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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.
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.
Purpose: This study investigated the effectiveness of the stuttering modification intervention Kinder Dürfen Stottern (KIDS) in school-age children who stutter.
Method: Seventy-three children who stutter were included in this multicenter, two-group parallel, randomized, wait-list controlled trial with a follow-up of 12 months. Children aged 7–11 years were recruited from 34 centers for speech therapy and randomized to either the immediate-treatment group or the 3 months delayed-treatment group. KIDS was provided by 26 clinicians who followed a treatment manual. Although the primary outcome measure was the impact of stuttering (Overall Assessment of the Speaker’s Experience of Stuttering–School-Age [OASES-S]), the secondary outcomes included objective and subjective data on stuttering severity.
Results: At 3 months postrandomization, the mean score changes of the OASESS differed significantly between the experimental (n = 33) and control group (n = 29; p = .026). Furthermore, treatment outcomes up to 12 months were analyzed (n = 59), indicating large effects of time on the OASES-S score (p < .001, partial η2 = .324). This was paralleled by significant improvements in parental ratings and objective ratings (stuttering severity, frequency, and physical concomitants).
Conclusions: The significant short-term treatment effects in the OASES-S are in line with the (initial) focus of KIDS on cognitive and affective aspects of stuttering.
Over 12 months, these changes were maintained and accompanied by
behavioral improvements. The results suggest that individual treatment with KIDS is an adequate treatment option for this age group.
Background: Establishing a healthy lifestyle has a great potential to reduce the prevalence of non-communicable diseases (NCDs) and their risk factors. NCDs contribute immensely to the economic costs of the health care system arising from therapy, medication use, and productivity loss.
Aim: The aim of this study was to evaluate the effect of the Healthy Lifestyle Community Program (cohort 2; HLCP-2) on medication use and consequently on medication costs for selected NCDs (diabetes, hypertension, and dyslipidemia).
Methods: Data stem from a 24-month non-randomised, controlled intervention trial aiming to improve risk factors for NCDs. Participants completed questionnaires at six measurement time points assessing medication use, from which costs were calculated. The following medication groups were included in the analysis as NCD medication: glucose-lowering medications (GLM), antihypertensive drugs (AHD) and lipid-lowering drugs (LLD). Statistical tests for inter- and intra-group comparison and multiple regression analysis were performed.
Results: In total, 118 participants (intervention group [IG]: n = 79; control group [CG]: n = 39) were considered. Compared to baseline medication use decreased slightly in the IG and increased in the CG. Costs for NCD medication were significantly lower in the IG than in the CG after 6 (p = 0.004), 12 (p = 0.040), 18 (p = 0.003) and 24 months (p = 0.008). After multiple regression analysis and adjusting for confounders, change of costs differed significantly between the groups in all final models.
Conclusion: The HLCP-2 was able to moderately prevent an increase of medication use and thus reduce costs for medication to treat NCDs with the greatest impact on AHD.
Non-communicable diseases (NCD) are associated with high costs for healthcare systems. We evaluated changes in total costs, comprising direct and indirect costs, due to a 24-month non-randomized, controlled lifestyle intervention trial with six measurement time points aiming to improve the risk profile for NCDs. Overall, 187 individuals from the general population aged ≥18 years were assigned to either the intervention group (IG; n = 112), receiving a 10-week intensive lifestyle intervention focusing on a healthy, plant-based diet; physical activity; stress management; and community support, followed by a 22-month follow-up phase including monthly seminars, or a control group (CG; n = 75) without intervention. The complete data sets of 118 participants (IG: n = 79; CG: n = 39) were analyzed. At baseline, total costs per person amounted to 67.80 ± 69.17 EUR in the IG and 48.73 ± 54.41 EUR in the CG per week. The reduction in total costs was significantly greater in the IG compared to the CG after 10 weeks (p = 0.012) and 6 months (p = 0.004), whereas direct costs differed significantly after 10 weeks (p = 0.017), 6 months (p = 0.041) and 12 months (p = 0.012) between the groups. The HLCP-2 was able to reduce health-related economic costs, primarily due to the reduction in direct costs.
Die Bedeutung des Einsatzes von Verfahren, die unter dem Begriff der Künstlichen Intelligenz (KI) zusammenzufassen sind, wird sowohl für gesellschaftliche Prozesse als auch den Auftrag an die Soziale Arbeit zunehmend erkannt und diskutiert. Mit diesem Artikel wird ein Beitrag zum Diskurs geleistet, indem vertieft der Bereich der Sprachverarbeitung durch KI, das Natural Language Processing (NLP), in den Blick genommen wird. Verarbeitung natürlicher Sprache ist aufgrund der hohen Bedeutung kommunikativer Prozesse für die Praxis der Sozialen Arbeit von besonderer Relevanz, zugleich wird die Profession der Sozialen Arbeit tangiert. Bezugnehmend auf Staub-Bernasconis Handlungstheorie werden Implikationen und Diskussionspunkte von NLP identifiziert und diskutiert. Zudem werden mögliche Gratifikationen für Klient*innen herausgearbeitet, die sich u. a. aus der Wirkung und sozialen Interaktion ergeben. Hier wird die Media-Equation-Theorie von Nass und Reeves als Erkenntnisfolie herangezogen. Vor diesen Perspektiven ergeben sich sowohl Risiken (u. a. die Gefahr einer modularisierten Herauslösung genuin sozialarbeiterischer Tätigkeit) als auch Chancen (u. a. Teilhabe, niederschwelliger Zugang, Zugriff auf breitere Datenbasis).
Fürther Gespräche: Expertenforum für den Rettungsdienst mit Forderungen an Politik und Gesellschaft
(2023)
Die rettungsdienstliche Versorgung, die Rettungsfachkräfteausbildung und die rechtlichen Vorraussetzungen in Deutschland bieten aktuell keine guten Rahmenbedingungen für die Zukunft. Ein Expertenforum mit deutschlandweiten Akteuren aus Wissenschaft, Bildung und Rettungsdienst diskutierten im Rahmen der Fürther Gespräche die Herausforderungen der Professionalisierung und der Kompetenzentwicklung des Rettungsdienstes und der präklinischen Versorgung in Deutschland. Die zentralen Ergebnisse und Schwerpunktthemen wurden in 7 Thesen zusammengefasst und konkrete Lösungsansätze entwickelt. Das Spannungsfeld umfasst hierbei die Anforderungen und Versorgungsbedarfe, die Prävention und erweiterte Versorgungskomponenten, die Digitalisierungsstrategie, integrierte Leitstellen und vernetzte Gesundheitsdienstleistungen, Personalentwicklung und -bindung sowie Qualifikationsniveaus und Notarztqualifikation.