Formulation of a Biodegradable Detergent for Cleaning Oily Residues Generated during Industrial Processes

The use of toxic petroleum-based heavy oils is common in industrial processes. The cleaning of machines, equipment, and other surface covered in these oils is achieved with expensive products that are often also toxic and harmful to both the health of workers and the environment. The present paper proposes the development of a sustainable biodetergent made from plant-based materials. Tests were performed to determine the properties of the biodetergent in terms of its surfactant and emulsifying capacities, stability, toxicity, and the removal of heavy oil from glass plates and metallic surfaces. The formulation was composed of a natural solvent (cottonseed oil), a plant-based surfactant agent (saponin), and two natural stabilizers (carboxymethylcellulose and glycerine). The formulation was stable, nontoxic, and highly efficient, removing 100% of the heavy oil from glass and metallic surfaces. This solution developed in this study could be used in diverse industries with the need to clean machines and parts encrusted with oil and grease as well as the cleaning of floors covered with petroleum-based residues. A preliminary analysis of the economic feasibility of using the detergent was carried out at a Brazilian power plant. Besides the reduction in environmental impact due to the use of a nontoxic, biodegradable product as well as the reduction in health risks to workers associated with toxic cleaning products, this new product can have a considerable impact on the market as an environmentally friendly solution.     

La_(1.8)Ca_(0.2)Mg_(14)Ni_3合金在甲苯溶液中球磨改性后的储氢性能

采用在甲苯介质中球磨以改善La1.8Ca0.2Mg14Ni3的储氢性能。随着球磨时间的增加,合金的吸放氢性能得到显著地提高,在20 h达到最高。其在513 K,4.0 MPa氢压下初次活化时,吸氢质量分数达到了3.95%,在3次活化后,300 K时的吸氢质量分数达到3.85%,在613 K,一个大气压的放氢质量分数在900 s内达到了4.92%。通过XRD和SEM分析,球磨后合金颗粒粒径明显减小且有非晶化趋势。在球磨过程中形成了电子络合体(electrondonor-acceptor,EDA)体系。合金颗粒粒径、非晶化程度和EDA共同作用使球磨20 h的合金表现出最优异的吸放氢性能。     

Characterization of the Ion Beam Current Density of the RF Ion Source with Flat and Convex Extraction Systems

Silicon - The characterization of ion beam current density distribution and beam uniformity is crucial for improving broad-beam ion source technologies. The design of the broad ion beam extraction...     

Text entry for the Blind on Smartwatches

Universal Access in the Information Society - The interaction challenges presented by touch-screen enabled devices for blind and visually impaired people has been addressed extensively in the...     

嫦娥二号工程X频段测控通信系统设计与试验

针对月球以远深空探测任务中,远距离、长时延、弱信号的测控通信难点,有效手段之一就是提高射频频率.为此,在嫦娥二号工程中首次开展了X频段测控通信技术试验.本文重点研究了X频段测控通信系统天地一体化的设计与实现,阐述了技术试验系统的组成、关键技术攻关情况、系统试验与测试方法.试验结果表明,与S频段测控通信系统相比,X频段在链路性能、测定轨精度方面有显著提高,将正式作为后续月球及深空探测的主要频段.     

DNA fragment assembly using a grid-based genetic algorithm

In this paper we propose a genetic algorithm (GA) for solving the DNA fragment assembly problem in a computational grid. The algorithm, which is named GrEA, is a steady-state GA which uses a panmitic population, and it is based on computing parallel function evaluations in an asynchronous way. We have implemented GrEA on top of the Condor system, and we have used it to solve the DNA assembly problem. This is an NP-hard combinatorial optimization problem which is growing in importance and complexity as more research centers become involved on sequencing new genomes. While previous works on this problem have usually faced 30 K base pairs (bps) long instances, we have tackled here a 77 K bps long one to show how a grid system can move research forward. After analyzing the basic grid algorithm, we have studied the use of an improvement method to still enhance its scalability. Then, by using a grid composed of up to 150 computers, we have achieved time reductions from tens of days down to a few hours, and we have obtained near optimal solutions when solving the 77 K bps long instance (773 fragments). We conclude that our proposal is a promising approach to take advantage of a grid system to solve large DNA fragment assembly problem instances and also to learn more about grid metaheuristics as a new class of algorithms for really challenging problems.     

Proteomics identification of differentially expressed proteins in the muscle of dysferlin myopathy patients

The muscular dystrophies are a large and heterogeneous group of neuromuscular disorders that can be classified according to the mode of inheritance, the clinical phenotype and the molecular defect. To better understand the pathological mechanisms of dysferlin myopathy we compared the protein-expression pattern in the muscle biopsies of six patients with this disease with six patients with limb girdle muscular dystrophy 2A, five with facioscapulohumeral dystrophy and six normal control subjects. To investigate differences in the expression levels of skeletal muscle proteins we used 2-DE and MS. Western blot or immunohistochemistry confirmed relevant results. The study showed specific increase expression of proteins involved in fast-to-slow fiber type conversion (ankyrin repeat protein 2), type I predominance (phosphorylated forms of slow troponin T), sarcomere stabilization (actinin-associated LIM protein), protein ubiquitination (TRIM 72) and skeletal muscle differentiation (Rho-GDP-dissociation inhibitor ly-GDI) in dysferlin myopathy. As anticipated, we also found differential expression of proteins common to all the muscular dystrophies studied. This comparative proteomic analysis suggests that in dysferlin myopathy (i) the type I fiber predominance is an active process of fiber type conversion rather than a selective loss of type II fibers and (ii) the dysregulation of proteins involved in muscle differentiation further confirms the role of dysferlin in this process.     

Multi-Objective Optimization using Grid Computing

This paper analyzes some technical and practical issues concerning the use of parallel systems to solve multi-objective optimization problems using enumerative search. This technique constitutes a conceptually simple search strategy, and it is based on evaluating each possible solution from a given finite search space. The results obtained by enumeration are impractical for most computer platforms and researchers, but they exhibit a great interest because they can be used to be compared against the values obtained by stochastic techniques. We analyze here the use of a grid computing system to cope with the limits of enumerative search. After evaluating the performance of the sequential algorithm, we present, first, a parallel algorithm targeted to multiprocessor systems. Then, we design a distributed version prepared to be executed on a federation of geographically distributed computers known as a computational grid. Our conclusion is that this kind of systems can provide to the community with a large and precise set of Pareto fronts that would be otherwise unknown.     

Structural design using multi-objective metaheuristics. Comparative study and application to a real-world problem

Many structural design problems in the field of civil engineering are naturally multi-criteria, i.e., they have several conflicting objectives that have to be optimized simultaneously. An example is when we aim to reduce the weight of a structure while enhancing its robustness. There is no a single solution to these types of problems, but rather a set of designs representing trade-offs among the conflicting objectives. This paper focuses on the application of multi-objective metaheuristics to solve two variants of a real-world structural design problem. The goal is to compare a representative set of state-of-the-art multi-objective metaheuristic algorithms aiming to provide civil engineers with hints as to what optimization techniques to use when facing similar problems as those selected in the study presented in this paper. Accordingly, our study reveals that MOCell, a cellular genetic algorithm, provides the best overall performance, while NSGA-II, the de facto standard multi-objective metaheuristic technique, also demonstrates a competitive behavior.     

Design and behavior study of a grammar-guided genetic programming algorithm for mining association rules

This paper presents a proposal for the extraction of association rules called G3PARM (Grammar-Guided Genetic Programming for Association Rule Mining) that makes the knowledge extracted more expressive and flexible. This algorithm allows a context-free grammar to be adapted and applied to each specific problem or domain and eliminates the problems raised by discretization. This proposal keeps the best individuals (those that exceed a certain threshold of support and confidence) obtained with the passing of generations in an auxiliary population of fixed size n. G3PARM obtains solutions within specified time limits and does not require the large amounts of memory that the exhaustive search algorithms in the field of association rules do. Our approach is compared to exhaustive search (Apriori and FP-Growth) and genetic (QuantMiner and ARMGA) algorithms for mining association rules and performs an analysis of the mined rules. Finally, a series of experiments serve to contrast the scalability of our algorithm. The proposal obtains a small set of rules with high support and confidence, over 90 and 99% respectively. Moreover, the resulting set of rules closely satisfies all the dataset instances. These results illustrate that our proposal is highly promising for the discovery of association rules in different types of datasets.