Iron(III)-Pivalate-Based Complexes with Tetranuclear {Fe-4(mu(3)-O)(2)}(8+) Cores and N-Donor Ligands: Formation of Cluster and Polymeric Architectures

Different synthetic routes have been used for the preparation of a new tetranuclear [Fe4O2(O2CCMe3)(8)(bpm)] cluster (1) and a one-dimensional coordination polymer [Fe4O2-(O2CCMe3)(8)(hmta)](n) (2) (bpm = 2,2'-bipyrimidine and hmta = hexamethylenetetramine). For cluster 1, two structural isomers, 1a and 1b center dot 3MeCN, have been found. X-ray crystallographic analysis showed that all complexes consist of a central {Fe-4(mu(3)-O)(2)}(8+) core. In 1a, metal ions in the core are additionally linked by six bridging pivalates as two other pivalates and a bpm ligand are chelated to Fe-III ions, whereas in cluster 1b, metal ions in the {Fe-4(mu(3)-O)(2)}(8+) core are linked by seven bridging pivalates and only one carboxylate as well as bpm are chelated to the iron centers. In coordination polymer 2, [Fe4O2(O2CCMe3)(8)] clusters are bridged by hmta ligands to form zigzag chains. Magnetic measurements have been carried out to characterize these complexes and revealed antiferromagnetic interactions between Fe-III ions with best-fit parameters of J(wb) = -72.2 (1a) and -88.7 cm(-1) (1b) for wing...body interactions.

The Meta in Meta-object Architectures

Behavioral reflection is crucial to support for example functional upgrades, on-the-fly debugging, or monitoring critical applications. However the use of reflective features can lead to severe problems due to infinite metacall recursion even in simple cases. This is especially a problem when reflecting on core language features since there is a high chance that such features are used to implement the reflective behavior itself. In this paper we analyze the problem of infinite meta-object call recursion and solve it by providing a first class representation of meta-level execution: at any point in the execution of a system it can be determined if we are operating on a meta-level or base level so that we can prevent infinite recursion. We present how meta-level execution can be represented by a meta-context and how reflection becomes context-aware. Our solution makes it possible to freely apply behavioral reflection even on system classes: the meta-context brings stability to behavioral reflection. We validate the concept with a robust implementation and we present benchmarks.

High depth-resolution laser ablation chemical analysis of additive-assisted Cu electroplating for microchip architectures

Laser ablation/ionisation mass spectrometry with a vertical resolution at a nanometre scale was applied for the quantitative characterisation of the chemical composition of additive-assisted Cu electroplated deposits used in the microchip industry. The detailed chemical analysis complements information gathered by optical techniques and allows new insights into the metal deposition process.

Atomistic simulations of 2D bicomponent self-assembly: from molecular recognition to self-healing

Supramolecular two-dimensional engineering epitomizes the design of complex molecular architectures through recognition events in multicomponent self-assembly. Despite being the subject of in-depth experimental studies, such articulated phenomena have not been yet elucidated in time and space with atomic precision. Here we use atomistic molecular dynamics to simulate the recognition of complementary hydrogen-bonding modules forming 2D porous networks on graphite. We describe the transition path from the melt to the crystalline hexagonal phase and show that self-assembly proceeds through a series of intermediate states featuring a plethora of polygonal types. Finally, we design a novel bicomponent system possessing kinetically improved self-healing ability in silico, thus demonstrating that a priori engineering of 2D self-assembly is possible.

Self-assembly of focal point oligo-catechol ethylene glycol dendrons on titanium oxide surfaces: adsorption kinetics, surface characterization, and nonfouling properties

This work covers the synthesis of second-generation, ethylene glycol dendrons covalently linked to a surface anchor that contains two, three, or four catechol groups, the molecular assembly in aqueous buffer on titanium oxide surfaces, and the evaluation of the resistance of the monomolecular adlayers against nonspecific protein adsorption in contact with full blood serum. The results were compared to those of a linear poly(ethylene glycol) (PEG) analogue with the same molecular weight. The adsorption kinetics as well as resulting surface coverages were monitored by ex situ spectroscopic ellipsometry (VASE), in situ optical waveguide lightmode spectroscopy (OWLS), and quartz crystal microbalance with dissipation (QCM-D) investigations. The expected compositions of the macromolecular films were verified by X-ray photoelectron spectroscopy (XPS). The results of the adsorption study, performed in a high ionic strength ("cloud-point") buffer at room temperature, demonstrate that the adsorption kinetics increase with increasing number of catechol binding moieties and exceed the values found for the linear PEG analogue. This is attributed to the comparatively smaller and more confined molecular volume of the dendritic macromolecules in solution, the improved presentation of the catechol anchor, and/or their much lower cloud-point in the chosen buffer (close to room temperature). Interestingly, in terms of mechanistic aspects of "nonfouling" surface properties, the dendron films were found to be much stiffer and considerably less hydrated in comparison to the linear PEG brush surface, closer in their physicochemical properties to oligo(ethylene glycol) alkanethiol self-assembled monolayers than to conventional brush surfaces. Despite these differences, both types of polymer architectures at saturation coverage proved to be highly resistant toward protein adsorption. Although associated with higher synthesis costs, dendritic macromolecules are considered to be an attractive alternative to linear polymers for surface (bio)functionalization in view of their spontaneous formation of ultrathin, confluent, and nonfouling monolayers at room temperature and their outstanding ability to present functional ligands (coupled to the termini of the dendritic structure) at high surface densities.

MaxMAC: a Maximally Traffic-Adaptive MAC Protocol for Wireless Sensor Networks

Energy efficiency is a major concern in the design of Wireless Sensor Networks (WSNs) and their communication protocols. As the radio transceiver typically accounts for a major portion of a WSN node’s power consumption, researchers have proposed Energy-Efficient Medium Access (E2-MAC) protocols that switch the radio transceiver off for a major part of the time. Such protocols typically trade off energy-efficiency versus classical quality of service parameters (throughput, latency, reliability). Today’s E2-MAC protocols are able to deliver little amounts of data with a low energy footprint, but introduce severe restrictions with respect to throughput and latency. Regrettably, they yet fail to adapt to varying traffic load at run-time. This paper presents MaxMAC, an E2-MAC protocol that targets at achieving maximal adaptivity with respect to throughput and latency. By adaptively tuning essential parameters at run-time, the protocol reaches the throughput and latency of energy-unconstrained CSMA in high-traffic phases, while still exhibiting a high energy-efficiency in periods of sparse traffic. The paper compares the protocol against a selection of today’s E2-MAC protocols and evaluates its advantages and drawbacks.

On the accuracy of Software-based Energy Estimation Techniques

This paper examines the accuracy of software-based on-line energy estimation techniques. It evaluates today’s most widespread energy estimation model in order to investigate whether the current methodology of pure software-based energy estimation running on a sensor node itself can indeed reliably and accurately determine its energy consumption - independent of the particular node instance, the traffic load the node is exposed to, or the MAC protocol the node is running. The paper enhances today’s widely used energy estimation model by integrating radio transceiver switches into the model, and proposes a methodology to find the optimal estimation model parameters. It proves by statistical validation with experimental data that the proposed model enhancement and parameter calibration methodology significantly increases the estimation accuracy.

Service-Centric Networking

Content-centric networking is a novel paradigm for the Future Internet that treats content as a first class citizen. This paper argues that content-centric networking should be generalized towards a service-centric networking scheme. We propose a service-centric networking design based on an object-oriented approach, in which content and services are considered objects. We show implementation architectures for example services and how these can benefit from service-oriented networking.

Back to the future in one week - implementing a Smalltalk VM in PyPy

We report on our experiences with the Spy project, including implementation details and benchmark results. Spy is a re-implementation of the Squeak (i.e., Smalltalk-80) VM using the PyPy toolchain. The PyPy project allows code written in RPython, a subset of Python, to be translated to a multitude of different backends and architectures. During the translation, many aspects of the implementation can be independently tuned, such as the garbage collection algorithm or threading implementation. In this way, a whole host of interpreters can be derived from one abstract interpreter definition. Spy aims to bring these benefits to Squeak, allowing for greater portability and, eventually, improved performance. The current Spy codebase is able to run a small set of benchmarks that demonstrate performance superior to many similar Smalltalk VMs, but which still run slower than in Squeak itself. Spy was built from scratch over the course of a week during a joint Squeak-PyPy Sprint in Bern last autumn.

Neural network based glucose - insulin metabolism models for children with Type 1 diabetes

In this paper two models for the simulation of glucose-insulin metabolism of children with Type 1 diabetes are presented. The models are based on the combined use of Compartmental Models (CMs) and artificial Neural Networks (NNs). Data from children with Type 1 diabetes, stored in a database, have been used as input to the models. The data are taken from four children with Type 1 diabetes and contain information about glucose levels taken from continuous glucose monitoring system, insulin intake and food intake, along with corresponding time. The influences of taken insulin on plasma insulin concentration, as well as the effect of food intake on glucose input into the blood from the gut, are estimated from the CMs. The outputs of CMs, along with previous glucose measurements, are fed to a NN, which provides short-term prediction of glucose values. For comparative reasons two different NN architectures have been tested: a Feed-Forward NN (FFNN) trained with the back-propagation algorithm with adaptive learning rate and momentum, and a Recurrent NN (RNN), trained with the Real Time Recurrent Learning (RTRL) algorithm. The results indicate that the best prediction performance can be achieved by the use of RNN.

Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers

The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifiers (ECs), are comparatively assessed.

VirtualMesh: An Emulation Framework for Wireless Mesh Networks in OMNeT++

Wireless Mesh Networks (WMN) have proven to be a key technology for increased network coverage of Internet infrastructures. The development process for new protocols and architectures in the area of WMN is typically split into evaluation by network simulation and testing of a prototype in a test-bed. Testing a prototype in a real test-bed is time-consuming and expensive. Irrepressible external interferences can occur which makes debugging difficult. Moreover, the test-bed usually supports only a limited number of test topologies. Finally, mobility tests are impractical. Therefore, we propose VirtualMesh as a new testing architecture which can be used before going to a real test-bed. It provides instruments to test the real communication software including the network stack inside a controlled environment. VirtualMesh is implemented by capturing real traffic through a virtual interface at the mesh nodes. The traffic is then redirected to the network simulator OMNeT++. In our experiments, VirtualMesh has proven to be scalable and introduces moderate delays. Therefore, it is suitable for predeployment testing of communication software for WMNs.