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Staff evaluation and selection involves conducting job- and job-specific analyzes, with the aim of fully and multidimensional describing professional activity and capturing individual tasks at work. Knowing the person, his individual characteristics, establishing a psychological diagnosis is important in order to recruit and employ, promote or reorient a person. The essence of any organization lies in human effort, and its efficiency and effectiveness are largely influenced by the behavior of the people within the organization. Competent, well-organized and motivated employees are a strategic resource and the stake of a company's success.

It is undeniable that the last years have been dominated by an extensive development of computer and mobile hardware/software and Internet access that has become more dynamic, powerful and affordable. This growth influences our lives in all its aspects but more importantly it influences, now more than ever, our education. The growing use of digital learning platforms blurs the lines between mobile phones, tablets and desktop computers and opens multiple possibilities for educational programs. Using different devices for eLearning and/or mLearning (from the first levels to Lifelong Learning) has become essential in our existence and with this, the need of using our own devices in the process of learning, in business, or in vast array of conncted domains. From a cyber security point of view, the BYOD perspective has a great future in education but also in the business area due to its benefits (in equipment cost or maintenance, human and time resources). Regardless of the benefits, however, studies have also identified issues concerning security (being connected from your own device but using the resources from different organizations), because most of those systems are open and interconnected, the security aspect is a challenge which has to be addressed. This paper highlights the trends in eLearning and mLearning and the extent to which using BYOD poses cyber security threats to educational organizations. Moreover, this article highlights a few possible scenarios that could represent the future of eLearning and mLearning based on lessons learned, best practices, cyber security practices and cyber security education.

In this paper, we present an algorithm for image registration utilizing the shearlet representation. The shearlet framework allows one to collect multi-scale and multi-directional feature information from multidimensional data that can be used to create key feature vectors that are scale, rotation, and shift invariant. These key feature vectors produce a transformation that will align the sensed image to the source image. We demonstrate our registration algorithm on various medical databases.

The purpose of this work is to evaluate the performance of a computer based analysis system aimed at the quantitative detection of changes in hip osteolytic lesions in subjects with hip implants. The computer system is based on the supervised segmentation of a baseline x-ray computed-tomography (CT) scan and an automated segmentation of a follow-up CT scan using an object based tracking algorithm. The segmentation process outlines the pelvic bone and lesions present in the pelvis. The size and CT density of the osteolytic lesions are computed in both baseline and follow-up segmentations and the change in both these quantities are evaluated. The system analysis consisted of the direct comparison of the quantitative results obtained from an expert manual segmentation to the quantitative results obtained using the automated system on 20 subjects. The system bias was evaluated by performing forwards and backwards analysis of the CT data. Furthermore, the stability of the proposed tracking system was compared to the variability of the manual tracking. The results show that the system enhances the human ability to detect changes in lesions size and density regardless of the inherent observer variability in the definition of the baseline manual segmentation.

The analysis of the focal changes in the morphology of a tissue such as cartilage is completed through statistical parametric mapping by first detecting the amount of thickness changes; followed by the point by point estimation of the variance in the thickness delta estimation. Once the change and the variance are estimated, the z-map is computed. The z-map is used to compute single change parameters. i.e: volume significant change, area of significant change, average thickness of the significant changes, and D values from the probability distributions. That can be used for treatment decisions.

Abnormal MR findings including cartilage defects, cartilage denuded areas, osteophytes, and bone marrow edema (BME) are used in staging and evaluating the degree of osteoarthritis (OA) in the knee. The locations of the abnormal findings have been correlated to the degree of pain and stiffness of the joint in the same location. The definition of the anatomic region in MR images is not always an objective task, due to the lack of clear anatomical features. This uncertainty causes variance in the location of the abnormality between readers and time points. Therefore, it is important to have a reproducible system to define the anatomic regions. This works present a computerized approach to define the different anatomic knee regions. The approach is based on an algorithm that uses unique features of the femur and its spatial relation in the extended knee. The femur features are found from three dimensional segmentation maps of the knee. From the segmentation maps, the algorithm automatically divides the femur cartilage into five anatomic regions: trochlea, medial weight bearing area, lateral weight bearing area, posterior medial femoral condyle, and posterior lateral femoral condyle. Furthermore, the algorithm automatically labels the medial and lateral tibia cartilage. The unsupervised definition of the knee regions allows a reproducible way to evaluate regional OA changes. This works will present the application of this automated algorithm for the regional analysis of the cartilage tissue.

The objective of this work was to develop a procedure for detecting focal and local thickness cartilage changes due to osteoarthritis (OA). These measurements are used to quantify the disease progression over time. Therefore, it is essential to establish a method for measuring the precision of such changes for each subject involved in a longitudinal study. Secondly, we have developed a method which establishes the variation in natural characteristics, such as shape and thickness within a set of normal and mild OA cartilage thickness maps. This method will allow the analysis of disease progression over time and among subjects participating in clinical studies. Both procedures depend on a previously validated segmentation and computation of the cartilage thickness maps from three dimensional multispectral MRI data.

This work presents a system for realistic computer simulations of quantification systems from three-dimensional (3D) medical images. The system is based on accurate computer models of anatomical parts derived from 3D medical images. The models can be realistically manipulated in the virtual domain reflecting actual scan-rescan situations. The manipulated object can be used to reconstruct 3D images. Furthermore, the model can be used to simulate observer segmentations of the reconstructed anatomy. Because segmentations are fundamental for comprehensive quantification of anatomical structures quantifications performances can be derived from the simulated segmentations. The proposed simulation system has been used to predict joint space width variations between two different imaging protocols without the need of analyzing several volunteers' scans. The system has been used to predict intra observer performance helping in the selection of the best imaging protocol. Performance results and comparison with actual scan rescan performance evaluations are presented.

Methods for quantifying hip prosthesis induced changes in the adjacent bone are of great interest to orthopedics. In this work, we present a semi-automated technique for measuring the differences in bone density between the prosthetic and contra lateral hips within a CT volumetric data set. In order to reliably compare the bone-density measurements between the prosthetic and the contra laterals hips, a standardized zoning was developed. Using a spherical model of the outer surface of the prosthetic cup, the superior volume of the acetabular region was subdivided into four distance zones: 0-1mm, 1-6mm, 6-11mm, and 11-16mm, respectively. Furthermore, these regions were divided into four positional zones: medial, lateral, anterior, and posterior. At the same time, the positional zones were divided into four angular regions 15, 30, 45, and 60 degrees from the apex of the acetabular cup. The bone density is computed as the average density in Hounsfield unit (HU) measured from the CT scan using all the voxels within each of the 64 zones. Preliminary analysis has been completed on 3 subjects with total hip replacement. The zonal densities on the prosthetic hip and the contra lateral hip were computed and compared. Contrary to initial expectations, a paired t-test showed no statistical significance between the prosthetic and contra lateral bone-densities at any of the four distance zones. Further analysis with a larger sample subject is needed to detect differences in bone-density between the hips in the stress/weight bearing areas in the 15 to 30 degree regions. A method for reliably and consistently measuring the bone-density within standardized zones has been developed and applied on prosthetic and contra lateral hips. The average bone-density for each of the zones takes into account the entire volumetric data set within that region, which is a considerable improvement over the subjective, user driven region of interest estimate selected within one slice practiced in previous methods. Therefore, our method will offer a more reliable metric in prosthetic changes within the bone. Furthermore, this method may be applicable to analysis of density measurements of different prosthetis and surgical procedures.

A finite element model of the knee joint could be helpful in providing insight on mechanisms of injury, effects of treatment, and the role of mechanical factors in degenerative conditions. However, preparation of such a model involves many geometric simplifications and input of material properties, some of which are poorly understood. Therefore, a method to compare model predictions to actual behaviors under controlled conditions could provide confidence in the model before exploration of other loading scenarios. Our laboratory has developed a method to apply axial loads to the in vivo human knee during magnetic resonance imaging, resembling weightbearing conditions. Image processing algorithms may then be used to assess the three-dimensional kinematics of the tibia and femur during loading. A three-dimensional model of the tibio-menisco-femoral contact has been generated and the image-based kinematic boundary conditions were applied to investigate the distribution of stresses and strains in the articular cartilage and menisci throughout the loading period. In this study, our goal is to investigate the contact patterns during long term loading of up to twenty minutes in the healthy knee. Specifically, we assess the use of both elastic and poroelastic material properties in the cartilage, and compare model predictions to known loading conditions and images of tissue deformations.Copyright © 2003 by ASME

Ultrasound speckle carries information about the interrogated scattering microstructure. The complex signal is represented as a superposition of signals due to all scatterers within a resolution cell volume, VE. A crossbeam geometry with separate transmit and receive transducers is well suited for such studies. The crossbeam volume, VE is defined in terms of the overlapping diffraction beam patterns. Given the focused piston transducer's radius and focal distance, a Lommel diffraction formulation suitable for monochromatic excitation is used to calculate VE as a function of frequency and angle. This formulation amounts to a Fresnel approximation to the diffraction problem and is not limited to the focal zone or the far field. Such diffraction corrections as VE are needed to remove the system effects when trying to characterize material using moment analysis. Theoretically, VE is numerically integrated within the overlapping region of the product of the transmit-receive transfer functions. Experimentally, VE was calculated from the field pattern of a medium-focused transducer excited by a monochromatic signal detected by a 0.5mm diameter PVDF membrane hydrophone. We present theoretical and experimental evaluations of VE for the crossbeam geometry at frequencies within the transducers' bandwidth, and its application to tissue microstructure characterization.© (2002) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

twice was evaluated using pairwise differences and linear correlation between pairs of measures. The surface area estimate from automatic segmentation gave a linear correlation coefficient of 0.96 between pairs of estimates on the same knee, and the mean absolute pairwise difference was 2.9%. Conclusions: The cartilage surface area estimate obtained from the automatic cartilage segmentation method can separate healthy populations from OA populations with statistical significance (p = 0.014). Since the method is fully automatic it has full intra-scan reproducibility, and the results show that is also has high inter-scan reproducibility (absolute pairwise difference of 2.9%, linear correlation coefficient of 0.96). Because the method is fully automatic, reproducible and can separate healthy populations from OA populations using the cartilage area estimate obtained from the method, it may be a useful tool in clinical studies using MRI.