41 research outputs found

    A universal method for depositing patterned materials in-situ

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    Current techniques of patterned material deposition require separate steps for patterning and material deposition. The complexity and harsh working conditions post serious limitations for fabrication. Here, we introduce a novel single-step and easy-to-adapt method that can deposit materials in-situ. Its unique methodology is based on the semiconductor nanoparticle assisted photon-induced chemical reduction and optical trapping. This universal mechanism can be used for depositing a large selection of materials including metals, insulators and magnets, with quality on par with current technologies. Patterning with several materials together with optical-diffraction-limited resolution accuracy can be achieved from macroscopic to microscopic scale. Furthermore, the setup is naturally compatible with optical microscopy based measurements, thus sample characterisation and material deposition can be realised in-situ. Various devices fabricated with this method in 2D or 3D show it is ready for deployment in practical applications. This revolutionary method will provide a distinct tool in material technology

    An intelligent system for trading signal of cryptocurrency based on market tweets sentiments

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    The purpose of this study is to examine the efficacy of an online stock trading platform in enhancing the financial literacy of those with limited financial knowledge. To this end, an intelligent system is proposed which utilizes social media sentiment analysis, price tracker systems, and machine learning techniques to generate cryptocurrency trading signals. The system includes a live price visu�alization component for displaying cryptocurrency price data and a prediction function that provides both short-term and long-term trading signals based on the sentiment score of the previous day’s cryptocurrency tweets. Additionally, a method for refining the sentiment model result is outlined. The results illustrate that it is feasible to incorporate the Tweets sentiment of cryptocurrencies into the system for generating reliable trading signals

    The effects of a mindfulness-based family psychoeducation intervention for the caregivers of young adults with first-episode psychosis: A randomized controlled trial

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    Objective: In this study, we investigated the effects of a mindfulness-based family psychoeducation (MBFPE) program on the mental-health outcomes of both caregivers and young adults with first-episode psychosis with an onset in the past three years through a multi-site randomized controlled trial. We also studied the outcomes of three potential mediating effects of interpersonal mindfulness, expressed emotions, and non-attachment on the program. Method: We randomly assigned 65 caregivers of young adults with psychosis to MBFPE (n = 33) or an ordinary family psychoeducation (FPE) program (n = 32); among them, 18 young adults in recovery also participated in the evaluation of outcomes. Results: Intent-to-treat analyses were conducted. No significant time × group interaction effects of MBFPE and FPE programs were found in any of the caregivers’ outcomes. However, the young adults with psychosis reported higher levels of recovery after the MBFPE program than after the ordinary FPE program (F = 8.268, p = 0.012, d = 1.484). They also reported a larger reduction in over-involvement of their caregivers (F = 4.846, p = 0.044, d = 1.136), showing that MBFPE had a superior effect to FPE in promoting recovery and reducing over-involvement. Conclusions: A brief psychoeducation program may not reduce the burden on or improve the mental-health outcome of caregivers of individuals with recent-onset psychosis. However, integrating mindfulness into a conventional family psychoeducation program may reduce the expressed emotions of caregivers, especially over-involvement. Further studies should explore how psychoeducation programs can reduce the impact of psychosis on family through sustainable effects in terms of reducing their burden and expressed emotions, using a rigorous study and adequate sample size

    Old Technique -New Evidence: Topical agents for musculo-skeletal injuries

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    The popular use of topical agents for the treatment of musculoskeletal injuries has persisted for centuries but not much scientific evaluations have been done. Since medicinal herbs are particularly popular in Asia, we started a systematic exploration on their choices, and their pharmacological activities; whether transcutaneous transport of bioactive components occur and above all, whether quality clinical evidences could be generated. We found that a search on the vast literature pool would reveal the favourable choices of herbal agents. Biological screening of those selected herbs showed that they probably follow three major common pathways to help with healing after injury, viz, anti-inflammation, pro-angiogenesis and cellular proliferation. Using a simple formula of selected herbs with the ideal bioactivities, evidence based clinical trials could be organized to further prove the efficacy. We have created two such formulae to be put on clinical trial. Our early pilot clinical trials on two minor injuries on the foot and one chronic inflammatory condition have yielded positive data on the value of such topical agents on pain and oedema control, as well as functional maintenance. There was also suggestion of more rapid bone healing. Although limitations exist clear with the small number of study subjects, the positive data and safe application support more studies

    Chronic intermittent hypoxia induces local inflammation of the rat carotid body via functional upregulation of proinflammatory cytokine pathways

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    Maladaptive changes in the carotid body (CB) induced by chronic intermittent hypoxia (IH) account for the pathogenesis of cardiovascular morbidity in patients with sleep-disordered breathing. We postulated that the proinflammatory cytokines, namely interleukin (IL)-1β, IL-6 and tumor necrosis factor (TNF)-α, and cytokine receptors (IL-1r1, gp130 and TNFr1) locally expressed in the rat CB play a pathophysiological role in IH-induced CB inflammation. Results showed increased levels of oxidative stress (serum 8-isoprostane and nitrotyrosine in the CB) in rats with 7-day IH treatment resembling recurrent apneic conditions when compared with the normoxic control. Local inflammation shown by the amount of ED1-containing cells (macrophage infiltration) and the gene transcripts of NADPH oxidase subunits (gp91phox and p22phox) and chemokines (MCP-1, CCR2, MIP-1α, MIP-1β and ICAM-1) in the CB were significantly more in the hypoxic group than in the control. In addition, the cytokines and receptors were expressed in the lobules of chemosensitive glomus cells containing tyrosine hydroxylase and the levels of expressions were significantly increased in the hypoxic group. Exogenous cytokines elevated the intracellular calcium ([Ca2+]i) response to acute hypoxia in the dissociated glomus cells. The effect of cytokines on the [Ca2+]i response was significantly greater in the hypoxic than in the normoxic group. Moreover, daily treatment of IH rats with anti-inflammatory drugs (dexamethasone or ibuprofen) attenuated the levels of oxidative stress, gp91phox expression and macrophage infiltration in the CB. Collectively, these results suggest that the upregulated expression of proinflammatory cytokine pathways could mediate the local inflammation and functional alteration of the CB under chronic IH conditions

    To determine the cost-effectiveness of smoking cessation clinics undermanagement of Department of Health in Hong Kong

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    published_or_final_versionMedical SciencesMasterMaster of Medical Science

    Recursive likelihood evaluation and fast search algorithm for polynomial segment model, with application to speech recognition

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    Polynomial segment models (PSMs), which are generalization,of the hidden Markov models (HMMs), have opened an alternative research direction for speech recognition. However, they have been limited by their computational complexity. Traditionally, any change in PSM segment boundary requires likelihood recomputation of all the frames within the segment. This makes the PSM's segment likelihood evaluation an order of magnitude more expensive than the HMM's. Furthermore, because recognition using segment models needs to search over all possible segment boundaries, the PSM recognition is computationally unfeasible beyond N-best rescoring. By exploiting the properties of the time normalization in PSM, and by decomposing the PSM segment likelihood into a simple function of "sufficient statistics," in this paper, we show that segment likelihood can be evaluated efficiently in an order of computational complexity similar to HMM. In addition, by reformulating the PSM recognition as a search for the optimal,path through a graph, this paper introduces a fast PSM search algorithm that intelligently prunes the. number of hypothesized segment boundaries, such that PSM recognition can be performed in an order of complexity similar to HMM. We demonstrate the effectiveness of the proposed algorithms with experiments using a PSM-based recognition system on two different recognition tasks: TIDIGIT digit recognition and the Wall Street Journal dictation task. In both tasks, PSM recognition is feasible and out-performed traditional HMM by more than 14\%
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