key drivers for health promotion in home care settings. The use of VC gains an augmented attention in the considerations of medical innovations. In this paper, we propose an approach that exploits semantic technologies and Embodied Conversational Agents to help patients training cognitive abilities using mobile devices. In this work, semantic technologies are used to provide knowledge about the memory of a specific person, who exploits the structured data stored in a linked data repository and take advantage of the flexibility provided by ontologies to define search domains and expand the agent’s capabilities. Our Memory Bank Embodied Conversational Agent (MBECA) is used to interact with the patient and ease the interaction with new devices. The framework is oriented to Alzheimer’s patients, caregivers, and therapists

How Humans Judge Machines

How people judge humans and machines differently, in scenarios involving natural disasters, labor displacement, policing, privacy, algorithmic bias, and more. How would you feel about losing your job to a machine? How about a tsunami alert system that fails? Would you react differently to acts of discrimination depending on whether they were carried out by a machine or by a human? What about public surveillance? How Humans Judge Machines compares people's reactions to actions performed by humans and machines. Using data collected in dozens of experiments, this book reveals the biases that permeate human-machine interactions. Are there conditions in which we judge machines unfairly? Is our judgment of machines affected by the moral dimensions of a scenario? Is our judgment of machine correlated with demographic factors such as education or gender? César Hidalgo and colleagues use hard science to take on these pressing technological questions. Using randomized experiments, they create revealing counterfactuals and build statistical models to explain how people judge artificial intelligence and whether they do it fairly. Through original research, How Humans Judge Machines bring us one step closer to understanding the ethical consequences of AI. Written by César A. Hidalgo, the author of Why Information Grows and coauthor of The Atlas of Economic Complexity (MIT Press), together with a team of social psychologists (Diana Orghian and Filipa de Almeida) and roboticists (Jordi Albo-Canals), How Humans Judge Machines presents a unique perspective on the nexus between artificial intelligence and society. Anyone interested in the future of AI ethics should explore the experiments and theories in How Humans Judge Machines