Partitioning Technique Research Articles

Machine learning based simulation of an anti-cancer drug (busulfan) solubility in supercritical carbon dioxide: ANFIS model and experimental validation

In this work, a novel machine learning method (MLM) was developed based on Neuro fuzzy system, i.e., ANFIS (Adaptive neuro fuzzy inference system). This neuro fuzzy algorithm was implemented for simulating the solubility of pharmaceuticals in supercritical carbon dioxide. The model of drug was busulfan which is an anti-cancer drug with large applications for patients. The drug efficacy can be improved by production at nanosize using supercritical technology. The processing parameters including pressure and temperature were evaluated in the process and were taken into account in the modeling of supercritical system. Also, the simulated output which is the only predicted variable was the solubility of drug in the solvent. Several collected data from literature were used to train the ANFIS structure, and the trained model was then used for testing step. Both training and testing steps in developing ANFIS model indicated great accuracy and very close to the experimental values. Indeed, 32 data points were used for the ANFIS modeling among which 12 were used in testing the ANFIS model. In developing ANFIS model, Trimf membership function was used for generation of FIS structure. The Fuzzy system was generated using the Grid Partitioning technique which indicated great accuracy in prediction of busulfan solubility data. The model developed in this work indicated that the ANFIS model can be used to accurately predict the solubility of drugs in supercritical solvents which can be consequently used for production of drugs with improved efficacy.

Phytochemical analysis, total phenolic, total flavonoid contents and ferric reducing power of extracts from roots and leaves of Searsia burchellii

Searsia burchellii has therapeutic applications in traditional medicine. The aim of the present study was to analyse the phytochemical profiles, total phenolic contents (TPC), total flavonoid contents (TFC) and ferric reducing power of various extracts from roots and leaves of S. burchellii collected from the kingdom of Lesotho. A combination of maceration, hot solvent extraction and solvent-solvent partition techniques were used to obtain methanolic extracts, fractions such as hexane, chloroform, ethyl acetate and methanol/water from methanolic extracts and water extracts separately from the roots and leaves of S. burchellii. The presence of alkaloids, steroids, terpenoids, phenolics, tannins, flavonoids, coumarins, saponins, glycosides, carbohydrates, proteins and phlobatannins were identified from these extracts/fractions. The TPC of these extracts/fractions was found to be in the range of 3.22±0.37 to 247.46±4.33 mg TAE/g DW, respectively. The TFC of these extracts/fractions was found to be in the range of 34.70±4.31 to 278.20±2.01 mg QE/g DW, respectively. Additionally, these extracts/fractions showed a moderate to strong ferric reducing power compared to ascorbic acid. The IC50 values were found to be in the range of 5.35-143.12 and 65.64-348.28 µg/mL for extracts/fractions from the roots and leaves, respectively. Ascorbic acid showed an IC50 value of 20.93 µg/mL. Therefore, the extracts/fractions from S. burchellii have the potential to replace the existing synthetic antioxidants

A Hybrid Grid Partitioning and Fuzzy Goal Programming Model for Enhanced Performance and Decision Support

This study introduces an innovative Hybrid Grid Partitioning and Fuzzy Goal Programming Model for Enhanced Performance and Decision Support. The proposed model combines grid partitioning and fuzzy goal programming techniques in order to address the challenges of complex systems and optimize performance while taking into account subjective preferences and uncertainties. Using grid partitioning, the hybrid model divides the problem space into smaller grid cells, facilitating localized analysis and efficient processing. Fuzzy goal programming handles ambiguous objectives and uncertain constraints, providing decision-making flexibility and robustness. The effectiveness of the model is demonstrated through a numerical example, highlighting enhanced performance and decision support for complex problems. The applicability of the hybrid paradigm to diverse fields, including transportation, logistics, and resource management, is discussed. The study also acknowledges limitations, such as computational complexity and the need for validation in the actual world. In the future, it will be necessary to resolve these limitations and improve the hybrid model. Overall, the research advances optimization methodologies and decision support systems by providing a comprehensive framework for confronting complex systems and facilitating well-informed decision making.

A generic framework for decomposing block cipher structure with secret components

In this paper, we present a generic framework for decomposing block cipher structure with secret components. The generic framework is based on the zero-correlation linear cryptanalysis and exploits clusters of zero-correlation linear hulls to establish a system of equations to recover the secret components. In order to solve such a system, we convert the operations into the field of real numbers. By utilizing the coordinate partition technique, both the time and data complexity will be mitigated to make the framework more competitive. We apply the framework to the Feistel structure with SP-type round functions and both the attack rounds and complexity perform better than previous results. Finally, we apply the proposed framework to the Type-1 Feistel structure to demonstrate its generality and improve the previous results against such structure. Our work provides further insights into the security of block cipher structure with secret components.

A variational formulation for 2-D vibro-acoustic analysis of a circular ring in unbounded domain

This paper develops a variational formulation for two-dimensional (2-D) vibro-acoustic analysis of a circular ring in unbounded acoustic domain. The Reissner–Naghdi’s generalized shell theory is adopted to formulate the energy functional of the circular ring, while the Helmholtz equation is used to derive the stored energy in the acoustic domain. A modified variational principle combined with a domain partitioning technique is adopted to formulate the coupled vibro-acoustic model. The continuity constraints on the common interfaces of adjacent acoustic partitions are imposed by using a least square weighted residual method. To satisfy the Sommerfeld radiation condition, the local absorbing boundary condition is adopted at an artificial acoustic boundary. With the spectral polynomials or series employed as the admissible functions, the structural displacements and acoustic pressure are expanded by a spectral method. The convergence, accuracy and efficiency of the present method for predicting the vibro-acoustic responses are demonstrated by comparing the obtained results with the exact solutions or FEM results. The effects of the local absorbing boundary and the radius of the exterior acoustic boundary are also investigated in both the light and heavy acoustic mediums.

Delineating Precipitation Regions of the Contiguous United States from Cluster Analyzed Gridded Data

Spatially homogenous precipitation regions were delineated for the contiguous United States using a gridded data set of daily precipitation. Seasonal means (1981–2010) of four variables, together characterizing seasonal precipitation, were computed and subjected to a principal component analysis (PCA). PCA reduced the original 30,665 grid cells by sixteen precipitation variables (four variables, four seasons) in the data set. The standardized scores of the three retained principal components, which together retain 78.4 percent of the original data set’s variance, were then subjected to three agglomerative hierarchical clustering techniques. Using an objective method, several cluster solutions were examined, and the average linkage thirteen-cluster solution was deemed optimal. The average linkage solution was then subjected to a k-means partitioning technique under the premise that objects are not considered for reassignment during agglomerative hierarchical cluster procedures. The result is fifteen precipitation regions across the contiguous United States. Results indicate that the regions successfully minimize intraregion variability and maximize interregion variability when compared to the nine climate regions defined by the United States National Centers for Environmental Information. It is therefore suggested that the regions defined by this work will better serve research aimed at an improved understanding of long-term hydroclimate change and variability at regional to synoptic scales across the United States.

On Computations of Topological Descriptors of Kagome Lattice

The analysis of graphs and networks is a multidisciplinary research field from its origin. As such, it has been extensively used also in biomedicine, cheminformatics, and in bioinformatics, where approaches based on graph descriptors have been offered for communicating with the numerous challenging tasks. In quantitative structure-activity (QSAR) and quantitative structure-property (QSPR) relationships studies, graph invariants are used to forecast the biological activities and properties of nanomaterials. In these studies, degree-based topological descriptors have founded comprehensive acceptance among the different types of descriptors, because of the ease of generation and the speed with which these calculations can be performed. In this article, we first compute the M-polynomials of 2D kagome lattice and then derive the closed form of various significant topological degree-based indices in terms of the M-polynomials and partition techniques. Furthermore, we sketch surfaces that show the dependence of some topological indices on the parameters of the structure.

Balanced content space partitioning for pub/sub: a study on impact of varying partitioning granularity

The increasing volume of live content poses new challenges to publish/subscribe services at the cloud scale. Providing efficient publish/subscribe services for live content is a complex task because most subscriptions occupy only a small portion of the entire subscription space, i.e., use limited live content. Thus, the real-world workload of a publish/subscribe service for live content becomes skewed, and the distribution of subscriptions becomes seriously imbalanced, causing an inefficient processing of events. We present a correlation-based balanced content space partitioning technique for a publish/subscribe service. Our proposed technique reduces the degree of imbalance from a skewed subscription workload in a content-based publish/subscribe service, using the correlation coefficient between attributes to build dimension groups. We assign attributes of low correlation to the same dimension group to balance the subscription workloads. Moreover, we present our analysis on the load balance impacts of varying partitioning granularity for efficient message processing. We conducted empirical experiments evaluating the effectiveness of our partitioning technique and measuring the impact of varying partitioning granularity. The results show that the proposed technique outperforms conventional partitioning techniques by evaluating the ways in which subscriptions are evenly distributed among brokers. Moreover, the results show that the load balance can be improved by increasing the partitioning granularity with an adjustment of two degrees, i.e., the segment and dimension group degrees.

Dynamic analysis of coupled composite laminated shells with elastic boundary condition using a domain decomposition method

In this paper, the free and forced vibration behavior of coupled composite laminated shell are investigated by using a domain decomposition method under the elastic boundary condition. The coupled shell structure is combined in the form of a double curved shell-cylindrical shell-double curved shell, and the double curved shells at both ends are combined with the cylindrical shell upside down (figure 1). The double curved shell contains the elliptical, paraboloidal and hyperbolical shells. multi-segment partitioning technique is employed to establish the theoretical model based on the first-order shear deformation theory (FSDT). Regardless of the shell types and boundary conditions, the displacement functions of each shell are expended by the orthogonal ultraspherical polynomials in the meridional direction, and by the standard Fourier series in the circumferential direction. The boundary and continue conditions are generalized by the artificial spring technology. The convergence study and numerical comparison with finite element method (FEM) demonstrates that the proposed method has good reliability and accuracy to analyze the dynamic characteristics of the coupled composite laminated shell. The effects of material properties, geometric dimension, boundary condition and external force on the coupled composite laminated shell structure are proposed with some numerical results.

Ensuring confidentiality and availability of sensitive data over a network system under cyber threats

The online storage of sensitive data enjoys many benefits such as flexibility, cost-savings, scalability, and convenience but it also poses a big concern on the data confidentiality and availability. To ensure the confidentiality and availability of sensitive data over a network system, the data partition technique is often employed. We study the optimal data partition strategy over an arbitrary network under cyber threats. Both the outside attack and the risk propagation (i.e., inside attack) are considered for the data partition. The data breach probability and retrieve probability are discussed under both limited and unlimited risk propagation for various scenarios. It is discovered that the risk propagation can have much more impact on the optimal partition strategy than that of outside attacks, and the unlimited risk propagation leads to more severer cyber risk. The network topology significantly impacts the partition strategy which hints that the network topology should never be overlooked in practice. The corruption due to compromise can lead to different partition strategies. An optimal partition model is developed for determining the optimal strategy and the pareto non-dominated solutions are recommended for practical use.

Investigations of butterfly species identification from images in natural environments

It has been a challenging problem to identify species of butterflies, especially from images taken in natural environments. Therefore the First international butterfly species recognition competition was organized at the third Data Mining Competition in China in 2018, so as to find good solutions to this challenging problem. The baseline for the competition was based on the Faster R-CNN for it was the latest deep learning algorithm at that time. Nearly all the competition teams chose the Faster R-CNN, or its variations, to solve the problem. But the identification rates were not good enough, and Faster R-CNN is very time consuming. As a result we have been trying to find the most suitable algorithm to solve the butterfly species identification challenge. This paper will present some investigations we have undertaken in this field over the past two years, and show the results we have obtained. We propose a new partition and augmentation technique for the extremely unbalanced ecological butterfly database. We found that RetinaNet is, so far, the best deep learning algorithm to tackle butterfly species identification based on butterfly images taken in natural environments. The best result we obtained was 79.7% in terms of mAP (mean average precision). This is the best result compared to the state-of-the-art studies in this field on the same database so far.

A hybrid approach for text document clustering using Jaya optimization algorithm

In this digital era, millions of Internet users are contributing vast amounts of data in the form of unstructured text documents. Organizing this material is a tedious task. The clustering of text document plays a vital role for organizing these unstructured text documents. In our paper, we make use of Hybrid Jaya Optimization algorithm (HJO) for text Document Clustering (DC), referred to as HJO-DC. We have used the Silhouette index as a metric to measure the quality of a solution. The proposed work is compared with partitioning techniques such as K-Means and K-Medoids and metaheuristic techniques such as Genetic algorithm, Cuckoo Search, Particle Swarm Optimizer, Firefly and Grey Wolf Optimizer. Remarkably, the proposed algorithm achieves the highest quality clustering in all benchmark examples.

A unified vibration modeling and dynamic analysis of FRP-FGPGP cylindrical shells under arbitrary boundary conditions

In this study, a unified vibration model of fiber reinforced polymer (FRP) cylindrical shell with functionally graded porous graphene platelets (FGPGP) coating is proposed. The material properties of four types of FGPGP coating are presented first, and then the displacements of the structure are expressed by using the first-order shear deformation theory. The energy expressions of coating are introduced by the multi-segment partition technique. Then, an artificial spring method is employed to simulate arbitrary boundary conditions. With the application of the Jacobi orthogonal polynomials and the multi-segment partition techniques, the equation of motion of FRPCS with FG-PGP coating is derived to solve the free and forced vibrations. Finally, the model is systematically verified with some references and dynamic analysis with different coating patterns, boundary conditions and the thickness of coating are comprehensively investigated.

An analysis of the graph processing landscape

The value of graph-based big data can be unlocked by exploring the topology and metrics of the networks they represent, and the computational approaches to this exploration take on many forms. For the use-case of performing global computations over a graph, it is first ingested into a graph processing system from one of many digital representations. Extracting information from graphs involves processing all their elements globally, which can be done with single-machine systems (with varying approaches to hardware usage), distributed systems (either homogeneous or heterogeneous groups of machines) and systems dedicated to high-performance computing (HPC). For these systems focused on processing the bulk of graph elements, common use-cases consist in executing for example algorithms for vertex ranking or community detection, which produce insights on graph structure and relevance of their elements. Many distributed systems (such as Flink, Spark) and libraries (e.g. Gelly, GraphX) have been built to enable these tasks and improve performance. This is achieved with techniques ranging from classic load balancing (often geared to reduce communication overhead) to exploring trade-offs between delaying computation and relaxing accuracy. In this survey we firstly familiarize the reader with common graph datasets and applications in the world of today. We provide an overview of different aspects of the graph processing landscape and describe classes of systems based on a set of dimensions we describe. The dimensions we detail encompass paradigms to express graph processing, different types of systems to use, coordination and communication models in distributed graph processing, partitioning techniques and different definitions related to the potential for a graph to be updated. This survey is aimed at both the experienced software engineer or researcher as well as the graduate student looking for an understanding of the landscape of solutions (and their limitations) for graph processing.

Vibration suppression effect of porous graphene platelet coating on fiber reinforced polymer composite plate with viscoelastic damping boundary conditions resting on viscoelastic foundation

This work is devoted to the investigation of vibration suppression effects of the viscoelastic foundation, viscoelastic damping boundary condition and porous graphene platelet coating (PGPC) on the fiber reinforced polymer (FRP) composite plate. A dynamic model is built by combining the first-order shear deformation theory, the multi-segment partition technique, the virtual spring technology, the Rayleigh-Ritz approach, and the mode strain energy method. Afterwards, the structural natural frequencies, modal shapes and modal damping ratios are calculated, as well as the verification of the convergence and the correctness of the constructed model. Finally, the vibration suppression effect of the FRP-PGPC composite plate with different parameters, such as geometrical and material parameters, foundation and boundary condition types, are discussed in details.

Scalable online first-order monitoring

Online monitoring is the task of identifying complex temporal patterns while incrementally processing streams of data-carrying events. Existing state-of-the-art monitors for first-order patterns, which may refer to and quantify over data values, can process streams of modest velocity in real-time. We show how to scale up first-order monitoring to substantially higher velocities by slicing the stream, based on the events’ data values, into substreams that can be monitored independently. Because monitoring is not embarrassingly parallel in general, slicing can lead to data duplication. To reduce this overhead, we adapt hash-based partitioning techniques from databases to the monitoring setting. We implement these techniques in an automatic data slicer based on Apache Flink and empirically evaluate its performance using two tools—MonPoly and DejaVu—to monitor the substreams. Our evaluation attests to substantial scalability improvements for both tools.

Free vibration analysis of inversely coupled composite laminated shell structures with general boundary condition

In this paper, the free vibration characteristics of various coupled composite laminated doubly curved revolution shells are investigated under generalized boundary conditions (BCs). The joint shell structure consists of a doubly curved revolution shell–cylindrical shell–doubly curved revolution shell structure, and here, unlike previous structures, the doubly curved revolution shells are inversely joined together. In this paper, doubly curved shells such as elliptical, paraboloidal, and spherical shells are considered. The first order shear deformation theory and multi-segment partitioning technique are adopted to establish the theoretical model of coupled shell structures. Regardless of the individual shell structures and the BCs, the displacement functions of each shell segment are expanded using ultraspherical polynomials in the meridional direction and using the standard Fourier series in the circumferential direction. In order to generalize the BCs at both ends of a coupled shell and the connecting conditions at the interface, the virtual spring technique is employed. Then, the natural frequencies and mode shapes of the coupled shell structures are obtained by the Ritz method. The reliability and accuracy of the proposed method are verified by the convergence study and numerical comparison with results of the finite element method. In addition, some numerical results are also reported for the free vibration of coupled composite laminated doubly curved revolution shell structures under classical and elastic BCs, which can provide the reference data for future studies.

Hydro-conditioning and seed partitioning for Inga laurina (SW.) Willd Propagation

The Inga laurina (Sw.) Willd species comes from tropical and subtropical regions, in several phytogeographic areas of distribution, including Brazil. It belongs to the leguminous family, present in riparian forests and used in urban afforestation, it provides shading and a pleasant microclimate, ornamental value, in addition to the fruit being consumed by man and birds. Species of this genus have compounds that are used in popular medicine. The propagation method is still by seeds, making it difficult to obtain the seedling, as they present recalcitrance, impairing its storage viability. The objective of this work was to verify the possibility of I. laurina seeds partitioning for the species propagation, and the storage viability, if previously subjected to hydro-conditioning. A completely randomized design was adopted, in a 2 x 4 factorial (Lot x storage time), with four replications of 50 seeds per experimental unit. The treatments were formed by the lots of whole and partitioned seeds, plus the hydro-conditioning application in the stored period. After the treatment’s application, sowing was done in tubes, being kept in a 120-day evaluation period. After this period, emergence, emergence speed index, average emergence time, air length and roots (cm), the aerial part and roots dry biomass (g), number of roots, leaves and stems (g) were evaluated. Seed partitioning was not proved to be advantageous. The seed partitioning technique was not proved to be advantageous. The hydro-conditioning adoption during storage allowed the seeds to remain viable and with the desired vigor.

Improving Indoor Localization System Using a Partitioning Technique Based on RSS and ToA

Designing a localization system for an indoor environment faces more challenges because of multipath and interference problems. In this field, the most important techniques used for such environment, are RSS and ToA which need to be improved especially from more interference because of the huge multipath problems. In this paper, a case study of a selected building is chosen in order to apply the proposed technique of this research. Such proposal is based on the PT ‎ of the area in the case study into MZ. Each zone is allocated special values for the parameters used to estimate the target positions. WI package is used to simulate the case study area and apply such proposal based on RSS and ToA. The results confirm that the estimated locations are close to the real locations by the average error of (2.8) meter and (0.192) meter for ToA corresponding one zone and four zones ‎respectively. ‎ In contrast, the results of our experiment show that the accuracy is improved from an average error of (2.4) meter and (0.217) meter for RSS corresponding one zone and four zones ‎respectively‎. Such results confirm that dividing the case study area into more zones leads to more accuracy.

Sequence and preference in the use of electron acceptors in flooded agricultural soils

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