Hasil untuk "Technology (General)"

L. Savary, L. Balents

Quantum spin liquids may be considered ‘quantum disordered’ ground states of spin systems, in which zero-point fluctuations are so strong that they prevent conventional magnetic long-range order. More interestingly, quantum spin liquids are prototypical examples of ground states with massive many-body entanglement, which is of a degree sufficient to render these states distinct phases of matter. Their highly entangled nature imbues quantum spin liquids with unique physical aspects, such as non-local excitations, topological properties, and more. In this review, we discuss the nature of such phases and their properties based on paradigmatic models and general arguments, and introduce theoretical technology such as gauge theory and partons, which are conveniently used in the study of quantum spin liquids. An overview is given of the different types of quantum spin liquids and the models and theories used to describe them. We also provide a guide to the current status of experiments in relation to study quantum spin liquids, and to the diverse probes used therein.

I. Sirés, E. Brillas, M. Oturan et al.

M. Ibáñez-Espiga, C. D. Kloos

Abstract This study presents a systematic review of the literature on the use of augmented reality technology to support science, technology, engineering and mathematics (STEM) learning. It synthesizes a set of 28 publications from 2010 to 2017. A qualitative content analysis is used to investigate the general characteristics of augmented reality applications in STEM education, the instructional strategies and techniques deployed in the studies reviewed, and the evaluation approaches followed in the interventions. This review found that most augmented reality applications for STEM learning offered exploration or simulation activities. The applications reviewed offered a number of similar design features based on digital knowledge discovery mechanisms to consume information through the interaction with digital elements. However, few studies provided students with assistance in carrying out learning activities. Most of the studies reviewed evaluated the effects of augmented reality technology in fostering students' conceptual understanding, followed by those that investigated affective learning outcomes. A number of suggestions for future research arose from this review. Researchers need to design features that allow students to acquire basic competences related with STEM disciplines, and future applications need to include metacognitive scaffolding and experimental support for inquiry-based learning activities. Finally, it would be useful to explore how augmented reality learning activities can be part of blended instructional strategies such as the flipped classroom.

Anil K. Jain, R. Bolle, Sharath Pankanti

J. Rao, K. Geckeler

Imtiaz Parvez, A. Rahmati, I. Guvenc et al.

The fifth generation (5G) wireless network technology is to be standardized by 2020, where main goals are to improve capacity, reliability, and energy efficiency, while reducing latency and massively increasing connection density. An integral part of 5G is the capability to transmit touch perception type real-time communication empowered by applicable robotics and haptics equipment at the network edge. In this regard, we need drastic changes in network architecture including core and radio access network (RAN) for achieving end-to-end latency on the order of 1 ms. In this paper, we present a detailed survey on the emerging technologies to achieve low latency communications considering three different solution domains: 1) RAN; 2) core network; and 3) caching. We also present a general overview of major 5G cellular network elements such as software defined network, network function virtualization, caching, and mobile edge computing capable of meeting latency and other 5G requirements.

Thomas Dietz, E. Rosa

C. Farrar, Jerry J. Czarnecki, H. Sohn et al.

D. MacKenzie, J. Wajcman

F. H. Adler

L. Mello

J. Sanders, Edward Kandrot

Steve Jones

A. Gambardella, A. McGahan

Heather K. Holden, R. Rada

A. Morgan, J. Gilman

Jan, Prof Nieuwenhuis

Early childhood education (ECE) lays the foundation for a child's lifelong learning journey. More and more, new insights and understandings of child development are evolving, and concomitantly, new preschool education has seen several exciting trends in recent years, with innovative approaches to enhance children's learning experiences. Current trends in early childhood education, inter alia, focus on increasing physical activity, social-emotional learning (SEL), inclusive classrooms, integrating STEM/STEAM through play-based methods, a heightened focus on mental health, outdoor activity, and holistic development requiring higher degrees for educators, and incorporating more technology into learning. In this section of Perspectives in Education, we explore the latest trends and developments shaping the landscape of early childhood education.

Yuqi Ye, Li You, Hao Xu et al.

The integrated sensing and communication (ISAC) technology has been extensively researched to enhance communication rates and radar sensing capabilities. Additionally, a new technology known as fluid antenna system (FAS) has recently been proposed to obtain higher communication rates for future wireless networks by dynamically altering the antenna position to obtain a more favorable channel condition. The application of the FAS technology in ISAC scenarios holds significant research potential. In this paper, we investigate a FAS-assisted multiple-input multiple-output (MIMO) ISAC system for maximizing the radar sensing signal-clutter-noise ratio (SCNR) under communication signal-to-interference-plus-noise ratio (SINR) and antenna position constraints. We devise an iterative algorithm that tackles the optimization problem by maximizing a lower bound of SCNR with respect to the transmit precoding matrix and the antenna position. By addressing the non-convexity of the problem through this iterative approach, our method significantly improves the SCNR. Our simulation results demonstrate that the proposed scheme achieves a higher SCNR compared to the baselines.

Somporn Sahachaiseree, Takashi Oguchi

ABSTRACT Reinforcement learning (RL) is a promising machine‐learning solution to traffic signal control problems, which have been extensively studied. However, variants of non‐linear, deep artificial neural network (ANN) function approximators (FAs) have been predominantly employed in previous studies proposing RL‐based controllers, leaving a significant interpretability issue due to their black‐box nature. In this work, the use of the linear FA for a value‐based RL agent in traffic signal control problems is investigated along with the least‐squares Q‐learning method, abbreviated as LSTDQ. The interpretable linear FA was found to be adequate for the RL agent to learn an optimal policy. This leads to the proposal to replace a non‐linear ANN FA with the linear FA counterpart, resolving the interpretability issue. Moreover, the LSTDQ learning method shows superior behaviour convergence compared to a gradient descent method. In a low‐intensity arrival pattern scenario, the control by the RL agent cuts about half of the average delay resulting from the pretimed control. Owing to the conciseness of the linear FA, a direct interpretation analysis of the converged linear‐FA parameters is presented. Lastly, two online relearning tests of the agents under non‐stationary arrivals are conducted to demonstrate the online performance of LSTDQ. In conclusion, the linear‐FA specification and the LSTDQ method are together proposed to be used for its control algorithm interpretability property, superior convergence quality, and lack of hyperparameters.

C. J. Horwell, S. Ravenhall, R. Clarkson et al.

Abstract Volcanic eruptions produce plumes of ash, gas and aerosols that present a risk to aviation at all standard flight levels. Here, we investigate atmospheric dispersal of volcanic emissions, whether and how they infiltrate aircraft, and whether ground-level public health exposure thresholds can be related to the pressurised cabin environment. We then review the limited evidence for physical and mental health, and behavioural impacts, resulting from volcanic emissions entering aircraft. Serious health risks are considered low for healthy individuals, but respiratory irritation is likely for a high exposure scenario to sulfur dioxide (SO2). Asthmatics are particularly sensitive to SO2, with even relatively low, short exposures, potentially resulting in severe respiratory impacts. Negative group behaviours are not expected but individual distress is possible. Communicating this evidence to the aviation industry may result in more informed decision-making on flightpath alterations and triggering of emergency protocols, both before and during volcanic emission encounters.