Partitioning Technique Research Articles

Valorization of waste Syzygium cumini seed kernels by three-phase partitioning extraction and evaluation of in vitro antioxidant and hypoglycemic potential

Diabetes is one of the leading causes of morbidity and mortality worldwide. Replacement of the chemically synthesized medicines with phytomedicines is of great importance. Syzygium cumini fruit has around 30% hard seed kernel part that is considered solid waste and can be a good source of bioactive phenolic compounds. Thus, this work was performed to valorize Syzygium cumini kernels powder (SCKP) using a three-phase partitioning technique (TPP) and evaluate in vitro antioxidant and hypoglycemic activities. The extraction yield of catechin, gallic acid, TFC, and TPC obtained through TPP was 0.52, 33.24 mg/g, 7.78 mg CE/g, and 82.66 mg GAE/g, respectively, in 45 min, 40% ammonium sulfate, 1:1 (V/V) extraction slurry to t-butanol, 30 ± 2 °C temperature, pH 4, 1:20 (g/mL) SCKP to water and SCKP size 106 µm. IC50 values 12.15, 9.33, and 7.55 in µg/mL for TPP extract were obtained from DPPH, α-amylase, and α-glucosidase in vitro assays, respectively. TPP for extraction of bioactive compounds was found superior over traditional extraction techniques (devoid of t-butanol and ammonium sulfate). The extract obtained shows enormous antioxidant and hypoglycemic potential for use in nutraceutical and pharmaceutical industries. Highlights First report on three-phase partitioning (TPP) extraction of catechin and gallic acid from SCKP. Effective extraction of total phenolic and flavonoid compounds. Proved the enormous antioxidant and hypoglycemic potential of SCKP’s TPP extract. TPP extraction, the selective, efficient, and economically viable option.

A Survey of Clustering Algorithms for High-Dimensional Data Mining

The increasing complexity and dimensionality of data in numerous domains, including bioinformatics, text mining, multimedia, and social network analysis, pose significant challenges to traditional clustering algorithms. This paper provides a comprehensive review of various clustering algorithms suitable for high-dimensional data mining. We begin by elaborating on the challenges and difficulties intrinsic to high-dimensional spaces, such as the curse of dimensionality and the concentration of measure. Following that, we investigate a variety of different approaches to clustering, such as partitioning techniques, grid-based methods, hierarchical methods, density-based methods, and model-based methods. These algorithms are examined with a particular emphasis on how well they deal with high-dimensional data and how well they deal with data noise, as well as how well they can scale and how easily they can be interpreted. The most current developments in subspace and correlation clustering, as well as embedding approaches and the application of deep learning to the clustering of high-dimensional data, are also included in this overview. With the help of this in-depth analysis, our goals are to give insights into the benefits and drawbacks of each algorithm and to aid academics and practitioners in picking the optimal approach for the high-dimensional data mining projects they are working on.

An Exploration of Stress: Leveraging Online Data from Crowdsourcing Platforms

Background: Early detection of community health risk factors such as stress is of great interest to health policymakers, but representative data collection is often expensive and time-consuming. It is important to investigate the use of alternative means of data collection such as crowdsourcing platforms. Methods: An online sample of Amazon Mechanical Turk (MTurk) workers (N = 500) filled out, for themselves and their child, demographic information and the 10-item Perceived Stress Scale (PSS-10), designed to measure the degree to which situations in one’s life are appraised as stressful. Internal consistency reliability of the PSS-10 was examined via Cronbach’s alpha. Analysis of variance (ANOVA) was utilized to explore trends in the average perceived stress of both adults and their children. Last, Rasch trees were utilized to detect differential item functioning (DIF) in the set of PSS-10 items. Results: The PSS-10 showed adequate internal consistency reliability (Cronbach’s alpha = 0.73). ANOVA results suggested that stress scores significantly differed by education (p = 0.024), employment status (p = 0.0004), and social media usage (p = 0.015). Rasch trees, a recursive partitioning technique based on the Rasch model, indicated that items on the PSS-10 displayed DIF attributable to physical health for adults and social media usage for children. Conclusion: The key conclusion is that this data collection scheme shows promise, allowing public health officials to examine health risk factors such as perceived stress quickly and cost effectively.

Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using IoT assisted mobile fog cloud

ABSTRACT These days, fog-cloud based healthcare application partitioning techniques have been growing progressively. However, existing static fog-cloud based application partitioning methods are static and cannot adopt dynamic changes in the dynamic environment (e.g., where network and computing nodes have resource value variation) during the execution process. This study devises a Deep Neural Networks Energy Cost-Efficient Partitioning and Task Scheduling (DNNECTS) algorithm framework which consists of the following components: application partitioning, task sequencing, and scheduling. Experimental results show the suggested methods in terms of energy consumption and the applications' cost in the dynamic environment.

Encapsulation and characterization of cellulase purified with three-phase partitioning technique

Purification of Trichoderma reesei cellulase by three-phase partitioning (TPP) technique and encapsulation of the enzyme in calcium alginate gel were studied. These two basic experiments were optimized with all variables and finally optimum conditions for both purification and encapsulation studies were determined. Ideal TPP condition for the purification of T. reesei cellulase was determined as; 50% (w/v) ammonium sulphate saturation, 1:1 (v/v) enzyme: t-butanol ratio, pH 4.5 and 0.1 mg of the protein. By using this condition, the cellulase enzyme was successfully purified with 113% activity yield and 1.42 purification fold. As a result of the encapsulation studies, optimum immobilization condition for cellulase was determined as 0.5 mg enzyme (protein amount) and 2% (w/v) alginate, 0.5% (w/v) chitosan in 0.15 M CaCl2, 3 h for immobilization, 2.0% (v/v) glutaraldehyde and 2 h for cross-linking. The enzyme was immobilized with 82% activity yield by using this conditions. The cellulase, which was concentrated in the middle phase of TPP, could also immobilized with this condition. The optimal temperature of the free and immobilized cellulase were determined as 65 °C and the optimum pHs were found as pH 4.0 and pH 5.0, respectively. The immobilized cellulase was also used three times with high activity yields. The biochemical characterization and stability studies confirm that this immobilized enzyme show promise for industry that could be safely used especially both in the feed and food industry and food processing.

Molecular Structural Descriptors of Donut Benzenoid Systems

Circumcised donut benzenoid systems are a class of polycyclic aromatic hydrocarbons (PAHs) derived from planar benzenoid networks by strategically eliminating a few hexagons. The study of donut benzenoid systems has been gaining ground because of their neoteric and fascinating characteristics like superaromaticity, ascribed to their unique structure. The distinct nature and properties of donut benzenoids open the door for applications in several industries such as biotechnology, electronics, pharmaceuticals and so on. The investigation of the properties of circumcised donut benzenoids and their relationship to the underlying molecular structures using graph-theoretical techniques is advantageous in the design and synthesis of new donut benzenoid compounds. Studies on topological indices have conclusively proved that they have a strong correlation to various properties of chemical compounds. They also provide a mathematical representation of the molecular structure for QSPR/QSAR techniques. In this paper, the degree-based molecular structural descriptors are calculated for two circumcised donut benzenoid structures using M-polynomial and edge partition techniques, effectively doubling the number of known topological indices for these structures. Also, the numerical values of these indices are computed and compared pictorially.

Pengujian Black Box pada Aplikasi Keamanan Data Multimedia Message Service (MMS) Berbasis Android Menggunakan Teknik Equivalence Partitions

Currently, almost everyone has used electronic devices in which there are applications that have been installed. To meet user satisfaction, this application must go through a testing process to check all errors in the system so that they can be fixed later. This test is performed on a previously created Data Security Application. Software testing that has been done is using the Black Box method with the Equivalence Partition technique. From the test results, it was found that cases that need to be fixed are the fields in the Create Message Form.

Local routing in WSPD-based spanners

The well-separated pair decomposition (WSPD) of the complete Euclidean graph defined on points in $R^2$, introduced by Callahan and Kosaraju [JACM, 42 (1): 67-90, 1995], is a technique for partitioning the edges of the complete graph based on length into a linear number of sets. Among the many different applications of WSPDs, Callahan and Kosaraju proved that the sparse subgraph that results by selecting an arbitrary edge from each set (called WSPD-spanner) is a $1 + 8/(s − 4)$-spanner, where $s > 4$ is the separation ratio used for partitioning the edges.Although competitive local-routing strategies exist for various spanners such as Yao-graphs, $\Theta$-graphs, and variants of Delaunay graphs, few local-routing strategies are known for any WSPD-spanner. Our main contribution is a local-routing algorithm with a near-optimal competitive routing ratio of $1 + O(1/s)$ on a WSPD-spanner.Specifically, using Callahan and Kosaraju’s fair split-tree, we show how to build a WSPD-spanner with spanning ratio $1 + 4/s + 4/(s − 2)$ which is a slight improvement over $1 + 8/(s − 4)$. We then present a 2-local and a 1-local routing algorithm on this spanner with competitive routing ratios of $1 + 6/(s − 2) + 4/s$ and $1 + 8/(s − 2) + 4/s + 8/s^2$, respectively. Moreover, we prove that there exists a point set for which our WSPD-spanner has a spanning ratio of at least $1 + 8/s$, thereby proving the near-optimality of its spanning ratio and the near-optimality of the routing ratio of both our routing algorithms.

CoPart: a context-based partitioning technique for big data

The MapReduce programming paradigm is frequently used in order to process and analyse a huge amount of data. This paradigm relies on the ability to apply the same operation in parallel on independent chunks of data. The consequence is that the overall performances greatly depend on the way data are partitioned among the various computation nodes. The default partitioning technique, provided by systems like Hadoop or Spark, basically performs a random subdivision of the input records, without considering the nature and correlation between them. Even if such approach can be appropriate in the simplest case where all the input records have to be always analyzed, it becomes a limit for sophisticated analyses, in which correlations between records can be exploited to preliminarily prune unnecessary computations. In this paper we design a context-based multi-dimensional partitioning technique, called CoPart, which takes care of data correlation in order to determine how records are subdivided between splits (i.e., units of work assigned to a computation node). More specifically, it considers not only the correlation of data w.r.t. contextual attributes, but also the distribution of each contextual dimension in the dataset. We experimentally compare our approach with existing ones, considering both quality criteria and the query execution times.

How Much Influence Do CEOs Have on Company Actions and Outcomes? The Example of Corporate Social Responsibility

How much influence do CEOs have on corporate social responsibility (CSR)? To answer this question, we apply the CEO in context (CiC) variance partitioning technique to estimate the CEO effect—the a...

Ready-time partitioning algorithm for computation offloading of workflow applications in mobile cloud computing

To augment the capabilities of mobile devices, application partitioning solutions in mobile cloud computing have emerged to decide the execution location of each application part between mobile device and cloud servers. To adapt to the network and server status changes during application execution, we propose a cloudlet-assisted ready-time partitioning technique, which partitions each task of the users’ workflow exactly when it is ready to run. It aims at minimizing the energy consumption of each mobile user’s device, while meeting user-defined deadlines. The proposed deadline distribution–ready-time partitioning algorithm has two phases: deadline distribution phase, which distributes each user deadline among its workflow tasks before application execution, and ready-time partitioning phase, which partitions each ready task by effectively considering the network and cloudlet status. Using real mobile applications, the experimental results prove its adaptability to environmental changes, its energy-efficiency, and its ability to satisfy the deadline over other methods.

Bacterioplankton composition as an indicator of environmental status: proof of principle using indicator value analysis of estuarine communities

Increasing awareness of environmental impacts caused by anthropogenic activities highlights the need to determine indicators of environmental status that can be routinely assessed at large spatial and temporal scales. Microbial communities comprise the greatest share of biological diversity on Earth and can rapidly reflect recent environmental changes while providing a record of past events. However, they have rarely been targeted in the search for ecological indicators of habitat types, environmental conditions, or environmental changes. Here, as a proof of principle, we analysed the bacterioplankton community composition of 4 estuaries in North and South America, Europe, and Asia, and looked for indicators of groups of samples defined using partition techniques, according to primary physicochemical variables typically monitored to infer water quality. Indicator value analysis (IndVal) was conducted to identify indicator operational taxonomic units (OTUs; analogous to species in other fields of ecology) in each group. These bacterioplankton-based indicators exhibited a high capacity to predict the group membership of the samples within each estuary and to correctly assign the samples to the appropriate estuary in a combined data set, employing different machine learning techniques. The indicators were composed of OTUs belonging to several bacterial phyla, which responded significantly and differentially to the environmental variables used to define the groups of samples. Moreover, the predictive values of these bacterial indicators were generally higher than those of other biological assemblages commonly used for environmental monitoring. Therefore, this approach appears to be a promising tool to complement existing strategies for monitoring and conservation of aquatic systems worldwide.

A Review on Deep Learning Techniques for the Diagnosis of Novel Coronavirus (COVID-19).

Novel coronavirus (COVID-19) outbreak, has raised a calamitous situation all over the world and has become one of the most acute and severe ailments in the past hundred years. The prevalence rate of COVID-19 is rapidly rising every day throughout the globe. Although no vaccines for this pandemic have been discovered yet, deep learning techniques proved themselves to be a powerful tool in the arsenal used by clinicians for the automatic diagnosis of COVID-19. This paper aims to overview the recently developed systems based on deep learning techniques using different medical imaging modalities like Computer Tomography (CT) and X-ray. This review specifically discusses the systems developed for COVID-19 diagnosis using deep learning techniques and provides insights on well-known data sets used to train these networks. It also highlights the data partitioning techniques and various performance measures developed by researchers in this field. A taxonomy is drawn to categorize the recent works for proper insight. Finally, we conclude by addressing the challenges associated with the use of deep learning methods for COVID-19 detection and probable future trends in this research area. The aim of this paper is to facilitate experts (medical or otherwise) and technicians in understanding the ways deep learning techniques are used in this regard and how they can be potentially further utilized to combat the outbreak of COVID-19.

MINIATURIZED LIQUID-LIQUID EXTRACTION WITH LOW-TEMPERATURE PARTITIONING TECHNIQUE (LLE/LTP) FOR PESTICIDE ANALYSIS IN A BIOLOGICAL MATRIX BY GAS CHROMATOGRAPHY

The liquid-liquid extraction with low-temperature partitioning technique (LLE/LTP) was miniaturized to determine the pesticides chlorpyrifos, bifenthrin, λ-cyhalothrin, and deltamethrin in a biological matrix, by gas chromatography. The method was proposed for fetal-bovine serum samples (500 μL), by using a full factorial design (23) with one center point. The evaluated factors included the volume of water added to the sample (1000 μL), volume of extraction solvent (500 μL) and stirring time (60 s) – a cleanup step was not necessary. The method was validated for the main figures of merit. The results indicated that the LLE/LTP/GC/ECD miniaturized method was efficient in extracting pesticides from blood-serum samples (75 to 115%). It showed both selectivity and sensitiveness, with limits of quantification (LOQ) ranging from 8 at 17 μg L-1. Once validated, the method was applied to blood-serum samples of rural workers from a city in the Brazilian state of Minas Gerais. In that case, the presence of chlorpyrifos was detected in two of the samples.

The Projection Extension Method: A Spectrally Accurate Technique for Complex Domains

An essential ingredient of a spectral method is the choice of suitable bases for test and trial spaces. On complex domains, these bases are harder to devise, necessitating the use of domain partitioning techniques such as the spectral element method. In this study, we introduce the Projection Extension (PE) method, an approach that yields spectrally accurate solutions to various problems on complex geometries without requiring domain decomposition. This technique builds on the insights used by extension methodologies such as the immersed boundary smooth extension and Smooth Forcing Extension (SFE) methods that are designed to improve the order of accuracy of the immersed boundary method. In particular, it couples an accurate extension procedure, that functions on arbitrary domains regardless of connectedness or regularity, with a least squares minimization of the boundary conditions. The resulting procedure is stable under iterative application and straightforward to generalize to higher dimensions. Moreover, it rapidly and robustly yields exponentially convergent solutions to a number of challenging test problems, including elliptic, parabolic, Newtonian fluid flow, and viscoelastic problems.

Crystal structure and electrochemical properties of phosphosulphate NaFe2PO4(SO4)2

NASICON-type NaFe2(PO4)(SO4)2 (NFPS) electrode material is successfully synthesized via a rheological route. DSC/TG analysis shows that it is thermally stable up to 760 °C. A carbon composite NFPS/C was obtained using ball milling. NFPS and NFPS/C were investigated as cathode and anode materials for sodium-ion batteries. Analysis of migration paths was performed by the Voronoi-Dirichlet partition technique to determine all possible Na+ ion migration paths. The diffusion coefficient, estimated by GITT, is in the 10-12-10-13 cm2∙s-1 range, which corresponds to the fast Na+ ion migration in the structure. According to calculations, NFPS is a wide band gap material, which indicates its poor electrical conductivity.

A Mass-Based Approach for Local Outlier Detection

This paper proposes a new outlier detection approach that measures the degree of outlierness for each instance in a given dataset. The proposed model utilizes a mass-based dissimilarity measure to address the weaknesses of neighbor-based outlier models while detecting local outliers in the dataset within a variety of data point densities. In particular, it first applies a hierarchical partitioning technique to generate a set of tree-like nested structure partitions for the input dataset, and then a mass-based dissimilarity measure is defined to quantify the dissimilarity between two data instances given the generated hierarchical partition structure. After that, for each data instance, a context set is obtained by gathering the neighbors around it with the $k$ lowest mass dissimilarities, and based on those context sets, a mass-based local outlier score model is introduced to compute the outlierness for each individual instance. The proposed approach fundamentally changes the perspective of the outlier model by using the mass-based measurement instead of the distance-based functions used in most neighbor-based methods. A comprehensive experiment conducted on both synthetic and real-world datasets demonstrates that the proposed approach is not only competitive with the existing state-of-the-art outlier detection models but is also an efficient and effective alternative for local outlier detection methods.

Reachability Analysis of Neural Feedback Loops

Neural Networks (NNs) can provide major empirical performance improvements for closed-loop systems, but they also introduce challenges in formally analyzing those systems' safety properties. In particular, this work focuses on estimating the forward reachable set of \textit{neural feedback loops} (closed-loop systems with NN controllers). Recent work provides bounds on these reachable sets, but the computationally tractable approaches yield overly conservative bounds (thus cannot be used to verify useful properties), and the methods that yield tighter bounds are too intensive for online computation. This work bridges the gap by formulating a convex optimization problem for the reachability analysis of closed-loop systems with NN controllers. While the solutions are less tight than previous (semidefinite program-based) methods, they are substantially faster to compute, and some of those computational time savings can be used to refine the bounds through new input set partitioning techniques, which is shown to dramatically reduce the tightness gap. The new framework is developed for systems with uncertainty (e.g., measurement and process noise) and nonlinearities (e.g., polynomial dynamics), and thus is shown to be applicable to real-world systems. To inform the design of an initial state set when only the target state set is known/specified, a novel algorithm for backward reachability analysis is also provided, which computes the set of states that are guaranteed to lead to the target set. The numerical experiments show that our approach (based on linear relaxations and partitioning) gives a $5\times$ reduction in conservatism in $150\times$ less computation time compared to the state-of-the-art. Furthermore, experiments on quadrotor, 270-state, and polynomial systems demonstrate the method's ability to handle uncertainty sources, high dimensionality, and nonlinear dynamics, respectively.

An efficient time-domain method for the electromagnetic transient response of multiconductor transmission lines excited by an electromagnetic field

An efficient time-domain method based on the waveform relaxation with transverse partitioning (WR-TP) techniques and the delay extraction passive compact circuit (DEPACT) macromodel is presented for evaluating the electromagnetic (EM) transient response of multiconductor transmission lines (MTLs) excited by an external EM field. Firstly, the equivalent distributed sources due to the tangential components of the electric field are introduced into the MTLs equations. Then, the TLs are divided into a cascaded of DEPACT subnetworks, where the telegrapher’s equations of lossless TLs are decoupled by applying the WR-TP techniques. And the relaxation sources over the lossy TLs are updated by using the waveform scaling algorithm, which can completely avoid performing the numerical convolution. Finally, the equivalent circuit is established and the transient responses induced on the lines can be obtained by using the circuit simulation software PSCAD.

Robustness Analysis of Neural Networks via Efficient Partitioning With Applications in Control Systems

Neural networks (NNs) are now routinely implemented on systems that must operate in uncertain environments, but the tools for formally analyzing how this uncertainty propagates to NN outputs are not yet commonplace. Computing tight bounds on NN output sets (given an input set) provides a measure of confidence associated with the NN decisions and is essential to deploy NNs on safety-critical systems. Recent works approximate the propagation of sets through nonlinear activations or partition the uncertainty set to provide a guaranteed outer bound on the set of possible NN outputs. However, the bound looseness causes excessive conservatism and/or the computation is too slow for online analysis. This letter unifies propagation and partition approaches to provide a family of robustness analysis algorithms that give tighter bounds than existing works for the same amount of computation time (or reduced computational effort for a desired accuracy level). Moreover, we provide new partitioning techniques that are aware of their current bound estimates and desired boundary shape (e.g., lower bounds, weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{\infty }$ </tex-math></inline-formula> -ball, convex hull), leading to further improvements in the computation-tightness tradeoff. The letter demonstrates the tighter bounds and reduced conservatism of the proposed robustness analysis framework with examples from model-free RL and forward kinematics learning.