The efficacy of ultrasound-guided oblique subcostal transversus abdominis plane block in patients undergoing open cholecystectomy

Background: Ultrasound-guided oblique subcostal transversus abdominis plane (TAP) blockade has been described recently as providing a wider analgesic blockade than the posterior approach, with the possibility of being suitable for surgery both superior and inferior to the umbilicus. The objective of this study was to report the authors’ experience of intraoperative oblique subcostal TAP blockade during open cholecystectomy. Case report: This is a case series of 10 patients who had bilateral oblique subcostal TAP blockade for elective laparoscopic cholecystectomy which was subsequently converted to open cholecystectomy. Intraoperative haemodynamic parameters (pulse rate, systolic and diastolic blood pressure and mean arterial blood pressure) were recorded every five minutes. A rescue bolus of intravenous fentanyl (0.5 μg/kg) was given when any of the above-mentioned parameters were raised more than 15% from the baseline. The postoperative visual analogue score (VAS) was recorded in the recovery room. Intraoperative administration of rescue fentanyl bolus was minimal with a mean postoperative VAS of 2.1 ± 1.60. No complications were noted related to TAP blockade. Conclusion: Ultrasound-guided oblique subcostal TAP blockade can be effective as intraoperative analgesia in abdominal surgery. Randomised controlled studies comparing TAP blockade with other modes of analgesia are needed to determine its efficacy for abdominal surgery.Keywords: oblique subcostal TAP block, open cholecystectomy, anterior abdominal wall, thoracolumbar intercostal nerves, intraoperative analgesi

Behavior Prediction Based on Obstacle Motion Patterns in Dynamically Changing Environments

This paper proposes a behavior prediction method for navigation application in dynamically changing environments, which predicts obstacle behaviors based on learned obstacle motion patterns (OMP) from observed obstacle motion trajectories. A multi-level prediction model is then proposed that predicts long-term or short-term obstacle behaviors. Simulation results show that it works well in a complex environment and the prediction is consistent with actual behaviors. © 2008 IEEE.published_or_final_versio

A practical regularization technique for modified nodal analysis in large-scale time-domain circuit simulation

Fast full-chip time-domain simulation calls for advanced numerical integration techniques with capability to handle the systems with (tens of) millions of variables resulting from the modified nodal analysis (MNA). General MNA formulation, however, leads to a differential algebraic equation (DAE) system with singular coefficient matrix, for which most of explicit methods, which usually offer better scalability than implicit methods, are not readily available. In this paper, we develop a practical two-stage strategy to remove the singularity in MNA equations of large-scale circuit networks. A topological index reduction is first applied to reduce the DAE index of the MNA equation to one. The index-1 system is then fed into a systematic process to eliminate excess variables in one run, which leads to a nonsingular system. The whole regularization process is devised with emphasis on exact equivalence, low complexity, and sparsity preservation, and is thus well suited to handle extremely large circuits. © 2012 IEEE.published_or_final_versio

Time-domain analysis of large-scale circuits by matrix exponential method with adaptive control

We propose an explicit numerical integration method based on matrix exponential operator for transient analysis of large-scale circuits. Solving the differential equation analytically, the limiting factor of maximum time step changes largely from the stability and Taylor truncation error to the error in computing the matrix exponential operator. We utilize Krylov subspace projection to reduce the computation complexity of matrix exponential operator. We also devise a prediction-correction scheme tailored for the matrix exponential approach to dynamically adjust the step size and the order of Krylov subspace approximation. Numerical experiments show the advantages of the proposed method compared with the implicit trapezoidal method. © 1982-2012 IEEE.published_or_final_versio

Review Radix rule application of heat in clinic

為深入了解柴胡在退熱方面的特點及臨床運用規律,從柴胡的配伍、炮制、用量、煎煮方法以及劑型等方面,對柴胡用治發熱的相關文獻進行歸納分析,探討其臨床特點及運用規律。認為柴胡的退熱配伍不少,然對其藥理研究尚不夠;有關使用炮制柴胡的報道亦較少,且炮制的方法也有爭議;柴胡的用量、煎煮方法對臨床治療發熱性疾病有一定的影響,但研究的深度不夠,各家意見不一,有待進一步研究。link_to_OA_fulltex

A Low Complexity Pilot Scheduling Algorithm for Massive MIMO

Pilot contamination is a fundamental bottleneck in massive multiple-input multiple-output (MIMO) cellular networks. In this letter, we aim to design a pilot scheduling method to reduce the effect of pilot contamination in multi-user multi-cell massive MIMO systems. Mathematically, the pilot scheduling problem can be formulated as a permutation-based optimization problem. However, finding the optimal solution requires an exhaustive search and is computationally prohibitive. Therefore, we propose a low-complexity near-optimal algorithm developed from the cross-entropy optimization framework to solve this problem. Simulation results reveal that our algorithm not only significantly outperforms the existing pilot-scheduling schemes but also achieves excellent performance with low complexity

Circuit simulation via matrix exponential method for stiffness handling and parallel processing

We propose an advanced matrix exponential method (MEXP) to handle the transient simulation of stiff circuits and enable parallel simulation. We analyze the rapid decaying of fast transition elements in Krylov subspace approximation of matrix exponential and leverage such scaling effect to leap larger steps in the later stage of time marching. Moreover, matrix-vector multiplication and restarting scheme in our method provide better scalability and parallelizability than implicit methods. The performance of ordinary MEXP can be improved up to 4.8 times for stiff cases, and the parallel implementation leads to another 11 times speedup. Our approach is demonstrated to be a viable tool for ultra-large circuit simulations (with 1.6M ∼ 12M nodes) that are not feasible with existing implicit methods. © 2012 ACM.published_or_final_versio

Mitigation of low-frequency current ripple in fuel-cell inverter systems through waveform control

published_or_final_versio

A partial-dithering strategy for edge-illumination X-ray phase-contrast tomography enabled by a joint reconstruction method

Edge-illumination X-ray phase-contrast tomography (EIXPCT) is a promising imaging technology where partially opaque masks are utilized with laboratory-based X-ray sources to estimate the distribution of the complex-valued refractive index. EIXPCT resolution is mainly determined by the period of a sample mask, but can be significantly improved by a dithering technique. Here, dithering means that multiple images per tomographic view angle are acquired as the object is moved over sub-pixel distances. Drawbacks of dithering include increased data-acquisition times and radiation doses. Motivated by the flexibility in data-acquisition designs enabled by a recently developed joint reconstruction (JR) method, a novel partial-dithering strategy for EIXPCT data-acquisition is proposed. In this strategy, dithering is implemented at only a subset of the tomographic view angles. The strategy can result in spatial resolution comparable to that of the conventional full-dithering strategy, where dithering is performed at every view angle, but the acquisition time is substantially decreased. Here, the effect of dithering parameters on image resolution are explored

The wavelet-NARMAX representation : a hybrid model structure combining polynomial models with multiresolution wavelet decompositions

A new hybrid model structure combing polynomial models with multiresolution wavelet decompositions is introduced for nonlinear system identification. Polynomial models play an important role in approximation theory, and have been extensively used in linear and nonlinear system identification. Wavelet decompositions, in which the basis functions have the property of localization in both time and frequency, outperform many other approximation schemes and offer a flexible solution for approximating arbitrary functions. Although wavelet representations can approximate even severe nonlinearities in a given signal very well, the advantage of these representations can be lost when wavelets are used to capture linear or low-order nonlinear behaviour in a signal. In order to sufficiently utilise the global property of polynomials and the local property of wavelet representations simultaneously, in this study polynomial models and wavelet decompositions are combined together in a parallel structure to represent nonlinear input-output systems. As a special form of the NARMAX model, this hybrid model structure will be referred to as the WAvelet-NARMAX model, or simply WANARMAX. Generally, such a WANARMAX representation for an input-output system might involve a large number of basis functions and therefore a great number of model terms. Experience reveals that only a small number of these model terms are significant to the system output. A new fast orthogonal least squares algorithm, called the matching pursuit orthogonal least squares (MPOLS) algorithm, is also introduced in this study to determine which terms should be included in the final model