Efficient matching of nonrectangular shapes

Most known two-dimensional matching algorithms have a rectangular text (image) and rectangular pattern (template) as their input. These matching algorithms perform a row by row analysis followed by some column processing. These techniques are only efficient if all the rows are of equal length, hence the necessity for rectangular patterns.We present a novel method for analysing patterns with rows of different lengths. This enables finding all occurrences of a nonrectangular figure of heightm in ann×n text in time . We make use of thesmaller matching problem.The smaller matching problem is that of finding all appearances of a numerical one dimensional pattern in a numerical one-dimensional text, where every element of the pattern is no greater than the corresponding text element.Partially supported by NSF grant CCR-88-03641 and a University of Maryland full year research award.Supported by a University of Maryland Graduate Fellowship, an ACM Samuel M. Alexander Fellowship and NSF grant CCR-88-03641.     

Fluorescence in situ hybridization of three oncogenes on a leiomyoma

Using fluorescence in situ hybridization technique, expression of three oncogenes, C-myc, RARa, and cyclin-D was tested on a uterine leiomyoma. C-myc and RARa were amplified in approximately 30% and 90% of the cells, respectively. Numerous small signals of C-myc were indicative of the presence of double minutes. Amplification of RARa is being reported for the first time in a leiomyoma. Cyclin-D was normal in diploid cells while it was highly amplified in polyploid cells. Low levels of amplified C-myc and cyclin-D cells seem to be the reason for this tumor to be benign, while RARa could not be effective without the association of some other gene such as PML. Information presented here are significant toward developing new curative strategies such as gene-specific drugs and molecular manipulation to stop the activity of cancer gene. Further study may elucidate that how fibroids grow and maintain their rare benign nature.     

Stores clustering using a data mining approach for distributing automotive spare-parts to reduce transportation costs

Clustering of retail stores in a distribution network with specific geographical limits plays an important and effective role in distribution and transportation costs reduction. In this paper, the relevant data and information for an established automotive spare-parts distribution and after-sales services company (ISACO) for a 3-year period have been analyzed. With respect to the diversity and lot size of the available information such as stores location, order, goods, transportation vehicles and road and traffic information, three effecting factors with specific weights have been defined for the similarity function: 1. Euclidean distance, 2. Lot size 3. Order concurrency. Based on these three factors, the similarity function has been examined through 5 steps using the Association Rules principles, where the clustering of the stores is performed using k-means algorithm and similar stores are allocated to the clusters. These steps include: 1. Similarity function based on the Euclidean distances, 2. Similarity function based on the order concurrency, 3. Similarity function based on the combination of the order concurrency and lot size, 4. Similarity function based on the combination of these three factors and 5. Improved similarity function. The above mentioned clustering operation for each 5 cases addressed in data mining have been carried out using R software and the improved combinational function has been chosen as the optimal clustering function. Then, trend of each retail store have been analyzed using the improved combinational function and along with determining the priority of the depot center establishment for every cluster, the appropriate distribution policies have been formulated for every cluster. The obtained results of this study indicate a significant cost reduction (32%) in automotive spare-parts distribution and transportation costs.     

Failure analysis of unidirectional polymeric matrix composites with two serial pin loaded-holes

The objective of this study is to investigate the effects of geometrical and physical parameters on failure modes and failure loads in unidirectional polymeric matrix composites with two serial pin loaded holes, analytically and experimentally. It is assumed that all of unidirectional fibers in the laminate lie in one direction while loaded by a load p ₀ at infinity, parallel to the direction of the fibers. To derive equilibrium equations based on a Shear-Lag theory, a rectangular arrangement of fibers is considered and with the proper use of boundary and boundness conditions, stress and displacement fields are computed within the laminate, along with the surrounding pinholes. Finally by using the Hashin criterion failure modes and failure loads are estimated. To validate analytical results based on shear-lag theory, an experimental program is carried out. A very good agreement is observed between two procedures. Based on results, in small sizes of two pins, the dominant failure mode is bearing and with the increasing of hole sizes, failure modes are changed to tension and shear modes.     

Application of ion-engineered Persian Gulf seawater in EOR:effects of different ions on interfacial tension,contact angle,zeta potential,and oil recovery

In this study,we initially performed interfacial tension(IFT)tests to investigate the potential of using the Persian Gulf sea-water(PGSW)as smart water with dif...     

Behavior of square concrete columns reinforced with Prefabricated Cage System

Behavior of normal strength concrete columns reinforced with a new reinforcement, termed Prefabricated Cage System (PCS) is investigated. A total of 16 small-scale PCS and rebar reinforced column specimens were constructed and tested under monotonic axial displacement. The experimental results indicate that the overall behavior of rebar and PCS reinforced specimens are comparable prior to achieving the peak column load. In general, PCS specimens are more ductile and absorb more energy than similar rebar specimens after the peak load is reached. The effect of different parameters, such as the steel tube thickness, number of longitudinal reinforcements, transverse steel spacing, and crossties on the specimens’ strength and deformation capacity are investigated. A confined concrete model is proposed and used to predict the load–displacement response. The theoretical load–displacement relations obtained from the proposed model are compared with those obtained from the Mander et al. (J Struct Eng 114(8):1804–1826, 1988) confinement model.     

Protein–Protein Interaction Prediction for Targeted Protein Degradation

Protein–protein interactions (PPIs) play a fundamental role in various biological functions; thus, detecting PPI sites is essential for understanding diseases and developing new drugs. PPI prediction is of particular relevance for the development of drugs employing targeted protein degradation, as their efficacy relies on the formation of a stable ternary complex involving two proteins. However, experimental methods to detect PPI sites are both costly and time-intensive. In recent years, machine learning-based methods have been developed as screening tools. While they are computationally more efficient than traditional docking methods and thus allow rapid execution, these tools have so far primarily been based on sequence information, and they are therefore limited in their ability to address spatial requirements. In addition, they have to date not been applied to targeted protein degradation. Here, we present a new deep learning architecture based on the concept of graph representation learning that can predict interaction sites and interactions of proteins based on their surface representations. We demonstrate that our model reaches state-of-the-art performance using AUROC scores on the established MaSIF dataset. We furthermore introduce a new dataset with more diverse protein interactions and show that our model generalizes well to this new data. These generalization capabilities allow our model to predict the PPIs relevant for targeted protein degradation, which we show by demonstrating the high accuracy of our model for PPI prediction on the available ternary complex data. Our results suggest that PPI prediction models can be a valuable tool for screening protein pairs while developing new drugs for targeted protein degradation.     

Selection of the optimum decomposition level using the discrete wavelet transform for automobile suspension system

Journal of Mechanical Science and Technology - This paper discusses the determination of the optimum decomposition level by using the discrete wavelet transform (DWT) method for an automobile...