Pain following burn injuries can be severe and may persist after hospital discharge. The experience of pain is influenced by multiple biological and psychosocial factors. Post-discharge pain may be related to pain experienced during hospitalization as well as anxiety associated with these pain experiences. There are also protective factors; one notable example is optimism. However, the role of optimism in burn-related pain has not yet been investigated. This study aimed to describe the extent of pain measured over 14 consecutive days post-discharge and to examine its relationship with background pain, procedural pain, pain-related anxiety, and optimism. This multi-center longitudinal cohort study was conducted in five burns centres. The results showed that 50 % of the patients had a pain score ≥ 2 on a 0 – 10 scale after discharge, which on average decreased further over the next 14 days. However, a subgroup of patients maintained elevated pain levels. Patients with higher pain scores postdischarge were more likely to have experienced higher levels of background pain and procedural pain in-hospital and they scored lower on optimism. Pain-related anxiety did not independently contribute to pain postdischarge. The results indicate that patients with high pain scores during hospital admission may need specific attention regarding pain management when they leave the hospital. Furthermore, patients may benefit from optimism-inducing interventions in the hospital and thereafter.
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This paper addresses the procedural generation of levels for collaborative puzzle-platform games. To address this issue, we distinguish types of multiplayer interaction, focusing on two-player collaboration, and identify relevant game mechanics for a puzzle-platform game, addressing player movement, interaction with moving game objects, and physical interaction involving both players. These are further formalized as game design patterns. To test the feasibility of the approach, a level generator has been implemented based on a rule-based approach, using the existing tool called Ludoscope and a prototype game developed in the Unity game engine. The level generation procedure results in over 3.7 million possible playable level variations that can be generated automatically. Each of these levels encourages or even requires both players to engage in collaborative gameplay.
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Pain following burn injuries can be severe and may persist after hospital discharge. The experience of pain is influenced by multiple biological and psychosocial factors. Post-discharge pain may be related to pain experienced during hospitalization as well as anxiety associated with these pain experiences. There are also protective factors; one notable example is optimism. However, the role of optimism in burn-related pain has not yet been investigated. This study aimed to describe the extent of pain measured over 14 consecutive days post-discharge and to examine its relationship with background pain, procedural pain, pain-related anxiety, and optimism. This multi-center longitudinal cohort study was conducted in five burns centres. The results showed that 50 % of the patients had a pain score ≥ 2 on a 0 – 10 scale after discharge, which on average decreased further over the next 14 days. However, a subgroup of patients maintained elevated pain levels. Patients with higher pain scores post-discharge were more likely to have experienced higher levels of background pain and procedural pain in-hospital and they scored lower on optimism. Pain-related anxiety did not independently contribute to pain post-discharge. The results indicate that patients with high pain scores during hospital admission may need specific attention regarding pain management when they leave the hospital. Furthermore, patients may benefit from optimism-inducing interventions in the hospital and thereafter.
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Grammar-based procedural level generation raises the productivity of level designers for games such as dungeon crawl and platform games. However, the improved productivity comes at cost of level quality assurance. Authoring, improving and maintaining grammars is difficult because it is hard to predict how each grammar rule impacts the overall level quality, and tool support is lacking. We propose a novel metric called Metric of Added Detail (MAD) that indicates if a rule adds or removes detail with respect to its phase in the transformation pipeline, and Specification Analysis Reporting (SAnR) for expressing level properties and analyzing how qualities evolve in level generation histories. We demonstrate MAD and SAnR using a prototype of a level generator called Ludoscope Lite. Our preliminary results show that problematic rules tend to break SAnR properties and that MAD intuitively raises flags. MAD and SAnR augment existing approaches, and can ultimately help designers make better levels and level generators.
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Many organizations use business process management to manage and model their processes. Currently, flow-based process formalisms, such as BPMN, are considered the standard for modeling processes. However, recent literature describes several limitations of this type of formalism that can be solved by adopting a constraint-based formalism. To preserve economic investments in existing process models, transformation activities needed to be limited. This paper presents a methodical approach for performing the tedious parts of process model transformation. Executing the method results in correctly transformed process models and reduces the effort required for converting the process models.
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ConceptThe goal of the worksop/tutorial is to introduce participants to the fundamentals of Procedural Content Generation (PCG) based on generative grammars, have them experience an example of such a system first-hand, and discuss the potential of this approach for various areas of procedural content generation for games. The principles and examples are based on Ludoscope, a software tool developed at the HvA by Dr. Joris Dormans, e.a.Duration: 2 hoursOverviewWe will use the first 30 minutes to explain the basics of how to use generative grammars to generate levels. The principles of these grammars and model transformations will be demonstrated by means of the level generation system of Spelunky, which we have modeled in Ludoscope.Spelunky focuses solely on the generation of geometry, but grammar-based systems can also be used to transform more abstract concepts of level design into level geometry. In the next hour, the participants will be able to get some hands-on experience with Ludoscope. The assignment will be to generate a Mario-like level based on specific requirements, adapted to the interests of workshop participants.Finally, we are interested in the participants’ evaluation of this approach to PCG. We will use the last 20 minutes to discuss alternative techniques, and possible applications to other areas of PCG, like asset creation, scripting and game generation.Workshop participants are asked to bring a (PC) laptop to work on during the workshop, and are encouraged to work in pairs.
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Background & aims: Accurate diagnosis of sarcopenia requires evaluation of muscle quality, which refers to the amount of fat infiltration in muscle tissue. In this study, we aim to investigate whether we can independently predict mortality risk in transcatheter aortic valve implantation (TAVI) patients, using automatic deep learning algorithms to assess muscle quality on procedural computed tomography (CT) scans. Methods: This study included 1199 patients with severe aortic stenosis who underwent transcatheter aortic valve implantation (TAVI) between January 2010 and January 2020. A procedural CT scan was performed as part of the preprocedural-TAVI evaluation, and the scans were analyzed using deep-learning-based software to automatically determine skeletal muscle density (SMD) and intermuscular adipose tissue (IMAT). The association of SMD and IMAT with all-cause mortality was analyzed using a Cox regression model, adjusted for other known mortality predictors, including muscle mass. Results: The mean age of the participants was 80 ± 7 years, 53% were female. The median observation time was 1084 days, and the overall mortality rate was 39%. We found that the lowest tertile of muscle quality, as determined by SMD, was associated with an increased risk of mortality (HR 1.40 [95%CI: 1.15–1.70], p < 0.01). Similarly, low muscle quality as defined by high IMAT in the lowest tertile was also associated with increased mortality risk (HR 1.24 [95%CI: 1.01–1.52], p = 0.04). Conclusions: Our findings suggest that deep learning-assessed low muscle quality, as indicated by fat infiltration in muscle tissue, is a practical, useful and independent predictor of mortality after TAVI.
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Rationale: Although ultrasound has been reported as valid and reliable tool to assess muscle size in older adults1, little is known about intra-rater reliability (intra-RR) and inter-rater reliability (inter-RR) of BodyMetrix in specific to assess small muscles. Therefore, in this study we aimed to assess intra-RR and inter-RR of biceps muscle size (thickness) in elderly.Methods: Thirty elderly (81.9±6.3 years; 80% women; BMI 26.7±5.3 kg/m2) living in a Portuguese nursing home/residence were included. To assess procedural intra-RR and inter-RR, ultrasound measurements were performed by two raters (R1, R2, beginners level) by BodyMetrixTM BX2000, on the biceps of the right arm. R1 repeated the ultrasound measurement once. To assess measurement intra-RR and inter-RR, images were analyzed by three raters (R1, R2, and R3 [experienced level]). Agreement was analyzed by intraclass correlation coefficient. ICC values of 0.50 to 0.75 were considered moderate to good, and >0.75 as good to excellent. Statistical significance was set at p<0.05.Results: Mean muscle thickness at 1st and 2nd measurement (R1) was 23.4±4.5 and 23.7±3.8 mm, respectively. For procedural intra-RR, ICC was 0.630. For inter-RR of image 1 (R1) vs. image 2 (R2), ICC was 0.622. For inter-RR of image 2 (R2) vs. image 3 (R1) ICC was 0.534. For measurement reliability, ICCs for intra-RR of R1 and R2 were 0.865 and 0.766, respectively. ICCs for inter-RR of R1 vs. R2, R2 vs. R3, and R1 vs. R3 were 0.865, 0.800, and 0.815, respectively. All ICCs were statistically significant (p≤0.001).Conclusion: The results of our study indicate that procedural reliability of biceps muscle size as assessed by BodyMetrix in elderly is moderate to good, and measurement reliability is good to excellent. Increasing the level of experience may further improve procedural reliability.
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