Search results

The health workforce of the 21st century has enormous challenges; health professionals need to be both experts in their field and equipped with leadership and managerial skills. These skills are not part of the regular curriculum, so specific programs bridging this gap are required. Since 2001, FAIMER®, with eight centers across the globe, has worked to create health professions education leaders through transformational learning experiences, developing a global community of practice encompassing over 40 countries. We describe the design, implementation, evaluation, and evolution of the leadership and management curriculum component of the global Institute over 15 years. The curriculum is developed and updated through practices that keep faculty and fellows connected, aligned, and learning together. The article highlights the unique features, challenges faced, and sustainability issues. With a robust mixed methods evaluation, there are substantial reasons to believe that the model works, is adaptable and replicable to meet local needs. The program is playing an important role of answering the call for training positive, strengths-based, collaborative leaders who are socially accountable and embrace the challenges for high quality equitable health care around the globe

In this application paper, we present an analytical process to identify teaching/learning (T/L) methods used in leadership education. Applying this process to a global program for leadership development of healthcare professionals, we highlight nine methods that teachers most often used, and learners viewed as most impactful. Seven of the pedagogies identified were aligned with literature, indicating the applicability of the process for leadership education in general. We identified two methods that had not been previously or explicitly described and that learners validated as important: building a respectful and inclusive environment and sharing personal narratives. These methods appear critical for success in a diverse group of learners. The process we describe for analyzing T/L methods will be a useful addition for designers of leadership development programs.

At airport security checkpoints, baggage screening is aimed to prevent transportation of prohibited and potentially dangerous items. Observing the projection images generated by X-rays scanner is a critical method. However, when multiple objects are stacked on top of each other, distinguishing objects only by a two-dimensional picture is difficult, which prompts the demand for more precise imaging technology to be investigated for use. Reconstructing from 2D X-ray images to 3D-computed tomography (CT) volumes is a reliable solution.

To more accurately distinguish the specific contour shape of items when stacked, multi-information fusion network (MFCT-GAN) based on generative adversarial network (GAN) and U-like network (U-NET) is proposed to reconstruct from two biplanar orthogonal X-ray projections into 3D CT volumes. The authors use three modules to enhance the reconstruction qualitative and quantitative effects, compared with the original network. The skip connection modification (SCM) and multi-channels residual dense block (MRDB) enable the network to extract more feature information and learn deeper with high efficiency; the introduction of subjective loss enables the network to focus on the structural similarity (SSIM) of images during training.

On account of the fusion of multiple information, MFCT-GAN can significantly improve the value of quantitative indexes and distinguish contour explicitly between different targets. In particular, SCM enables features more reasonable and accurate when expanded into three dimensions. The appliance of MRDB can alleviate problem of slow optimization during the late training period, as well as reduce the computational cost. The introduction of subjective loss guides network to retain more high-frequency information, which makes the rendered CT volumes clearer in details.

The authors' proposed MFCT-GAN is able to restore the 3D shapes of different objects greatly based on biplanar projections. This is helpful in security check places, where X-ray images of stacked objects need to be distinguished from the presence of prohibited objects. The authors adopt three new modules, SCM, MRDB and subjective loss, as well as analyze the role the modules play in 3D reconstruction. Results show a significant improvement on the reconstruction both in objective and subjective effects.

A user-friendly HTML-based open-source software has been developed for structural shielding design of medical X-ray imaging facilities. Based on values published by the NCRP Report N° 147 the software allows thickness calculations for different materials used in conventional X-ray rooms, mammography rooms and computed tomography rooms, diminishing errors resulting from the use of curves. The software focuses on the optimization principle by considering workload distributions instead of applying all the workload at a single high operating potential. The input data can be those recommended by the NCRP Report N° 147 or, if the facility has its own data, they can be used instead. With the implemented methodology, the code validation was performed by comparison of the results with a study case provided by the Report. The software application is available in two languages (English and Spanish) and provides the accuracy of the method presented, as well as assisting the physicist in shielding computations in a user-friendly manner. This software tool is available upon request to the corresponding author.

This paper aims to deal with the design optimization of a synchronous reluctance machine to be used in an X-ray tube, where the goal is to maximize the torque while keeping low the amount of material used, by means of gradient-based free-form shape optimization.

The presented approach is based on the mathematical concept of shape derivatives and allows to obtain new motor designs without the need to introduce a geometric parametrization. This paper presents an extension of a standard gradient-based free-form shape optimization algorithm to the case of multiple objective functions by determining updates, which represent a descent of all involved criteria. Moreover, this paper illustrates a way to obtain an approximate Pareto front.

The presented method allows to obtain optimal designs of arbitrary, non-parametric shape with very low computational cost. This paper validates the results by comparing them to a parametric geometry optimization in JMAG by means of a stochastic optimization algorithm. While the obtained designs are of similar shape, the computational time used by the gradient-based algorithm is in the order of minutes, compared to several hours taken by the stochastic optimization algorithm.

This paper applies the presented gradient-based multi-objective optimization algorithm in the context of free-form shape optimization using the mathematical concept of shape derivatives. The authors obtain a set of Pareto-optimal designs, each of which is a shape that is not represented by a fixed set of parameters. To the best of the authors’ knowledge, this approach to multi-objective free-form shape optimization is novel in the context of electric machines.