String analysis by sliding positioning strategy

Discovering frequent factors from long strings is an important problem in many applications, such as biosequence mining. In classical approaches, the algorithms process a vast database of small strings. However, in this paper we analyze a small database of long strings. The main difference resides in the high number of patterns to analyze. To tackle the problem, we have developed a new algorithm for discovering frequent factors in long strings. We present an Apriori-like solution which exploits the fact that any super-pattern of a non-frequent pattern cannot be frequent. The SANSPOS algorithm does a multiple-pass, candidate generation and test approach. Multiple length patterns can be generated in a pass. This algorithm uses a new data structure to arrange nodes in a trie. A Positioning Matrix is defined as a new positioning strategy. By using Positioning Matrices, we can apply advanced prune heuristics in a trie with a minimal computational cost. The Positioning Matrices let us process strings including Short Tandem Repeats and calculate different interestingness measures efficiently. Furthermore, in our algorithm we apply parallelism to transverse different sections of the input strings concurrently, speeding up the resulting running time. The algorithm has been successfully used in natural language and biological sequence contexts.     

Ontology-based similarity for product information retrieval

Product development of today is becoming increasingly knowledge intensive. Specifically, design teams face considerable challenges in making effective use of increasing amounts of information. In order to support product information retrieval and reuse, one approach is to use case-based reasoning (CBR) in which problems are solved “by using or adapting solutions to old problems.” In CBR, a case includes both a representation of the problem and a solution to that problem. Case-based reasoning uses similarity measures to identify cases which are more relevant to the problem to be solved. However, most non-numeric similarity measures are based on syntactic grounds, which often fail to produce good matches when confronted with the meaning associated to the words they compare. To overcome this limitation, ontologies can be used to produce similarity measures that are based on semantics. This paper presents an ontology-based approach that can determine the similarity between two classes using feature-based similarity measures that replace features with attributes. The proposed approach is evaluated against other existing similarities. Finally, the effectiveness of the proposed approach is illustrated with a case study on product–service–system design problems.     

Chemical analysis of ozonized theobroma fat

Ozonized theobroma fat is used as raw material in the manufacture of pessaries and cosmetic creams. Ozonization of theobroma fat with water was carried out at different applied ozone dosages, and the resultant PV, acid value, iodine value, total hydroperoxide content, and FA content were determined. PV and total hydroperoxide content showed a notable increase with applied ozone dosage up to 35.7 mg/g. Acid value varied slightly from 4.1 to 9.9 mg KOH/g, and the iodine value fell to zero. PV and total hydroperoxide content increased slightly with a higher applied ozone dosage. The comparison of total hydroperoxide measurement using ferrous oxidation in xylenol orange assay and traditional iodometric assay for PV determination showed a significant linear correlation. Small amounts of oleic acid were found in ozonized theobroma fat samples with iodine value equaling zero, which demonstrated that iodine value determination is an inexact assay. During ozonization of theobroma fat, an increase in acid value of 18.9-fold with respect to the initial value was observed owing to decomposition of peroxide.     

Data collection in global software engineering research: learning from past experience

Global Software Engineering has become a standard in today’s software industry. Research in distributed software development poses severe challenges that are due to the spatial and temporal distribution of the actors, as well as to language, intercultural and organizational aspects. These challenges occur in addition to “traditional” challenges of the domain itself in large-scale software projects, like coordination and communication issues, requirements volatily, lack of domain knowledge, among others. While several authors have reported empirical studies of global software development projects, the methodological difficulties and challenges of this type of studies have not been sufficiently discussed. In this paper, we share our experiences of collecting and analysing qualitative data in the context of Global Software Engineering projects. We discuss strategies for gaining access to field sites, building trust and documenting distributed and complex work practices in the context of several research projects we have conducted in the past 9 years. The experiences described in this paper illustrate the need to deal with fundamental problems, such as understanding local languages and different cultures, observing synchronous interaction, or dealing with barriers imposed by political conflicts between the sites. Based on our findings, we discuss some practical implications and strategies that can be used by other researchers and provide some recommendations for future research in methodological aspects of Global Software Engineering.     

Fast training procedure for Viola–Jones type object detectors using Laplacian clutter models

This paper presents a fast training strategy for the Viola–Jones (VJ) type object-detection systems. The VJ object- detection system, popular for its high accuracy at real-time testing speeds, has a drawback that it is slow to train. A face detector, for example, can take days to train. In content-based image retrieval (CBIR), where search needs to be performed instantaneously, VJ’s long training time is not affordable. Therefore, VJ’s method is hardly used for such applications. This paper proposes two modifications to the training algorithm of VJ-type object detection systems which reduces the training time to the order of seconds. Firstly, Laplacian clutter (non-object) models are used to train the weak classifier, thus eliminating the need to read and evaluate thousands of clutter images. Secondly, the training procedure is simplified by removing the time-consuming AdaBoost-based feature selection procedure. An object detector, trained with 500 images, approximately takes 2 s for training in a conventional 3 GHz machine. Our results show that the accuracy of the detector, built with the proposed approach, is inferior to that of VJ for difficult object class such as frontal faces. However, for objects with lesser degree of intra-class variations such as hearts, state-of-the-art accuracy can be obtained. Importantly, for CBIR applications, the fast testing speed of the VJ type object detector is maintained.     

Enantiospecific synthesis of (R)-1,7-dioxaspiro [5.5]undecane [major component of olive fruit fly (Dacus Oleae) sex pheromone] from d-Fructose

The synthesis of the title compound13 has been carried out through the preparation of its precursor, (3R,4R,5S,6R)-3,4,5-trihydroxy-1,7-dioxaspiro[5.5]undecane (6), obtained fromd-fructose using Wittig's methodology, reduction, and spiroketalation. Compound6 was transformed into13 by a Barton deoxygenation at C-5 followed by a Corey dideoxygenation at C-3,4 of the appropriately protected derivatives.Enantiospecific synthesis of spiroacetals. Part II. For Part I, see Izquierdo and Plaza (1990).     

Random Copolymers with 2,2′-Dioxy-1,1′-biphenyl-phosphazene and Chiral 2,2′-dioxy-1,1′-binaphthylphosphazene Units Functionalized in the 6,6′-Positions with PPh<Subscript>2</Subscript> Groups as Precursors for Ru(arene) Catalysts

The chiral phosphazene copolymers {[NP(O₂C₁₂H₈)]_0.9[NP(O₂C₂₀H₁₂)]_0.1} (1) and {[NP(O₂C₁₂H₈)]_0.9[NP(O₂C₂₀H₁₀Br₂)]_0.1} _n (2) [(O₂C₁₂H₈) = 2,2′-dioxy-1,1′-biphenyl; (O₂C₂₀H₁₂) = R-2,2′-dioxy-1,1′-binaphthyl and (O₂C₂₀H₁₀Br₂) = R-6,6′-dibromo-2,2′-dioxy-1,1′-binaphthyl] were prepared by sequential substitution from [NPCl₂] _n and the corresponding dihydroxy-biphenyl or binaphthyl reagents in the presence of Cs₂CO₃ and K₂CO₃. The reaction of (2) with ^tBuLi in THF, followed by addition of PPh₂Cl and a treatment with SiHCl₃/PPh₃ to eliminate any oxidized OC₆H₄P(O)Ph₂ groups, gave the phosphine containing copolymer {[NP(O₂C₁₂H₈)]_0.9[NP(O₂C₂₀H₁₀[PPh₂]₂)]_0.1} _n (3), that was used as a chiral ligand to support [Ru(p-cymene)Cl] complexes. The resulting catalyst was active for hydrogen transfer from isopropyl alcohol to acetophenone but the placement of the Ru centers in the 6,6′-positions of the binaphthoxyphosphazene units induced no enantioselectivity. Dedicated to Professor Christopher Allen.     

Extraction of rice brain oil using supercritical carbon dioxide and propane

Extraction of rice bran lipids was performed using supercritical carbon dioxide (SC−CO₂) and liquid propane. To provide a basis for extraction efficiency, accelerated solvent extraction with hexane was performed at 100°C and 10.34 MPa. Extraction pressure was varied for propane and SC−CO₂ extractions. Also, the role of temperature in SC−CO₂ extraction efficiency was investigated at 45,65, and 85°C. For the SC−CO₂ experiments, extraction efficiencies were proportional to pressure and inversely proportional to temperature, and the maximal yield of oil achieved using SC−CO₂ was 0.222±0.013 kg of oil extracted per kg of rice bran for conditions of 45°C and 35 MPa. The maximal yield achieved with propane was 0.224±0.016 kg of oil per kg of rice bran at 0.76 MPa and ambient temperature. The maximum extraction efficiencies of both SC−CO₂ and propane were found to be significantly different from the hexane extraction baseline yield, which was 0.261±0.005 kg oil extracted per kg of rice bran. A simulated economic analysis was performed on the possibility of using SC−CO₂ and propane extraction technologies to remove oil from rice bran generated in Mississippi. Although the economic analysis was based on the maximal extraction efficiency for each technology, neither process resulted in a positive rate of return on investment.     

Restructuring butterfat through blending and chemical interesterification. 3. Rheology

Interesterified and noninteresterified butterfat-canola oil blends, ranging from 100% butterfat to 60∶40 butterfat-canola oil (w/w) in 10% increments, were evaluated for hardness index (HI), dropping point, viscosity, and viscoelastic properties at small deformation. Both blending and chemical interesterification diminished HI in a nonlinear fashion. HI changes in interesterified blends were more pronounced than in noninteresterified blends. Dropping points yielded information on the structure of the blends. Butterfat’s dropping point (DP) was 34.4°C, whereas that of interesterified butterfat was 37.0°C, which is indicative of a more structured network for the latter. DP values of blends with 60–90% butterfat (interesterified vs. noninteresterified) were not significantly different (P<0.05). Interesterified blends had a higher crystallization onset temperature than did noninteresterified blends. All blends in the liquid state displayed Newtonian behavior. Oscillatory frequency sweep measurements at small amplitude showed that interesterified blends generally had lower storage moduli (G′) than their noninteresterified counterparts. BothG′ andG″ were frequency-dependent. Replacement of 30% butterfat by canola oil led to notable changes in small deformation measurements, whereas replacement of 20% butterfat led to big changes in large deformation measurements.