The development of a framework for enterprise security architecture and its application in organizations

The main purpose of this study is to develop an enterprise security framework that is based on a comprehensive following the Zachman EA architecture. The enterprise architecture (EA) is a long-term view or blueprint for an organization. It is a very important blueprint for balancing business and IT technology and for adding value to an organization. Security is also an essential dimension for enterprises nowadays. This paper will incorporate the security dimension with the Zachman EA framework, which intends to serve as an enterprise security framework in assisting an organization to successfully and effectively implement security

Software for Modeling Ultrasound Breast Cancer Imaging

Computer-based models are increasingly used in biomedical imaging research to clarify links between anatomical structure, imaging physics, and the information content of medical images. A few three-dimensional breast tissue software models have been developed for mammography simulations to optimize current mammography systems or to test novel systems. It would be beneficial in the development of ultrasound breast imaging to have a similar computational model for simulation. A three-dimensional breast anatomy model with the lobular ducts, periductal and intralobular loose fibrous tissue, interlobular dense fibrous tissue, fat, and skin has been implemented. The parenchymal density of the model can be varied from about 20 to 75% to represent a range of clinically relevant densities. The anatomical model was used as a foundation for a three-dimensional breast tumour model. The tumour model was designed to mimic the ultrasound appearance of features used in tumour classification. Simulated two-dimensional ultrasound images were synthesized from the models using a first-order k-space propagation simulator. Similar to clinical ultrasound images, the simulated images of normal breast tissue exhibited non-Rayleigh speckle in regions of interest consisting of primarily fatty, primarily fibroglandular, and mixed tissue types. The simulated images of tumours reproduced several shape and margin features used in breast tumour diagnosis. The ultrasound wavefront distortion produced in simulations using the anatomical model was evaluated and a second method of modeling wavefront distortion was also proposed in which 10 to 12 irregularly shaped, strongly scattering inclusions were iii superimposed on multiple parallel time-shift screens to create the screen-inclusion model. Simulations of planar pulsed wave propagation through the two proposed models, a conventional parallel time-shift screen model, and digitized breast tissue specimens were compared. The anatomical model and screen-inclusion model were able to produce arrival-time fluctuation and energy-level fluctuation characteristics comparable to the digitized tissue specimens that the parallel-screen model was unable to reproduce. This software is expected to be valuable for imaging simulations that require accurate and detailed representation of the ultrasound characteristics of breast tumours

Design and Estimation of an AUV Portable Intelligent Rescue System Based on Attitude Recognition Algorithm

This research is based on the attitude sensing algorithm to design a portable intelligent rescue system for autonomous underwater vehicles (AUVs). To lower the possibility of losing the underwater vehicle and reduce the difficulty of rescuing, when an AUV intelligent rescue system (AIRS) detects the fault of AUVs and they could not be reclaimed, AIRS can pump carbon dioxide into the airbag immediately to make the vehicle resurface. AIRS consists of attitude sensing module, double-trigger inflator mechanism, and activity recognition algorithm. The sensing module is an eleven-DOF sensor that is made up of a six-axis inertial sensor, a three-axis magnetometer, a barometer, and a thermometer. Furthermore, the signal calibration and extended Kalman filter (SC-EKF) is proposed to be used subsequently to calibrate and fuse the data from the sensing module. Then, the attitude data are classified with the principle of feature extraction (FE) and backpropagation network (BPN) classifier. Finally, the designed double-trigger inflator can be triggered not only by electricity but also by water damage when the waterproof cabin is severely broken. With the AIRS technology, the safety of detecting and investigating the use AUVs can be increased since there is no need to send divers to engage in the rescue mission under water

A Framework for Enterprise Security Architecture and Its Application in Information Security Incident Management

An enterprise architecture (EA) plan is a long-term view or blueprint for an organization. It is a very important blueprint for balancing business and Information Technology (IT) and for adding value to an organization. Security is also nowadays an essential dimension for enterprises. It can prevent confidential information from being leaked, and/or stolen, lost succumbing to other serious disasters. There are many studies focusing on EA or on specific aspects of security. However, there are very few studies focusing on enterprise security architecture. This paper focuses on integrating the security dimension into the Zachman EA framework (Zachman, 2007) and is intended to serve as an enterprise security framework (ESA) to assist an organization in successfully and effectively implementing security. The efficacy of the ESA implementation is illustrated through an application in an organization

The motivation in two different industries companies—manufacture and service—in Taiwan

The importance of motivation has been noticed in the recent decades. It is because motivation has the function of enhancing the efficiency of the company’s operation without increase the cost. However, motivation is a complicated issue that many researchers are challenging. Since there are many factors that can influence the attitude and decision of motivation, researchers need to probe into the reasons. For example, what factors are the needs of individuals as well as what affects the individual’s decision process. Motivation is defined by many scholars and there are theories developed in order to explain individual’s behaviour. Usually, researchers examine the notion of motivation through two sorts of theories, which are content theories and process theories. The first type discussed in this research includes Maslow’s hierarchy model, Alderfer’s ERG theory, and Herzberg’s dual-factors theory. On the other hand, Vroom’s expectancy theory, Adams’ equity theory, and Locke’s goal-setting theory are in the sector of process theories. As for the culture dimension, Hofstede’s idea is utilized. These academic views are used to analyze the data collected from the questionnaire. It is concluded that the firm can group the individuals’ various perceptions and the consensus is useful in the modification of the motivation mechanism and the pattern of the motivation attitudes of the firms in research are identified

Systemic Associations with Residual Subretinal Fluid after Ranibizumab in Diabetic Macular Edema

Purpose. To investigate the impact of systemic diseases on the occurrence of subretinal fluid (SRF) in diabetic macular edema (DME) and prognostic factors for residual SRF following three consecutive monthly intravitreal ranibizumab. Methods. Ninety-seven eyes from 68 patients with DME who completed 3 consecutive monthly injections of ranibizumab were enrolled. Systemic parameters mainly included chronic kidney disease (CKD), hypertension, HbA1c, and insulin dependence. Renal parameters for CKD were serum creatinine, estimated glomerular filtration rate (eGFR), and serum albumin. Ocular factors were baseline central macular thickness (CMT), severity of diabetic retinopathy (DR), and status of panretinal photocoagulation (PRP). Results. Chronic kidney disease had significant correlation with baseline SRF (R=0.397, p<0.001 after partial correlation with adjustment for age and DR severity). As for CKD, lower serum albumin, but not eGFR or serum creatinine, was associated with baseline presence of SRF (p=0.026, p=0.08 and p=0.53, resp., after adjustment for age and DR severity). Overall, lower eGFR and lower HbA1c values, contrary to popular belief, predicted the presence of residual SRF following intravitreal injections (p=0.016 and p<0.001, resp.). Conclusions. Tight sugar control and poorer baseline kidney function may slow the resorption of SRF after anti-VEGF injections in patients with DME in the short term

Progressive Transformation Learning for Leveraging Virtual Images in Training

To effectively interrogate UAV-based images for detecting objects of interest, such as humans, it is essential to acquire large-scale UAV-based datasets that include human instances with various poses captured from widely varying viewing angles. As a viable alternative to laborious and costly data curation, we introduce Progressive Transformation Learning (PTL), which gradually augments a training dataset by adding transformed virtual images with enhanced realism. Generally, a virtual2real transformation generator in the conditional GAN framework suffers from quality degradation when a large domain gap exists between real and virtual images. To deal with the domain gap, PTL takes a novel approach that progressively iterates the following three steps: 1) select a subset from a pool of virtual images according to the domain gap, 2) transform the selected virtual images to enhance realism, and 3) add the transformed virtual images to the training set while removing them from the pool. In PTL, accurately quantifying the domain gap is critical. To do that, we theoretically demonstrate that the feature representation space of a given object detector can be modeled as a multivariate Gaussian distribution from which the Mahalanobis distance between a virtual object and the Gaussian distribution of each object category in the representation space can be readily computed. Experiments show that PTL results in a substantial performance increase over the baseline, especially in the small data and the cross-domain regime.Comment: CVPR 2023 (Selected as Highlight