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  • 1
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    Online Resource
    Osteogenic Differentiation of Human Adipose-Derived Stem Cells Seeded on a Biomimetic Spongiosa-like Scaffold: Bone Morphogenetic Protein-2 Delivery by Overexpressing Fascia
    MDPI AG ; 2022
    In:  International Journal of Molecular Sciences Vol. 23, No. 5 ( 2022-02-28), p. 2712-
    Details
    In: International Journal of Molecular Sciences, MDPI AG, Vol. 23, No. 5 ( 2022-02-28), p. 2712-
    Abstract: Human adipose-derived stem cells (hADSCs) have the capacity for osteogenic differentiation and, in combination with suitable biomaterials and growth factors, the regeneration of bone defects. In order to differentiate hADSCs into the osteogenic lineage, bone morphogenetic proteins (BMPs) have been proven to be highly effective, especially when expressed locally by route of gene transfer, providing a constant stimulus over an extended period of time. However, the creation of genetically modified hADSCs is laborious and time-consuming, which hinders clinical translation of the approach. Instead, expedited single-surgery gene therapy strategies must be developed. Therefore, in an in vitro experiment, we evaluated a novel growth factor delivery system, comprising adenoviral BMP-2 transduced fascia tissue in terms of BMP-2 release kinetics and osteogenic effects, on hADSCs seeded on an innovative biomimetic spongiosa-like scaffold. As compared to direct BMP-2 transduction of hADSCs or addition of recombinant BMP-2, overexpressing fascia provided a more uniform, constant level of BMP-2 over 30 days. Despite considerably higher BMP-2 peak levels in the comparison groups, delivery by overexpressing fascia led to a strong osteogenic response of hADSCs. The use of BMP-2 transduced fascia in combination with hADSCs may evolve into an expedited single-surgery gene transfer approach to bone repair.
    Type of Medium: Online Resource
    ISSN: 1422-0067
    URL: Article
    DOI: 10.3390/ijms23052712
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2019364-6
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  • 2
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    Classification and Automated Interpretation of Spinal Posture Data Using a Pathology-Independent Classifier and Explainable Artificial Intelligence (XAI)
    MDPI AG ; 2021
    In:  Sensors Vol. 21, No. 18 ( 2021-09-21), p. 6323-
    Details
    In: Sensors, MDPI AG, Vol. 21, No. 18 ( 2021-09-21), p. 6323-
    Abstract: Clinical classification models are mostly pathology-dependent and, thus, are only able to detect pathologies they have been trained for. Research is needed regarding pathology-independent classifiers and their interpretation. Hence, our aim is to develop a pathology-independent classifier that provides prediction probabilities and explanations of the classification decisions. Spinal posture data of healthy subjects and various pathologies (back pain, spinal fusion, osteoarthritis), as well as synthetic data, were used for modeling. A one-class support vector machine was used as a pathology-independent classifier. The outputs were transformed into a probability distribution according to Platt’s method. Interpretation was performed using the explainable artificial intelligence tool Local Interpretable Model-Agnostic Explanations. The results were compared with those obtained by commonly used binary classification approaches. The best classification results were obtained for subjects with a spinal fusion. Subjects with back pain were especially challenging to distinguish from the healthy reference group. The proposed method proved useful for the interpretation of the predictions. No clear inferiority of the proposed approach compared to commonly used binary classifiers was demonstrated. The application of dynamic spinal data seems important for future works. The proposed approach could be useful to provide an objective orientation and to individually adapt and monitor therapy measures pre- and post-operatively.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s21186323
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2052857-7
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  • 3
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    Tool Downscaling Effects on the Friction Stir Spot Welding Process and Properties of Current-Carrying Welded Aluminum–Copper Joints for E-Mobility Applications
    MDPI AG ; 2021
    In:  Metals Vol. 11, No. 12 ( 2021-12-03), p. 1949-
    Details
    In: Metals, MDPI AG, Vol. 11, No. 12 ( 2021-12-03), p. 1949-
    Abstract: According to the technical breakthrough towards E-Mobility, current-carrying dissimilar joints between aluminum and copper are gaining an increasing relevance for the automotive industry and thus, coming into focus of many research activities. The joining of dissimilar material in general is well known to be a challenging task. Furthermore, the current-carrying joining components in E-Drive consist of pure aluminum and copper materials with relatively thin sheet thickness, which are thermally and mechanically very sensitive, as well as highly heat and electrically conductive. This results in additional challenges for the joining process. Due to their properties, friction stir welding and especially fiction stir spot welding (FSSW) using pinless tools—i.e., as hybrid friction diffusion bonding process (HFDB) is more and more attractive for new application fields and particularly promising for aluminum–copper joining tasks in E-Mobility. However, the feasibility is restricted because of the relatively high process forces required during friction stir welding. Thus, to fulfill the high process and quality requirements in this above-mentioned application field, further research and process development towards process force reduction are necessary. This work deals with the application of the tool downscaling strategy as a mean of process force reduction in FSSW of thin aluminum and copper sheets for current-carrying applications in E-Mobility, where the components are very sensitive to high mechanical loads. The tool downscaling approach enables constant weld quality in similar process time of about 0.5 s despite reduced process forces and torques. By reducing the tool diameter from 10 mm to 6 mm, the process force could be reduced by 36% and the torque by over 50%. Furthermore, a similar heat propagation behavior in the component is observable. These results provide a good basis for the joining of E-Drive components with thermal and mechanical sensitive sheet materials using the pinless FSSW process.
    Type of Medium: Online Resource
    ISSN: 2075-4701
    URL: Article
    DOI: 10.3390/met11121949
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2662252-X
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  • 4
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    Development of a New Solver to Model the Fish-Hook Effect in a Centrifugal Classifier
    MDPI AG ; 2021
    In:  Minerals Vol. 11, No. 7 ( 2021-06-22), p. 663-
    Details
    In: Minerals, MDPI AG, Vol. 11, No. 7 ( 2021-06-22), p. 663-
    Abstract: Centrifugal air classifiers are often used for classification of particle gas flows in the mineral industry and various other sectors. In this paper, a new solver based on the multiphase particle-in-cell (MP-PIC) method, which takes into account an interaction between particles, is presented. This makes it possible to investigate the flow process in the classifier in more detail, especially the influence of solid load on the flow profile and the fish-hook effect that sometimes occurs. Depending on the operating conditions, the fish-hook sometimes occurs in such apparatus and lead to a reduction in classification efficiency. Therefore, a better understanding and a representation of the fish-hook in numerical simulations is of great interest. The results of the simulation method are compared with results of previous simulation method, where particle–particle interactions are neglected. Moreover, a validation of the numerical simulations is carried out by comparing experimental data from a laboratory plant based on characteristic values such as pressure loss and classification efficiency. The comparison with experimental data shows that both methods provide similar good values for the classification efficiency d50; however, the fish-hook effect is only reproduced when particle-particle interaction is taken into account. The particle movement prove that the fish-hook effect is due to a strong concentration accumulation in the outer area of the classifier. These particle accumulations block the radial transport of fine particles into the classifier, which are then entrained by coarser particles into the coarse material.
    Type of Medium: Online Resource
    ISSN: 2075-163X
    URL: Article
    DOI: 10.3390/min11070663
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655947-X
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  • 5
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    Online Resource
    Effects of Flow Baffles on Flow Profile, Pressure Drop and Classification Performance in Classifiers
    MDPI AG ; 2021
    In:  Processes Vol. 9, No. 7 ( 2021-07-15), p. 1213-
    Details
    In: Processes, MDPI AG, Vol. 9, No. 7 ( 2021-07-15), p. 1213-
    Abstract: This paper presents a study of the use of flow baffles inside a centrifugal air classifier. An air classifier belongs to the most widely used classification devices in mills in the mineral industry, which is why there is a great interest in optimizing the process flow and pressure loss. Using Computational Fluid Dynamics (CFD), the flow profile in a classifier without and with flow baffles is systematically compared. In the simulations, turbulence effects are modeled with the realizable k–ε model, and the Multiple Reference Frame approach (MRF) is used to represent the rotation of the classifier wheel. The discrete phase model is used to predict the collection efficiency. The effects on the pressure loss and the classification efficiency of the classifier are considered for two operating conditions. In addition, a comparison with experimental data is performed. Firstly, the simulations and experiments show good agreement. Furthermore, the investigations show that the use of flow baffles is suitable for optimizing the flow behavior in the classifier, especially in reducing the pressure loss and therefore energy costs. Moreover, the flow baffles have an impact on the classification performance. The impact depends on the operation conditions, especially the classifier speed. At low classifier speeds, the classifier without flow baffles separates more efficiently; as the speed increases, the classification performance of the classifier with flow baffles improves.
    Type of Medium: Online Resource
    ISSN: 2227-9717
    URL: Article
    DOI: 10.3390/pr9071213
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2720994-5
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  • 6
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    Optimized Identification of Advanced Chronic Kidney Disease and Absence of Kidney Disease by Combining Different Electronic Health Data Resources and by Applying Machine Learning Strategies
    MDPI AG ; 2020
    In:  Journal of Clinical Medicine Vol. 9, No. 9 ( 2020-09-12), p. 2955-
    Details
    In: Journal of Clinical Medicine, MDPI AG, Vol. 9, No. 9 ( 2020-09-12), p. 2955-
    Abstract: Automated identification of advanced chronic kidney disease (CKD ≥ III) and of no known kidney disease (NKD) can support both clinicians and researchers. We hypothesized that identification of CKD and NKD can be improved, by combining information from different electronic health record (EHR) resources, comprising laboratory values, discharge summaries and ICD-10 billing codes, compared to using each component alone. We included EHRs from 785 elderly multimorbid patients, hospitalized between 2010 and 2015, that were divided into a training and a test (n = 156) dataset. We used both the area under the receiver operating characteristic (AUROC) and under the precision-recall curve (AUCPR) with a 95% confidence interval for evaluation of different classification models. In the test dataset, the combination of EHR components as a simple classifier identified CKD ≥ III (AUROC 0.96[0.93–0.98]) and NKD (AUROC 0.94[0.91–0.97] ) better than laboratory values (AUROC CKD 0.85[0.79–0.90], NKD 0.91[0.87–0.94] ), discharge summaries (AUROC CKD 0.87[0.82–0.92], NKD 0.84[0.79–0.89] ) or ICD-10 billing codes (AUROC CKD 0.85[0.80–0.91], NKD 0.77[0.72–0.83] ) alone. Logistic regression and machine learning models improved recognition of CKD ≥ III compared to the simple classifier if only laboratory values were used (AUROC 0.96[0.92–0.99] vs. 0.86[0.81–0.91] , p 〈 0.05) and improved recognition of NKD if information from previous hospital stays was used (AUROC 0.99[0.98–1.00] vs. 0.95[0.92–0.97]] , p 〈 0.05). Depending on the availability of data, correct automated identification of CKD ≥ III and NKD from EHRs can be improved by generating classification models based on the combination of different EHR components.
    Type of Medium: Online Resource
    ISSN: 2077-0383
    URL: Article
    DOI: 10.3390/jcm9092955
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2662592-1
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  • 7
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    County-Level Economic Changes and Drug Mortality in the United States: Evidence from the Great Recession
    MDPI AG ; 2022
    In:  International Journal of Environmental Research and Public Health Vol. 19, No. 23 ( 2022-12-05), p. 16261-
    Details
    In: International Journal of Environmental Research and Public Health, MDPI AG, Vol. 19, No. 23 ( 2022-12-05), p. 16261-
    Abstract: We aimed at examining whether county-level economic changes were associated with changes in county-level drug mortality rates since the Great Recession and whether the association is equally distributed across major sociodemographic subgroups. Using the Centers for Disease Control and Prevention’s Wide-Ranging Online Data for Epidemiologic Research (2004–2019), combined with census data, we conducted fixed effects analyses by including county-level economic changes as primary exposures and county-level drug-related mortality rates (per 100,000 people) from 2004–2007 (i.e., prior to the recession) to 2008–2011, 2012–2015, and 2016–2019 as an outcome variable based on 1833 counties. Our findings showed that drug mortality rates increased from 13.9 (2004–2007) to 16.0 (2008–2011), 18.0 (2012–2015), and 23.0 (2016–2019). Counties experiencing smaller median household income growth during and/or after the recession were associated with greater increase in drug mortality than counties experiencing larger median household income growth among the total population and all sociodemographic subgroups. Counties experiencing larger increases in unemployment rates and percentage of vacant housing units were associated with greater increase in drug mortality than counties experiencing smaller or no increase in unemployment rates and percentage of vacant housing units among certain sociodemographic subgroups. Findings suggest the importance of local economic contexts in understanding drug mortality risk since the recession. Drug overdose prevention polices need to be formulated by taking local economic changes following a major recession into consideration.
    Type of Medium: Online Resource
    ISSN: 1660-4601
    URL: Article
    DOI: 10.3390/ijerph192316261
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2175195-X
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  • 8
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    Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport
    MDPI AG ; 2020
    In:  Applied Sciences Vol. 10, No. 12 ( 2020-06-19), p. 4229-
    Details
    In: Applied Sciences, MDPI AG, Vol. 10, No. 12 ( 2020-06-19), p. 4229-
    Abstract: Applying an optimal race strategy is a decisive factor in achieving the best possible result in a motorsport race. This mainly implies timing the pit stops perfectly and choosing the optimal tire compounds. Strategy engineers use race simulations to assess the effects of different strategic decisions (e.g., early vs. late pit stop) on the race result before and during a race. However, in reality, races rarely run as planned and are often decided by random events, for example, accidents that cause safety car phases. Besides, the course of a race is affected by many smaller probabilistic influences, for example, variability in the lap times. Consequently, these events and influences should be modeled within the race simulation if real races are to be simulated, and a robust race strategy is to be determined. Therefore, this paper presents how state of the art and new approaches can be combined to modeling the most important probabilistic influences on motorsport races—accidents and failures, full course yellow and safety car phases, the drivers’ starting performance, and variability in lap times and pit stop durations. The modeling is done using customized probability distributions as well as a novel “ghost” car approach, which allows the realistic consideration of the effect of safety cars within the race simulation. The interaction of all influences is evaluated based on the Monte Carlo method. The results demonstrate the validity of the models and show how Monte Carlo simulation enables assessing the robustness of race strategies. Knowing the robustness improves the basis for a reasonable determination of race strategies by strategy engineers.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app10124229
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2704225-X
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  • 9
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    Virtual Strategy Engineer: Using Artificial Neural Networks for Making Race Strategy Decisions in Circuit Motorsport
    MDPI AG ; 2020
    In:  Applied Sciences Vol. 10, No. 21 ( 2020-11-04), p. 7805-
    Details
    In: Applied Sciences, MDPI AG, Vol. 10, No. 21 ( 2020-11-04), p. 7805-
    Abstract: In circuit motorsport, race strategy helps to finish the race in the best possible position by optimally determining the pit stops. Depending on the racing series, pit stops are needed to replace worn-out tires, refuel the car, change drivers, or repair the car. Assuming a race without opponents and considering only tire degradation, the optimal race strategy can be determined by solving a quadratic optimization problem, as shown in the paper. In high-class motorsport, however, this simplified approach is not sufficient. There, comprehensive race simulations are used to evaluate the outcome of different strategic options. The published race simulations require the user to specify the expected strategies of all race participants manually. In such simulations, it is therefore desirable to automate the strategy decisions, for better handling and greater realism. It is against this background that we present a virtual strategy engineer (VSE) based on two artificial neural networks. Since our research is focused on the Formula 1 racing series, the VSE decides whether a driver should make a pit stop and which tire compound to fit. Its training is based on timing data of the six seasons from 2014 to 2019. The results show that the VSE makes reasonable decisions and reacts to the particular race situation. The integration of the VSE into a race simulation is presented, and the effects are analyzed in an example race.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app10217805
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2704225-X
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  • 10
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    Erratum: Heilmeier, A., et al. Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport. Appl. Sci. 2020, 10, 4229
    MDPI AG ; 2020
    In:  Applied Sciences Vol. 10, No. 17 ( 2020-08-19), p. 5745-
    Details
    In: Applied Sciences, MDPI AG, Vol. 10, No. 17 ( 2020-08-19), p. 5745-
    Abstract: We wish to make the following corrections to the published paper [...]
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app10175745
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2704225-X
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