Contemporary achievements in astronautics: Salyut-7, the Vega Project and Spacelab

The latest achievements in Soviet aeronautics are described; the new stage in the space program to study Venus using Soviet automated space probes, and the next space mission by cosmonauts to the Salyut-7 station. Information is also presented on the flight of the Spacelab orbiting laboratory created by Western European specialists

Workshop on R&D projects: proceedings

P.PORTO Research Workshops are thematic meetings, to present and discuss R&D activities and outcomes – be it in the form of new knowledge, applied technology, industrial or intellectual property – providing a space for debate, networking and creation of synergies. This volume provides the contributions of the Research Workshop of April 2019, dedicated to the set of R&D projects led by P.PORTO researchers, in collaboration with companies and end-users, in the scope of the national projects call 02/SAICT/2016.info:eu-repo/semantics/publishedVersio

Art therapy with latency period boys exhibiting overt anger and/or aggression

The aim of this study is to determine the effectiveness of using art as therapy, with latency period boys, to develop anger management skills. The respondents for this study were male children selected by an outpatient psychiatric clinic, using the criteria of age (latency period) and problem (anger/aggression). The six subjects assigned to the group agreed to attend a therapy session for one hour per week for six consecutive weeks. Sessions involved the use of art to therapeutically achieve the aims of anger management and ego strength, while incorporating projective drawing tests as part of the evaluative process. The Achenbach Child Behaviour Check List was used as a pre- and post-test to determine perceivable changes in each cbild1S anger management skills. The naturalistic mode of research was used to attend to the credibility of the study. The data obtained is information verifying rather than information generating. Many young clients who enter therapy are treated for a conspicuous difficulty which may include impaired social skills or learning difficulties, while for many the underlying problem of anger is the primary issue. To surmount this anomaly there has been negligible art therapy research into anger management from which a therapeutic approach could be developed and/or replicated. In this study, research on anger therapy is divided into three schools of thought: the Humanistic, the Gestalt and Cognitive- Behavioural therapies, none of which are conclusive in their treatment of anger management. As many of the models appear promising, an eclectic approach based on art therapy is perceived by the author as the most desirable and is used in this study. The knowledge gained in this study provides a basis for further art therapy research in the clinical and private sector

Treatment Planning Automation for Rectal Cancer Radiotherapy

Background Rectal cancer is a common type of cancer. There is an acute health disparity across the globe where a significant population of the world lack adequate access to radiotherapy treatments which is a part of the standard of care for rectal cancers. Safe radiotherapy treatments require specialized planning expertise and are time-consuming and labor-intensive to produce. Purpose: To alleviate the health disparity and promote the safe and quality use of radiotherapy in treating rectal cancers, the entire treatment planning process needs to be automated. The purpose of this project is to develop automated solutions for the treatment planning process of rectal cancers that would produce clinically acceptable and high-quality plans. To achieve this goal, we first automated two common existing treatment techniques, 3DCRT and VMAT, for rectal cancers, and then explored an alternative method for creating a treatment plan using deep learning. Methods: To automate the 3DCRT treatment technique, we used deep learning to predict the shapes of field apertures for primary and boost fields based on CT and location and the shapes of GTV and involved lymph nodes. The results of the predicted apertures were evaluated by a GI radiation oncologist. We then designed an algorithm to automate the forward-planning process with the capacity of adding fields to homogenize the dose at the target volumes using the field-in-field technique. The algorithm was validated on the clinical apertures and the plans produced were scored by a radiation oncologist. The field aperture prediction and the algorithm were combined into an end-to-end process and were tested on a separate set of patients. The resulting final plans were scored by a GI radiation oncologist for their clinical acceptability. To automate of VMAT treatment technique, we used deep learning models to segment CTV and OARs and automated the inverse planning process, based on a RapidPlan model. The end-to-end process requires only the GTV contour and a CT scan as inputs. Specifically, the segmentation models could auto-segment CTV, bowel bag, large bowel, small bowel, total bowel, femurs, bladder, bone marrow, and female and male genitalia. All the OARs were contoured under the guidance of and reviewed by a GI radiation oncologist. For auto-planning, the RapidPlan model was designed for VMAT delivery with 3 arcs and validated separately by two GI radiation oncologists. Finally, the end-to-end pipeline was evaluated on a separate set of testing patients, and the resulting plans were scored by two GI radiation oncologists. Existing inverse planning methods rely on 1D information from DVH values,2D information from DVH lines,or 3D dose distributions using machine learning for plan optimizations. The project explored the possibility of using deep learning to create 3D dose distributions directly for VMAT treatment plans. The training data consisted of patients treated by the VMAT treatment technique in the short-course fractionation scheme that uses 5 Gy per fraction for 5 fractions. Two deep learning architectures were investigated for their ability to emulate clinical dose distributions: 3D DDUNet and 2D cGAN. The top-performing model for each architecture was identified based on the difference in DVH values, DVH lines, and dose distribution between the predicted dose and the corresponding clinical plans. Results: For 3DCRT automation, the predicted apertures were 100%, 95%, and 87.5% clinically acceptable for the posterior-anterior, laterals, and boost apertures, respectively. The forward planning algorithm created wedged plans that were 85% clinically acceptable with clinical apertures. The end-to-end workflow generated 97% clinically acceptable plans for the separate test patients. For the VMAT automation, CTV contours were 89% clinically acceptable without necessary modifications and all the OAR contours were clinically acceptable without edits except for large and small bowels. The RaidPlan model was evaluated to produce 100% and 91% of clinically acceptable plans per two GI radiation oncologists. For the testing of end-to-end workflow, 88% and 62% of the final plans were accepted by two GI radiation oncologists. For the evaluation of deep learning architectures, the top-performing model of the DDUNet architecture used the medium patch size and inputs of CT, PTV times prescription dose mask, CTV, PTV 10 mm expansion, and the external body structure. The model with inputs CT, PTV, and CTV masks performed the best for the cGAN architecture. Both the DDUNet and cGAN architectures could predict 3D dose distributions that had DVH values that were statistically the same as the clinical plans. Conclusions: We have successfully automated the clinical workflow for generating either 3DCRT or VMAT radiotherapy plans for rectal cancer for our institution. This project showed that the existing treatment planning techniques for rectal cancer can be automated to generate clinically acceptable and safe plans with minimal inputs and no human intervention for most patients. The project also showed that deep learning architectures can be used for predicting dose distributions

3D Shape Descriptor-Based Facial Landmark Detection: A Machine Learning Approach

Facial landmark detection on 3D human faces has had numerous applications in the literature such as establishing point-to-point correspondence between 3D face models which is itself a key step for a wide range of applications like 3D face detection and authentication, matching, reconstruction, and retrieval, to name a few. Two groups of approaches, namely knowledge-driven and data-driven approaches, have been employed for facial landmarking in the literature. Knowledge-driven techniques are the traditional approaches that have been widely used to locate landmarks on human faces. In these approaches, a user with sucient knowledge and experience usually denes features to be extracted as the landmarks. Data-driven techniques, on the other hand, take advantage of machine learning algorithms to detect prominent features on 3D face models. Besides the key advantages, each category of these techniques has limitations that prevent it from generating the most reliable results. In this work we propose to combine the strengths of the two approaches to detect facial landmarks in a more ecient and precise way. The suggested approach consists of two phases. First, some salient features of the faces are extracted using expert systems. Afterwards, these points are used as the initial control points in the well-known Thin Plate Spline (TPS) technique to deform the input face towards a reference face model. Second, by exploring and utilizing multiple machine learning algorithms another group of landmarks are extracted. The data-driven landmark detection step is performed in a supervised manner providing an information-rich set of training data in which a set of local descriptors are computed and used to train the algorithm. We then, use the detected landmarks for establishing point-to-point correspondence between the 3D human faces mainly using an improved version of Iterative Closest Point (ICP) algorithms. Furthermore, we propose to use the detected landmarks for 3D face matching applications