An intelligent swarm based-wavelet neural network for affective mobile phone design

In this paper, an intelligent swarm based-wavelet neural network for affective mobile designed is presented. The contribution on this paper is to develop a new intelligent particle swarm optimization (iPSO), where a fuzzy logic system developed based on human knowledge is proposed to determine the inertia weight for the swarm movement of the PSO and the control parameter of a newly introduced cross-mutated operation. The proposed iPSO is used to optimize the parameters of wavelet neural network. An affective design of mobile phones is used to evaluate the effectiveness of the proposed iPSO. It has been found that significantly better results in a statistical sense can be obtained by the iPSO comparing with the existing hybrid PSO methods

Minimal residual disease-based risk stratification in Chinese childhood acute lymphoblastic leukemia by flow cytometry and plasma DNA quantitative polymerase chain reaction

published_or_final_versio

Hong Kong–Macau Severe Hives and Angioedema Referral Pathway

BackgroundUrticaria (defined as the presence of hives, angioedema, or both) can be caused by a variety of etiologies ranging from more common conditions such as chronic spontaneous urticaria (CSU) to rarer conditions such as hereditary angioedema (HAE). Specialist referral may be necessary in cases of severe urticaria or HAE, but access to specialist services remains limited in certain regions, such as the Greater Bay Area (GBA) of China. To address this, the Hong Kong–Macau Severe Hives and Angioedema Referral Pathway (SHARP) was initiated by the Hong Kong Institute of Allergy and Macau Society of Dermatology to promote multidisciplinary collaboration and regional exchange of expertise in the diagnosis and management of severe urticaria.MethodsA nominated task force of dermatologists and immunologists who manage patients with severe urticaria formulated the consensus statements (CS) using the Delphi method. The consensus was defined a priori as an agreement of ≥80%.ResultsA total of 24 CS were formulated, including four statements on classifications and definitions, seven statements on diagnosis, and 13 statements on management and referral. The definitions for acute/chronic urticaria and severe CSU were stated. Unnecessary investigations and inappropriate medications were discouraged. The characteristics and recommended approach to suspected bradykinergic angioedema were specified. Stepwise treatment options using second-generation antihistamines, omalizumab, or cyclosporin for patients with CSU were addressed, and the importance of access to HAE-specific medications was emphasized. Furthermore, an integrated referral pathway for patients with severe hives and angioedema was constructed.ConclusionThe SHARP provides guidance for the management and specialist referral of patients with severe hives and angioedema in Hong Kong and Macau

Light-convolution dense selection u-net (Lds u-net) for ultrasound lateral bony feature segmentation

Scoliosis is a widespread medical condition where the spine becomes severely deformed and bends over time. It mostly affects young adults and may have a permanent impact on them. A periodic assessment, using a suitable modality, is necessary for its early detection. Conventionally, the usually employed modalities include X-ray and MRI, which employ ionising radiation and are expensive. Hence, a non-radiating 3D ultrasound imaging technique has been developed as a safe and economic alternative. However, ultrasound produces low-contrast images that are full of speckle noise, and skilled intervention is necessary for their processing. Given the prevalent occurrence of scoliosis and the limitations of scalability of human expert interventions, an automatic, fast, and low-computation assessment technique is being developed for mass scoliosis diagnosis. In this paper, a novel hybridized light-weight convolutional neural network architecture is presented for automatic lateral bony feature identification, which can help to develop a fully-fledged automatic scoliosis detection system. The proposed architecture, Light-convolution Dense Selection U-Net (LDS U-Net), can accurately segment ultrasound spine lateral bony features, from noisy images, thanks to its capabilities of smartly selecting only the useful information and extracting rich deep layer features from the input image. The proposed model is tested using a dataset of 109 spine ultrasound images. The segmentation result of the proposed network is compared with basic U-Net, Attention U-Net, and MultiResUNet using various popular segmentation indices. The results show that LDS U-Net provides a better segmentation performance compared to the other models. Additionally, LDS U-Net requires a smaller number of parameters and less memory, making it suitable for a large-batch screening process of scoliosis without a high computational requirement

A new measurement of antineutrino oscillation with the full detector configuration at Daya Bay

We report a new measurement of electron antineutrino disappearance using the fully-constructed Daya Bay Reactor Neutrino Experiment. The final two of eight antineutrino detectors were installed in the summer of 2012. Including the 404 days of data collected from October 2012 to November 2013 resulted in a total exposure of 6.9$\times$10$^5$ GW$_{\rm th}$-ton-days, a 3.6 times increase over our previous results. Improvements in energy calibration limited variations between detectors to 0.2%. Removal of six $^{241}$Am-$^{13}$C radioactive calibration sources reduced the background by a factor of two for the detectors in the experimental hall furthest from the reactors. Direct prediction of the antineutrino signal in the far detectors based on the measurements in the near detectors explicitly minimized the dependence of the measurement on models of reactor antineutrino emission. The uncertainties in our estimates of $\sin^{2}2\theta_{13}$ and $|\Delta m^2_{ee}|$ were halved as a result of these improvements. Analysis of the relative antineutrino rates and energy spectra between detectors gave $\sin^{2}2\theta_{13} = 0.084\pm0.005$ and $|\Delta m^{2}_{ee}|= (2.42\pm0.11) \times 10^{-3}$ eV$^2$ in the three-neutrino framework.Comment: Updated to match final published versio

A side-by-side comparison of Daya Bay antineutrino detectors

The Daya Bay Reactor Neutrino Experiment is designed to determine precisely the neutrino mixing angle $\theta_{13}$ with a sensitivity better than 0.01 in the parameter sin$^22\theta_{13}$ at the 90% confidence level. To achieve this goal, the collaboration will build eight functionally identical antineutrino detectors. The first two detectors have been constructed, installed and commissioned in Experimental Hall 1, with steady data-taking beginning September 23, 2011. A comparison of the data collected over the subsequent three months indicates that the detectors are functionally identical, and that detector-related systematic uncertainties exceed requirements.Comment: 24 pages, 36 figure