Diazepam-ketamine anaesthesia for open heart surgery a "micro-mini" drip administration technique

Two hundred open-heart cases were anaesthetized with a diazepam-ketamine combination. The results were excellent. A "Micro-Mini" drip technique insured low, even, but adequate dose levels of ketamine and less drug was used. Induction and maintenance are simple and smooth. Effects on the cardiovascular system and respiratory system are minimal. The margin of safety is wide and 100% oxygen can be used whenever needed.     

Preparation of poly(acrylic acid)–chitosan hydrogels by gamma irradiation and in vitro drug release

Biocompatible and biodegradable pH‐responsive hydrogels based on poly(acrylic acid) (AAc) and chitosan were prepared for controlled drug delivery. These interpolymeric hydrogels were synthesized by a gamma irradiation polymerization technique. The degree of gelation was over 96% and increased as the chitosan or acrylic acid content increased. The equilibrium swelling studies of hydrogels prepared in various conditions were carried out in an aqueous solution, and the pH sensitivity in the range of pH 1–12 was investigated. The AAc/chitosan hydrogels showed the highest water content when the 30 vol % AAc and 0.1 wt % chitosan were irradiated with a 30‐kGy radiation dose. Also, an increase of swelling degree with an increase in the pH was noticed and showed the highest value at pH 12. The drug, 5‐fluorouracil, was loaded into these hydrogels and the release studies were carried out in simulated gastric and intestinal fluids. The in vitro release profiles of the drugs showed that more than 90% of the loaded drugs were released in the first 1 h at the intestinal pH and the rest of the drug had been released slowly. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 90: 3660–3667, 2003     

Nanostructural and optical features of nc-Si:H films prepared by nickel-induced crystallization

A 30-nm-thick Ni layer was deposited on top of the nc-Si:H (hydrogenated nanocrystalline Si) films by rf-magnetron sputter, and then heat-treatments were carried out at temperatures of 350–500^∘C. Si nanocrystallites were formed in the Ni/nc-Si:H bilayer films during the post-deposition heat-treatments. The intensity of the photoluminescence spectra of the post-deposition heat-treated films gradually increased at wavelengths of ∼420 as well as ∼580 nm with raising the annealing temperature from 350 to 500^∘C. It is highly likely that the increase of the photoluminescence intensity is caused by the increase in the total volume of the nanocrystallites in the films. It was found that the nickel-induced crystallization processing enhanced the formation of Si cystallites with the size of ∼2 and ∼5 nm in the films.     

Electrospun PEDOT:PSS/PVP nanofibers as the chemiresistor in chemical vapour sensing

Herein, PEDOT:PSS/PVP nanofibers were produced by electrospinning. The presence of PEDOT:PSS in the nanofibers was confirmed by FT-Raman spectroscopy. The applied voltage-dependent diameter of PEDOT:PSS/PVP nanofibers was observed. Also, sensing behaviors of electrospun PEDOT:PSS/PVP nanofibers were explored by measuring its response upon cyclic exposure to organic vapours such as ethanol, methanol, THF, and acetone at room temperature. When PEDOT:PSS/PVP nanofibers were exposed to each solvent, the protic and aprotic solvents resulted in opposite electrical responses. These findings exhibit that electrospun PEDOT:PSS/PVP nanofibers are the promising candidate for the organic vapour sensing material.     

A novel iterative shape from focus algorithm based on combinatorial optimization

Shape from focus (SFF) is a technique to estimate the depth and 3D shape of an object from a sequence of images obtained at different focus settings. In this paper, the SFF is presented as a combinatorial optimization problem. The proposed algorithm tries to find the combination of pixel frames which produces maximum focus measure computed over pixels lying on those frames. To reduce the high computational complexity, a local search method is proposed. After the estimate of the initial depth map solution of an object, the neighborhood is defined, and an intermediate image volume is generated from the neighborhood. The updated depth map solution is found from the intermediate image volume. This update process of the depth map solution continues until the amount of improvement is negligible. The results of the proposed SFF algorithm have shown significant improvements in both the accuracy of the depth map estimation and the computational complexity, with respect to the existing SFF methods.     

Effective pedestrian detection using deformable part model based on human model

Recently, pedestrian detection systems have become an important technology in the development of the advanced driver assistance system (ADAS) for the autonomous car. The histogram of oriented gradients (HOG) is currently the most basic algorithm for detecting pedestrians, but it treats the entire body of the pedestrian as one single feature. In other words, if the entire body of the pedestrian is not visible, the detection rate under HOG decreases markedly. To solve this problem, we propose a detection system using a deformable part model (DPM) that divides the pedestrian data into two parts using a latent support vector machine (SVM)-based machine-learning technique. Experimental results show that our approach achieves better performance in a detection system than the existing method. In practice, there are many occlusions in the environment in front of the vehicle. For example, the surrounding transport facilities, such as a car or another obstacle, can occlude a pedestrian. These occlusions can increase the false detection rate and cause difficulties during the detection process. Our proposed method uses a different approach and can easily be applied in real-world scenarios, regardless of occlusions.

     

An exploratory study of radio frequency identification (RFID) adoption in the healthcare industry

This study examines the radio frequency identification (RFID) adoption decision process and proposes a model predicting the likelihood of adopting RFID within organizations in the healthcare industry. A considerable number of studies have been conducted regarding organizational information technology (IT) adoption, but the nature of the organizational IT adoption process is still not well understood. Especially, although there are a number of variables and categories that have been found empirically to be related to adoption behavior, there is little in the way of evidence to suggest the origin or motivation behind the adoption. Thus, this study investigates the underlying motivations and driving forces behind the adoption of RFID using the theory of technology-push and need-pull. In this study, an organizational RFID adoption model is proposed and empirically tested by a survey using a sample of 126 senior executives in U.S. hospitals. The model posits that three categories of factors, technology push, need pull, and presence of champions, determine the likelihood of adopting RFID within organizations. This study also found that the relationships between those three categories and the likelihood of adopting RFID are strengthened or weakened by organizational readiness.     

An Empirical Study Evaluating Social Networking Continuance and Success

Social networking site (SNS) use decisions have led to major economic and social transformations worldwide. While many organizations seek to use SNSs from a strategic perspective to reach their customer, it is important to understand what makes SNSs successful in order to use them for competitive purposes. The current research evaluates the influence of the social capital theory on SNS success measures. A model was developed and empirically tested using two data samples to ensure valid and reliable results for success of SNSs. The results display the importance of social capital in SNS success followed by practitioner and academic implications.     

Design and development of a maintenance and virtual training system for ancient Chinese architecture

Ancient Chinese architecture is an important aspect of traditional Chinese culture and has been studied by many scholars around the world via historical documents, photographs, and three-dimensional models. In this paper, a building information model (BIM) and virtual reality (VR) and video analysing technology are used to develop a maintenance and virtual training system for ancient architecture. A digital ancient architecture model that includes a three-dimensional model and attributes is established, and the model can be visualized using a VR video processing system. Based on this system, we propose a method of fire detection in the maintenance system to ensure the safety of ancient buildings. After performing lightweight processing of the three-dimensional model, the Forge platform, which can achieve high-speed browsing via Web browsers, is used to perform the virtual construction, dismantling and other functions. By providing an immersive experience, users will develop a deeper understanding of ancient architectural structures and construction processes, which will accelerate research on ancient architecture.