Granular Reduction Research Articles

The impacts of red mud dosing on methane production and reduction of CO2: Activity of granules and formation

Experiments were conducted to investigate the effect of red mud (RM) dosing on granular sludge formation, biogas production and carbon dioxide reduction from palm oil mill effluent (POME) digestion. The results show that dosing RM adversely affected sludge granulation due to the formation of precipitates and hydrolyzates with poor settleability. However, at the optimal dosage (4.5 g RM/L), it could benefit granules formation and stability by improving the in extracellular polymeric substances and biogas production rate was 87.9 l g-VSadded/d at 4.5:30 mixing ratio. The ratio of 0.5:80–2.5:50 g-VS did not affect methane production and the highest methane yield average 79.9 l/g-VS added for RM:POME of 4.5:30, 14.5 higher respective to that of POME alone only. A CO2 reduction of 89.6% was obtained at RM of 4.5:30 ( r = 0.998). The chemical oxygen demand (COD) removal was 87% obtained at 30 g COD/l and 4.5:30 g-VS with growth of sp. Methanosarcina. The process evaluation was found model cone best fitted and actual production of CH4. The evidence by low root mean square prediction error (RMSPE) showed high correlation difference (Dif. %) with predicted value and actual values. Analyses were evaluated that the POME degradation with RM utilization, substantially enhanced the hydrolysis rate (khyd), lag phase time h (λ) and methane production rate ( Rm) of mixing ratio of RM:POME. Furthermore, the system showed solid reduction with the increased production of methane.

Knowledge granularity reduction for decision tables

Attribute reduction is a difficult topic in rough set theory and knowledge granularity reduction is one of the important types of reduction. However, up to now, its reduction algorithm based on a discernibility matrix has not been given. In this paper, we show that knowledge granularity reduction is equivalent to both positive region reduction and X-absolute reduction, and derive its corresponding algorithm based on a discernibility matrix to fill the gap. Particularly, knowledge granularity reduction is the usual positive region reduction for consistent decision tables. Finally, we provide a simple knowledge granularity reduction algorithm for finding a reduct with the help of binary integer programming, and consider six UCI datasets to illustrate our algorithms.

Optical plasticity of mammalian cells.

Transparency is widespread in nature, ranging from transparent insect wings to ocular tissues that enable you to read this text, and transparent marine vertebrates. And yet, cells and tissue models in biology are usually strongly light scattering and optically opaque, precluding deep optical microscopy. Here we describe the directed evolution of cultured mammalian cells toward increased transparency. We find that mutations greatly diversify the optical phenotype of Chinese Hamster Ovary cells, a cultured mammalian cell line. Furthermore, only three rounds of high-throughput optical selection and competitive growth are required to yield fit cells with greatly improved transparency. Based on 15 monoclonal cell lines derived from this directed evolution experiment, we find that the evolved transparency frequently goes along with a reduction of nuclear granularity and physiological shifts in gene expression profiles. In the future this optical plasticity of mammalian cells may facilitate genetic clearance of living tissues for in vivo microscopy.

Dealing with noise in crowdsourced GPS human trajectory logging data

SummaryAs a crowdsourcing map platform, OpenStreetMap (OSM) relies on public contributions to enhance its dataset where the contributors can create, modify or remove features from the maps or share their trajectory trips in the repository. The majority of the data provided in a crowdsourcing platform are manually created and reviewed to suit real‐world conditions, hence human perception is the key indicator to consider the correctness of the data. One of the data that is provided by crowdsourcing platform is public trajectory. Public trajectory data contains details of historical trips obtained from contributors' GPS logger devices that are embedded in mobile devices, wearable devices, satnavs, or vehicle GPS trackers to record the user's trajectory path. While public trajectory data can be used as an alternate data source for human movement analysis, this crowdsourced dataset is also prone to noise and inaccuracy which makes the preprocessing step an important phase prior of any processing step. In this article, we discuss the characteristics and the most common noise from crowdsourcing GPS trajectories and utilize a non‐map‐matching approach convex hull‐based reduction method to minimize spike noise, followed by granularity reduction to reduce the number of trajectory points while maintaining the nature of the trajectories.

Granular matrix method of attribute reduction in formal contexts

Granular reduction has been of great interests for formal context analysis. From the perspective of granular computing, granular matrices are proposed to represent the extensions and intensions of formal concepts. Within this framework, irreducible elements are studied. Furthermore, similarity degree, information granular and information entropy are developed to specify the significance of attribute. In this case, heuristic approaches for granular reduct are proposed. Finally, several data sets are experimented to demonstrate the feasibility and efficiency of our method. Our methods present a new framework for granular reduction in formal concept analysis.

Granular Matrix: A New Approach for Granular Structure Reduction and Redundancy Evaluation

Granular structure is a mathematical expression of knowledge in granular computing and a direct determinant of the data processing efficiency. To improve the efficiency of data processing, many scholars have studied the reduction of granular structure. The attribute reduction and the granular reduction are two types of reduction on different layers of a granular structure, with the latter being both an essential step for granular structure reduction and the foundation of the attribute reduction. Yet compared with the attribute reduction, the granular reduction has received less attention from scholars. Therefore, a fuzzy granular reduction theory and a granular matrix based on the fuzzy $\beta$ -coverings is proposed in this article. The insufficiency of the existing granular reduction theory for fuzzy $\beta$ -coverings is pointed out, and proper sufficient and necessary conditions for two fuzzy $\beta$ -coverings generating the same upper and lower approximations are also given in this article. In addition, to reduce and evaluate a fuzzy $\beta$ -covering, a novel reduction algorithm based on a granular matrix is proposed for the first time. Also, since fuzzy covering reduction is NP-hard, a heuristic greedy algorithm is designed to obtain a reduct. Numerical experiments show that the redundancy rates of neighborhood granule sets induced by some big-scale data sets exceed 99 $\%$ , which indicates that the existing neighborhood granulation methods need to be urgently improved. Based on this, concise granular structures and much more efficient feature selection algorithms can be proposed in the future.

Prediction of Shortening and Material Grain Size after Extrusion Using the ECAP Method

Equal channel angular pressing (ECAP) is a widespread severe plastic deformation (SPD) method to fabricate ultrafine-grained bulk materials. In the field of materials engineering, this method has already experienced rapid development over the past few decades. In this research, the authors sought to create a prediction of shortening and the material particle size after extrusion using ECAP. Behaviours of essential functions are analysed here on samples of pure copper Cu 99.9. It is the measurement and analytical processing of changes in the values of selected structural and mechanical parameters depending on the reduction of the structural granularity. Parameters such as deformation speed, deformation work and ECAP mechanical performance are also included in the results. The change in structure and mechanical parameters is also newly demonstrated by measuring the change in the velocity of the longitudinal ultrasound wave during the experimental passes. Based on the results obtained, new computational algorithms for Excel and Matlab were developed. The algorithms developed here contain many new findings, conclusions and derivations addressing the integrity of the surface with the inner structure of materials. Algorithms are very well-suited for obtaining results on different materials quickly, for predicting and checking measured values, and for designing optimal measurement technology parameters for the ECAP method.

A Boolean matrix approach for granular reduction in formal fuzzy contexts

A Boolean matrix approach for granular reduction in formal fuzzy contexts

Granular matrix-based knowledge reductions of formal fuzzy contexts

Knowledge reduction is an important issue in formal fuzzy contexts, which can simplify the structure of concept lattices. In this paper, a novel granular matrix-based for knowledge reduction of crisp-fuzzy concept is investigated. Firstly, matrix representations of extents and intents of concepts are defined, respectively, which are used to characterize the join-irreducible elements and propose the corresponding algorithm. In this framework, granular consistent set and granular reduct are developed. Then the judgement theorem of reduction and its corresponding algorithm in formal fuzzy context are proposed. Furthermore, we generalize the matrix approach to formal fuzzy decision contexts. Finally, numerical experiments are conducted to evaluate the effectiveness of the proposed approaches. Our methods present a new framework for knowledge reduction in formal fuzzy contexts.

In vitro effects of the asymmetric peptidomimetic 157, containing l-tartaric acid core and valine/leucine substituents, on Leishmania amazonensis promastigotes and amastigotes

The current treatments for leishmaniasis bump into several obstacles, including low efficacy, high costs, long monitoring, and several/severe side effects. Consequently, the search for promising compounds is a tangible need. Recently, we reported the anti-Leishmania amazonensis action of asymmetric peptidomimetic compounds containing tartaric acid as core, especially the 157 derivative that contains valine/leucine substituents in its structure. Herein, we decipher the multiple effects of 157 on the L. amazonensis physiology and on the interaction process with macrophages. The peptidomimetic 157 induced significant changes on the morphometric (internal granularity reduction as judged by flow cytometer) and on the ultrastructural (round-shaped parasites, presence of plasma membrane blebs and flagellum loss as visualized by scanning electron microscopy) aspects of treated promastigotes compared to untreated ones. The alteration on the plasma membrane permeability was confirmed by the passive incorporation of propidium iodide in 157-treated promastigotes. In parallel, the low viability of promastigotes was also associated to the perturbation of mitochondrial transmembrane electric potential. These combined results demonstrated that 157 induced irreversible metabolic damages that led to L. amazonensis death. The pre-treatment of promastigotes with 157 inhibited the association index with macrophages in a typically dose-dependent manner. Additionally, 157 significantly reduced the number of intramacrophage amastigotes after 72 h of drug contact, presenting an IC50 value of 30.2 μM. Under our experimental conditions, 157 showed higher toxicity to promastigotes and amastigotes when compared to RAW cells, resulting in good selective indexes. Therefore, 157 can be considered as an interesting candidate for further optimization, since its synthesis is simple and cheap.

Novel Three-Way Decisions Models with Multi-Granulation Rough Intuitionistic Fuzzy Sets

The existing construction methods of granularity importance degree only consider the direct influence of single granularity on decision-making; however, they ignore the joint impact from other granularities when carrying out granularity selection. In this regard, we have the following improvements. First of all, we define a more reasonable granularity importance degree calculating method among multiple granularities to deal with the above problem and give a granularity reduction algorithm based on this method. Besides, this paper combines the reduction sets of optimistic and pessimistic multi-granulation rough sets with intuitionistic fuzzy sets, respectively, and their related properties are shown synchronously. Based on this, to further reduce the redundant objects in each granularity of reduction sets, four novel kinds of three-way decisions models with multi-granulation rough intuitionistic fuzzy sets are developed. Moreover, a series of concrete examples can demonstrate that these joint models not only can remove the redundant objects inside each granularity of the reduction sets, but also can generate much suitable granularity selection results using the designed comprehensive score function and comprehensive accuracy function of granularities.

Granularity reduction method based on positive decision holding for multi‐granulation decision‐theoretic rough set

Multi-granulation decision-theoretic rough set (MG-DTRS) is a generalisation of the multi-granulation rough set (MGRS) model through integrating the related properties of MGRSs and decision-theoretic rough sets (DTRSs), and it can realise decision analysis and processing of information systems from multiple perspectives and multi-level. Taking the optimistic MG-DTRS and the pessimistic MG-DTRS model as an example, MG-DTRS model and its related properties were discussed according to the DTRSs which construct positive, boundary and negative regions on the basis of two thresholds given by experts. Especially, the positive decision and its properties in MG-DTRS model were analysed. At the same time, it was found and proved that the positive decision is monotonic with the change of granularity in MG-DTRS model. Further, the granular structure selection problem was investigated under the MG-DTRS model, the concept of α-decision granularity importance was introduced and a granularity reduction algorithm based on the positive decision holding for MG-DTRS model was designed and constructed. Finally, an example was given to prove that the algorithm is effective, and it has a small time complexity.

A graph approach for knowledge reduction in formal contexts

Knowledge reduction in formal concept analysis (FCA) is an important procedure for knowledge processing and data analysis. Currently, the knowledge reduction in FCA based on granular computing (GrC) provides another way to analyze and represent the structure of concept lattices. However, the granular reduction method is based on Boolean reasoning and thus is an NP-hard problem. It is therefore natural to develop some heuristic methods to deal with this problem especially for the large data. In this paper, a new framework based on graph theory is used to study the granular reduction in FCA. A graph representation for the granular reduction is first investigated. The results in this paper show that the granular reduction computation in FCA can be translated into a graph optimization problem. Two new heuristic graph-based algorithms for the granular reduction in formal contexts and formal decision contexts are then respectively presented. Furthermore, numerical experiments are conducted to evaluate the effectiveness of the proposed methods.

Object granular reduction of fuzzy formal contexts

Object granular reduction of fuzzy formal contexts

Attribute reduction of relative knowledge granularity in intuitionistic fuzzy ordered decision table

For the moment, the attribute reduction algorithm of relative knowledge granularity is very important research areas. It provides a new viewpoint to simplify feature set. Based on the decision information is unchanged, fast and accurate deletion of redundant attributes, which is the meaning of attribute reduction. Distinguishing ability of attribute sets can be well described by relative knowledge granularity in domain. Therefore, how to use the information based on relative knowledge granularity to simplify the calculation of attribute reduction. It is an important direction of research. For increasing productiveness and accuracy of attribute reduction, in this paper we investigate attribute reduction method of relative knowledge granularity in intuitionistic fuzzy ordered decision table(IFODT). More precisely, we redefine the granularity of knowledge and the relative knowledge granularity by ordered relation. And their relevant properties are proved. On the premise that the decision results remain unchanged, in order to accurately calculate the relative importance of any condition attributes about the decision attribute sets, the conditional attribute of internal and external significance are designed by relative knowledge granularity. And some important properties of relative attribute significance are proved. Therefore, we determine the importance of conditional attributes based on the size of the relative attribute significance. In the aspect of computation, the corresponding algorithm is designed and time complexity of algorithm is calculated. Moreover, the attribute reduction model of relative knowledge granularity of efficiency and accuracy is proved by test. Last, the validity of algorithm is demonstrated by an case about IFODT.

Redefining the effect of salt on thermophilic starter cell viability, culturability and metabolic activity in cheese

This study investigated the differential effect of salt concentration in the outside and inside layers of brine salted cheeses on viability, culturability and enzyme activity of starter bacteria. The high-salt environment of the outside layer caused a sharp decrease in L. helveticus viability as measured by traditional plate counts. Remarkably, this was associated with lower release of intracellular enzymes (LDH), reduced levels of proteolysis and larger membrane integrity as measured by flow cytometry (FC) following classical Live/Dead staining. FC analysis of light scattering properties highlighted a significant reduction in size and granularity of the microbiota located in the cheese surface, suggestive of cell shrinkage and condensation of internal macromolecules probably due to hyperosmotic stress. The microbiota of the cheese surface were found to experience greater oxidative stress, as measured by FC analysis of the total levels of reactive oxygen species, compared to that of the interior layer. These results lead us to postulate that the physiology and health status of the microbiota were significantly different in the outer and inner layers of the cheese. The hyperosmotic environment of the outer layer resulted in reduced cell lysis, as measurable by assays based upon membrane integrity, but rather triggered cell death via mechanisms involving cell shrinkage and ROS-mediated damage of vital intracellular components. This study challenges the current thinking on how salt controls microbial activity in ripening cheese, especially in cheeses which are brine salted as local variations in biochemical ripening indices can differ significantly from the outside to the inside of a ripening cheese.

Granular reducts of formal fuzzy contexts

Knowledge reduction is one of the key issues in knowledge discovery and data mining. During the construction of a concept lattice, it has been recognized that computational complexity is a major obstacle in deriving all the concept from a database. In order to improve the computational efficiency, it is necessary to preprocess the database and reduce its size as much as possible. Focusing on formal fuzzy contexts, we introduce in the paper the notions of granular consistent sets and granular reducts and propose granular reduct methods in the sense of reducing the attributes. With the proposed approaches, the attributes that are not essential to all the object concepts can be removed without loss of knowledge and, consequently, the computational complexity of constructing the concept lattice is reduced. Furthermore, the relationship between the granular reducts and the classification reducts in a formal fuzzy context is investigated.

Reversal of phenotypes of cellular senescence by pan-mTOR inhibition.

Cellular senescence, a state of essentially irreversible proliferation arrest, serves as a potent tumour suppressor mechanism. However, accumulation of senescent cells with chronological age is likely to contribute to loss of tissue and organ function and organismal aging. A crucial biochemical modulator of aging is mTOR; here, we have addressed the question of whether acute mTORC inhibition in near-senescent cells can modify phenotypes of senescence. We show that acute short term treatment of human skin fibroblasts with low dose ATP mimetic pan-mTORC inhibitor AZD8055 leads to reversal of many phenotypes that develop as cells near replicative senescence, including reduction in cell size and granularity, loss of SA-β-gal staining and reacquisition of fibroblastic spindle morphology. AZD8055 treatment also induced rearrangement of the actin cytoskeleton, providing a possible mechanism of action for the observed rejuvenation. Importantly, short-term drug exposure had no detrimental effects on cell proliferation control across the life-course of the fibroblasts. Our findings suggest that combined inhibition of both mTORC1 and mTORC2 may provide a promising strategy to reverse the development of senescence-associated features in near-senescent cells.

Tools for multiple granularity analysis of brain MRI data for individualized image analysis

Voxel-based analysis is widely used for quantitative analysis of brain MRI. While this type of analysis provides the highest granularity level of spatial information (i.e., each voxel), the sheer number of voxels and noisy information from each voxel often lead to low sensitivity for detection of abnormalities. To ameliorate this issue, granularity reduction is commonly performed by applying isotropic spatial filtering. This study proposes a systematic reduction of the spatial information using ontology-based hierarchical structural relationships. The 254 brain structures were first defined in multiple (n=29) geriatric atlases. The multiple atlases were then applied to T1-weighted MR images of each subject's data for automated brain parcellation and five levels of ontological relationships were established, which further reduced the spatial dimension to as few as 11 structures. At each ontology level, the amount of atrophy was evaluated, providing a unique view of low-granularity analysis. This reduction of spatial information allowed us to investigate the anatomical features of each patient, demonstrated in an Alzheimer's disease group.

Research on the <i>N</i>-Reduction of Fuzzy Covering Rough Sets

The granular reduction plays an important role in covering rough sets, and the aim of this paper is to explore the granular reduction of covering rough sets and fuzzy covering rough set Firstly, the covering rough sets based on neighborhood element and the N-reduction are introduced, and their properties are discussed; secondly, The definitions and properties of upper and lower approximation of fuzzy covering rough based on the neighborhood are given; Lastly, the N- reduction of fuzzy covering rough based on the neighborhood element is proposed, and we can get that covering rough sets remain the same upper and lower approximations for N-reduction.