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Renewable energies have an entirely different cost structure than fossil-fuel-based electricity generation. This is mainly due to the operation at zero marginal cost, whereas for fossil fuel plants the fuel itself is a major driver of the entire cost of energy. For a wind turbine, most of the materials and resources are spent up front. Over its lifetime, this initial capital and material investment is converted into usable energy. Therefore, it is desirable to gain the maximum benefit from the utilized materials for each individual turbine over its entire operating lifetime. Material usage is closely linked to individual damage progression of various turbine components and their respective failure modes. In this work, we present a novel approach for an optimal long-term planning of the operation of wind energy systems over their entire lifetime. It is based on a process for setting up a mathematical optimization problem that optimally distributes the available damage budget of a given failure mode over the entire lifetime. The complete process ranges from an adaptation of real-time wind turbine control to the evaluation of long-term goals and requirements. During this process, relevant deterministic external conditions and real-time controller setpoints influence the damage progression with equal importance. Finally, the selection of optimal planning strategies is based on an economic evaluation. The method is applied to an example for demonstration. It shows the high potential of the approach for an effective damage reduction in different use cases. The focus of the example is to effectively reduce power of a turbine under conditions where high loads are induced from wake-induced turbulence of neighbouring turbines. Through the optimization approach, the damage budget can be saved or spent under conditions where it pays off most in the long term. This way, it is possible to gain more energy from a given system and thus to reduce cost and ecological impact by a better usage of materials.

Aim To achieve international expert consensus and give recommendations on best practices in hair transplantation surgery, focusing on pre- and post-transplantation care. Methods A modified Delphi method was used to reach consensus. An international scientific committee developed an 81-statement questionnaire. A panel of 38 experts in hair transplantation from 17 countries across 4 continents assessed the questionnaire. Results Two consensus rounds were carried out, with 59 out of 81 statements (73%) reaching consensus. Expert recommendations emphasize the correct selection of candidates for hair transplantation and the need for patients to have received adequate medical treatment for alopecia before transplant. Comorbidities should be assessed and considered while planning surgery, and an individualized plan for perioperative care should be drawn up before transplant. Certain medications associated with increased risk of bleeding should be withdrawn before surgery. Specific recommendations for post-transplantation care are given. After transplantation, patients should gradually resume their normal haircare regimen. Close follow-up should be carried out during the first year after transplant. Conclusions This study presents numerous consensus-based recommendations on general aspects of hair transplantation, including candidate selection, medical therapy prior to transplantation, anesthesia, and resuming haircare after transplantation.

Quality assessments of horticultural products are still often carried out manually in breeding contexts, although computer vision systems have been reported to be able to overcome the limitations of manual assessments, e.g. in automated food processing. Here, a new computer vision workflow for quality trait assessment of bush bean pods (Phaseolus vulgaris) is introduced to replace physical measurements and visual scorings of expert breeders, while increasing consistency, accuracy, and objectivity of the measurements. A closed imaging box was used to take images of bean pods from 40 different varieties to develop and validate computer vision workflows to assess breeding relevant shape and color traits of bean pods. For the detection of beaks and peduncles via a neural network approach (Mask R-CNN) accuracies of 95.5% were reached. Computer vision estimations and manual reference measurements of length and caliber were highly correlated (R=0.99). Also, curvature and brightness of green bean pods well- correlated with visual scorings of expert breeders (R=0.81, R=-0.87). A Random Forest Classifier was trained to distinguish yellow and extremely rare bicolored pods and a cross validation accuracy of 83 ±7% was reached. An additional backlight LED panel enabled non-destructive analysis of seed formation inside the pod and promising results were achieved using a Faster R-CNN model. This new computer vision workflow provides the opportunity to replace well-established manual workflows for quality trait assessment of bush bean pods as it is more objective, reliable, and considerably faster.

The vegetation optical depth (VOD) variable contains information on plant water content and biomass. It can be estimated alongside soil moisture from currently operating satellite radiometer missions, such as SMOS (ESA) and SMAP (NASA). The estimation of water fluxes, such as plant water uptake (PWU) and transpiration rate (TR), from these earth system parameters (VOD, soil moisture) requires assessing water potential gradients and flow resistances in the soil, the vegetation and the atmosphere. Yet water flux estimation remains an elusive challenge especially on a global scale. In this concept study, we conduct a field-scale experiment to test mechanistic models for the estimation of seasonal water fluxes (PWU and TR) of a winter wheat stand using measurements of soil moisture, VOD, and relative air humidity (RH) in a controlled environment. We utilize microwave L-band observations from a tower-based radiometer to estimate VOD of a wheat stand during the 2017 growing season at the Selhausen test site in Germany. From VOD, we first extract the gravimetric moisture of vegetation and then determine the relative water content (RWC) and vegetation water potential (VWP) of the wheat field. Although the relative water content could be directly estimated from VOD, our results indicate this may be challenging for the phenological phases, when rapid biomass and plant structure development take place within the wheat canopy. We estimate water uptake from the soil to the wheat plants from the difference between the soil and vegetation potentials divided by the flow resistance from soil into wheat plants. The TR from the wheat plants into the atmosphere was obtained from the difference between the vegetation and atmosphere water potentials divided by the flow resistances from plants to the atmosphere. For this, the required soil matric potential (SMP), the vapor pressure deficit (VPD), and the flow resistances were obtained from on-site observations of soil, plant, and atmosphere together with simple mechanistic models. This pathfinder study shows that the L-band microwave radiation contains valuable information on vegetation water status that enables the estimation of water dynamics (up to fluxes) from the soil via wheat plants into the atmosphere, when combined with additional information of soil and atmosphere water content. Still, assumptions have to be made when estimating the vegetation water potential from relative water content as well as the water flow resistances between soil, wheat plants, and atmosphere. Moreover, direct validation of water flux estimates for the assessment of their absolute accuracy could not be performed due to a lack of in situ PWU and TR measurements. Nonetheless, our estimates of water status, potentials, and fluxes show the expected temporal dynamics, known from the literature, and intercompare reasonably well in absolute terms with independent TR estimates of the NASA ECOSTRESS mission, which relies on a Priestly–Taylor type of retrieval model. Our findings support that passive microwave remote-sensing techniques qualify for the estimation of vegetation water dynamics next to traditionally measured stand-scale or plot-scale techniques. They might shed light on future capabilities of monitoring water dynamics in the soil–plant–atmosphere system including wide-area, remote-sensing-based earth observation data.

Construction progress documentation is currently of great interest for the AEC (Architecture, Engineering and Construction) branch and BIM (Building Information Modeling). Subject of this work is the geometric accuracy assessment of image-based change detection in indoor environments based on a BIM. Line features usually serve well as geodetic references in indoor scenes in order to solve for camera orientation. However, building edges are never perfectly built as planned and often geometrically generalized for BIM compliant representation. As a result, in this approach, line correspondences for image-to-model co-registration are considered as statistically uncertain entities as this is essential for dealing with metric confidences in the field of civil engineering and BIM. We present an estimation model for camera pose refinement which is based on the incidence condition between model edges and corresponding image lines. Geometric accuracies are assigned to the model edges according to the Level of Accuracy (LOA) specification for BIM. The approach is demonstrated in a series of tests using a synthetic image of an indoor BIM. The effects of varying edge detection accuracies on the estimation are investigated as well as the effects of using model edges with different geometric quality by adding Gaussian noise to the synthetic observations, each within 100 simulation runs. The results show that the camera orientation can be improved with the presented estimation model as long as the BIM compliant references meet the conditions of LOA 30 or higher (σ

Abstract Background The lack of approved treatments for the majority of rare diseases is reflective of the unique challenges of orphan drug development. Novel methodologies, including new functionally relevant endpoints, are needed to render the development process more feasible and appropriate for these rare populations and thereby expedite the approval of promising treatments to address patients’ high unmet medical need. Here, we describe the development of an innovative master protocol and primary outcome assessment to investigate the modified amino acid N-acetyl-l-leucine (Sponsor Code: IB1001) in three separate, multinational, phase II trials for three ultra-rare, autosomal-recessive, neurodegenerative disorders: Niemann-Pick disease type C (NPC), GM2 gangliosidoses (Tay-Sachs and Sandhoff disease; “GM2”), and ataxia telangiectasia (A-T). Methods/design The innovative IB1001 master protocol and novel CI-CS primary endpoints were developed through a close collaboration between the Industry Sponsor, Key Opinion Leaders, representatives of the Patient Communities, and National Regulatory Authorities. As a result, the open-label, rater-blinded study design is considerate of the practical limitations of recruitment and retention of subjects in these ultra-orphan populations. The novel primary endpoint, the Clinical Impression of Change in Severity© (CI-CS), accommodates the heterogenous clinical presentation of NPC, GM2, and A-T: at screening, the principal investigator appoints for each patient a primary anchor test (either the 8-m walk test (8MWT) or 9-hole peg test of the dominant hand (9HPT-D)) based on his/her unique clinical symptoms. The anchor tests are videoed in a standardized manner at each visit to capture all aspects related to the patient’s functional performance. The CI-CS assessment is ultimately performed by independent, blinded raters who compare videos of the primary anchor test from three periods: baseline, the end of treatment, and the end of a post-treatment washout. Blinded to the time point of each video, the raters make an objective comparison scored on a 7-point Likert scale of the change in the severity of the patient’s neurological signs and symptoms from video A to video B. To investigate both the symptomatic and disease-modifying effects of treatment, N-acetyl-l-leucine is assessed during two treatment sequences: a 6-week parent study and 1-year extension phase. Discussion The novel CI-CS assessment, developed through a collaboration of all stakeholders, is advantageous in that it better ensures the primary endpoint is functionally relevant for each patient, is able to capture small but meaningful clinical changes critical to the patients’ quality of life (fine-motor skills; gait), and blinds the primary outcome assessment. The results of these three trials will inform whether N-acetyl-l-leucine is an effective treatment for NPC, GM2, and A-T and can also serve as a new therapeutic paradigm for the development of future treatments for other orphan diseases. Trial registration The three trials (IB1001-201 for Niemann-Pick disease type C (NPC), IB1001-202 for GM2 gangliosidoses (Tay-Sachs and Sandhoff), IB1001-203 for ataxia telangiectasia (A-T)) have been registered at www.clinicaltrials.gov (NCT03759639; NCT03759665; NCT03759678), www.clinicaltrialsregister.eu (EudraCT: 2018-004331-71; 2018-004406-25; 2018-004407-39), and https://www.germanctr.de (DR KS-ID: DRKS00016567; DRKS00017539; DRKS00020511).

Abstract Background There is a need for useful standardized Quality of Life (QoL) measures for people diagnosed with schizophrenia. Therefore, a short form of the self-administered Quality of Life in Schizophrenia (QLiS) scale was developed and validated. Methods Four steps were taken to develop the abridged version using samples from the Clinical Analysis of the Treatment of Schizophrenia (CATS) study. Firstly, a model with second order scales was developed using exploratory factor analysis (EFA). Secondly, it was tested in an independent sample using confirmatory factor analysis (CFA). Thirdly, this model served as the basis for selecting items for the short form. Distributional properties, content reviews, and factor loadings were taken into account in this step. Fourthly, the resulting short form was validated through confirmatory factor analysis (CFA). Composite reliability scores were calculated for the new subscales. Results Three second order scales were constructed: illness-related quality of life (QoL), social life and finances, and global subjective well-being. CFA of the new theoretical model resulted in a CFI of 0.67 and absolute fit indices of CMIN/df = 2.55, RMSEA = 0.08, SRMR = 0.09. The selected 13 items showed good statistical properties and good fit of content to subscale. Fit of the underlying theoretical model with the reduced number of items was tested in an independent sample. Absolute and fit indices of the short form model were satisfactory (CFI = 0.95, CMIN/df = 2.23, RMSEA = 0.06, SRMR = 0.04). Composite reliability scores for three subscales were above 0.70. Conclusions The short form of the QLIS (QLiS-SF) showed good model fit and reliability. It should only be considered for use if the application of the long version is not suitable.

In the non-destructive determination of material properties, the utilization of ultrasound has proven to be a viable tool. In the presented paper, a laser is used to create broadband acoustic waves in plate-shaped specimens by applying the photoacoustic effect. The waves are detected using a purpose-built ultrasonic transducer that is based on piezoceramics instead of the commonly used piezoelectric polymer films. This new transducer concept allows for detection of ultrasonic waves up to 10 MHz with high sensitivity, thereby allowing the characterization of highly damping materials such as polymers. The recorded data are analysed using different methods to obtain information on the propagation modes transmitted along the specimen. In an inverse procedure, the gained results are compared to simulations, yielding approximations for the specimen's material properties.

Multi-Camera-Rover are recently coming up for usual terrestrical surveying tasks. This technique is new for the surveyors. Although photogrammetric specialists realize the benefits of such systems immediately, surveyors have difficulties to find efficient usages. To approach this new measurement systems the technique has to be understood and the confidence on the accuray has to grow. In this paper we analyze the accuracy of a Multi-Camera-Rover using an indoor testfield by photogrammetric algorithms. The results show that the knowledge of the interior orientation parameter of the cameras and the relative orientation of the cameras is essential for precise geometric reconstructions. Knowing these additional data, high accurate results become possible.

Autistic youth frequently experience interfering anxiety, and schools may be an ideal setting to deliver mental health care. A type 1 hybrid-effectiveness, cluster randomized trial was used to examine the effectiveness of school-based Facing Your Fears compared to usual care. Change in provider cognitive behavior therapy knowledge and treatment fidelity (adherence and provider competence) were also examined. Seventy-seven interdisciplinary school providers across 25 elementary/middle schools were trained via a train-the-trainer approach. Eighty-one students, ages 8-14 years, with autism or suspected autism and anxiety participated. Students who received school-based Facing Your Fears displayed significantly greater reductions in anxiety compared to students in usual care according to caregiver and child report (Screen for Anxiety and Related Emotional Disorders). Significant reductions in total anxiety (p = 0.012), separation (p = 0.002), and social anxiety (p = 0.003) subscales occurred, according to parent report. Student self-report indicated significant reductions on the social anxiety subscales (p = 0.001). Interdisciplinary school providers demonstrated significantly increased cognitive behavior therapy knowledge following training (p < 0.001). Mean adherence and competence ratings were strong. The positive effectiveness and implementation outcomes are encouraging. Training interdisciplinary school providers to deliver school-based Facing Your Fears has the potential to increase access to care for anxious autistic students. Future directions and limitations are discussed.

Raman scattering based imaging represents a very powerful optical tool for biomedical diagnostics. Different Raman signatures obtained by distinct tissue structures and disease induced changes provoke sophisticated analysis of the hyperspectral Raman datasets. While the analysis of linear Raman spectroscopic tissue data is quite established, the evaluation of hyperspectral nonlinear Raman data has not yet been evaluated in great detail. The two most common nonlinear Raman methods are CARS (coherent anti-Stokes Raman scattering) and SRS (stimulated Raman scattering) spectroscopy. Specifically the linear concentration dependence of SRS as compared to the quadratic dependence of CARS has fostered the application of SRS tissue imaging. Here, we applied spectral processing to hyperspectral SRS and CARS data for tissue characterization. We could demonstrate for the first time that similar cluster distributions can be obtained for multispectral CARS and SRS data but that clustering is based on different spectral features due to interference effects in CARS and the different concentration dependence of CARS and SRS. It is shown that a direct combination of CARS and SRS data does not improve the clustering results.

Nonlinear optical endoscopy is an attractive technique for biomedical imaging since it promises to give access to high resolution imaging in vivo. Among the various techniques used for endoscopic contrast generation, coherent anti-Stokes Raman scattering (CARS) is especially interesting. CARS endoscopy allows molecule specific imaging of unlabeled samples. In this contribution, we describe the design, implementation, and experimental characterization of a rigid, compact CARS endoscope with a spatial resolution of 750 nm over a field of view of roughly 250 μm. Omission of the relay optics and use of a gradient index lens specifically designed for this application allow one to realize these specifications in an endoscopic unit which is 2.2 mm wide over a length of 187 mm, making clinical applications during surgical interventions possible. Multimodal use of the endoscope is demonstrated with images of samples with neurosurgical relevance.

The cryosphere is an important component of global organic contaminant cycles. Snow is an efficient scavenger of atmospheric organic pollutants while a seasonal snowpack, sea ice, glaciers and ice caps are contaminant reservoirs on time scales ranging from days to millennia. Important physical and chemical processes occurring in the various cryospheric compartments impact contaminant cycling and fate. A variety of interactions and feedbacks also occur within the cryospheric system, most of which are susceptible to perturbations due to climate change. In this article, we review the current state of knowledge regarding the transport and processing of organic contaminants in the global cryosphere with an emphasis on the role of a changing climate. Given the complexity of contaminant interactions with the cryosphere and limitations on resources and research capacity, interdisciplinary research and extended collaborations are essential to close identified knowledge gaps and to improve our understanding of contaminant fate under a changing climate.

Purpose. The prognosis for patients with Ewing's tumours who have metastases at presentation or who are refractory to standard chemotherapy regimens remains poor. There is therefore a need to evaluate the role of new agents. This report describes the initial results of a prospective phase II trial of docetaxel in patients with progressive or refractory Ewing's tumours.

Recently, focus on maintenance strategies has been shifted towards prognostic health management (PHM) and a number of state of the art algorithms based on data-driven prognostics have been developed to predict the health states of degrading components based on sensory data. Amongst these algorithms, Multiclass Support Vector Machines (MC-SVM) has gained popularity due to its relatively high classification accuracy, ability to classify multiple patterns and capability to handle noisy / incomplete data. However, its application is limited by the difficulty in determining the required kernel function and penalty parameters. To address this problem, this paper proposes a hybrid differential evolution – particle swarm optimization (DE-PSO) algorithm to optimize the MC-SVM kernel function and penalty parameters. The differential algorithm (DE) obtains the search limit for the SVM parameters, while the particle swarm optimization algorithm (PSO) determines the global optimum parameters for a given training data set. Since degrading machinery components display several degradation stages in their lifetime, the MC-SVM trained with optimum parameters are used to estimate the health states of a degrading machinery component, from which the remaining useful life (RUL) is predicted. This method improves the classification accuracy of MC-SVM in predicting the health states of a machinery component and consequently increases the accuracy of RUL predictions. The feasibility of the method is validated using bearing prognostic run-to-failure data obtained from NASA public data repository. A comparative study between MC-SVM with parameters obtained using simple grid search with n-fold cross validation and MC- SVM with DE-PSO based on prognostic performance metrics reveals that the proposed method has better performance, with all the cases considered falling within a 10 % error margin. The method also outperforms other soft computing methods proposed in literature.

Self-optimizing mechatronic systems offer possibilities well beyond those of traditional mechatronic systems. Among these is the adaptation of the system behavior to the current situation. To do so, they are able to choose from different working points, which are pre-calculated using multiobjective optimization and are thus Pareto-optimal with regard to the chosen objective functions. In this contribution, a method is presented that allows to continuously control the system degradation by adapting the behavior of a self-optimizing system throughout its complete lifetime. The current remaining useful lifetime is estimated and then related to the spent lifetime and the desired useful lifetime. Using this information, a reliability-related objective is prioritized using a closed-loop control, which in turn is used to determine the working point of the self-optimizing system. This way, the desired useful lifetime can be achieved. To exemplify the setup of the controller structure and to demonstrate the adaptation of the system behavior, a dynamic model of a clutch system is used. It can be seen that the closed loop controller is able to correct for external perturbations, such as changed requirements, as well as changed system parameters. This way, the modeled system is able to achieve the desired lifetime reliably.

Handling and storing chemicals in the industrial world require a conscientious hazards assessment and the implementation of appropriate measures to ensure safety for the workers, the company, the environment and the society. Reliable evaluations and predictions based solely on the molecular structure would represent a valuable tool in the preliminary hazard assessment process as it would reduce the necessary time and resources for extensive testing. This work presents how Quantitative Structure-Property Relationships (QSPR) of various hazardous chemical properties (i.e. thermal decomposition enthalpy or Minimal Ignition Energy) can be built to meet these needs. Results are to be illustrated with some examples. Two sets of chemicals were studied and the corresponding experimental results were correlated to 90 molecular descriptors. The models were built following a regression analysis. The best multi-linear regressions presenting 6 parameters or less with high determination and cross-validation coefficients are withheld as predictive models.

There is a need for a risk analysis technique specific for academic research laboratories. Since accurate accident data, normally required for quantitative risk analysis, are not available for this environment, expert judgements are often used to describe risks. However, these judgements are afflicted with linguistic, lexical or informal uncertainties. As a consequence, analyses made by different experts can lead to different results, which make risks incomparable. The purpose of this work is to analyse the effect of these uncertainties and to test strategies to improve the accuracy of the risk estimation based on expert judgements. Different calculation methods were used to compare the obtained risk scores. Results show that a multiplication-based formula, as used, for example, in the Failure Mode, Effects and Criticality Analysis (FMECA), has an inconsistent variance of the risk score distribution. Another approach, using a logarithm-sum-based formula, gives more consistent results but introduces other drawbacks. An estimation method based on Bayesian networks is giving more consistent variances, which are crucial for the risk estimation. With a higher precision of the risk score results, the prioritization of risks can be enhanced and resources can be better allocated to improve the level of occupational safety in academic research laboratories.