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The use of computational modeling and simulation (CMS) as a tool for gaining insight into the technical performance and safety of medical devices has emerged continuously over the past years. However, to rely on information and decisions derived from model predictions, it is essential to establish model credibility for the specific context of use. Limited regulatory requirements and lack of consensus on the level of verification and validation activities required result in rare use of CMS as a source of evidence in the medical device approval process. The American Society of Mechanical Engineers (ASME) developed a risk-informed framework to establish appropriate credibility requirements of a computational model: the ASME V&V 40?2018 standard. This paper aims to outline the concepts of this standard and to demonstrate its application using an example from the orthotics field. The necessary steps to establish model credibility for a custom?made 3D printed wrist hand orthosis (WHO) are presented. It is shown that the credibility requirements of each verification and validation activity depend on model risk by applying two different contexts of use to the same computational model.
The Spreadsheet Energy System Model Generator (SESMG) is a tool for modeling and optimizing energy systems with a focus on urban systems. The SESMG is easily accessible as it comes with a browser-based graphical user interface, spreadsheets to provide data entry, and detailed documentation on how to use it. Programming skills are not required for the installation or application of the tool. The SESMG includes advanced modeling features such as the application of the multi-energy system (MES) approach, multi-objective optimization, model-based methods for reducing computational requirements, and automated conceptualization and result processing of urban energy systems with high spatial resolution. Due to its accessibility and the applied modeling methods, urban energy systems can be modeled and optimized with comparatively low effort.
Purpose
The purpose of this paper is to investigate the relationships between technology orientations and export performance of small and medium-sized enterprises (SMEs).
Design/methodology/approach
A quantitative research design was adopted for this study. The paper formulates hypotheses from the literature review. These hypotheses are tested using structural equation modeling with data collected from 231 SMEs in Uganda. Data were analyzed using SPSS version 23 and AMOS.
Findings
The findings of this study showed technology orientation has a positive and significant relationship with the performance of Ugandan SMEs and that supply chain agility moderates technology orientation and export performance.
Research limitations/implications
The study discusses the findings, advances limitations and managerial implications. It also suggests future research avenues. It proposes some recommendations to help Ugandan SMEs to form flexible supply chains, use the latest technology and create strong relationship ties with their partners in the supply chain.
Practical implications
The study suggests that managers of Ugandan SMEs should use the latest technology in production, marketing, logistics and supply chain management which will enable them to respond quickly to customer tastes and preferences leading to higher levels of export performance.
Originality/value
This study contributes to the literature on strategic management showing the reliability of scales used and the confirmatory of the factor structure. This study shows that in strategic management technology, orientation is critical in increasing export performance. This study has extended the resource-based view (RBV) and dynamic capabilities theories.
Stormwater tree pits with storage elements enable the irrigation of urban trees and can potentially act as decentralized rainwater retention basins. This paper mainly focuses on analyzing this potential. Field tests were conducted to investigate the irrigation behavior and the storage effect of a storm water tree pit system using Perl hoses as irrigation elements over a period of two years.
The rainfall, storage volumes, and soil moisture within the employed planting pit were measured.
With the help of system modeling, the retention ability of the storm water tree pit system was analyzed. The available storage volume was sufficient to irrigate trees for several days. During the measurement period, about 15% of the inflowing rainwater was fed to the root zone of the tree. With practical storage volumes of 200 to 300 m3/ha, a remarkable amount of water from heavy rainfall could be completely stored, thus significantly reducing the risk of flooding. The retention effect and irrigation behavior largely depend on the soil conditions and the technical possibilities of the equipment supplying the root area (in this case, Perl hoses). Further investigations are required to determine the influence on the growth conditions of trees and optimize of the system for discharge into the root zone.
This study explores the intriguing relationship between personality traits, self-rated fitness (SRF), and physical activity (PA) variables among German university students (N = 4244) and sheds light on the impact of personality on adherence to PA guidelines. Employing an online cross-sectional study, the short-form of the Big Five Inventory-2 assessed five domains of personality traits (Extraversion, Negative Emotionality, Agreeableness, conscientiousness, and Open-Mindedness). PA, including sitting time, was assessed using the International Physical Activity Questionnaire (short-form). SRF and muscle-strengthening activities (MSA) were assessed with one item each. Multiple linear and logistic regression analyses examined associations of individual personality trait domains and all domains combined with SFR, PA variables, and adherence to PA guidelines, controlling for sociodemographic, behavioral, and (mental) health covariates. Most reliably, Extraversion and Conscientiousness revealed positive associations with PA variables, while Negative Emotionality yielded inverse relationships with PA variables. For instance, each unit increase in Extraversion corresponded to an additional 17 min of weekly MSA. On the contrary, daily sitting time was unrelated to personality. Of note, high Open-Mindedness was associated with lower odds for adhering to current PA guidelines. The findings have implications for developing targeted interventions that promote a physically active lifestyle and support students’ well-being and academic success.
This review paper presents a short overview of current power system modelling tools especially used for analysing energy and electricity systems for the supply and demand sector. The main focus of this review lies on open source tools and models which are written and used in the programming language “Python”. The modelling tools are represented in a comprehensive table with key information. Five modelling tools with an open source license can be filtered out. The modelling tool PyPSA can be considered as a high performing tool especially as the gap between power system analysis tool (PSAT) and energy system modelling tool.
Local and regional energy systems are becoming increasingly entangled. Therefore, models for optimizing these energy systems are becoming more and more complex and the required computing resources (run-time and random access memory usage) are increasing rapidly. The computational requirements can basically be reduced solver-based (mathematical optimization of the solving process) or model-based (simplification of the real-world problem in the model). This paper deals with identifying how the required computational requirements for solving optimization models of multi-energy systems with high spatial resolution change with increasing model complexity and which model-based approaches enable to reduce the requirements with the lowest possible model deviations. A total of 12 temporal model reductions (reduction of the number of modeled time steps), nine techno-spatial model reductions (reduction of possible solutions), and five combined reduction schemes were theoretically analyzed and practically applied to a test case. The improvement in reducing the usage of computational resources and the impact on the quality of the results were quantified by comparing the results with a non-simplified reference case. The results show, that the run-time to solve a model increases quadratically and memory usage increases linearly with increasing model complexity. The application of various model adaption methods have enabled a reduction of the run-time by over 99% and the memory usage by up to 88%. At the same time, however, some of the methods led to significant deviations of the model results. Other methods require a profound prior knowledge and understanding of the investigated energy systems to be applied. In order to reduce the run-time and memory requirements for investment optimization, while maintaining good quality results, we recommend the application of (1) a pre-model that is used to (1a) perform technological pre-selection and (1b) define reasonable technological boundaries, (2) spatial sub-modeling along network nodes, and 3) temporal simplification by only modeling every nth day (temporal slicing), where at least 20% of the original time steps are modeled. Further simplifications such as spatial clustering or larger temporal simplification can further reduce the computational effort, but also result in significant model deviations.