Knowledge-Based Decision Support for Concept Evaluation Using the Extended Impact Model of Modular Product Families

The design of modular product families enables a high external variety of products by a low internal variety of components and processes. This variety optimization leads to large economic savings along the entire value chain. However, when designing and selecting suitable modular product architecture concepts, often only direct costs are considered, and indirect costs as well as cross-cost center benefits are neglected. A lack of knowledge about the full savings potential often results in the selection of inferior solutions. Since available approaches do not adequately address this problem, this paper provides a new methodological support tool that ensures consideration of the full savings potentials in the evaluation of modular product architecture concepts. For this purpose, the visual knowledge base of the Impact Model of Modular Product Families (IMF) is used, extended and implemented in a model-based environment using SysML. The newly developed Sys-IMF is then applied to the product family example of electric medium-voltage motors. The support tool is dynamic, expandable and filterable and embedded in a methodical procedure for knowledge-based decision support. Sys-IMF supports decision makers in the early phase of interdisciplinary product development and enables the selection of the most suitable modular solution for the company.

From Awareness to Social Action: The Role of Dementia Friends in Sustaining Dementia Inclusiveness

According to the World Dementia Council, three components are important to effectively engage a community to become dementia inclusive, 1) raising awareness and consequently decreasing stigma, 2) enabling participation, and 3) providing support—including in health and care settings. Too many times these components are separate initiatives thus limiting their effectiveness and sustainability. By applying the collective impact model and utilizing the Dementia Friends program as the link between the three, all dementia inclusive efforts can be enhanced and sustained regardless of the range of activities and approaches a community chooses to adopt. This symposium provides both evidence and examples of how to personalize and employ the Dementia Friends program to optimize the process, outcome, and impact of dementia inclusive initiatives. By engaging the entire community, awareness is raised, the structure is in place to enable action, and cross-sector collaboration will ensure continuation and sustainability of these important efforts.

Improving Mood Promoting Access to Collaborative Treatment (IMPACT) Program in Geriatrics Primary Care

Improving Mood Promoting Access to Collaborative Treatment (IMPACT) is a well-established model for the treatment of depression in primary care. The COVID pandemic has caused increased distress and depression among the older patients in our New York City geriatric practice. This paper describes the establishment of a virtual IMPACT model during the pandemic in which IMPACT services have been provided via telephone. This effort was a multidisciplinary collaboration among geriatric medicine, geriatric psychiatry, social work, and geropsychology. Our IMPACT program uses a brief form of Interpersonal Psychotherapy (IPT) for depression as the psychosocial component instead of Problem Solving Treatment. Delivery of IMPACT by telephone appears to have enhanced engagement and sustained involvement in the program compared with prior efforts to deliver it by in-person meetings. IPT as a psychosocial modality was well-received by patients. To date, treatment outcomes have been favorable and will be reported in this presentation.

An analytical model to determine the impact force of drone strikes

In addition to the well-known threats of bird and hail strikes, small unmanned aerial vehicles (sUAV) pose a new threat to manned aviation. Determining the severity of collisions between sUAVs and aircraft structures is essential for the safe use and integration of drones in airspace. A generic analytical calculation model needs to be developed to supplement the existing test and simulation data. This paper presents an analytic model for drone collisions with perpendicular and inclined targets. The targets have a rigid or elastic material behavior. The aircraft impact model, which is used for the design of nuclear reactor structures, is transferred and adjusted for sUAV impacts to calculate the impact force. A mass- and a burst load distribution are needed as input parameters. Both distributions are determined for an sUAV design depending on the flight direction. Compared to previous calculations, the new approach is to consider a moving target structure, which produces more realistic results. We compare the calculation results with simulation data from sUAV collisions with a commercial airliner windshield from the literature. The calculations show plausible results and a good agreement with literature data. Subsequently, the influence of the input parameters on the impact force is investigated. We see that spring stiffness, target mass, burst load distribution and damping have minor influence on the overall impact force. The impact velocity, mass distribution and flight orientation on the other hand have a major influence on the impact force. Further tests are needed to validate the impact model.

Continuous Integration for Machine Learning Experiments Reproducibility: a Practical Study

The development of a Machine Learning (ML) model depends on many variables in its training. Both model architecture-related variables, such as initial weights and hyperparameters, and general variables, like datasets and framework versions, might impact model metrics and experiment reproducibility. An application cannot be trustworthy if it produces good results only in a specific environment. Therefore, in order to avoid reproducibility issues, some good practices need to be adopted. This paper aims to report a practical experience in developing a machine learning application adopting a workflow that assures the reproducibility of the experiments and, consequently, its reliability, improving the team productivity.