Background and Objectives: Graph processing is increasingly gaining attention during era of big data. However Graph processing applications are highly memory intensive due to nature of Graphs. processing-in-memory (PIM) is an old idea which revisited recently with the advent of technology specifically the ability to manufacture 3D stacked chipsets. PIM puts forward to enrich memory units with computational capabilities to reduce the cost of data movement between processor and memory system. This approach seems to be a way of dealing with large-scale Graph processing, considering recent advances in the field. Methods: This paper explores real-world PIM technology to improve Graph processing efficiency by reducing irregular access patterns and improving temporal locality using HMC. We propose NodeFetch, a new method to access nodes and their neighbors while processing a Graph by adding a new command to HMC system. Results: Results of our simulation on a set of real-world Graphs point out that the proposed idea can achieve 3. 3x speed up in average and 69% reduction of energy consumption over the baseline PIM architecture which is HMC. Conclusion: Most of the techniques in the field of processing-in-memory, hire methods to reduce movement of data between processor and memory. This paper proposes a method to reduce Graph processing execution time and energy consumption by reducing cache misses while processing a Graph.

Block-centric Graph processing systems have received significant attention in recent years. To produce the required partitions, most of these systems use general-purpose partitioning methods. As a result, the performance of them has been limited. To face this problem, special partitioning algorithms have been proposed by researchers. However, these methods focused on traditional partitioning measures like the number of cutting-edges and the load-balance. In return, the power of block-centric Graph processing systems is due to unique characteristics that are focused on the design of them. According to basic and important characteristics of these systems, in this paper two new measures are proposed as partitioning goals. To the best of our knowledge, the proposed method is the first work that considers the diameter and size of the high-level Graph as optimization factors for partitioning purposes. The evaluation of the proposed method over real Graphs showed that we could significantly reduce the diameter of the high-level Graph. Moreover, the number of cutting-edges of the proposed method are very close to Metis, one of most popular centralized partitioning methods. Since the number of required supersteps in block-centric Graph processing systems mainly depends on the diameter of the high-level Graph, the proposed method can significantly improve the performance of these systems.

In this paper, an alternative approach in operational modal analysis is presented, utilizing image processing technique and transmissibility functions. Imaging sensors do not impose additional mass on the structure due to their non-contact nature, while transmissibility functions, independent of excitation type, can directly extract mode shapes. The innovation of this research lies in combining these two techniques to record dynamic responses and identify modal properties. To capture the temporal response history from video signals, the block-matching method with sub-pixel accuracy was employed. Validation was conducted by recording the response of the tip of a cantilevered steel beam subjected to impact excitation, using a high-speed camera and a laser vibrometer, simultaneously. The RMSE plots in the time domain and the PSD in the frequency domain indicate high accuracy of this method. Using this approach, the displacement time histories of various points on the structure were extracted from the video signals, and the modal properties, including natural frequencies, damping ratios, and mode shapes, were identified using the transmissibility matrix method. The results obtained from the proposed method were compared with the stochastic subspace identification (SSI) method and analytical solutions. The findings reveal the accuracy of the modal identification approach introduced in this article. The highest relative error in estimating the natural frequencies of the first and second modes, compared to the values from the laser method, are 0.19% and 0.13%, respectively, and in comparison to the analytical values, they are 0.34% and 1.5%, respectively.

In traditional speech processing, feature extraction and classification were conducted as separate steps. The advent of deep neural networks has enabled methods that simultaneously model the relationship between acoustic and phonetic characteristics of speech while classifying it directly from the raw waveform. The first convolutional layer in these networks acts as a filter bank. To enhance interpretability and reduce the number of parameters, researchers have explored the use of parametric filters, with the SincNet architecture being a notable advancement. In SincNet's initial convolutional layer, rectangular bandpass filters are learned instead of fully trainable filters. This approach allows for modeling with fewer parameters, thereby improving the network's convergence speed and accuracy. Analyzing the learned filter bank also provides valuable insights into the model's performance. The reduction in parameters, along with increased accuracy and interpretability, has led to the adoption of various parametric filters and deep architectures across diverse speech processing applications. This paper introduces different types of parametric filters and discusses their integration into various deep architectures. Additionally, it examines the specific applications in speech processing where these filters have proven effective.

BACKGROUND AND OBJECTIVE: Varicocele is one of the causes of male infertility. It changes with temperature and drainage of testicular veins that due to spermatogenesis disturbance and decrease of fertility in these patients. However varicocelectomy is a treatment for varicocele, but there are other methods of treating this disease including assisted reproductive technology (ART), Sperm processing and swim up. METHODS: This interventional study was performed on 155 cases in fertility and infertility Center of Babol University of medical sciences in 2004. Sperm parameters in varicocele patients was assessed before and after sperm processing. FINDINGS: The mean age of patients was 31.8 years. Sperm motility before sperm processing in grade III and IV was 53.9% and 12.9%, respectively but after sperm processing in grade III and IV changed to 78.7% and 56.8%, respectively. Total motility before sperm processing was 40 % and changed after processing to 80%. CONCLUSION: The results show that with sperm processing and collection of appropriate sperms, we are able to increase fertility.

The independence Graph Ind(G) of a Graph G is the Graph with vertices as maximum independent sets of G and two vertices are adjacent, if and only if the corresponding maximum independent sets are disjoint. In this work, we find the independence Graph of Cartesian product of d copies of complete Graphs Kq, which is known as the Hamming Graph H(d, q). Greenwell and Lovasz [7] found that the independence number of direct product of d copies of Kq as qd−1. We prove that the independence number of Hamming Graph H(d, q), which is cartesian product of d copies of Kq, is also qd−1. As an application of our findings, we find answers for rook problem in higher dimensional square chess board.

In this paper, we find the star chromatic number of central Graph of complete bipartite Graph and corona Graph of complete Graph with path and cycle.

Let R be a commutative ring and M be an R-module. The M-intersection Graph of ideals of R, denoted by GM(R) is a Graph with the vertex set I(R) ∗, , and two distinct vertices I and J are adjacent if and only if IM ∩,JM ̸, = 0. In this paper, we study GR/J (R/I), where I and J are ideals of R and I ⊆,J. We characterize all ideals I and J for which GR/J (R/I) is planar, outerplanar or ring Graph.

‎Graph coloring is the assignment of one color to each vertex of a Graph so that two adjacent vertices are not of the same color‎. ‎The Graph coloring problem (GCP) is a matter of combinatorial optimization‎, ‎and the goal of GCP is determining the chromatic number $\chi(G)$‎. ‎Since GCP is an NP-hard problem‎, ‎then in this paper‎, ‎we propose a new approximated algorithm for finding the coloring number (it is an approximation of chromatic number) by using a Graph adjacency matrix to colorize or separate a Graph‎. ‎To prove the correctness of the proposed algorithm‎, ‎we implement it in MATLAB software‎, ‎and for analysis in terms of solution and execution time‎, ‎we compare our algorithm with some of the best existing algorithms that are already implemented in MATLAB software‎, ‎and we present the results in tables of various Graphs‎. ‎Several available algorithms used the largest degree selection strategy‎, ‎while our proposed algorithm uses the Graph adjacency matrix to select the vertex that has the smallest degree for coloring‎. ‎We provide some examples to compare the performance of our algorithm to other available methods‎. ‎We make use of the Dolan-Mor\'e performance profiles to assess the performance of the numerical algorithms‎, ‎and demonstrate the efficiency of our proposed approach in comparison with some existing methods‎.