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An Investigation into Helmet Use, Perceptions of Sports-Related Concussion, and Seeking Medical Care for Head Injury amongst Competitive Cyclists
Full text linkThe purpose of this study was to investigate competitive cyclists’ helmet use, perceptions of sports-related concussion (SRC), and medical-care-seeking behaviors. A mixed-method approach was used with qualitative and quantitative data presented. The study comprised of a cross-sectional analysis of 405 competitive cyclists who completed an online survey. Results indicated that most participants believed a bicycle helmet protects against SRC (79.5%) and considerable numbers of participants would not seek medical care for potential head injury in scenarios where this would be recommended. It was also discovered that marketing of concussion reduction technology influences cyclists’ helmet-purchasing behaviors. With the data presented, it is recommended that governing bodies in cycling need to develop educational resources to address gaps in knowledge regarding SRC amongst cyclists. We also suggest that more independent research on concussion reduction technologies in bicycle helmets is needed, with advertising supported by clear scientific evidence to avoid negatively influencing head injury management and reporting behaviors amongst cyclists
A blood atlas of COVID-19 defines hallmarks of disease severity and specificity
No full textTreatment of severe COVID-19 is currently limited by clinical heterogeneity and incomplete description of specific immune biomarkers. We present here a comprehensive multi-omic blood atlas for patients with varying COVID-19 severity in an integrated comparison with influenza and sepsis patients versus healthy volunteers. We identify immune signatures and correlates of host response. Hallmarks of disease severity involved cells, their inflammatory mediators and networks, including progenitor cells and specific myeloid and lymphocyte subsets, features of the immune repertoire, acute phase response, metabolism and coagulation. Persisting immune activation involving AP-1/p38MAPK was a specific feature of COVID-19. The plasma proteome enabled sub-phenotyping into patient clusters, predictive of severity and outcome. Systems-based integrative analyses including tensor and matrix decomposition of all modalities revealed feature groupings linked with severity and specificity compared to influenza and sepsis. Our approach and blood atlas will support future drug development, clinical trial design and personalized medicine approaches for COVID-19.[Display omitted]•Blood atlas delineating innate and adaptive immune dysregulation in COVID-19•Shared and specific immune signatures of COVID-19, influenza and all cause sepsis•Multi-omic immune profiling differentiates hospitalised patient severity in COVID-19•Immune activation and proliferation involving AP-1/p38MAPK associated with COVID-19A multi-omic analysis of patient blood samples reveals both similarities and specific features of COVID-19 when compared with samples obtained from sepsis or influenza patients which could yield better targetted therapies for severe COVID-19
Near-Field Investigation of Luminescent Hyperuniform Disordered Materials
No full textDisordered photonic nanostructures have attracted tremendous interest in the past three decades, not only due to the fascinating and complex physics of light transport in random media, but also for peculiar functionalities in a wealth of interesting applications. Recently, the interest in dielectric disordered systems has received new inputs by exploiting the role of long-range correlation within scatterer configurations. Hyperuniform photonic materials, that share features of photonic crystals and random systems, constitute the archetype of systems where light transport can be tailored from diffusive transport to a regime dominated by light localization due to the presence of photonic band gap. Here, advantage is taken of the combination of the hyperuniform disordered (HuD) design in slab photonics, the use of embedded quantum dots for feeding the HuD resonances, and near-field hyperspectral imaging with sub-wavelength resolution in the optical range to explore the transition from localization to diffusive transport. It is shown, theoretically and experimentally, that photonic HuD systems support resonances ranging from strongly localized modes to extended modes. It is demonstrated that Anderson-like modes with high Q/V are created, with small footprint, intrinsically reproducible and resilient to fabrication-induced disorder, paving the way for a novel photonic platform for quantum applications.</p
Load estimation in unsteady flows from sparse pressure measurements: Application of transition networks to experimental data
Full text linkInspired by biological swimming and flying with distributed sensing, we propose a data-driven approach for load estimation that relies on complex networks. We exploit sparse, real-time pressure inputs, combined with pre-trained transition networks, to estimate aerodynamic loads in unsteady and highly separated flows. The transition networks contain the aerodynamic states of the system as nodes along with the underlying dynamics as links. A weighted average-based (WAB) strategy is proposed and tested on realistic experimental data on the flow around an accelerating elliptical plate at various angles of attack. Aerodynamic loads are then estimated for angles-of-attack cases not included in the training dataset so as to simulate the estimation process. An optimization process is also included to account for the system's temporal dynamics. Performance and limitations of the WAB approach are discussed, showing that transition networks can represent a versatile and effective data-driven tool for real-time signal estimation using sparse and noisy signals (such as surface pressure) in realistic flows
Cell-Sweeping: A New Paradigm for Cells Deployment in Radio Access Networks
Full text linkGood network coverage is an important element ofQuality of Service (QoS) provision that mobile cellular operatorsaim to provide. The established requirements for the existingFifth Generation (5G) and the emerging scenarios for upcomingSixth Generation (6G) cellular communication technologieshighly depend on the coverage quality that the network is able toprovide. In addition, some proposed 5G solutions such as densification,are complex, costly, and tend to degrade network coveragedue to increased interference which is critical for the cell-edgeperformance. In this direction, we present a novel concept ofcell-sweeping for coverage enhancement in cellular networks.One of the main objectives behind this mechanism relies onovercoming the cell-edge problem which directly translates intobetter network coverage. In sequence, the concept operation isintroduced and compared to the conventional static cell scenarios.These comparisons target mostly the benefits at the cell-edgelocations. Additionally, the use of schedulers that take advantageof the sweeping system is expected to extend the cell-edge benefitsto the entire network. This is observed when deploying cellsweepingwith the Proportional Fair (PF) scheduler. A 5thpercentileimprovement of 125% and an average throughputincrease of 35% were obtained through system level simulations.The preliminary results presented in this paper suggest that cellsweepingcan be adopted as an emerging technology for futureRadio Access Network (RAN) deployments
Photoassisted ionization spectroscopy of few implanted bismuth orbitals in a silicon-on-insulator device
Full text linkThe electrically detected orbital spectrum of a mesoscopic silicon device containing a small number of donors has been investigated. The device was fabricated on silicon-on-insulator with an optically active channel containing 6 x 105 substitutional bismuth centers introduced by ion implantation. The 1s(A₁) → 2p± orbital transition at the energy associated with isolated bismuth donors was detected via a change in photocurrent when illuminated by THz light from a free electron laser. The spectral dependence on bias, temperature, and laser intensity is explored to determine optimum conditions for detecting orbital transitions in smaller devices with fewer donors. These results suggest that photo-induced impact ionization can offer a route for the spectroscopic detection of few impurities providing a useful tool for the development of solid-state quantum technologies
Do Travelers Trust Intelligent Service Robots?
Full text linkThis research investigates travelers' trust in intelligent autonomous technologies based on two studies involving self-driving transportation and robot bartenders. Targeting travelers residing in the United States, online questionnaire was distributed to test the relationships between trusting beliefs in intelligent robots, its antecedents, and its outcomes. The results demonstrate that the cognitive trust formation process holds in situations involving intelligent robots as objects of trust. Trust in intelligent machines is influenced by negative attitude toward technology and propensity to trust technology. Surprisingly, the physical form of robots does not affect trust. Finally, trust leads to adoption intention in both studies. The contribution of this research is in elucidating consumer trust in intelligent robots designed for socially-driven interactions in travel settings
Fast Data Driven Estimation of Cluster Number in Multiplex Images using Embedded Density Outliers
Full text link—The usage of chemical imaging technologies is becoming a routine accompaniment to traditional methods in pathology. Significant technological advances have developed these next generation techniques to provide rich, spatially resolved , multidimensional chemical images. The rise of digital pathology has significantly enhanced the synergy of these imaging modalities with optical microscopy and immunohistochemistry, enhancing our understanding of the biological mechanisms and progression of diseases. Techniques such as imaging mass cy-tometry provide labelled multidimensional (multiplex) images of specific components used in conjunction with digital pathology techniques. These powerful techniques generate a wealth of high dimensional data that create significant challenges in data analysis. Unsupervised methods such as clustering are an attractive way to analyse these data, however, they require the selection of parameters such as the number of clusters. Here we propose a methodology to estimate the number of clusters in an automatic data-driven manner using a deep sparse autoencoder to embed the data into a lower dimensional space. We compute the density of regions in the embedded space, the majority of which are empty, enabling the high density regions (i.e. clusters) to be detected as outliers and provide an estimate for the number of clusters. This framework provides a fully unsupervised and data-driven method to analyse multidimensional data. In this work we demonstrate our method using 45 multiplex imaging mass cytometry datasets. Moreover, our model is trained using only one of the datasets and the learned embedding is applied to the remaining 44 images providing an efficient process for data analysis. Finally, we demonstrate the high computational efficiency of our method which is two orders of magnitude faster than estimating via computing the sum squared distances as a function of cluster number
Reinforcement Learning based Latency Minimization in Secure NOMA-MEC Systems with Hybrid SIC
No full text—In this paper, physical layer security (PLS) in a non-orthogonal multiple access (NOMA)-based mobile edge computing (MEC) system is investigated, where hybrid successive interference cancellation (SIC) decoding is considered. Specifically , users intend to complete confidential tasks with the help of the MEC server, while an eavesdropper attempts to intercept the offloaded tasks. By jointly designing computational resource allocation, task assignment, and power allocation, a latency minimization problem is formulated. Based on the interactions between local computing time and MEC processing time, the closed-from solutions of computational resource allocation and task assignment are derived. After that, a strategy selection mechanism is established to select offloading strategies based on the corresponding conditions. Moreover, according to the analysis of hybrid SIC decoding, the conditions of different decoding orders in secure NOMA networks are derived. Furthermore, a reinforcement learning based algorithm is proposed to solve the power allocation problems for NOMA and OMA offloading strategies. This work is extended to a multiuser scenario, in which a matching-based algorithm is proposed to solve the formulated sub-channel assignment problem. Simulation results indicate that: i) the proposed solution can significantly reduce the latency and provide dynamic strategy selection for various scenarios; ii) the NOMA offloading strategy with hybrid SIC decoding can outperform other strategies in the considered system. Index Terms—Mobile edge computing (MEC), Non-orthogonal multiple access (NOMA), physical layer security (PLS), reinforcement learning, sub-channel assignment
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