Change of short-term memory effect in acute ischemic ventricular myocardium: A computational study

The ionic mechanism of change in short-term memory (STM) during acute myocardial ischemia has not been well understood. In this paper, an advanced guinea pig ventricular model developed by Luo and Rudy was used to investigate STM property of ischemic ventricular myocardium. STM response was calculated by testing the time to reach steady-state action potential duration (APD) after an abrupt shortening of basic cycling length (BCL) in the pacing protocol. Electrical restitution curves (RCs), which can simultaneously visualize multiple aspects of APD restitution and STM, were obtained from dynamic and local S1S2 restitution portrait (RP), which consist of a longer interval stimulus (S1) and a shorter interval stimulus (S2). The angle between dynamic RC and local S1S2 RC reflects the amount of STM. Our results indicated that compared with control (normal) condition, time constant of STM response in the ischemic condition decreased significantly. Meanwhile the angle which reflects STM amount is less in ischemic model than that in control model. By tracking the effect of ischemia on intracellular ion concentration and membrane currents, we declared that changes in membrane currents caused by ischemia exert subtle influences on STM; it is only the decline of intracellular calcium concentration that give rise to the most decrement of STM.     

Augment low-field intra-operative MRI with preoperative MRI using a hybrid non-rigid registration method

Background

Preoperatively acquired diffusion tensor image (DTI) and blood oxygen level dependent (BOLD) have been proved to be effective in providing more anatomical and functional information; however, the brain deformation induced by brain shift and tumor resection severely impairs the correspondence between the image space and the patient space in image-guided neurosurgery.

Method

To address the brain deformation, we developed a hybrid non-rigid registration method to register high-field preoperative MRI with low-field intra-operative MRI in order to recover the deformation induced by brain shift and tumor resection. The registered DTI and BOLD are fused with low-field intra-operative MRI for image-guided neurosurgery.

Results

The proposed hybrid registration method was evaluated by comparing the landmarks predicted by the hybrid registration method with the landmarks identified in the low-field intra-operative MRI for 10 patients. The prediction error of the hybrid method is 1.92 ± 0.54 mm, and the compensation accuracy is 74.3 ± 5.0%. Compared to the landmarks far from the resection region, those near the resection region demonstrated a higher compensation accuracy (P-value = .003) although these landmarks had larger initial displacements.

Conclusions

The proposed hybrid registration method is able to bring preoperatively acquired BOLD and DTI into the operating room and compensate for the deformation to augment low-field intra-operative MRI with rich anatomical and functional information.     

Belief propagation algorithms for finding the probable configurations over factor graph models

In this article, we study the belief propagation algorithms for solving the multiple probable configurations (MPC) problem over graphical models. Based on the loopy max-product methodology, we first develop an iterative belief propagation mechanism (IBPM), which aims to find the most probable configurations facing with the existence of multiple solutions. In applications ranging from low-density parity-check codes to combinatorial optimization one would like to find not just the best configurations but rather than the summary of all possible explanations. Not only can this problem be solved by our proposed loopy message-passing algorithm (LMPA), we also prove that, for tree factor graph models, this LMPA guarantees fast convergence. Moveover, we subsequently present a low-complexity approach to simplifying the message integration operation throughout the whole belief propagation circulation. Simulations built on various settings demonstrate that both IBPM and LMPA can accurately and rapidly approximate the MPC in acyclic graph with hundreds of variables.     

Security-aware intermediate data placement strategy in scientific cloud workflows

Massive computation power and storage capacity of cloud computing systems allow scientists to deploy data-intensive applications without the infrastructure investment, where large application datasets can be stored in the cloud. Based on the pay-as-you-go model, data placement strategies have been developed to cost-effectively store large volumes of generated datasets in the scientific cloud workflows. As promising as it is, this paradigm also introduces many new challenges for data security when the users outsource sensitive data for sharing on the cloud servers, which are not within the same trusted domain as the data owners. This challenge is further complicated by the security constraints on the potential sensitive data for the scientific workflows in the cloud. To effectively address this problem, we propose a security-aware intermediate data placement strategy. First, we build a security overhead model to reasonably measure the security overheads incurred by the sensitive data. Second, we develop a data placement strategy to dynamically place the intermediate data for the scientific workflows. Finally, our experimental results show that our strategy can effectively improve the intermediate data security while ensuring the data transfer time during the execution of scientific workflows.     

Data-based adaptive online prediction model for plant-wide production indices

A data-based adaptive online prediction model is proposed for plant-wide production indices based on support vector regression, a general method which we customized specifically to model very large data sets that are generated dynamically and periodically. The proposed model can update its parameters online according to the statistical properties of the training samples. Further, in order to improve the prediction precision, each sample is weighted with a dynamic penalty factor that considers the effect of each sample on the prediction model accuracy. Moreover, a customized procedure is introduced to handle large training sets. After having been convincingly evaluated on benchmark data, effectiveness and performance of our approach for plant-wide production indices is demonstrated using industrial data from an operating ore dressing plant over a range of scale in training data set size. The higher accuracy and shorter computation times than existing methods suggest that it may prove advantageous in actual application to dynamic production processes.     

Catalytic activities and properties of mesoporous sulfated Al2O3–ZrO2

Mesoporous sulfated Al₂O₃–ZrO₂ (MSAZ) catalysts with large surface areas and pore volumes after calcination at high temperature (650 °C) and with higher Al₂O₃ content than 20wt% were successfully prepared from a template of block copolymer (P84). The MSAZ catalysts were characterized by X-ray diffraction (XRD), N₂ adsorption, transmission electron microscopy (TEM), ²⁷Al magic-angle spinning nuclear magnetic resonance (MAS NMR), thermogravimetric analysis (TG–DTG), temperature-programmed desorption of ammonia (NH₃-TPD) and infrared spectra (IR) of adsorbed pyridine. It is shown that the resulting mesostructured sulfated Al₂O₃–ZrO₂ samples have a well-developed textural mesoporosity. The number of acid sites present on MSAZ catalysts is higher than that on conventional sulfated zirconia, and the former catalysts are more active than the latter one for various acid-catalyzed reactions.     

远红外日用陶瓷釉的研究

通过在普通日用陶瓷釉中掺入远红外陶瓷粉,研究了远红外陶瓷粉对釉面质量(白度、光泽度、显微硬度)及远红外陶瓷釉辐射率的影响,探讨了不同分散剂对远红外陶瓷粉在基釉中的分散情况及其对陶瓷釉显微结构的影响。制备出了符合国家日用瓷质量标准、且具有高的远红外辐射性能的远红外日用陶瓷釉。     

A helical drive in-pipe robot based on compound planetary gearing

The modern society is fuelled by very comprehensive grids of gas and liquid pipelines. In recent years, various in-pipe robots have been developed for inspection and maintenance tasks inside such pipes. In this paper, a novel in-pipe robot is proposed and developed for gas/oil well interventions at thousands of meters downhole. Due to the nature of such intervention, in-pipe robot design must be capable of carrying a very large payload, as large as 2500?N inside a pipe with diameter as small as 54?mm. The proposed design concept is based on a compound planetary gearing system. One of the major novelties of this design is the use of pipe wall as a ring gear for one stage of the compound planetary gear system; the other novelty is the generation of helical angle when the planetary gears are expanded to press on the pipe wall. The proposed concept is compact, efficient, and has never been reported before. In this paper, the helical angle, the velocity, and load capability of the proposed system will be analyzed. The load transportation capability of the proposed robot is also measured based on an experiment. Initial data have shown great potential in carrying large payloads.     

一种基于ARM的集中器的硬件实现

介绍了一种以ARM9为核心,以Windows CE5.0为操作系统的集中器。它符合国家电网的Q/GDW 374.2-2009技术标准,可以在国家电网中使用。给出了集中器的系统框图,介绍了集中器的主要实现电路,论述了其主要工作原理。     

Adaptive Neural Network Finite-Time Control for Uncertain Robotic Manipulators

An adaptive neural network finite-time controller (NNFTC) for a class of uncertain nonlinear systems is proposed by using the backstepping method, which employs an adaptive neural network (NN) system to approximate the structure uncertainties and uses a variable structure term to compensate the approximation errors, thus improving the robustness of the system to external disturbances. The controller is then applied to uncertain robotic manipulators, with a control objective of driving the system state to the original equilibrium point. It is proved that the closed-loop system is finite-time stable. Moreover, simulated and experimental results indicate that the proposed NNFTC is effective and robust.