Facial Electromyography-based Adaptive Virtual Reality Gaming for Cognitive Training

Cognitive training has shown promising results for delivering improvements in human cognition related to attention, problem solving, reading comprehension and information retrieval. However, two frequently cited problems in cognitive training literature are a lack of user engagement with the training programme, and a failure of developed skills to generalise to daily life. This paper introduces a new cognitive training (CT) paradigm designed to address these two limitations by combining the benefits of gamification, virtual reality (VR), and affective adaptation in the development of an engaging, ecologically valid, CT task. Additionally, it incorporates facial electromyography (EMG) as a means of determining user affect while engaged in the CT task. This information is then utilised to dynamically adjust the game's difficulty in real-time as users play, with the aim of leading them into a state of flow. Affect recognition rates of 64.1% and 76.2%, for valence and arousal respectively, were achieved by classifying a DWT-Haar approximation of the input signal using kNN. The affect-aware VR cognitive training intervention was then evaluated with a control group of older adults. The results obtained substantiate the notion that adaptation techniques can lead to greater feelings of competence and a more appropriate challenge of the user's skills

A deep generic to specific recognition model for group membership analysis using non-verbal cues

Automatic understanding and analysis of groups has attracted increasing attention in the vision and multimedia communities in recent years. However, little attention has been paid to the automatic analysis of the non-verbal behaviors and how this can be utilized for analysis of group membership, i.e., recognizing which group each individual is part of. This paper presents a novel Support Vector Machine (SVM) based Deep Specific Recognition Model (DeepSRM) that is learned based on a generic recognition model. The generic recognition model refers to the model trained with data across different conditions, i.e., when people are watching movies of different types. Although the generic recognition model can provide a baseline for the recognition model trained for each specific condition, the different behaviors people exhibit in different conditions limit the recognition performance of the generic model. Therefore, the specific recognition model is proposed for each condition separately and built on the top of the generic recognition model. We conduct a set of experiments using a database collected to study group analysis while each group (i.e., four participants together) were watching a number of long movie segments. The proposed deep specific recognition model (44%) outperforms the generic recognition model (26%). The recognition of group membership also indicates that the non-verbal behaviors of individuals within a group share commonalities

Fabrication and Thermoelectric Characterization of Transition Metal Silicide-Based Composite Thermocouples

Metal silicide-based thermocouples were fabricated by screen printing thick films of the powder compositions onto alumina tapes followed by lamination and sintering processes. The legs of the embedded thermocouples were composed of composite compositions consisting of MoSi2, WSi2, ZrSi2, or TaSi2 with an additional 10 vol % Al2O3 to form a silicide–oxide composite. The structural and high-temperature thermoelectric properties of the composite thermocouples were examined using X-ray diffraction, scanning electron microscopy and a typical hot–cold junction measurement technique. MoSi2-Al2O3 and WSi2-Al2O3 composites exhibited higher intrinsic Seebeck coefficients (22.2–30.0 μV/K) at high-temperature gradients, which were calculated from the thermoelectric data of composite//Pt thermocouples. The composite thermocouples generated a thermoelectric voltage up to 16.0 mV at high-temperature gradients. The MoSi2-Al2O3//TaSi2-Al2O3 thermocouple displayed a better performance at high temperatures. The Seebeck coefficients of composite thermocouples were found to range between 20.9 and 73.0 μV/K at a temperature gradient of 1000 ◦C. There was a significant difference between the calculated and measured Seebeck coefficients of these thermocouples, which indicated the significant influence of secondary silicide phases (e.g., Mo5Si3, Ta5Si3) and possible local compositional changes on the overall thermoelectric response. The thermoelectric performance, high sensitivity, and cost efficiency of metal silicide–alumina ceramic composite thermocouples showed promise for high-temperature and harsh-environment sensing applications

Copula-based multivariate input modeling

In this survey, we review the copula-based input models that are well suited to provide multivariate input-modeling support for stochastic simulations with dependent inputs. Specifically, we consider the situation in which the dependence between pairs of simulation input random variables is measured by tail dependence (i.e., the amount of dependence in the tails of a bivariate distribution) and review the techniques to construct copula-based input models representing positive tail dependencies. We complement the review with the parameter estimation from multivariate input data and the random-vector generation from the estimated input model with the purpose of driving the simulation. © 2012 Elsevier Ltd

Membrane-dependent activities of human 15-lox-2 and its murine counterpart implications for murine models of atherosclerosis

© 2016 by The American Society for Biochemistry and Molecular Biology, Inc. The enzyme encoded by the ALOX15B gene has been linked to the development of atherosclerotic plaques in humans and in a mouse model of hypercholesterolemia. In vitro, these enzymes, which share 78% sequence identity, generate distinct products from their substrate arachidonic acid: the human enzyme, a 15-S-hydroperoxy product; and the murine enzyme, an 8-S-product. We probed the activities of these enzymes with nanodiscs as membrane mimics to determine whether they can access substrate esterified in a bilayer and characterized their activities at the membrane interface. We observed that both enzymes transform phospholipid-esterified arachidonic acid to a 15-S-product. Moreover, when expressed in transfected HEK cells, both enzymes result in significant increases in the amounts of 15-hydroxyderivatives of eicosanoids detected. In addition, we show that 15-LOX-2 is distributed at the plasma membrane when the HEK293 cells are stimulated by the addition Ca 2+ ionophore and that cellular localization is dependent upon the presence of a putative membrane insertion loop. We also report that sequence differences between the human and mouse enzymes in this loop appear to confer distinct mechanisms of enzyme-membrane interaction for the homologues

Comparing Social Science and Computer Science Workflow Processes for Studying Group Interactions

In this article, a team of authors from the Geeks and Groupies workshop, in Leiden, the Netherlands, compare prototypical approaches to studying group interaction in social science and computer science disciplines, which we call workflows. To help social and computer science scholars understand and manage these differences, we organize workflow into three major stages: research design, data collection, and analysis. For each stage, we offer a brief overview on how scholars from each discipline work. We then compare those approaches and identify potential synergies and challenges. We conclude our article by discussing potential directions for more integrated and mutually beneficial collaboration that go beyond the producer–consumer model

A Prospective Look: Key Enabling Technologies, Applications and Open Research Topics in 6G Networks

The fifth generation (5G) mobile networks are envisaged to enable a plethora of breakthrough advancements in wireless technologies, providing support of a diverse set of services over a single platform. While the deployment of 5G systems is scaling up globally, it is time to look ahead for beyond 5G systems. This is driven by the emerging societal trends, calling for fully automated systems and intelligent services supported by extended reality and haptics communications. To accommodate the stringent requirements of their prospective applications, which are data-driven and defined by extremely low-latency, ultra-reliable, fast and seamless wireless connectivity, research initiatives are currently focusing on a progressive roadmap towards the sixth generation (6G) networks. In this article, we shed light on some of the major enabling technologies for 6G, which are expected to revolutionize the fundamental architectures of cellular networks and provide multiple homogeneous artificial intelligence-empowered services, including distributed communications, control, computing, sensing, and energy, from its core to its end nodes. Particularly, this paper aims to answer several 6G framework related questions: What are the driving forces for the development of 6G? How will the enabling technologies of 6G differ from those in 5G? What kind of applications and interactions will they support which would not be supported by 5G? We address these questions by presenting a profound study of the 6G vision and outlining five of its disruptive technologies, i.e., terahertz communications, programmable metasurfaces, drone-based communications, backscatter communications and tactile internet, as well as their potential applications. Then, by leveraging the state-of-the-art literature surveyed for each technology, we discuss their requirements, key challenges, and open research problems

A prospective look: key enabling technologies, applications and open research topics in 6G networks

The fifth generation (5G) mobile networks are envisaged to enable a plethora of breakthrough advancements in wireless technologies, providing support of a diverse set of services over a single platform. While the deployment of 5G systems is scaling up globally, it is time to look ahead for beyond 5G systems. This is mainly driven by the emerging societal trends, calling for fully automated systems and intelligent services supported by extended reality and haptics communications. To accommodate the stringent requirements of their prospective applications, which are data-driven and defined by extremely low-latency, ultra-reliable, fast and seamless wireless connectivity, research initiatives are currently focusing on a progressive roadmap towards the sixth generation (6G) networks, which are expected to bring transformative changes to this premise. In this article, we shed light on some of the major enabling technologies for 6G, which are expected to revolutionize the fundamental architectures of cellular networks and provide multiple homogeneous artificial intelligence-empowered services, including distributed communications, control, computing, sensing, and energy, from its core to its end nodes. In particular, the present paper aims to answer several 6G framework related questions: What are the driving forces for the development of 6G? How will the enabling technologies of 6G differ from those in 5G? What kind of applications and interactions will they support which would not be supported by 5G? We address these questions by presenting a comprehensive study of the 6G vision and outlining seven of its disruptive technologies, i.e., mmWave communications, terahertz communications, optical wireless communications, programmable metasurfaces, drone-based communications, backscatter communications and tactile internet, as well as their potential applications. Then, by leveraging the state-of-the-art literature surveyed for each technology, we discuss the associated requirements, key challenges, and open research problems. These discussions are thereafter used to open up the horizon for future research directions

Effect of arsenic-phosphorus interaction on arsenic-induced oxidative stress in chickpea plants

Arsenic-induced oxidative stress in chickpea was investigated under glasshouse conditions in response to application of arsenic and phosphorus. Three levels of arsenic (0, 30 and 60 mg kg−1) and four levels of P (50, 100, 200, and 400 mg kg−1) were applied to soil-grown plants. Increasing levels of both arsenic and P significantly increased arsenic concentrations in the plants. Shoot growth was reduced with increased arsenic supply regardless of applied P levels. Applied arsenic induced oxidative stress in the plants, and the concentrations of H2O2 and lipid peroxidation were increased. Activity of superoxide dismutase (SOD) and concentrations of non-enzymatic antioxidants decreased in these plants, but activities of catalase (CAT) and ascorbate peroxidase (APX) were significantly increased under arsenic phytotoxicity. Increased supply of P decreased activities of CAT and APX, and decreased concentrations of non-enzymatic antioxidants, but the high-P plants had lowered lipid peroxidation. It can be concluded that P increased uptake of arsenic from the soil, probably by making it more available, but although plant growth was inhibited by arsenic the P may have partially protected the membranes from arsenic-induced oxidative stress

Structural and Optical Properties of Diluted Magnetic Ga1−xMnxAs–AlAs Quantum Wells Grown on High-Index GaAs Planes

We report on the structural and optical properties of Ga₁₋ᵪ Mn ᵪ As-AlAs quantum wells (QWs) with χ=0.1% grown by molecular beam epitaxy (MBE) on semi-insulating GaAs substrates with orientations (100), (110), (311)B and (411)B. Atomic force microscopy (AFM), X-ray diffraction (XRD) and photoluminescence (PL) techniques were used to investigate these QWs. AFM results have evidenced the formation of Mn-induced islands, which are randomly distributed on the surface. These islands tend to segregate for samples grown on (110) and (411)B planes, while no clear segregation was observed for samples grown on (100) and (311)B orientations. Results show that the PL line width increases with Mn segregation. XRD measurements were used to determine 2θ,d and cell parameters