A new multi criteria decision making approach for medical imaging systems considering risk factors

Effective and good quality imaging is important for medical decision-making and can reduce unnecessary costs and procedures. Therefore, decision making regarding any technology can present serious problems for healthcare centers with multi criteria decision making problems (MCDM). This paper is the first to develop the fuzzy axiomatic design with risk factors (RFAD) approach and to use it in multi attribute comparisons of medical imaging systems in a university hospital. Although most MCDM approaches in the literature treat risk factors as separate criteria, in real life every alternative has its own risks related to each criterion. The proposed approach integrates the risk factors in each criterion and calculates the information content to compare alternatives. This paper applies three different approaches to MCDM problems related to the selection of medical imaging systems for a university hospital.     

Risk factors of enterprise internal control under the internet of things governance: A qualitative research approach

This study aims to (1) define the critical risk factors that influence the governance of enterprise internal control in an IoT environment, and (2) classify the risk factors and study their importance in such an environment. The study uses Gowin’s Vee knowledge map as a research strategy to mitigate the limitations of qualitative research through a set of strict research procedures. In addition, the Delphi method is used to test and provide feedback to justify and revise the critical risk factors. Finally, 83 items were obtained and categorized into eight different types of critical risk factors. For emphasizing how the risk factors of enterprise internal control involve diverse stakeholders, the critical risk factors are further classified based on the three-layer DCM architecture for mapping with various perceptions. The results of this research can be used as a reference in managing risk factors under the IoT environment. In the new generation of IoT governance practice, the related factors can also be regarded as the essential measurement items for enterprises in conducting effective internal control and auditing.     

A survey on essential components of a self-sovereign identity

This paper provides an overview of the Self-Sovereign Identity (SSI) concept, focusing on four different components that we identified as essential to the architecture. Self-Sovereign Identity is enabled by the new development of blockchain technology. Through the trustless, decentralised database that is provided by a blockchain, classic Identity Management registration processes can be replaced.We start off by giving a simple overview of blockchain based SSI, introducing an architecture overview as well as relevant actors in such a system. We further distinguish two major approaches, namely the Identifier Registry Model and its extension the Claim Registry Model.Subsequently we discuss identifiers in such a system, presenting past research in the area and current approaches in SSI in the context of Zooko’s Triangle. As the user of an SSI has to be linked with his digital identifier we also discuss authentication solutions.Most central to the concept of an SSI are the verifiable claims that are presented to relying parties. Resources in the field are only loosely connected. We will provide a more coherent view of verifiable claims in regards to blockchain based SSI and clarify differences in the used terminology.Storage solutions for the verifiable claims, both on- and off-chain, are presented with their advantages and disadvantages.     

How much information is associated with a particular stimulus?

Although the Shannon mutual information can be used to reveal general features of the neural code, it cannot directly address which symbols of the code are significant. Further insight can be gained by using information measures that are specific to particular stimuli or responses. The specific information is a previously proposed measure of the amount of information associated with a particular response; however, as I show, it does not properly characterize the amount of information associated with particular stimuli. Instead, I propose a new measure: the stimulus-specific information (SSI), defined to be the average specific information of responses given the presence of a particular stimulus. Like other information theoretic measures, the SSI does not rely on assumptions about the neural code, and is robust to non-linearities of the system. To demonstrate its applicability, the SSI is applied to data from simulated visual neurons, and identifies stimuli consistent with the neuron's linear kernel. While the SSI reveals the essential linearity of the visual neurons, it also successfully identifies the well-encoded stimuli in a modified example where linear analysis techniques fail. Thus, I demonstrate that the SSI is an appropriate measure of the information associated with particular stimuli, and provides a new unbiased method of analysing the significant stimuli of a neural code.     

Korean science teachers’ perceptions of the introduction of socio‐scientific issues into the science curriculum

Abstract:

This study examined Korean secondary science teachers’ perceptions of the introduction of socio‐scientific issues (SSI) into the science curriculum. The guiding research questions were (a) What are teachers’ perceptions of SSI and the place of these issues in the science curriculum? and (b) What are some factors that impede or facilitate addressing SSI in Korean secondary classrooms? The factors that were explored included teachers’ perceptions of the necessity of addressing SSI, teachers’ personal science‐teaching efficacy (PSTE) beliefs regarding SSI, and other relevant situational factors. The 86 participants (65% female) completed a questionnaire comprising Likert‐type and open‐ended questions. Next, follow‐up, semi‐structured interviews were conducted with a random sample of 12 participants. Results indicated that participants perceived a need to address SSI positively. However, only a minority dealt with such issues in their classrooms and then only sporadically. Participants had low PSTE beliefs related to teaching about SSI. They perceived the lack of instructional time and the unavailability of relevant materials as the primary obstacles that hindered the teaching of SSI. It is argued that the culture of pre‐college science education in Korea, which is still largely dominated by a preoccupation with preparing students for admission to institutions of higher education, subsumes those obstacles identified and experienced by the participant teachers.     

Review of studies that have used the Standard Shiftwork Index: evidence for the underlying model of shiftwork and health

The Standard Shiftwork Index (SSI) provides shiftwork researchers with a standardised battery for assessing the impact of shiftwork on psychological and physiological well-being. We review the published SSI literature and evaluate the extent to which the results of these publications support the model underpinning the SSI. A total of 70 SSI publications were identified, of which 41 were selected. The 41 studies were published between 1993 and 2006 and had a combined total of 21,420 respondents. The analysis indicated support for parts of the SSI model, particularly in relation to the impact of shift systems design on individual well-being, as well as the role of individual factors (e.g., personality, coping style). However, evidential support for the model as a whole is incomplete. The findings of the reviewed studies are summarised and methodological limitations are discussed.     

Haptic palpation for medical simulation in virtual environments

Palpation is a physical examination technique where objects, e.g., organs or body parts, are touched with fingers to determine their size, shape, consistency and location. Many medical procedures utilize palpation as a supplementary interaction technique and it can be therefore considered as an essential basic method. However, palpation is mostly neglected in medical training simulators, with the exception of very specialized simulators that solely focus on palpation, e.g., for manual cancer detection. In this article we propose a novel approach to enable haptic palpation interaction for virtual reality-based medical simulators. The main contribution is an extensive user study conducted with a large group of medical experts. To provide a plausible simulation framework for this user study, we contribute a novel and detailed interaction algorithm for palpation with tissue dragging, which utilizes a multi-object force algorithm to support multiple layers of anatomy and a pulse force algorithm for simulation of an arterial pulse. Furthermore, we propose a modification for an off–the–shelf haptic device by adding a lightweight palpation pad to support a more realistic finger grip configuration for palpation tasks. The user study itself has been conducted on a medical training simulator prototype with a specific procedure from regional anesthesia, which strongly depends on palpation. The prototype utilizes a co-rotational finite-element approach for soft tissue simulation and provides bimanual interaction by combining the aforementioned techniques with needle insertion for the other hand. The results of the user study suggest reasonable face validity of the simulator prototype and in particular validate medical plausibility of the proposed palpation interaction algorithm.     

Test Generation Techniques

Existing test generation procedures are rooted in the SSI/MSI era. New techniques will cope with today's vastly more complicated LSI/VLSI systems.     

An improved Gabor wavelet transform and rough K-means clustering algorithm for MRI brain tumor image segmentation

Multimedia Tools and Applications - Image processing is significant in the medical field which provides detailed information about medical images and image segmentation is an essential part of...     

The efficacy of a medical virtual reality simulator for training phlebotomy

OBJECTIVE: The present study compared the effectiveness of a virtual reality (VR) simulator for training phlebotomy with that of a more traditional approach using simulated limbs. BACKGROUND: Phlebotomy, or drawing blood, is one of the most common medical procedures; yet, there are no universal standards for training and assessing performance. The absence of any standards can lead to injuries and inaccurate test results if the procedure is improperly performed. METHOD: Twenty 3rd-year medical students were trained under one of the two methods and had their performance assessed with a 28-item checklist. RESULTS: The results showed that performance improvements were limited to those who trained with the simulated limbs, and a detailed comparison of the two systems revealed several functional and physical differences that may explain these findings. CONCLUSION: Participants trained with simulated limbs performed better than those trained with a VR simulator; however, the metrics recorded by the VR system may address some aspects of performance that could eventually prove beneficial. APPLICATION: The present study highlights the potential for medical simulators to improve patient safety by enabling trainees to practice procedures on devices instead of patients. Applications of this research include training, performance assessment, and design of simulator systems.     

Robotic surgery setup simulation with the integration of inverse-kinematics computation and medical imaging

At present, there are representative robot operation systems such as da Vinci and ZEUS which have realized minimally invasive surgery by the use of dexterous manipulators. In the operating room, medical staff must prepare and set up an environment in which the robot has optimal freedom of motion and its functions can be fully demonstrated for every case. The range of motion in which the robot can reach and be maneuvered is restricted by the fixed point of the trocar site. We have developed a preoperative planning system with the function of volume rendering of medical images and automatic positioning by applying an inverse-kinematics computation of surgical robot. The motion of a surgical robot can be simulated in advance with the intuitive interface and kinematics computation program running in the background of the system. If robotic surgery planning with volume rendering of DICOM images is possible, the discussion of a surgical plan can be directly made just after the diagnosis considering the patient-specific structure. This kind of setup platform would be essential for the future introduction of surgical robotics into an operating room.     

Targeting investments in small-scale groundwater irrigation using Bayesian networks for a data-scarce river basin in Sub-Saharan Africa

Irrigation for smallholder farming systems is an important approach for sustainable intensification and increased productivity in Sub-Saharan Africa, provided investments in irrigation are properly targeted and accompanied by complementary improvements. Many GIS-based tools have been developed to identify suitable areas for investments in different types of small scale irrigation (SSI), but they do not explicitly address uncertainty on the data input and on the determination of factors that affect success of an investment in a given context. This paper addresses this problem by presenting an application of a decision-support targeting tool based on Bayesian networks (BNs) that can be used by non-expert policy-makers and investors to assess the potential success of specific technologies used for groundwater-based SSI. A case study application for the White Volta Basin in West Africa is presented to illustrate the BN approach.     

基于神经网络和框架推理的风险应对专家系统

为了提高飞机装配项目风险应对的效率及可靠性,提出一种将神经网络和框架知识系统相结合的风险应对方法。该方法基于框架表示理论,以方案与风险因子之间存在映射为规则,将专家风险应对经验存储于专家系统知识库之中。使训练后的神经网络以连接权的形式获取知识。使用时,运用匹配算法对神经网络输出和知识库中的知识进行推理匹配,得到最终应对方案。该方法在某型飞机装配项目的风险管理中得到应用,验证了方法的有效性。     

基于判别式受限玻尔兹曼机的医学图像分类法

随着计算机技术的发展,越来越多的医学图像分析技术应运而生.利用数据挖掘方法对医学图像做分析是目前研究的热点之一,该方法首先从医学图像中提取统计特征,在此基础上进一步挖掘,这种方法对所提取的特征有很强的依赖性而且受到经验等主观因素的影响.针对乳腺X光图像,采用一种可以从图像中自动学习特征并利用学习到的特征对图像进行分类的医学图像分析新方法——判别式受限玻尔兹曼机(Discriminative Restricted Boltzmann Machine,DRBM).DRBM是一种无向判别模型,它可以自动地从图像中学习特征.在乳腺X光图像标准数据集上的实验结果表明,DRBM对医学图像的分类准确率明显高于其它基于统计特征提取的医学图像分类方法.     

ADAPTIVE STRATEGIES FORNC MACHINING COMPOUND FREE-FORM SURFACES

ＡＤＡＰＴＩＶＥＳＴＲＡＴＥＧＩＥＳＦＯＲＮＣＭＡＣＨＩＮＩＮＧＣＯＭＰＯＵＮＤＦＲＥＥ－ＦＯＲＭＳＵＲＦＡＣＥＳＧａｏＳａｎｄｅ；ＺｈｏｕＹｕｎｆｅｉ；ＺｈａｎｇＸｉｎｆａｎｇ；ＺｈｏｕＪｉＡＤＡＰＴＩＶＥＳＴＲＡＴＥＧＩＥＳＦＯＲＮＣＭＡＣＨＩＮ...     

Designing a Framework for Digital KYC Processes Built on Blockchain-Based Self-Sovereign Identity

Know your customer (KYC) processes place a great burden on banks, because they are costly, inefficient, and inconvenient for customers. While blockchain technology is often mentioned as a potential solution, it is not clear how to use the technology’s advantages without violating data protection regulations and customer privacy. We demonstrate how blockchain-based self-sovereign identity (SSI) can solve the challenges of KYC. We follow a rigorous design science research approach to create a framework that utilizes SSI in the KYC process, deriving nascent design principles that theorize on blockchain’s role for SSI.     

Fuzzy cognitive map architectures for medical decision support systems

Medical decision support systems can provide assistance in crucial clinical judgments, particularly for inexperienced medical professionals. Fuzzy cognitive maps (FCMs) is a soft computing technique for modeling complex systems, which follows an approach similar to human reasoning and the human decision-making process. FCMs can successfully represent knowledge and human experience, introducing concepts to represent the essential elements and the cause and effect relationships among the concepts to model the behavior of any system. Medical decision systems are complex systems that can be decomposed to non-related and related subsystems and elements, where many factors have to be taken into consideration that may be complementary, contradictory, and competitive; these factors influence each other and determine the overall clinical decision with a different degree. Thus, FCMs are suitable for medical decision support systems and appropriate FCM architectures are proposed and developed as well as the corresponding examples from two medical disciplines, i.e. speech and language pathology and obstetrics, are described.     

Overview of Human Factors and Ergonomics in the OR,with an Emphasis on Minimally Invasive Surgeries

Operating rooms (OR) are where medical teams work to improve the health of a patient. However, because of the complexity of the procedures, errors and unsafe situations are likely to occur. These complications can lead to harm to the patient or medical professionals. Human factors and ergonomic professionals have been working to improve these unsafe conditions in the OR for over a century. However, with advances in technology, increased surgical complexity, as well as an increase in medical team members in the OR, there are still numerous improvements yet to be accomplished. Minimally invasive surgeries require an even more advanced approach to prevent errors due to technology, fatigue, and miscommunication. Human factors and ergonomic techniques provide the ability to greatly improve conditions for patients and surgeons alike. It is imperative that human factors and ergonomics continue to grow and facilitate technological advancements in healthcare. © 2012 Wiley Periodicals, Inc.