Distributed Processing of Similarity Queries

Many modern applications in diverse fields demand the efficient manipulation of very large multidimensional datasets. It is evident, that efficient and effective query processing techniques need to be developed, in order to provide acceptable response times in query processing. In this paper, we study the processing of similarity nearest neighbor queries in large distributed multidimensional databases, where objects are represented as vectors in a vector space, and are distributed in a multi-computer environment. The departure from the centralized case embodies a number of advantages and (unfortunately) a number of difficulties that need to be successfully overcome. In this perspective, four query evaluation strategies are presented, namely Concurrent Processing (CP), Selective Processing (SP), Two-Phase Processing (2PP) and Probabilistic Processing (PRP). The proposed techniques are compared analytically and experimentally, in order to discover the advantages of each one, as well as the best cases where each one should be applied. Experimental results are presented, demonstrating the performance of each method under different parameters values. Also, we investigate the impact of derived data that should be maintained in order to process similarity queries efficiently.     

Nearest Neighbor Queries in Shared-Nothing Environments

In this paper, we propose an efficient solution to the problem of nearest neighbor query processing in declustered spatial databases. Recently a branch-and-bound nearest neighbor finding (BB-NNF) algorithm has been designed to process nearest neighbor queries in R-trees. However, this algorithm is strictly serial (branch-and-bound oriented) and its performance degrades, during processing of a nearest neighbor query, if applied to a parallel environment, since it does not exploit any kind of parallelization. We develop an efficient query processing strategy for parallel nearest neighbor finding (P-NNF), assuming a shared nothing multi-processor architecture, where the processors communicate via a network. In our method, the relevant sites are activated simultaneously. In order to achieve this goal, statistical information is used. The efficiency measure is the response time of a given query. Experimental results, based on real-life and synthetic datasets, show that the proposed method outperforms the branch-and-bound method by factors.     

The temporality of technical debt introduction on new code and confounding factors

Software Quality Journal - Code Technical Debt (TD) is intentionally or unintentionally created when developers introduce inefficiencies in the codebase. This can be attributed to various reasons...     

Scale-up of pore-level relative permeability from micro- to macro-scale

Scaling up relative permeability curves of wetting and nonwetting phase of drainage and imbibition processes from pore scale to macro scale is a challenge. A new method for scaling up relative permeability from micro- to macro-scale is proposed based on electrical analogy of multiphase fluid flow at pore scale. The method is validated against four synthetic porous media generated using homogeneous and heterogeneous grain size distributions, each of which were cut into eight sub-segments. Single-phase and two-phase flow properties were calculated for the main blocks and the subsequent sub-segments using random network modelling technique. Then, the subsegments were randomly distributed in space to reconstruct the main blocks and the proposed scale-up method was employed to calculate the relative permeability curves of the reconstructed blocks. Results were compared to the ones obtained directly from the network model of the original blocks and show good agreement between the calculated and scaled-up relative permeability curves of primary drainage and secondary imbibition. Furthermore, the model was tested on real media. Eight network models were extracted from pore size distribution of core samples obtained from the Green River basin located in the Mesaverde Formation. Flow properties obtained from the network models were validated against experimental data and good agreement was observed. These network models show a higher level of heterogeneity at micro-scale. Then, the scale-up methods were employed in order to reconstruct the macro-scale sample and predict its properties. Scale-up methods successfully predict the single-phase and two-phase flow properties of the sample.     

Tensile Behavior and Formability Evaluation of Titanium-40 Material Based on the Forming Limit Diagram Approach

The formability of Titanium alloy sheet material Ti-40 has been experimentally assessed in the present investigation. The investigation is divided into two parts: In the first part, the effect of the strain rate applied during testing, as well as the effect of material axes’ orientation, on the tensile behavior is evaluated via standard tensile tests. In addition, the hardening characteristics as well as the anisotropy parameters (plastic strain ratio) have also been extracted. In the second part, the formability limits of Ti-40 material are experimentally derived using Nakajima tests and the corresponding forming limit diagrams are compared against other commercially available titanium sheet alloys.     

Analysis and Interpretation of Multiple Radioactive Particle Tracking Data on Laboratory Scale Air/Polyethylene Fluidized Beds

Multiple radioactive particle tracking is a noninvasive technique used to study flow phenomena within gas‐solid fluidized beds. Five tagged polyethylene particles were added to a fluidized bed and tracked using two gamma cameras set perpendicular to one another. One camera collected x‐z particle locations and the other collected y‐z particle locations, while both cameras collected particle count rate values. The x‐z and y‐z particle movement information was combined to form three‐dimensional particle trajectories, and in turn used to calculate instantaneous flow information. The information from the five particles was then averaged and used to calculate three‐dimensional information regarding solid circulation parameters.     

Geographical Differentiation of Greek Extra Virgin Olive Oil from Late-Harvested Koroneiki Cultivar Fruits

Seventy‐four monovarietal olive oil samples belonging to the Koroneiki cultivar were collected from four selected olive oil‐producing regions of Greece (Messinia, Lakonia, Irakleio and Etoloakarnania), during two harvesting periods (2012/2013 and 2013/2014) at the stage of full maturation (maturation index 5–6). Determination of volatile compounds (VC), fatty acid (FA) composition, total phenolic content (TPC) and color parameters was carried out in an effort to classify Koroneiki olive oil samples according to geographical origin, while conventional quality parameters (CQP) were used to characterize the samples. The analytical data were then subjected to statistical analysis using multivariate analysis of variance (MANOVA) and linear discriminant analysis (LDA). The results showed a correct classification rate of 79.7% based on VC analysis, 81.1% based on the combination of VC analysis and FA composition, and 87.8% based on the combination of VC analysis and color parameters.     

Modelling of austenite stability in low-alloy triple-phase steels

A model for the stability of dispersed austenite in low alloy triple-phase steels has been developed. The model was based on the dislocation dissociation model for classical heterogeneous martensitic nucleation by considering stress effects on the nucleation site potency distribution. The driving force for martensitic transformation has been calculated with the aid of computational thermodynamics. The model allows for the effects of chemical composition of austenite, mean austenite particle size, yield strength of the steel and stress state on austenite stability. Chemical enrichment in C and Mn, as well as size refinement of the austenite particles lead to stabilization. On the contrary, the increase in the yield strength of the steel and triaxiality of the stress state lead to destabilization. The model can be used to determine the microstructural characteristics of the austenite dispersion, i.e. chemical composition and size, for optimum transformation plasticity interactions at the particular stress state of interest and can then be useful in the design of low-alloy triple-phase steels.