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The phase behavior of a dendritic amphiphile containing a Newkome-type dendron as the hydrophilic moiety and a cholesterol unit as the hydrophobic segment is investigated at the air-liquid interface. The amphiphile forms stable monomolecular films at the airliquid interface on different subphases. Furthermore, the mineralization of calcium phosphate beneath the monolayer at different calcium and phosphate concentrations versus mineralization time shows that at low calcium and phosphate concentrations needles form, whereas flakes and spheres dominate at higher concentrations. Energy-dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy, and electron diffraction confirm the formation of calcium phosphate. High-resolution transmission electron microscopy and electron diffraction confirm the predominant formation of octacalcium phosphate and hydroxyapatite. The data also indicate that the final products form via a complex multistep reaction, including an association step, where nano-needles aggregate into larger flake-like objects.
We study those nonlinear partial differential equations which appear as Euler-Lagrange equations of variational problems. On defining weak boundary values of solutions to such equations we initiate the theory of Lagrangian boundary value problems in spaces of appropriate smoothness. We also analyse if the concept of mapping degree of current importance applies to Lagrangian problems.
The molecular biomarker composition of two sediment cores from Sanabria Lake (NW Iberian Peninsula) and a survey of modern plants in the watershed provide a reconstruction of past vegetation and landscape dynamics since deglaciation. During a proglacial stage in Lake Sanabria (prior to 14.7 cal ka BP), very low biomarker concentration and carbon preference index (CPI) values similar to 1 suggest that the n-alkanes could have derived from eroded ancient sediment sources or older organic matter with high degree of maturity. During the Late glacial (14.7-11.7 cal ka BP) and the Holocene (last 11.7 cal ka BP) intervals with higher biomarker and triterpenoid concentrations (high %nC(29) , nC(31) alkanes), higher CPI and average carbon length (ACL), and lower P-aq (proportion of aquatic plants) are indicative of major contribution of vascular land plants from a more forested watershed (e.g. Mid Holocene period 7.0-4.0 cal ka BP). Lower biomarker concentrations (high %nC(27) alkanes), CPI and ACL values responded to short phases with decreased allochthonous contribution into the lake that correspond to centennial-scale periods of regional forest decline (e.g. 4-3 ka BP, Roman deforestation after 2.0 ka, and some phases of the LIA, seventeenth-nineteenth centuries). Human activities in the watershed were significant during early medieval times (1.3-1.0 cal ka BP) and since 1960 CE, in both cases associated with relatively higher productivity stages in the lake (lower biomarker and triterpenoid concentrations, high %nC(23) and %nC(31) respectively, lower ACL and CPI values and higher P-aq). The lipid composition of Sanabria Lake sediments indicates a major allochthonous (watershed-derived) contribution to the organic matter budget since deglaciation, and a dominant oligotrophic status during the lake history. The study constrains the climate and anthropogenic forcings and watershed versus lake sources in organic matter accumulation processes and helps to design conservation and management policies in mountain, oligotrophic lakes.
Multimodal representation learning has gained increasing importance in various real-world multimedia applications. Most previous approaches focused on exploring inter-modal correlation by learning a common or intermediate space in a conventional way, e.g. Canonical Correlation Analysis (CCA). These works neglected the exploration of fusing multiple modalities at higher semantic level. In this paper, inspired by the success of deep networks in multimedia computing, we propose a novel unified deep neural framework for multimodal representation learning. To capture the high-level semantic correlations across modalities, we adopted deep learning feature as image representation and topic feature as text representation respectively. In joint model learning, a 5-layer neural network is designed and enforced with a supervised pre-training in the first 3 layers for intra-modal regularization. The extensive experiments on benchmark Wikipedia and MIR Flickr 25K datasets show that our approach achieves state-of-the-art results compare to both shallow and deep models in multimodal and cross-modal retrieval.
In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
Earthquake localization is both a necessity within the field of seismology, and a prerequisite for further analysis such as source studies and hazard assessment. Traditional localization methods often rely on manually picked phases. We present an alternative approach using deep learning that once trained can predict hypocenter locations efficiently. In seismology, neural networks have typically been trained with either single-station records or based on features that have been extracted previously from the waveforms. We use three-component full-waveform records of multiple stations directly. This means no information is lost during preprocessing and preparation of the data does not require expert knowledge. The first convolutional layer of our deep convolutional neural network (CNN) becomes sensitive to features that characterize the waveforms it is trained on. We show that this layer can therefore additionally be used as an event detector. As a test case, we trained our CNN using more than 2000 earthquake swarm events from West Bohemia, recorded by nine local three-component stations. The CNN successfully located 908 validation events with standard deviations of 56.4 m in east-west, 123.8 m in north-south, and 136.3 m in vertical direction compared to a double-difference relocated reference catalog. The detector is sensitive to events with magnitudes down to M-L = -0.8 with 3.5% false positive detections.
A very sensitive X-ray investigation of the giant HII region N11 in the Large Megallanic Cloud was performed using the Chandra X-ray Observatory. The 300 ks observation reveals X-ray sources with luminosities down to 10(32) erg s(-1), increasing the number of known point sources in the field by more than a factor of five. Among these detections are 13 massive stars (3 compact groups of massive stars, 9 O stars, and one early B star) with log(L-X/L-BOL) similar to -6.5 to -7, which may suggest that they are highly magnetic or colliding-wind systems. On the other hand, the stacked signal for regions corresponding to undetected O stars yields log(L-X/L-BOL) similar to -7.3, i.e., an emission level comparable to similar Galactic stars despite the lower metallicity. Other point sources coincide with 11 foreground stars, 6 late-B/A stars in N11, and many background objects. This observation also uncovers the extent and detailed spatial properties of the soft, diffuse emission regions, but the presence of some hotter plasma in their spectra suggests contamination by the unresolved stellar population.
We present an experimental approach to study the three-dimensional microstructure of gas diffusion layer (GDL) materials under realistic compression conditions. A dedicated compression device was designed that allows for synchrotron-tomographic investigation of circular samples under well-defined compression conditions. The tomographic data provide the experimental basis for stochastic modeling of nonwoven GDL materials. A plain compression tool is used to study the fiber courses in the material at different compression stages. Transport relevant geometrical parameters, such as porosity, pore size, and tortuosity distributions, are exemplarily evaluated for a GDL sample in the uncompressed state and for a compression of 30 vol.%. To mimic the geometry of the flow-field, we employed a compression punch with an integrated channel-rib-profile. It turned out that the GDL material is homogeneously compressed under the ribs, however, much less compressed underneath the channel. GDL fibers extend far into the channel volume where they might interfere with the convective gas transport and the removal of liquid water from the cell. (C) 2015 AIP Publishing LLC.
Faced with the increasing needs of companies, optimal dimensioning of IT hardware is becoming challenging for decision makers. In terms of analytical infrastructures, a highly evolutionary environment causes volatile, time dependent workloads in its components, and intelligent, flexible task distribution between local systems and cloud services is attractive. With the aim of developing a flexible and efficient design for analytical infrastructures, this paper proposes a flexible architecture model, which allocates tasks following a machine-specific decision heuristic. A simulation benchmarks this system with existing strategies and identifies the new decision maxim as superior in a first scenario-based simulation.
Acetanilides can be deacetylated and diazotized in situ, and subsequently used in Pd-catalyzed coupling reactions without isolation of the diazonium intermediate. Heck reactions, Suzuki cross-coupling reactions, and a Pd-catalyzed [2+2+1]cycloaddition have been investigated as terminating CC bond-forming steps of this one-flask sequence. The sequence does not require the exchange of solvents or removal of by-products between the individual steps, but proceeds by addition of reagents and catalysts in due course.