Partitioning Technique Research Articles

The Potential of Parijoto Fruit Extract Fraction (Medinilla speciosa Blume) as Antibiofilm against Staphylococcus aureus Bacteria

Parijoto fruit extract (Medinilla speciosa Blume) contains flavonoids, saponins, tannins, and terpenoids and has antibacterial potential in inhibiting the growth of Staphylococcus aureus bacteria. S. aureus bacteria are bacteria that are capable of forming biofilms. This research aims to determine the potential of extracts and fractions (water, ethyl acetate, and n-hexane) in inhibiting the formation and degradation of S. aureus biofilms. Parijoto fruit was extracted using the maceration method with 96% ethanol solvent. The extract was fractionated using a liquid-liquid partition technique with water, ethyl acetate, and n-hexane as solvents. The antibiofilm activity test against the S. aureus bacteria ATCC 25923 was carried out by treatment inhibiting biofilm formation and biofilm degradation. The method is to read absorbance using a microplate reader with a wavelength of 595 nm. Optical density results to calculate IC50 and EC50 values. The results showed that the extract and fractions (water, ethyl acetate, and n-hexane) had the potential to inhibit the formation and degradation of S. aureus bacterial biofilms. The IC50 of biofilm inhibition in the extract, water fraction, ethyl acetate, and n-hexane against S. aureus bacteria was 2.51; 1.03; 1.10; 1.52 mg/ml. The EC50 value of the biofilm degradation test on the extract, water fraction, ethyl acetate, and n-hexane against S. aureus bacteria was 1.03; 1.17; 1.37; 1.88 mg/ml. The most active fraction of parijoto fruit (Medinilla specosa Blume) in inhibiting and degrading S. aureus bacterial biofilms is the water fraction.

Detecting Differential Item Functioning in Multidimensional Graded Response Models With Recursive Partitioning.

Differential item functioning (DIF) is a common challenge when examining latent traits in large scale surveys. In recent work, methods from the field of machine learning such as model-based recursive partitioning have been proposed to identify subgroups with DIF when little theoretical guidance and many potential subgroups are available. On this basis, we propose and compare recursive partitioning techniques for detecting DIF with a focus on measurement models with multiple latent variables and ordinal response data. We implement tree-based approaches for identifying subgroups that contribute to DIF in multidimensional latent variable modeling and propose a robust, yet scalable extension, inspired by random forests. The proposed techniques are applied and compared with simulations. We show that the proposed methods are able to efficiently detect DIF and allow to extract decision rules that lead to subgroups with well fitting models.

Adaptive fuzzy-evidential classification based on association rule mining

As one of the most promising classification approaches, association classification (AC) integrates data classification and association discovery techniques for generating a compact set of classification association rules. Recently, the fuzzy set and evidence theories are successively applied into AC in order to improve the classification performance in terms of accuracy and interpretability. However, from the perspectives of applicability and universality, there still exists two important issues in the current AC framework. On one hand, several key parameters, such as the number of fused rules in classification and the minimum support threshold in association discovery, are difficult to be accurately predefined in practice. On the other hand, the fixed grid-based fuzzy partition is not benefit to adapt for those datasets with large number of features. In this paper, an association rule-based adaptive fuzzy-evidential classification framework (AR-AFEC) is developed for overcoming the above limitations. To do so, an optimal rule fusion strategy and a dynamic minimum support threshold setting scheme are proposed for adaptively learning the parameters during classification and association mining respectively. In addition, an entropy-based trapezoidal fuzzy partition technique is proposed to adaptively obtain the fuzzy sets defined on each continuous feature domain. Experiments on 26 benchmark datasets and a human activity recognition application demonstrate that the proposal can achieve better accuracy than some state-of-the-art rule-based classification approaches, using less rules with more general structure.

Four Families of Summation Formulas for 4F3(1) with Application

A collection of functions organized according to their indexing based on non-negative integers is grouped by the common factor of fixed integer N. This grouping results in a summation of N series, each consisting of functions partitioned according to this modulo N rule. Notably, when N is equal to two, the functions in the series are divided into two subseries: one containing even-indexed functions and the other containing odd-indexed functions. This partitioning technique is widely utilized in the mathematical literature and finds applications in various contexts, such as in the theory of hypergeometric series. In this paper, we employ this partitioning technique to establish four distinct families of summation formulas for F34(1) hypergeometric series. Subsequently, we leverage these summation formulas to introduce eight categories of integral formulas. These integrals feature compositions of Beta function-type integrands and F23(x) hypergeometric functions. Additionally, we highlight that our primary summation formulas can be used to derive some well-known summation results.

Enhancing Storage Efficiency and Performance: A Survey of Data Partitioning Techniques

Enhancing Storage Efficiency and Performance: A Survey of Data Partitioning Techniques

Stability analysis of linear delayed systems based on an allowable delay set partitioning approach

This note is concerned with the stability analysis of linear systems with a time-varying delay, where the delay is assumed to be a periodically varying bounded function with restricted derivatives. Combining the allowable delay set (ADS) definition and the region partitioning technique, an ADS partitioning approach is proposed, and then by with aid of this approach, a novel Lyapunov–Krasovskii functional (LKF), namely delay-set-partition-based (DSPB) LKF, is developed. Compared with the previous LKF method, the main advantage of the DSPB LKF lies in allowing the considered system to construct its own LKF in each sub-ADS. By utilizing the proposed LKF, less conservative stability criteria are derived. Finally, two simulation examples are provided to verify the effectiveness and superiority of the proposed method.

Verification of a virtual lunar regolith simulant

Introduction: Physical regolith simulants are valuable tools for developing In-Situ Resource Utilisation hardware. However, using virtual models of regolith instead can reduce costs, limit exposure to hazardous materials, and offer a practical method of testing the effects of reduced gravity.Methods: We verify a virtual model of regolith as macroparticles against physical tests. Using space partitioning techniques to identify neighbouring particles, we present a scalable model of regolith, in which the computation time increases roughly proportionally with the number of particles. We evaluated the performance of this virtual simulant vs. a physical simulant (Exolith LMS-1) by comparing the flow rate through funnels of various diameters, and the resultant angle of repose of material on both large (500 g) and small (16 g) scale tests.Results: For large scale tests, the flow rates were within the predicted range for macroparticles with radii 3–7 mm, with the greatest accuracy achieved for radii 4–5 mm. However, the macroparticles blocked the simulated funnels more easily than in the physical trials, due to their high cohesion. The angle of repose was not accurately represented by this model for either of the tests.Discussion: The high efficiency of this model makes it best suited for applications which require large scale approximations of regolith with real-time execution, such as virtual training for robot operators or providing visual and haptic feedback in model-mediated teleoperation systems. The results of this model in reduced gravity could be further verified against data from upcoming lunar missions in future work.

A randomized block extended Kaczmarz method with hybrid partitions for solving large inconsistent linear systems

We propose a randomized block extended Kaczmarz method with hybrid partitioning techniques for solving large inconsistent linear systems. It employs the k-means clustering to partition the columns of the coefficient matrix while applying the uniform sampling to derive the row partition of the coefficient matrix. It is proved that the proposed algorithm converges to the unique least-squares least-norm solution in expectation. Numerical examples validate that the new algorithm is competitive when compared with other randomized extended Kaczmarz-type methods.

Machine learning-based characterisation of urban morphology with the street pattern

Streets are a crucial part of the built environment, and their layouts, the street patterns, are widely researched and contribute to a quantitative understanding of urban morphology. However, traditional street pattern analysis only considers a few broadly defined characteristics. It uses administrative boundaries and grids as units of analysis that fail to encompass the diversity and complexity of street networks. To address these challenges, this research proposes a machine learning-based approach to automatically recognise street patterns that employs an adaptive analysis unit based on street-based local areas (SLAs). SLAs use a network partitioning technique that can adapt to distinct street networks, making it particularly suitable for different urban contexts. By calculating several streets’ network metrics and performing a hierarchical clustering method, streets with similar characters are grouped under the same street pattern. A case study is carried out in six cities worldwide. The results show that street pattern types are rather diverse and hierarchical, and categorising them into clearly demarcated taxonomy is challenging. The study derives a set of new morphometrics-based street patterns with four major types that resemble conventional street patterns and eleven sub-types to significantly increase their diversity for broader coverage of urban morphology. The new patterns capture urban structural differences across cities, such as the urban-suburban division and the number of urban centres present. In conclusion, the proposed machine learning-based morphometric street pattern to characterise urban morphology has an enhanced ability to encompass more information from the built environment while maintaining the intuitiveness of using patterns.

A bivariate spectral linear partition method for solving nonlinear evolution equations

This work develops a method for solving nonlinear evolution equations. The method, termed a bivariate spectral linear partition method (BSLPM), combines the Chebyshev spectral collocation method, bivariate Lagrange interpolation, and a linear partition technique as an underlying linearization method. It is developed for an th‐order nonlinear differential equation and then used to solve three known evolution problems. The results are compared with known exact solutions from literature. The method's applicability, reliability, and accuracy are confirmed by the congruence between the numerical and exact solutions. Tables, error graphs, and convergence graphs were generated using MATLAB (R2015a), to confirm the order of accuracy of the method and verify its convergence. The performance of the method is also observed against other methods performing well in these types of differential equations and is found to be comparable in terms of accuracy. The proposed method is also efficient as it uses minimal computation time.

Perioperative Changes in Serum Transaminase Levels: Impact on Postoperative Morbidity Following Liver Resection of Hepatocellular Carcinoma.

To define how dynamic changes in pre- versus post-operative serum aspartate aminotransferase (AST) and alanine aminotransaminase (ALT) levels may impact postoperative morbidity after curative-intent resection of hepatocellular carcinoma (HCC). Hepatic ischemia/reperfusion can occur at the time of liver resection and may be associated with adverse outcomes following liver resection. Patients who underwent curative resection for HCC between 2010-2020 were identified from an international multi-institutional database. Changes in AST and ALT (CAA) on postoperative day (POD) 3 versus preoperative values () were calculated using the formula: based on a fusion index via Euclidean norm, which was examined relative to the comprehensive complication index (CCI). The impact of CAA on CCI was assessed by the restricted cubic spline regression and Random Forest analyses. A total of 759 patients were included in the analytic cohort. Median CAA was 1.7 (range, 0.9 to 3.25); 431 (56.8%) patients had a CAA<2, 215 (28.3%) patients with CAA 2-5, and 113 (14.9%) patients had CAA ≥5. The incidence of post-operative complications was 65.0% (n=493) with a median CCI of 20.9 (IQR, 20.9-33.5). Spline regression analysis demonstrated a non-linear incremental association between CAA and CCI. The optimal cutoff value of CAA=5 was identified by the recursive partitioning technique. After adjusting for other competing risk factors, CAA≥5 remained strongly associated with risk of post-operative complications (Ref. CAA<5, OR 1.63, 95%CI 1.05-2.55, P=0.03). In fact, the use of CAA to predict post-operative complications was very good in both the derivative (AUC 0.88) and external (ACU 0.86) cohorts (n=1137). CAA was an independent predictor of CCI after liver resection for HCC. Use of routine labs such as AST and ALT can help identify patients at highest risk of post-operative complications following HCC resection.

Three-phase partitioning technique for the green separation of crude polysaccharides from Schizophyllum commune and its effect on macrophage activation

This study aimed to investigate a non-chromatographic method for separation polysaccharides from Schizophyllum commune (ScP1) using three-phase partitioning (TPP) for facile large-scale implementation. A crude slurry was extracted with hot water extraction, then separated using TPP at the following conditions: 40% (w/v) (NH4)2SO4, 1:2 (v/v) slurry:t-butanol ratio, pH 6, temperature of 40 °C, and incubation duration of 75 min to form the three phases. The results showed that extraction yield of the ScP1 under the optimized conditions was 20.08%. The ScP1 contained the most abundant mannose, with minor amounts of glucose and galactose. Two different equivalent molecular weights were found in ScP1 (709.54 ± 20.04 and 8.10 ± 0.31 kDa). An analysis of the Fourier transform-infrared spectrometer spectra, X-ray diffraction, and Congo red reaction indicated that the ScP1 were polysaccharides with semi-crystalline and random coil conformations. The solubility and microstructure of the ScP1 revealed a rough surface with small cavities features. Nuclear magnetic resonance analyses suggested that the backbone of ScP1 was primarily (1→3)-α-d-mannose and (1→3,6)-linkage residues. ScP1 enhanced the production of nitric oxide (NO) and mRNA expression of iNOS and TNFα of RAW264.7 cell in a dose-dependent manner. Moreover, ScP1 likely plays an immune stimulation role through MR, GR and DC-1 receptors on RAW264.7 cell. Thus, this technology may encourage economical and environmentally friendly separation of immune-enhancing agent polysaccharides from S. commune, which can be applied in the downstream industrial processing of functional foods and pharmaceuticals.

Exploration of movie evaluation analysis and data preprocessing impact based on RNN technology

In order to provide valuable models for the film and television industry, this study aims to introduce recurrent neural network (RNN) techniques for effective movie evaluation analysis. Semantic analysis using machine learning is a very important means of extracting and understanding the meaning behind the text. The study evaluates different RNN techniques to identify the optimal neural network model. Data preprocessing includes tokenization and embedding, including dataset partitioning, tokenization process and word embedding techniques. The comparative analysis involves the predictive performance of simple RNN, Long Short-Term Memory Network (LSTM) and LSTM with attention. This study also explores the impact of including emoji and punctuation analysis in the data preprocessing process on semantic analysis. The results of the study show that preprocessing emoticons and punctuation improves accuracy, and LSTM with attention shows excellent performance. Notably, the study concludes that LSTM with attention performs well in terms of runtime efficiency, convergence speed, and accuracy compared to other models. The effect of punctuation and emoticons is that it will improve the accuracy. This study helps to improve the quality of the movie by constructing an effective analytical model thus.

PANDA: Processing in Magnetic Random-Access Memory-Accelerated de Bruijn Graph-Based DNA Assembly

In this work, we present an efficient Processing in MRAM-Accelerated De Bruijn Graph-based DNA Assembly platform, named PANDA, based on an optimized and hardware-friendly genome assembly algorithm. PANDA is able to assemble large-scale DNA sequence datasets from all-pair overlaps. We first design a PANDA platform that exploits MRAM as computational memory and converts it to a potent processing unit for genome assembly. PANDA can not only execute efficient bulk bit-wise X(N)OR-based comparison/addition operations heavily required for the genome assembly task but also a full set of 2-/3-input logic operations inside the MRAM chip. We then develop a highly parallel and step-by-step hardware-friendly DNA assembly algorithm for PANDA that only requires the developed in-memory logic operations. The platform is then configured with a novel data partitioning and mapping technique that provides local storage and processing to utilize the algorithm level’s parallelism fully. The cross-layer simulation results demonstrate that PANDA reduces the run time and power by a factor of 18 and 11, respectively, compared with CPU. Moreover, speed-ups of up to 2.5 to 10× can be obtained over other recent processing in-memory platforms to perform the same task, like STT-MRAM, ReRAM, and DRAM.

Vehicle Driving Safety of Underground Interchanges Using a Driving Simulator and Data Mining Analysis

In the process of driving in an underground interchange, drivers are faced with many challenges, such as being in a closed space, visual changes alternating between light and dark conditions, complex road conditions in the confluence section, and dense signage, which directly affect the safety and comfort of drivers in an underground interchange. Thus, driving simulation, building information modeling (BIM), and data mining were used to analyze the impact of underground interchange safety facilities on driving safety and comfort. Acceleration disturbance and steering wheel comfort loss values were used to assist the comfort analysis. The CART algorithm, classification decision trees, and neural networks were used for data mining, which uses a dichotomous recursive partitioning technique where multiple layers of neurons are superimposed to fit and replace very complex nonlinear mapping relationships. Ten different scenarios were designed for comparison. Multiple linear regression combined with ANOVA was used to calculate the significance of the control variables for each scenario on the evaluation index. The results show that appropriately reducing the length of the deceleration section can improve driving comfort, setting reasonable reminder signs at the merge junction can improve driving safety, and an appropriate wall color can reduce speed oscillation. This study indicates that the placement of traffic safety facilities significantly influences the safety and comfort of driving in underground interchanges. This study may provide support for the optimization of the design of underground interchange construction and internal traffic safety facilities.

SOCIAL NETWORK STRUCTURE AND FIRMS’ INTERNATIONALIZATION: NEW INSIGHTS FROM A WHOLE-NETWORK PERSPECTIVE

Objective: The aim of this study was to analyze the social network structure of the oil and gas (O&amp;G) cluster in Rio de Janeiro, Brazil. We explore whether internationalized firms concentrate in specific sections of the social network, and scrutinize whether internationalization is associated with a firm’s attributes and its direct and indirect network ties. Method: This research applies social network analysis, which allows us to identify dense sections, popular nodes, and strategic position. We apply partitioning techniques to determine the extent to which a firm’s internationalization is affected by those to whom it is connected. Main results: Our findings show that internationalized firms concentrate in dense sections of the network, which are characterized by high interconnectedness. Furthermore, there is indicative evidence that interaction among firms and their attributes embedded in the social network structure is associated, at least in part, with the diffusion of internationalization. Relevance / Originality: Most studies targeting the network focus on the so-called ego network. This study applies a whole-network perspective to consider popular communities and central nodes and, ultimately, show how social behavior is diffused within the social network structure. Theoretical / Methodological Contributions: Drawing upon the network theory, we emphasize interdisciplinarity to advance international business (IB) scholarship. In particular, we qualify the Uppsala model’s assumption of network/relational capability, that is, the ability to build, sustain, and coordinate relationships by demonstrating the relevance of the social network structure to the internationalization process.

Enhancing the efficiency and functionality of xylanase from Bacillus sp. RTS11: Optimization, purification, characterization, and prospects in kraft pulp bleaching.

Bacillus sp. RTS11, a xylanolytic strain, was isolated from the Algerian desert rocks. Genetic analysis revealed a remarkable 98.69% similarity to Bacillus pumilus. We harnessed optimization techniques, including Plackett-Burman screening and Box-Behnken optimization design, to amplify xylanase production and activity. The outcome of these efforts was an optimized medium that yielded an impressive xylanase production titer of 448.89 U, a threefold increase compared to the non-optimized medium (146 U). The Purification of xylanase was achieved through the three-phase partitioning technique, employing t-butanol and various chromatographic methods. Notably, anion exchange chromatography led to isolating a highly pure enzyme with a molecular weight of 60 kDa. The xylanase exhibited its peak activity at a temperature of 60°C and a pH of 9.0. When applied to pulp pretreatment, 20 U/g of xylanase demonstrated a substantial increase in the release of phenolic and chromophore compounds while reducing sugar content in the pulp. Furthermore, this versatile xylanase shows its ability to efficiently hydrolyze a variety of agro-industrial residues, including wheat bran, corn and grape waste, wheat straw, and sugarcane bagasse. These findings underscore the significant potential of this xylanase enzyme in biobleaching processes and the utilization of agro-industrial waste, opening up exciting avenues for sustainable and environmentally friendly industrial applications.

Predictive potential of K‐Banhatti and Zagreb type molecular descriptors in structure–property relationship analysis of some novel drug molecules

AbstractTopological indices (TIs) can be used to forecast molecules' biological activity, physicochemical features, and toxicity. The use of topological indices and regression analysis can help us better understand drug behavior and contribute to the development of tailored drugs. This study investigates the predictive potential and efficacy of K‐Banhatti and Zagreb type degree‐based topological indices in quantitative structure–property relationship (QSPR) analysis of a comprehensive set of medications used for diabetes type‐I and type‐II disease. The K‐Banhatti and Zagreb type degree‐based topological indices are computed for 14 anti‐diabetes drug molecules using edge/vertex partitioning techniques. By leveraging these topological indices, QSPR regression models are developed to predict the physicochemical properties of the understudy drugs. The results show that the values of these topological indices are highly correlated with certain physicochemical properties of the anti‐diabetes drugs. Furthermore, the comparative analysis revealed that, for all the considered properties except enthalpy of vaporization, Zagreb type indices outperform K‐Banhatti indices with high predictive ability. Hence, it can be concluded that the Zagreb type indices are the best alternatives to theoretically predict the properties of anti‐diabetes drugs. This theoretical analysis can help chemists in their right choice of the topological indices to theoretically predict the properties of anti‐diabetes drugs without going into laborious experimentation.

Planktic diatom responses to spatiotemporal environmental variation in high‐mountain tropical lakes

Abstract Tropical lakes harbour high levels of biodiversity, but the temporal and spatial variability of biological communities are still inadequately characterised, making it difficult to predict the impact of accelerated rates of environmental change in these regions. Our goal was to identify the spatiotemporal dynamics of the planktic diatom community in the Cajas Massif in the tropical Andes. We analysed seasonal diatom and environmental data over a period of 1 year from 10 lakes located in geologically distinct basins and modelled community–environment relationships using multivariate ordination and variation partitioning techniques. Generalised additive models with a full‐subset information theoretic approach also were used to determine which environmental variables explain single‐species abundance. Although the lakes are monomictic and thus have variable thermal structure across the year, seasonal variability of water chemistry conditions was negligible, and seasonal differences in diatom community composition were small. Across space, diatom community composition was correlated primarily with ionic content (divalent cations and alkalinity), related to bedrock composition, and secondly with lake thermal structure and productivity. The ionic gradient overrode the effect of the thermal structure–productivity gradient at the diatom community level, whereas individual diatom species responded more sensitively to variables related to in‐lake and catchment productivity, including chlorophyll‐a and iron, and the proportion of wetlands in the catchment. Our results indicate that the spatiotemporal variability of Cajas lakes and their diatom communities is the result of multiple intertwined environmental factors. The emergence of the ionic and thermal structure–productivity gradients in a rather small tropical lake district suggests segregation of ecological niches for diatoms that also may be important in other high‐elevation lake regions. Future studies that track tropical Andean lakes under natural and anthropogenically mediated change, both in contemporary times and in palaeoenvironmental reconstructions, would benefit from the modelling approach (community and species levels) developed here.

Block size estimation for data partitioning in HPC applications using machine learning techniques

The extensive use of HPC infrastructures and frameworks for running data-intensive applications has led to a growing interest in data partitioning techniques and strategies. In fact, application performance can be heavily affected by how data are partitioned, which in turn depends on the selected size for data blocks, i.e. the block size. Therefore, finding an effective partitioning, i.e. a suitable block size, is a key strategy to speed-up parallel data-intensive applications and increase scalability. This paper describes a methodology, namely BLEST-ML (BLock size ESTimation through Machine Learning), for block size estimation that relies on supervised machine learning techniques. The proposed methodology was evaluated by designing an implementation tailored to dislib, a distributed computing library highly focused on machine learning algorithms built on top of the PyCOMPSs framework. We assessed the effectiveness of the provided implementation through an extensive experimental evaluation considering different algorithms from dislib, datasets, and infrastructures, including the MareNostrum 4 supercomputer. The results we obtained show the ability of BLEST-ML to efficiently determine a suitable way to split a given dataset, thus providing a proof of its applicability to enable the efficient execution of data-parallel applications in high performance environments.