Counting Fluorescent Dye Molecules on DNA Origami by Means of Photon Statistics

Obtaining quantitative information about molecular assemblies with high spatial and temporal resolution is a challenging task in fluorescence microscopy. Single‐molecule techniques build on the ability to count molecules one by one. Here, a method is presented that extends recent approaches to analyze the statistics of coincidently emitted photons to enable reliable counting of molecules in the range of 1–20. This method does not require photochemistry such as blinking or bleaching. DNA origami structures are labeled with up to 36 dye molecules as a new evaluation tool to characterize this counting by a photon statistics approach. Labeled DNA origami has a well‐defined labeling stoichiometry and ensures equal brightness for all dyes incorporated. Bias and precision of the estimating algorithm are determined, along with the minimal acquisition time required for robust estimation. Complexes containing up to 18 molecules can be investigated non‐invasively within 150 ms. The method might become a quantifying add‐on for confocal microscopes and could be especially powerful in combination with STED/RESOLFT‐type microscopy.     

Holistic process planning chain for robot machining

Today, machining of large, integral constructed structural parts requires expensive machining centers. In contrast, modern industrial robots are suitable for a wide field of applications and are characterized large working spaces and low capital investment. Therefore, they provide high economical potential for machining applications in aerospace industry, especially for the machining of near to shape pre-products like extruded profiles. However, their constructive characteristics like low stiffness and high sensitivity to vibrations lead to disadvantages compared with conventional machining centers and have to be considered during process planning. Therefore, several methods for offline and online optimization of robot machining processes were developed and integrated in a new process chain for manufacturing of structural fuselage parts. Thereby, the conventional CAD–CAM process planning chain was extended with simulation based analysis and optimization methods and a load-depending trajectory planning. These methods for offline process optimization within this novel process chain are presented in this paper.     

Damage Model and Progressive Failure Analyses for Filament Wound Composite Laminates

Recent improvements in manufacturing processes and materials properties associated with excellent mechanical characteristics and low weight have made composite materials very attractive for application on civil aircraft structures. However, even new designs are still very conservative, because the composite failure phenomenon is very complex. Several failure criteria and theories have been developed to describe the damage process and how it evolves, but the solution of the problem is still open. Moreover, modern filament winding techniques have been used to produce a wide variety of structural shapes not only cylindrical parts, but also “flat” laminates. Therefore, this work presents the development of a damage model and its application to simulate the progressive failure of flat composite laminates made using a filament winding process. The damage model was implemented as a UMAT (User Material Subroutine), in ABAQUS^TM Finite Element (FE) framework. Progressive failure analyses were carried out using FE simulation in order to simulate the failure of flat filament wound composite structures under different loading conditions. In addition, experimental tests were performed in order to identify parameters related to the material model, as well as to evaluate both the potential and the limitations of the model. The difference between numerical and the average experimental results in a four point bending set-up is only 1.6 % at maximum load amplitude. Another important issue is that the model parameters are not so complicated to be identified. This characteristic makes this model very attractive to be applied in an industrial environment.     

Anticipation and flexibility in dynamic scheduling

Many real-world optimization problems change over time and require frequent re-optimization. We suggest that in such environments, an optimization algorithm should reflect the problem's dynamics and explicitly take into account that changes to the current solution are to be expected. We claim that this can be achieved by having the optimization algorithm search for solutions that are not only good, but also flexible, i.e. easily adjustable if necessary in the case of problem changes. For the example of a job-shop with jobs arriving non-deterministically over time, we demonstrate that avoiding early idle times increases flexibility, and thus that the incorporation of an early idle time penalty as secondary objective into the scheduling algorithm can greatly enhance the overall system performance.     

Hydrogenation of p‐Nitrophenol to p‐Aminophenol as a Test Reaction for the Catalytic Activity of Supported Pt Catalysts

Hydrogenation of p‐nitrophenol (PNP) to p‐aminophenol (PAP) using NaBH₄ as a reducing agent was studied as a test reaction for determining the catalytic activity of supported Pt catalysts. The initial reaction rate, which is accessible within less than 10 min via online UV‐vis spectroscopy at room temperature, ambient pressure, and in water as solvent, was applied as measure for catalytic activity. For three Pt catalysts supported on porous SiO₂, porous glass, and Al₂O₃, respectively, significant differences in the catalytic activity by almost one order of magnitude were observed. However, especially in the case of very active catalysts, limitations of the reaction by internal or external mass transfer have to be considered.     

Allocating reforestation areas for sediment flow minimization: an?integer programming formulation and?a?heuristic solution method

Policy and decision makers dealing with environmental conservation and land use planning often require identifying potential sites for contributing to minimize sediment flow reaching riverbeds. This is the case of reforestation initiatives, which can have sediment flow minimization among their objectives. This paper proposes an Integer Programming (IP) formulation and a Heuristic solution method for selecting a predefined number of locations to be reforested in order to minimize sediment load at a given outlet in a watershed. Although the core structure of both methods can be applied for different sorts of flow, the formulations are targeted to minimization of sediment delivery. The proposed approaches make use of a Single Flow Direction (SFD) raster map covering the watershed in order to construct a tree structure so that the outlet cell corresponds to the root node in the tree. The results obtained with both approaches are in agreement with expert assessments of erosion levels, slopes and distances to the riverbeds, which in turn allows concluding that this approach is suitable for minimizing sediment flow. Since the results obtained with the IP formulation are the same as the ones obtained with the Heuristic approach, an optimality proof is included in the present work. Taking into consideration that the heuristic requires much less computation time, this solution method is more suitable to be applied in large sized problems.     

Weak signal identification with semantic web mining

We investigate an automated identification of weak signals according to Ansoff to improve strategic planning and technological forecasting. Literature shows that weak signals can be found in the organization’s environment and that they appear in different contexts. We use internet information to represent organization’s environment and we select these websites that are related to a given hypothesis. In contrast to related research, a methodology is provided that uses latent semantic indexing (LSI) for the identification of weak signals. This improves existing knowledge based approaches because LSI considers the aspects of meaning and thus, it is able to identify similar textual patterns in different contexts. A new weak signal maximization approach is introduced that replaces the commonly used prediction modeling approach in LSI. It enables to calculate the largest number of relevant weak signals represented by singular value decomposition (SVD) dimensions. A case study identifies and analyses weak signals to predict trends in the field of on-site medical oxygen production. This supports the planning of research and development (R&D) for a medical oxygen supplier. As a result, it is shown that the proposed methodology enables organizations to identify weak signals from the internet for a given hypothesis. This helps strategic planners to react ahead of time.