Improvement on thermal performance of a disk-shaped miniature heat pipe with nanofluid

The present study aims to investigate the effect of suspended nanoparticles in base fluids, namely nanofluids, on the thermal resistance of a disk-shaped miniature heat pipe [DMHP]. In this study, two types of nanoparticles, gold and carbon, in aqueous solution are used respectively. An experimental system was set up to measure the thermal resistance of the DMHP with both nanofluids and deionized [DI] water as the working medium. The measured results show that the thermal resistance of DMHP varies with the charge volume and the type of working medium. At the same charge volume, a significant reduction in thermal resistance of DMHP can be found if nanofluid is used instead of DI water

Experimental demonstration of RGB LED-based optical camera communications

Red, green, and blue (RGB) light-emitting diodes (LEDs) are widely used in everyday illumination, particularly where color-changing lighting is required. On the other hand, digital cameras with color filter arrays over image sensors have been also extensively integrated in smart devices. Therefore, optical camera communications (OCC) using RGB LEDs and color cameras is a promising candidate for cost-effective parallel visible light communications (VLC). In this paper, a single RGB LED-based OCC system utilizing a combination of undersampled phase-shift on off keying (UPSOOK), wavelength-division multiplexing (WDM), and multiple-input multiple-output (MIMO) techniques is designed, which offers higher space efficiency (3 bits/Hz/LED), long-distance, and nonflickering VLC data transmission. A proof-of-concept test bed is developed to assess the bit-error-rate performance of the proposed OCC system. The experimental results show that the proposed system using a single commercially available RGB LED and a standard 50-frame/s camera is able to achieve a data rate of 150 bits/s over a range of up to 60 m

ECG Signal Super-resolution by Considering Reconstruction and Cardiac Arrhythmias Classification Loss

With recent advances in deep learning algorithms, computer-assisted healthcare services have rapidly grown, especially for those that combine with mobile devices. Such a combination enables wearable and portable services for continuous measurements and facilitates real-time disease alarm based on physiological signals, e.g., cardiac arrhythmias (CAs) from electrocardiography (ECG). However, long-term and continuous monitoring confronts challenges arising from limitations of batteries, and the transmission bandwidth of devices. Therefore, identifying an effective way to improve ECG data transmission and storage efficiency has become an emerging topic. In this study, we proposed a deep-learning-based ECG signal super-resolution framework (termed ESRNet) to recover compressed ECG signals by considering the joint effect of signal reconstruction and CA classification accuracies. In our experiments, we downsampled the ECG signals from the CPSC 2018 dataset and subsequently evaluated the super-resolution performance by both reconstruction errors and classification accuracies. Experimental results showed that the proposed ESRNet framework can well reconstruct ECG signals from the 10-times compressed ones. Moreover, approximately half of the CA recognition accuracies were maintained within the ECG signals recovered by the ESRNet. The promising results confirm that the proposed ESRNet framework can be suitably used as a front-end process to reconstruct compressed ECG signals in real-world CA recognition scenarios

High ERCC1 expression predicts cisplatin-based chemotherapy resistance and poor outcome in unresectable squamous cell carcinoma of head and neck in a betel-chewing area

<p>Abstract</p> <p>Background</p> <p>This study was to evaluate the effect of excision repair cross-complementation group 1(ERCC1) expression on response to cisplatin-based induction chemotherapy (IC) followed by concurrent chemoradiation (CCRT) in locally advanced unresectable head and neck squamous cell carcinoma (HNSCC) patients.</p> <p>Methods</p> <p>Fifty-seven patients with locally advanced unresectable HNSCC who received cisplatin-based IC followed by CCRT from January 1, 2006 through January 1, 2008. Eligibility criteria included presence of biopsy-proven HNSCC without a prior history of chemotherapy or radiotherapy. Immunohistochemistry was used to assess ERCC1 expression in pretreatment biopsy specimens from paraffin blocks. Clinical parameters, including smoking, alcohol consumption and betel nuts chewing, were obtained from the medical records.</p> <p>Results</p> <p>The 12-month progression-free survival (PFS) and 2-year overall survival (OS) rates of fifty-seven patients were 61.1% and 61.0%, respectively. Among these patients, thirty-one patients had low ERCC1 expression and forty-one patients responded to IC followed by CCRT. Univariate analyses showed that patients with low expression of ERCC1 had a significantly higher 12-month PFS rates (73.3% vs. 42.3%, p < 0.001) and 2-year OS (74.2 vs. 44.4%, p = 0.023) rates. Multivariate analysis showed that for patients who did not chew betel nuts and had low expression of ERCC1 were independent predictors for prolonged survival.</p> <p>Conclusions</p> <p>Our study suggest that a high expression of ERCC1 predict a poor response and survival to cisplatin-based IC followed by CCRT in patients with locally advanced unresectable HNSCC in betel nut chewing area.</p

Learning Fine-Grained Visual Understanding for Video Question Answering via Decoupling Spatial-Temporal Modeling

While recent large-scale video-language pre-training made great progress in video question answering, the design of spatial modeling of video-language models is less fine-grained than that of image-language models; existing practices of temporal modeling also suffer from weak and noisy alignment between modalities. To learn fine-grained visual understanding, we decouple spatial-temporal modeling and propose a hybrid pipeline, Decoupled Spatial-Temporal Encoders, integrating an image- and a video-language encoder. The former encodes spatial semantics from larger but sparsely sampled frames independently of time, while the latter models temporal dynamics at lower spatial but higher temporal resolution. To help the video-language model learn temporal relations for video QA, we propose a novel pre-training objective, Temporal Referring Modeling, which requires the model to identify temporal positions of events in video sequences. Extensive experiments demonstrate that our model outperforms previous work pre-trained on orders of magnitude larger datasets.Comment: BMVC 2022. Code is available at https://github.com/shinying/des

Subcutaneous nerve activity is more accurate than heart rate variability in estimating cardiac sympathetic tone in ambulatory dogs with myocardial infarction

BACKGROUND: We recently reported that subcutaneous nerve activity (SCNA) can be used to estimate sympathetic tone. OBJECTIVE: The purpose of this study was to test the hypothesis that left thoracic SCNA is more accurate than heart rate variability (HRV) in estimating cardiac sympathetic tone in ambulatory dogs with myocardial infarction (MI). METHODS: We used an implanted radiotransmitter to study left stellate ganglion nerve activity (SGNA), vagal nerve activity (VNA), and thoracic SCNA in 9 dogs at baseline and up to 8 weeks after MI. HRV was determined based on time-domain, frequency-domain, and nonlinear analyses. RESULTS: The correlation coefficients between integrated SGNA and SCNA averaged 0.74 (95% confidence interval [CI] 0.41-1.06) at baseline and 0.82 (95% CI, 0.63-1.01) after MI (P <.05 for both). The absolute values of the correlation coefficients were significantly larger than that between SGNA and HRV analysis based on time-domain, frequency-domain, and nonlinear analyses, respectively, at baseline (P <.05 for all) and after MI (P <.05 for all). There was a clear increment of SGNA and SCNA at 2, 4, 6, and 8 weeks after MI, whereas HRV parameters showed no significant changes. Significant circadian variations were noted in SCNA, SGNA, and all HRV parameters at baseline and after MI, respectively. Atrial tachycardia (AT) episodes were invariably preceded by SCNA and SGNA, which were progressively increased from 120th, 90th, 60th, to 30th seconds before AT onset. No such changes of HRV parameters were observed before AT onset. CONCLUSION: SCNA is more accurate than HRV in estimating cardiac sympathetic tone in ambulatory dogs with MI

A novel randomly textured phosphor structure for highly efficient white light-emitting diodes

We have successfully demonstrated the enhanced luminous flux and lumen efficiency in white light-emitting diodes by the randomly textured phosphor structure. The textured phosphor structure was fabricated by a simple imprinting technique, which does not need an expensive dry-etching machine or a complex patterned definition. The textured phosphor structure increases luminous flux by 5.4% and 2.5% at a driving current of 120 mA, compared with the flat phosphor and half-spherical lens structures, respectively. The increment was due to the scattering of textured surface and also the phosphor particles, leading to the enhancement of utilization efficiency of blue light. Furthermore, the textured phosphor structure has a larger view angle at the full width at half maximum (87°) than the reference LEDs