Large‐Stroke Electrochemical Carbon Nanotube/Graphene Hybrid Yarn Muscles

Artificial muscles are reported in which reduced graphene oxide (rGO) is trapped in the helical corridors of a carbon nanotube (CNT) yarn. When electrochemically driven in aqueous electrolytes, these coiled CNT/rGO yarn muscles can contract by 8.1%, which is over six times that of the previous results for CNT yarn muscles driven in an inorganic electrolyte (1.3%). They can contract to provide a final stress of over 14 MPa, which is about 40 times that of natural muscles. The hybrid yarn muscle shows a unique catch state, in which 95% of the contraction is retained for 1000 s following charging and subsequent disconnection from the power supply. Hence, they are unlike thermal muscles and natural muscles, which need to consume energy to maintain contraction. Additionally, these muscles can be reversibly cycled while lifting heavy loads.     

Primary structure of gene H of cattle plague virus strain K]

The paper analyzes the standard legal and methodological assurance of the quality and safety of animal food raw materials and foodstuffs (meat, meat products, fish, shellfish, crayfish and their processing products) by the parasitic purity rates according the requirements under the Russian Federation's laws "On Sanitary and Epidemiological Well-Being of the Population", "On Protection of Consumer's Rights", "On Certification of Products and Services", those of SanPiN, such as 2.3.2.560-96 "Sanitary Requirements for the Quality and Safety of Food Raw Materials and Foodstuffs" and 3.2.569-96 "Prevention of Parasitic Diseases in the Russian Federation".     

DDAVP use during pregnancy: an analysis of its safety for mother and child

Synthetic 1-deamino-8-D-arginine-vasopressin (DDAVP) is used in the management of diabetes insipidus (DI). We conducted a systematic literature review of DDAVP use during pregnancy, with particular attention to its safety for both mother and infant. Studies were identified through Ovid MEDLINE from 1976 to July 1997 using the combined terms "desmopressin," "DDAVP," and "pregnancy". Review articles and published letters were also explored. One hundred one articles were retrieved, of which 20 met all the inclusion criteria. Included in the 20 articles were 53 cases with the use of DDAVP for the management of DI. The therapeutic daily dose of DDAVP was approximately 29 micrograms intranasally (range 7.5-100 micrograms), with adequate DI control observed. Three of 14 women with sufficient information developed preeclampsia, a nonsignificant difference from the expected rate of 5 percent (the Fisher exact test, 2-P = .08). The mode of delivery was defined for 22 cases, with 16 uneventful vaginal births, and six cesarean delivery. There was no evidence of a drug interaction among the five women who received both DDAVP and intravenous oxytocin. Information was available on 49 live births born to DI mothers on DDAVP. The mean gestational age at delivery was 37.4 weeks (SD 1.3 weeks), with an estimated mean birth weight of 2963.8 gm (range 2000-4420 gm). Forty-three offspring were reported as healthy (event rate 87.8 percent; 95 percent CI 77.2-95.3 percent). Of the remaining six infants, one developed DI at 18 months of age; a second was under 2500 gm at birth, but survived; the third developed hypotonia and failure to thrive at 21 months, two others had Down syndrome; and the sixth died of severe cardiac anomalies. Similar data were seen among the 41 infants whose mothers had used DDAVP throughout pregnancy. In conclusion, DDAVP use during pregnancy seems to be safe for both mother and child. Delivery does not seem to be augmented by its use, nor are there likely any associated adverse neonatal effects. A large database of DDAVP use during pregnancy is needed to confirm these findings.     

User sensitive research in e-learning: exploring the role of individual user characteristics

The increasing need for active and accessible learning in the inclusive knowledge society drives the demand for e-learning that engages users much more effectively than ever before. In this context, it is crucial to conduct research that embraces innovation in user sensitive design, or else influential individual user differences may be overlooked. The objective of this paper is to explore the creation of successful e-learning systems that are able to increase users’ learning performance and enhance their personal learning experiences. The paper reports two converging and complimentary approaches, namely case studies and experimentation. First, case studies are used to explore the extent to which effective e-learning systems comply with eight specific factors. Of the eight, accessibility, individual differences and student modeling turn out to be the weakest points in current practice. Second, an empirical study investigates the influences of user individual user differences on users’ learning outcomes in an e-learning environment. The experiment found that individual differences in motivation to learn and expectations about e-learning significantly impacted users’ learning achievements. Third, based on these studies, improvements in research methodology are identified towards greater consideration of user sensitive research issues, thus enabling us to outline improved experimental procedures. Further experiment results should provide us with better insights into the arguments needed to carefully assess benefits of developing and involving a user model in an e-learning application. Consequently, evaluation and justification could now encompass both system performance as well as user performance.     

C-PSA: Constrained Pareto simulated annealing for constrained multi-objective optimization

In recent years, evolutionary algorithms (EAs) have been extensively developed and utilized to solve multi-objective optimization problems. However, some previous studies have shown that for certain problems, an approach which allows for non-greedy or uphill moves (unlike EAs), can be more beneficial. One such approach is simulated annealing (SA). SA is a proven heuristic for solving numerical optimization problems. But owing to its point-to-point nature of search, limited efforts has been made to explore its potential for solving multi-objective problems. The focus of the presented work is to develop a simulated annealing algorithm for constrained multi-objective problems. The performance of the proposed algorithm is reported on a number of difficult constrained benchmark problems. A comparison with other established multi-objective optimization algorithms, such as infeasibility driven evolutionary algorithm (IDEA), Non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective Scatter search II (MOSS-II) has been included to highlight the benefits of the proposed approach.     

Feature-based statistical analysis of combustion simulation data

We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing and reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion science; however, it is applicable to many other science domains.     

On challenges of model transformation from UML to Alloy

The Unified Modeling Language (UML) is the de facto language used in the industry for software specifications. Once an application has been specified, Model Driven Architecture (MDA) techniques can be applied to generate code from such specifications. Since implementing a system based on a faulty design requires additional cost and effort, it is important to analyse the UML models at earlier stages of the software development lifecycle. This paper focuses on utilizing MDA techniques to deal with the analysis of UML models and identify design faults within a specification. Specifically, we show how UML models can be automatically transformed into Alloy which, in turn, can be automatically analysed by the Alloy Analyzer. The proposed approach relies on MDA techniques to transform UML models to Alloy. This paper reports on the challenges of the model transformation from UML class diagrams and OCL to Alloy. Those issues are caused by fundamental differences in the design philosophy of UML and Alloy. To facilitate better the representation of Alloy concepts in the UML, the paper draws on the lessons learnt and presents a UML profile for Alloy.     

Supplier behavior modeling and winner determination using parallel MDP

Behavioral uncertainty of a supplier is a major challenge to a buyer operating in e-procurement setting. Modeling suppliers’ behavior from past transactions, estimation of possible future performance and integrating this knowledge with the winner determination process can bring a new dimension to procurement process automation. We propose a states-space model to capture the uncertainty involved in long-term supplier behavior. The states represent the performance level of a supplier. This behavioral aspect is then integrated with the winner determination process of a multi-attribute reverse auction for efficient supplier selection using parallel MDP. We also propose an implementation framework to collect the feedback on supplier, generate an aggregate performance score and integrate it with the winner determination process. The performance aggregation and winner determination with help of Markov decision process effectively uses the past performance information. In addition, it updates performance information in regular invervals and allevates the problem of maintaining a long history. We compare the MDP-based selection with that of performance score-based selection through a simulation experiment. It is observed that our scheme gives better buyer utility, selects best suppliers and fetches better quality product. The benefits realized through these attributes to the buyer increases the efficiency of the MDP-based selection process.