Multichromic polymers based on pyrene clicked thienylpyrrole

In this study, the effect of pendant pyrene on the optical and electronic properties of poly(2,5‐dithienylpyrrole)s was studied. For this purpose a new pyrene coupled 2,5‐dithienylpyrrole derivative (SNS‐pyrene) was synthesized through click reaction. SNS‐pyrene was electrochemically polymerized and its electrochemical and optical properties were investigated by electrochemical and optical techniques. The polymer had a band gap of 3.36 eV and displayed light green to blue color variation upon oxidation in less than 2.48 s. Additionally, electrochemical copolymerization of SNS‐pyrene with 3,4‐ethylenedioxythiophene was achieved whilst a detailed investigation was performed on the effect of electrochemical polymerization conditions on the optoelectronic properties of the copolymers. Studies revealed that the copolymers exhibit multichromic reversible redox behavior with lower band gaps and shorter switching times than their parent polymer, P(SNS‐pyrene) © 2014 Society of Chemical Industry.     

Enhancing electrochromic properties of polypyrrole by silsesquioxane nanocages

A new monomer, octa(thiophenephenyl)silsesquioxane (OThiophenePS) was synthesized via click chemistry. The chemical structure of OThiophenePS was characterized by nuclear magnetic resonance (¹H NMR) and Fourier transform infrared (FT-IR) spectroscopies. Electrochemical polymerization of pyrrole with OThiophenePS was performed resulting in polypyrrole-attached, polyhedral oligomeric silsesquioxane (OPS–PPy). The spectroelectrochemical studies show that the electrochromic properties of (OPS–PPy) are superior to those of polypyrrole (PPy). This great improvement can be attributed to the more accessible doping sites and the facile ion movement during the redox switching brought by the loose packing of the PPy chains.     

The influence of initial water curing on the strength development of ordinary portland and pozzolanic cement concretes

The effect of initial water-curing period on the strength properties of concretes was investigated. Three types of cement, one ordinary Portland cement (OPC) and two natural pozzolanic cements (blended and trass cements), were used in the concrete mixtures. Six different curing regimes were applied to the specimens, the first of which was continuous water storing, and the second continuous air storing. In the remaining four regimes, the specimens were stored under varying initial water-curing periods of 3, 7, 14, and 28 days, respectively. The compressive strength tests were carried out on the cubic specimens at the ages of 7, 14, 28, 90, and 180 days. The variation of compressive strength with time was evaluated by using a semilogarithmic function and the strength-gaining rates were calculated by using this equation for different curing conditions. It was found that poor curing conditions are more adversely effective on the strength of concretes made by pozzolanic cements than that of OPC, and it is necessary to apply water curing to the former concretes at least for the initial 7 days to expose the pozzolanic activity. However, when the pozzolanic cement concretes have sufficient initial curing, they can reach the strength of OPC concretes in reasonable periods of time.     

Global structure constrained local shape prior estimation for medical image segmentation

Organ shape plays an important role in clinical diagnosis, surgical planning and treatment evaluation. Shape modeling is a critical factor affecting the performance of deformable model based segmentation methods for organ shape extraction. In most existing works, shape modeling is completed in the original shape space, with the presence of outliers. In addition, the specificity of the patient was not taken into account. This paper proposes a novel target-oriented shape prior model to deal with these two problems in a unified framework. The proposed method measures the intrinsic similarity between the target shape and the training shapes on an embedded manifold by manifold learning techniques. With this approach, shapes in the training set can be selected according to their intrinsic similarity to the target image. With more accurate shape guidance, an optimized search is performed by a deformable model to minimize an energy functional for image segmentation, which is efficiently achieved by using dynamic programming. Our method has been validated on 2D prostate localization and 3D prostate segmentation in MRI scans. Compared to other existing methods, our proposed method exhibits better performance in both studies.     

TORC: test plan optimization by requirements clustering

Acceptance testing is a time-consuming task for complex software systems that have to fulfill a large number of requirements. To reduce this effort, we have developed a widely automated method for deriving test plans from requirements that are expressed in natural language. It consists of three stages: annotation, clustering, and test plan specification. The general idea is to exploit redundancies and implicit relationships in requirements specifications. Multi-viewpoint techniques based on RM-ODP (Reference Model for Open Distributed Processing) are employed for specifying the requirements. We then use linguistic analysis techniques, requirements clustering algorithms, and pattern-based requirements collection to reduce the total effort of testing against the requirements specification. In particular, we use linguistic analysis for extracting and annotating the actor, process and object of a requirements statement. During clustering, a similarity function is computed as a measure for the overlap of requirements. In the test plan specification stage, our approach provides capabilities for semi-automatically deriving test plans and acceptance criteria from the clustered informal textual requirements. Two patterns are applied to compute a suitable order of test activities. The generated test plans consist of a sequence of test steps and asserts that are executed or checked in the given order. We also present the supporting prototype tool TORC, which is available open source. For the evaluation of the approach, we have conducted a case study in the field of acceptance testing of a national electronic identification system. In summary, we report on lessons learned how linguistic analysis and clustering techniques can help testers in understanding the relations between requirements and for improving test planning.     

Simultaneously learning actions and goals from demonstration

Our research aim is to develop interactions and algorithms for learning from naïve human teachers through demonstration. We introduce a novel approach to leverage the goal-oriented nature of human teachers by learning an action model and a goal model simultaneously from the same set of demonstrations. We use robot motion data to learn an action model for executing the skill. We use a generic set of perceptual features to learn a goal model and use it to monitor the executed action model. We evaluate our approach with data from 8 naïve teachers demonstrating two skills to the robot. We show that the goal models in the perceptual feature space are consistent across users and correctly recognize demonstrations in cross-validation tests. We additionally observe that a subset of users were not able to teach a successful action model whereas all of them were able to teach a mostly successful goal model. When the learned action models are executed on the robot, the success was on average 66.25 %. Whereas the goal models were on average 90 % correct at deciding on success/failure of the executed action, which we call monitoring.     

Sodium and ultrafiltration profiling in hemodialysis: A long-forgotten issue revisited

Sodium and ultrafiltration profiling are method of dialysis in which dialysate sodium concentration and ultrafiltration rate are altered during the course of the dialysis session. Sodium and ultrafiltration profiling have been used, commonly simultaneously, to improve hemodynamic stability during hemodialysis. Sodium profiling is particularly effective in decreasing the incidence of intradialytic hypotension, while ultrafiltration profiling is suggested to decrease subclinical repeated end organ ischemia during dialysis. However, complications such as increased interdialytic weight gain and thirst due to sodium excess have prevented widespread use of sodium profiling. Evidence suggest that different sodium profiling techniques may lead to different clinical results, and preferring sodium balance neutral sodium profiling may mitigate adverse effects related to sodium overload. However, evidence is lacking on the long-term clinical outcomes of different sodium profiling methods. Optimal method of sodium profiling as well as the utility of sodium/ultrafiltration profiling in routine practice await further clinical investigation.     

Transferrin-Decorated Niosomes with Integrated InP/ZnS Quantum Dots and Magnetic Iron Oxide Nanoparticles: Dual Targeting and Imaging of Glioma

The development of multifunctional nanoscale systems that can mediate efficient tumor targeting, together with high cellular internalization, is crucial for the diagnosis of glioma. The combination of imaging agents into one platform provides dual imaging and allows further surface modification with targeting ligands for specific glioma detection. Herein, transferrin (Tf)-decorated niosomes with integrated magnetic iron oxide nanoparticles (MIONs) and quantum dots (QDs) were formulated (PEGNIO/QDs/MIONs/Tf) for efficient imaging of glioma, supported by magnetic and active targeting. Transmission electron microscopy confirmed the complete co-encapsulation of MIONs and QDs in the niosomes. Flow cytometry analysis demonstrated enhanced cellular uptake of the niosomal formulation by glioma cells. In vitro imaging studies showed that PEGNIO/QDs/MIONs/Tf produces an obvious negative-contrast enhancement effect on glioma cells by magnetic resonance imaging (MRI) and also improved fluorescence intensity under fluorescence microscopy. This novel platform represents the first niosome-based system which combines magnetic nanoparticles and QDs, and has application potential in dual-targeted imaging of glioma.     

Momentum Transfer from Preshape to Grasping

A fully immersed object, suspended in water can be rotated from distance by a preshaped robot hand approaching and closing upon the object prior to contacting it. Momentum transfer from robot fingers closing into a grasp, to the fluid medium particles, and from these particles to the object surface generates the motion tendencies of that object in terms of rotational and translational displacements. In this paper, we propose the novel concept of a controller that determines either: 1) given initial position and orientation of a robot hand, what preshape is suitable for generating a desired momentum distribution on the surface of a given object in order to trigger a desired rotation in a desired direction when approaching with this preshaped hand; or 2) given a predetermined hand preshape, what initial position, orientation and hand aperture are suitable to generate a desired rotation upon approach and, without causing the retroceeding of the object. The desired object motion generated from distance by the approach of a hand preshape is to be used seamlessly for the subsequent manipulation of the object upon grasp. Towards this end, we propose in our work, a new model based on computational fluid dynamics, for determining the continuity in momentum transfer from robot hand fingers to the fluid medium, and to the object, until landing on that immersed object. Our experimental results demonstrate how different hand preshapes initiated from different locations in the medium surrounding an object of different cross sections suspended in equilibrium in the fluid, affects its motion tendencies in terms of rotation and translation. Our further contribution, in this paper, includes the modelling of robot fingers and object as fluidic elements which rigidity can be relaxed to induce compliance.     

Event Distortion-Based Clustering Algorithm for Energy Harvesting Wireless Sensor Networks

Wireless Personal Communications - Wireless sensor networks (WSNs) consist of compact deployed sensor nodes which collectively report their sensed readings about an event to the Base Station (BS)....