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The physical modification of starch to produce resistant starch (RS) is a viable strategy for the glycemic index (GI) lowering of foods and functionality improvement in starchy food products. RS cannot be digested in the small intestine but can be fermented in the colon to produce short-chain fatty acids rather than being broken down by human digestive enzymes into glucose. This provides major health advantages, like better blood sugar regulation, weight control, and a lower chance of chronic illnesses. This article provides a concise review of the recent developments in physical starch modification techniques, including annealing, extrusion, high-pressure processing, radiation, and heat–moisture treatment. Specifically, the focus of this paper is on the alteration of the crystalline structure of starch caused by the heat–moisture treatment and annealing and its impact on the resistance of starch to enzymatic hydrolysis, as well as the granular structure and molecular arrangement of starch caused by extrusion and high-pressure processing, and the depolymerization and crosslinking that results from radiation. The impacts of these alterations on starch's textural qualities, stability, and shelf life are also examined. This review demonstrates how physically modified resistant starch can be used as a flexible food ingredient with both functional and health benefits. These methods are economically and ecologically sustainable since they successfully raise the RS content and improve its functional characteristics without the need for chemical reagents. The thorough analysis of these methods and how they affect the structural characteristics and health advantages of RS emphasizes the material's potential as an essential component in the creation of functional foods that satisfy contemporary dietary and health requirements. [ABSTRACT FROM AUTHOR]

Abstract The dimension and size of data is growing rapidly with the extensive applications of computer science and lab based engineering in daily life. Due to availability of vagueness, later uncertainty, redundancy, irrelevancy, and noise, which imposes concerns in building effective learning models. Fuzzy rough set and its extensions have been applied to deal with these issues by various data reduction approaches. However, construction of a model that can cope with all these issues simultaneously is always a challenging task. None of the studies till date has addressed all these issues simultaneously. This paper investigates a method based on the notions of intuitionistic fuzzy (IF) and rough sets to avoid these obstacles simultaneously by putting forward an interesting data reduction technique. To accomplish this task, firstly, a novel IF similarity relation is addressed. Secondly, we establish an IF rough set model on the basis of this similarity relation. Thirdly, an IF granular structure is presented by using the established similarity relation and the lower approximation. Next, the mathematical theorems are used to validate the proposed notions. Then, the importance-degree of the IF granules is employed for redundant size elimination. Further, significance-degree-preserved dimensionality reduction is discussed. Hence, simultaneous instance and feature selection for large volume of high-dimensional datasets can be performed to eliminate redundancy and irrelevancy in both dimension and size, where vagueness and later uncertainty are handled with rough and IF sets respectively, whilst noise is tackled with IF granular structure. Thereafter, a comprehensive experiment is carried out over the benchmark datasets to demonstrate the effectiveness of simultaneous feature and data point selection methods. Finally, our proposed methodology aided framework is discussed to enhance the regression performance for IC50 of Antiviral Peptides.

Abstract Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been effectively and efficiently applied for feature selection to handle the uncertainty in real-valued datasets. Further, Fuzzy rough mutual information has been presented by integrating information entropy with fuzzy rough set to measure the importance of features. However, none of the methods till date can handle noise, uncertainty and vagueness simultaneously due to both judgement and identification, which lead to degrade the overall performances of the learning algorithms with the increment in the number of mixed valued conditional features. In the current study, these issues are tackled by presenting a novel intuitionistic fuzzy (IF) assisted mutual information concept along with IF granular structure. Initially, a hybrid IF similarity relation is introduced. Based on this relation, an IF granular structure is introduced. Then, IF rough conditional and joint entropies are established. Further, mutual information based on these concepts are discussed. Next, mathematical theorems are proved to demonstrate the validity of the given notions. Thereafter, significance of the features subset is computed by using this mutual information, and corresponding feature selection is suggested to delete the irrelevant and redundant features. The current approach effectively handles noise and subsequent uncertainty in both nominal and mixed data (including both nominal and category variables). Moreover, comprehensive experimental performances are evaluated on real-valued benchmark datasets to demonstrate the practical validation and effectiveness of the addressed technique. Finally, an application of the proposed method is exhibited to improve the prediction of phospholipidosis positive molecules. RF(h2o) produces the most effective results till date based on our proposed methodology with sensitivity, accuracy, specificity, MCC, and AUC of 86.7%, 90.1%, 93.0% , 0.808, and 0.922 respectively.

Fuzzy rough entropy established in the notion of fuzzy rough set theory, which has been effectively and efficiently applied for feature selection to handle the uncertainty in real-valued datasets. Further, Fuzzy rough mutual information has been presented by integrating information entropy with fuzzy rough set to measure the importance of features. However, none of the methods till date can handle noise, uncertainty and vagueness simultaneously due to both judgement and identification, which lead to degrade the overall performances of the learning algorithms with the increment in the number of mixed valued conditional features. In the current study, these issues are tackled by presenting a novel intuitionistic fuzzy (IF) assisted mutual information concept along with IF granular structure. Initially, a hybrid IF similarity relation is introduced. Based on this relation, an IF granular structure is introduced. Then, IF rough conditional and joint entropies are established. Further, mutual information based on these concepts are discussed. Next, mathematical theorems are proved to demonstrate the validity of the given notions. Thereafter, significance of the features subset is computed by using this mutual information, and corresponding feature selection is suggested to delete the irrelevant and redundant features. The current approach effectively handles noise and subsequent uncertainty in both nominal and mixed data (including both nominal and category variables). Moreover, comprehensive experimental performances are evaluated on real-valued benchmark datasets to demonstrate the practical validation and effectiveness of the addressed technique. Finally, an application of the proposed method is exhibited to improve the prediction of phospholipidosis positive molecules. RF(h2o) produces the most effective results till date based on our proposed methodology with sensitivity, accuracy, specificity, MCC, and AUC of 86.7%, 90.1%, 93.0% , 0.808, and 0.922 respectively. [ABSTRACT FROM AUTHOR]

The physical modification of starch to produce resistant starch (RS) is a viable strategy for the glycemic index (GI) lowering of foods and functionality improvement in starchy food products. RS cannot be digested in the small intestine but can be fermented in the colon to produce short-chain fatty acids rather than being broken down by human digestive enzymes into glucose. This provides major health advantages, like better blood sugar regulation, weight control, and a lower chance of chronic illnesses. This article provides a concise review of the recent developments in physical starch modification techniques, including annealing, extrusion, high-pressure processing, radiation, and heat–moisture treatment. Specifically, the focus of this paper is on the alteration of the crystalline structure of starch caused by the heat–moisture treatment and annealing and its impact on the resistance of starch to enzymatic hydrolysis, as well as the granular structure and molecular arrangement of starch caused by extrusion and high-pressure processing, and the depolymerization and crosslinking that results from radiation. The impacts of these alterations on starch’s textural qualities, stability, and shelf life are also examined. This review demonstrates how physically modified resistant starch can be used as a flexible food ingredient with both functional and health benefits. These methods are economically and ecologically sustainable since they successfully raise the RS content and improve its functional characteristics without the need for chemical reagents. The thorough analysis of these methods and how they affect the structural characteristics and health advantages of RS emphasizes the material’s potential as an essential component in the creation of functional foods that satisfy contemporary dietary and health requirements.

A single-stage centrifugation method is used for the precipitation and crystallization of a solution of a hybrid halide perovskite compound of the CH3NH3PbI3 composition. The perovskite films are annealed in the temperature range of 80–140°C, during which the excess of the N-methylpyrrolidone solvent was removed by evaporation. The X-ray phase analysis of the synthesized perovskite layer is carried out. The morphology of the surface of the layers after crystallization and the transmission spectra in the optical range are studied. The experiments and research results showed that the optimal temperature regime for the formation and crystallization of lead triiodide methylammonium perovskite films is 100–110°C. The perovskite layers obtained in these processing regimes have a surface morphology with a uniform granular crystal structure and are highly uniform. Moreover, in solar cells based on perovskite-like CH3NH3PbI3 structures with an annealing temperature of 100–110°C, the short-circuit currents reached 16.0 mA/cm2. At the same time, at annealing temperatures of perovskite layers above 120°С, the maximum value of the short-circuit currents did not exceed 14.0 mA/cm2. [ABSTRACT FROM AUTHOR]

The decision tree is one of the most important and representative classification algorithms in the field of machine learning, and it is an important technique for solving data mining classification tasks. In this paper, a decision tree classification algorithm based on granular matrices is proposed on the basis of granular computing theory. Firstly, the bit-multiplication and bit-sum operations of granular matrices are defined. The logical operations between granules are replaced by simple multiplication and addition operations, which reduces the operation time. Secondly, the similarity between granules is defined, the similarity metric matrix of the granular space is constructed, the classification actions are extracted from the similarity metric matrix, and the classification accuracy is defined by weighting the classification actions with the probability distribution of the granular space. Finally, the classification accuracy of the conditional attribute is used to select the splitting attributes of the decision tree as the nodes to form forks in the tree, and the similarity between granules is used to judge whether the data types in the sub-datasets are consistent to form the leaf nodes. The feasibility of the algorithm is demonstrated by means of case studies. The results of tests conducted on six UCI public datasets show that the algorithm has higher classification accuracy and better classification performance than the ID3 and C4.5. [ABSTRACT FROM AUTHOR]

Uncertainty measures are instrumental in describing the classification abilities in information systems, and uncertain information has been measured and processed with granular computing theory. While the fuzzy set-valued information system is a generalization of fuzzy information systems, the relationship between the information granulation and the uncertainty in fuzzy set-valued information systems remains to be studied. This paper probes into uncertainty measures in fuzzy set-valued information systems based on the fuzzy β -neighborhood and the idea of granulation. Specifically, the fuzzy β -neighborhood similarity relation that reflects the similarity between two objects is defined in terms of the nearness degree. We propose the concepts of information granules and granular structures induced by fuzzy β -neighborhood similarity relations, based on which we introduce the granularity measures and rough approximation measures of granular structures in fuzzy set-valued information systems. Given the situation of decision information systems, we propose the granularity-based rough approximation measures by combining granularity measures with rough approximation measures. Experiment results and effectiveness analysis show that the measures we proposed are reasonable and feasible. [ABSTRACT FROM AUTHOR]

This study aimed to explore the relationship between the granular structure of foxtail millet starch and the palatability of foxtail millet congee. The palatability and starch granular characteristics of congee from 10 cultivars of foxtail millet were analyzed and the effect of the latter on the former was evaluated by correlation analysis. A predictive model for the palatability of foxtail millet congee was proposed by stepwise regression analysis. The results showed that small granules accounted for a high percentage of foxtail millet starch together with a low percentage of tiny granules, and the Barrett-Joyner-Halenda (BJH) pore volume of foxtail millet starch granules was small, resulting in low gelatinization conclusion temperature (Tc) and fast gelatinization rate and finally good palatability of congee. The amylose content, gel hardness and gelatinization temperature range of foxtail millet starch were significantly negatively correlated with the sensory score of foxtail millet congee (P < 0.01), while the gel viscosity, elasticity, cohesiveness, gumminess, chewiness, resilience and initial gelatinization temperature were all significantly positively correlated with the sensory score for palatability of foxtail millet congee (P < 0.05, P < 0.01). The granular structure of foxtail millet starch indirectly affected the palatability of foxtail millet congee by affecting the key physicochemical properties. There was a significantly negative correlation between the proportion of small starch granules and Tc (P < 0.01) and gelatinization temperature range (P < 0.05). The proportion of medium starch granules had a significantly positive correlation with Tc (P < 0.01), and the proportion of large starch granules had a significantly positive correlation with the water-binding capacity of starch granules (P < 0.01). Finally, the predictive model for the palatability of foxtail millet porridge was developed using the gel cohesiveness, amylose content, proportion of tiny granules, gelatinization temperature range and Brunauer-Emmett-Teller (SBET) of foxtail millet starch as independent variables, and the correlation coefficient between the predictive score and the sensory score was 0.998 (P < 0.01). The above results can provide a theoretical basis for the selection of foxtail millet suitable for cooking and processing in China.

A sputtered FePt(BN, Re, C) film, here boron nitride (BN), was compared to a reference sample FePt(BN, Ag, C). Intrinsically, these films illustrate a high anisotropy field (Hk) and perpendicular magnetocrystalline anisotropy (Ku),although the reference sample shows a higher value (Hk = 69.5 kOe, Ku = 1.74 × 107 erg/cm3) than the FePt(BN, Re, C) film (Hk = 66.9 kOe, Ku = 1.46 × 107 erg/cm3). However, the small difference in the anisotropy constant (K2/K1) ratio presents a close tendency in the angular dependence of the switching field. Extrinsically, the out-of-plane coercivity for the reference sample is 32 kOe, which is also higher than the FePt(BN, Re, C) film (Hc = 27 kOe), and both films present lower remanence (Mr(parallel)/Mr(perpendicular) = 0.08~0.12), that is, the index for perpendicular magnetic anisotropy. The higher perpendicular magnetization for both films was due to highly (001) textured FePt films, which was also evidenced by the tight rocking width of 4.1°/3.0° for (001)/(002) X-ray diffraction peaks, respectively, and high-enough ordering degree. The reference sample was measured to have a higher ordering degree (S = 0.84) than FePt(BN, Re, C) (S = 0.63). As a result, the Ag segregant shows stronger ability to promote the ordering of the FePt film; however, the FePt(BN, Re, C) film still has comparable magnetic properties without Ag doping. From the surface and elemental composition analysis, the metallic Re atoms found in the FePt lattice result in a strong spin–orbital coupling between transition metal Fe (3d electron) and heavy metals (Re, Pt) (5d electron) and we conducted high magnetocrystalline anisotropy (Ku). Above is the explanation that the lower-ordered FePt(BN, Re, C) film still has high-enough Ku and out-of-plane Hc. Regarding the microstructure, both the reference sample and FePt(BN, Re, C) show granular structure and columnar grains, and the respective average grain size and distributions are 6.60 nm (12.5%) and 11.2 nm (15.9%). The average widths of the grain boundaries and the aspect ratio of the columnar grain height are 2.05 nm, 1.00 nm, 2.35 nm, and 1.70 nm, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

TAP is a general mRNA export receptor and is highly conserved among eukaryotes. The nematode Caenorhabditis elegans has another TAP‐like protein, NXF‐2, but little is known about its function. In this study, we show that NXF‐2 is specifically expressed in germ cells and forms a novel granular structure that is different from that of P granules and that NXF‐2 granules are anchored to the nuclear periphery in the mitotic region of the hermaphrodite gonad. In contrast, NXF‐2 granules are released within the whole cytoplasm in the meiotic region, where the feminization gene tra‐2 starts to function. Both inhibition of XPO‐1 (an ortholog of the export receptor CRM1) and mutation of the nuclear export signal of NXF‐2 caused the release of NXF‐2 granules from the nuclear periphery, indicating that anchoring of NXF‐2 granules depends on XPO‐1 function. Moreover, inhibition of NXF‐2 resulted in a substantial nuclear accumulation of the reporter mRNA carrying the tra‐2 3′UTR. These results suggest that, together with XPO‐1, NXF‐2 exports and anchors tra‐2 mRNA to the nuclear periphery to avoid precocious translation until the germ cells reach the meiotic region, thereby contributing to the regulation of tra‐2 mRNA expression. [ABSTRACT FROM AUTHOR]

Quinoa starch granular structure as affected by nonenyl succinic anhydride (NSA) substitution was investigated by multiple approaches, including scattering, spectroscopic, and microscopic techniques. The modification had little impact on the morphology of starch granules. The NSA substitution was found mainly in the amorphous lamellae and amorphous growth rings. The NSA modification increased the thickness of the amorphous lamellae. The homogeneity of the ordered structure in the granules was improved, probably because the NSA modification reduced the amount of defects in the semi-crystalline growth ring. Compared to other chemical modifications such as acylation, succinylation was more effective in modifying the starch lamellar structure. A possible reaction pattern of NSA modification on quinoa starch is proposed, in which the NSA modification may follow the sequence of amorphous growth rings, the amorphous matrices among blocklets, amorphous and crystalline lamellae in semi-crystalline growth rings. This study provides new insights on the structural changes of starch granules induced by succinylation on the supramolecular level. • NSA modification changed granular and supramolecular structure of quinoa starch. • NSA substitution affected packing of double helices in the crystallites of granules. • NSA modification at high DS increased the value of d a but decreased that of d c. • NSA substitution took place in regions with loose structure and defects. • A possible reaction mechanism of NSA substitution on quinoa starch is proposed. [ABSTRACT FROM AUTHOR]

Cabbage core (CC) is regarded as a waste part of the vegetable, despite being edible and containing various nutritional and functional compounds. We investigated the properties of CC powder with particle sizes < 1 mm as a new food material. CC powder was more resistant to structural deformation than leaf-derived powder, particularly CC powder with particles = 0.3 mm in size. To examine the application of CC powder in 3D printed foods, we investigated the effects of "nata puree," a disintegrated nata de coco made with tamarind seed gum (NPTG), on paste made with CC powder. NPTG promoted stable binding of paste made using CC powder, which was successfully extruded using a syringe to form a bar with a granular structure. Thus, CC powder possesses unique textural/structural properties for its application in next-generation foods. [ABSTRACT FROM AUTHOR]

As an extension of traditional information systems, interval-set information systems have a strong expressive ability to describe uncertain information. Study of the rough set theory and the attribute reduction of interval-set information system are worth discussing. Here, the granularity structure of similar equivalence classes in an interval-set information system is mined, and an attribute reduction algorithm is constructed. The upper and lower approximation operators in the interval-set information system are defined. The accuracy and roughness are determined by these operators. At the same time, using rough sets, a concept of three branches of rough sets on the interval-set information system is constructed. The concepts of attribute dependency and attribute importance are induced by the positive number domain of the three branch domains, and they then lead to the attribute reduction algorithm. Experiments on the UCI datasets show that the uncertainty measure proposed in this paper is sensitive to the attributes and can effectively reduce redundant information of the interval-set information system. [ABSTRACT FROM AUTHOR]

• Frequency dependence of elastic modulus of sea ice in bending. • Dependence of elastic modulus of sea ice in bending from ice structure. • In-situ tests with floating vibrating fixed ends beams of sea ice. • Laboratory test with free-free vibrating beams of sea ice. Thirteen laboratory and field tests were carried out with vibrating sea ice beams to study the dependence of the elastic modulus of sea ice in the spectral range from 1 Hz to 500 Hz. Six full-scale tests with floating fixed ends beams were carried out on the land fast ice of the Spitsbergen fjords. For laboratory testing, smaller ice beams were made sea ice of the same fjords. Three tests with columnar fresh lake ice S2 were conducted to validate the method for calculating of the added mass of a floating beam with fixed ends. A 60% increase in the elastic modulus of columnar sea ice S2 was found due to an increase in the frequency of flexural deformations in the range from 10 Hz to 500 Hz. The paper also discusses the influence of ice structure on the elastic modulus. [ABSTRACT FROM AUTHOR]

Binary information table, multi-valued information table and set-valued information table are three kinds of information systems often encountered in information processing. For any information system, we can often induce different information granular structures, and then construct the corresponding rough set models. Generally speaking, for the same information system, three models of Pawlak rough set, covering rough set and multi-granulation rough set can be induced according to different rules. These three kinds of rough set models are effective tools for data mining and information processing. This paper studies the relationship among Pawlak rough set, covering rough set and multi-granularity rough set induced in binary information table, multi-valued information table and set-valued information table, and obtains many important conclusions. The research content of this paper effectively connects the theories, methods and applications of Pawlak rough set, covering rough set and multi-granularity rough set, which not only enriches the rough set theory, but also expands the application prospect of rough set. [ABSTRACT FROM AUTHOR]

The superparamagnetic thin films with a granular structure show a characteristic magnetization curve. In this study, Co-Al2O3 granular thin films were fabricated by the sputtering method with normal DC source for Co deposition, RF source for Al2O3 deposition, and pulsed DC reactive sputtering were used for fabrication of non-magnetic matrix of oxide or nitride of Al or Si. As a result, Co-Al2O3 superparamagnetic thin films with high magnetization could be obtained with high deposition rate. Very high deposition rate was obtained for fabrication of oxide or nitride films of Al or Si using the pulsed DC reactive sputtering method. This deposition rate was 5 times larger than RF sputtering method. It is expected that a sputtering method with DC source for Co deposition and pulsed DC source for matrix deposition will be effective to realize very high magnetization in the superparamagnetic granular thin films. [ABSTRACT FROM AUTHOR]

Copyright of Food Research & Development is the property of Food Research & Development Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A granular structure of intuitionistic fuzzy (IF) information presents simultaneously the similarity and diversity of samples. However, this structural representation has rarely displayed its technical capability in data mining and information processing due to the lack of suitable constructive methods and semantic interpretation for IF information with regard to real data. To pursue better performance of the IF-based technique in real-world data mining, in this article, we examine information granularity, information entropy of IF granular structures, and their applications to data reduction of IF information systems. First, several types of partial-order relations at different hierarchical levels are defined to reveal the granularity of IF granular structures. Second, the granularity invariance between different IF granular structures is characterized by using relational mappings. Third, Shannon’s entropies are generalized to IF entropies and their relationships with the partial-order relations are addressed. Based on the theoretical analysis above, the significance of intuitionistic attributes using the information measures is then introduced and the information-preserving algorithm for data reduction of IF information systems is constructed. Finally, by inducing substantial IF relations from public datasets that take both the similarity/diversity between the samples from the same/different classes into account, a collection of numerical experiments is conducted to confirm the performance of the proposed technique. [ABSTRACT FROM AUTHOR]

A sputtered FePt(BN, Re, C) film, here boron nitride (BN), was compared to a reference sample FePt(BN, Ag, C). Intrinsically, these films illustrate a high anisotropy field (Hk) and perpendicular magnetocrystalline anisotropy (Ku),although the reference sample shows a higher value (Hk = 69.5 kOe, Ku = 1.74 × 107 erg/cm3) than the FePt(BN, Re, C) film (Hk = 66.9 kOe, Ku = 1.46 × 107 erg/cm3). However, the small difference in the anisotropy constant (K2/K1) ratio presents a close tendency in the angular dependence of the switching field. Extrinsically, the out-of-plane coercivity for the reference sample is 32 kOe, which is also higher than the FePt(BN, Re, C) film (Hc = 27 kOe), and both films present lower remanence (Mr(parallel)/Mr(perpendicular) = 0.08~0.12), that is, the index for perpendicular magnetic anisotropy. The higher perpendicular magnetization for both films was due to highly (001) textured FePt films, which was also evidenced by the tight rocking width of 4.1°/3.0° for (001)/(002) X-ray diffraction peaks, respectively, and high-enough ordering degree. The reference sample was measured to have a higher ordering degree (S = 0.84) than FePt(BN, Re, C) (S = 0.63). As a result, the Ag segregant shows stronger ability to promote the ordering of the FePt film; however, the FePt(BN, Re, C) film still has comparable magnetic properties without Ag doping. From the surface and elemental composition analysis, the metallic Re atoms found in the FePt lattice result in a strong spin–orbital coupling between transition metal Fe (3d electron) and heavy metals (Re, Pt) (5d electron) and we conducted high magnetocrystalline anisotropy (Ku). Above is the explanation that the lower-ordered FePt(BN, Re, C) film still has high-enough Ku and out-of-plane Hc. Regarding the microstructure, both the reference sample and FePt(BN, Re, C) show granular structure and columnar grains, and the respective average grain size and distributions are 6.60 nm (12.5%) and 11.2 nm (15.9%). The average widths of the grain boundaries and the aspect ratio of the columnar grain height are 2.05 nm, 1.00 nm, 2.35 nm, and 1.70 nm, respectively.

In multigranulation environments, variable precision multigranulation rough set (VPMGRS) is a useful framework that has a tolerance for errors. Approximations are basic concepts for knowledge acquisition and attribute reductions. Accelerating update of approximations can enhance the efficiency of acquiring decision rules by utilizing previously saved information. In this study, we focus on exploiting update mechanisms of approximations in VPMGRS with the addition of granular structures. By analyzing the basic changing trends of approximations in VPMGRS, we develop accelerating update mechanisms for acquiring approximations. In addition, an incremental algorithm to update variable precision multigranulation approximations is proposed when adding multiple granular structures. Finally, extensive comparisons elaborate the efficiency of the incremental algorithm.

A comprehensive analysis and characterization of granular structure (GS) and granular bainite (GB) were conducted in a low-carbon alloy steel with the objective to elucidate the mechanisms that contributed to strength–ductility combination. Ferrite + martensite–austenite (M-A) islands were observed in both GS and GB, but a transformation mechanism produced a variation in the matrix and in the M-A size, distribution and morphology. The differences in the matrix and M-A islands were carefully studied by electron backscatter diffraction. Furthermore, tensile tests were interrupted at different strains to study the microstructure evolution during tensile straining and mechanical properties determined from samples tensile strained to fracture. It was observed that the specimen with GB exhibited higher tensile strength, ductility and strain hardening compared to GS. Dislocation strengthening of GB was more apparent than GS. The higher volume fraction and uniform distribution of M-A islands also contributed to the higher strength of GB. The improvement in ductility may partly be attributed to the sub-structural refinement. An increased presence of geometrically necessary dislocations array in GB also simultaneously enhanced strength and ductility. During deformation, GB exhibited a more uniform local strain distribution compared to GS because of superior plastic ability of M-A islands. Based on the experimental results and analysis, we propose models describing the different failure mechanism in GS and GB. [ABSTRACT FROM AUTHOR]

Granule size is a critical parameter affecting starch processing properties. Ultrasound treatments of up to 22 h were applied on two starches differing in granule size (quinoa starch and maize starch). The two starches showed significantly different trends in both structural and physicochemical aspects affected by the ultrasound treatments. For the small granule starch (volume-weighted mean particle size of 1.79 μm), short-term ultrasonication caused an increase of swelling power. As the treatment time increased, the physicochemical properties were influenced by the degradation of amylopectin external chains. The X-ray diffraction results showed a decrease of relative crystallinity and changes of peak areas with long-term treatment. On the other hand, a balance between amylose leaching and surface damages was seen for the large granule starch (volume-weighted mean particle size of 18.3 μm). The effect of ultrasound modification on starches with different molecular and granular structures was discussed. A possible mechanism of the ultrasound effect was proposed. [ABSTRACT FROM AUTHOR]

Current extensive research on aerobic granular sludge (AGS) largely focuses on improving its microbial biodiversity, settlement behavior, nitrogen and phosphorus removal efficiency, and shock load resistance. Great challenges that have to be faced are the bottleneck of slow-speed granulation and easy disintegration after granulation, which are key to the extended application of AGS technology. In the present review, the typical morphological structures of AGS are firstly summarized as well as the granulation model hypotheses, and then, we analyze the dominant microflora and their spatial distribution features. The influencing factors on particle structure stability are discussed thereafter on a macro and micro scale. Prospects and future research trends are also discussed based on the current study results for AGS technology.

• Micromagnetic code based on Voronoi tessellation. • Magnetostatic field computed using a non-uniform fast Fourier method augmented with exact near field computation. • Exchange field computed on Voronoi geometry. • GPU accelerated computation. • Used to simulate SNR and Domain Wall Motion in granular recording media. A micromagnetic code based on Voronoi tessellation and the non-uniform fast Fourier transform (NUFFT) method is presented. The code is capable of efficiently and accurately simulating magnetization dynamics in large and structurally complex granular systems, such as multilayer granular media used for perpendicular magnetic recording, bit patterned media, granular nanowires, and read heads. In these systems the granular microstructure and distributions in grain and interface properties play an important role in device performance. The presented Voronoi simulator allows comprehensive studies to be performed as it accounts for the detailed granular microstructure and distributions that characterize true systems. Simulation time is greatly reduced by a non-uniform fast Fourier transform algorithm and implementation on graphics processing units (GPUs). Simulations of conventional magnetic recording, heat-assisted magnetization reversal, domain wall dynamics in granular nanowires, and particulate tape recording are presented. [ABSTRACT FROM AUTHOR]

Attribute subset selection is an important issue in data mining and information processing. However, most automatic methodologies consider only the relevance factor between samples while ignoring the diversity factor. This may not allow the utilization value of hidden information to be exploited. For this reason, we propose a hybrid model named intuitionistic fuzzy (IF) rough set to overcome this limitation. The model combines the technical advantages of rough set and IF set and can effectively consider the above-mentioned statistical factors. First, fuzzy information granules based on IF relations are defined and used to characterize the hierarchical structures of the lower and upper approximations of IF rough set within the framework of granular computing. Then, the computation of IF rough approximations and knowledge reduction in IF information systems are investigated. Third, based on the approximations of IF rough set, significance measures are developed to evaluate the approximation quality and classification ability of IF relations. Furthermore, a forward heuristic algorithm for finding one optimal reduct of IF information systems is developed using these measures. Finally, numerical experiments are conducted on public datasets to examine the effectiveness and efficiency of the proposed algorithm in terms of the number of selected attributes, computational time, and classification accuracy. [ABSTRACT FROM AUTHOR]

An approximation space is one basic concept in rough set theory. This paper investigates measures of uncertainty for an approximation space from granular computing viewpoint. Granular structures of an approximation spaces are first described by means of set vectors. Then, relationships between granular structures of an approximation spaces are studied from the two aspects of dependence and separation. Next, properties of granular structures of an approximation space are given. Furthermore, as an application for granular structures of an approximation spaces, measuring uncertainty of approximation spaces is investigated. Finally, one example is employed to illustrate features of the proposed measures for uncertainty of approximation spaces. These results will be helpful for understanding the essence of uncertainty for an approximation space. [ABSTRACT FROM AUTHOR]

Abstract Granular computing is an emerging field of study in which the complexity of problem solving is reduced through granulation. Researchers have proposed various granularity measures of partitions to quantify the effects of granulation with respect to simplification. However, two important issues still remain and require careful investigation. The first issue is that a partition is only a simple two-level granular structure, which may not be sufficient for the full scope of granular computing. The second issue is a clarification of the differences between granularity and complexity. Although they are related to each other, they represent different things. To address the two issues, this paper makes three contributions. First, we extend the partition granulation scheme into multilevel granular structures based on progressive partitioning. Second, we propose a complexity measure of a partition that incorporates both the block-level interactions (interactions within a block) and the partition-level interactions (interactions between blocks of the partition). Third, we generalize the complexity measure to multilevel granular structures generated from a progressive partitioning process. [ABSTRACT FROM AUTHOR]

An approximation space is one basic concept in rough set theory. This paper investigates measures of uncertainty for an approximation space from granular computing viewpoint. Granular structures of an approximation spaces are first described by means of set vectors. Then, relationships between granular structures of an approximation spaces are studied from the two aspects of dependence and separation. Next, properties of granular structures of an approximation space are given. Furthermore, as an application for granular structures of an approximation spaces, measuring uncertainty of approximation spaces is investigated. Finally, one example is employed to illustrate features of the proposed measures for uncertainty of approximation spaces. These results will be helpful for understanding the essence of uncertainty for an approximation space. [ABSTRACT FROM AUTHOR]

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The internal structure of a sheared assembly of monodisperse ellipsoids is investigated using three-dimensional Voronoi analysis. The discrete element method is employed to simulate isotropic and triaxial compression tests of ellipsoidal particles. A recently developed Voronoi tessellation technique, i.e., Set Voronoi tessellation, is applied to constructing Voronoi cells of assemblies at a series of shearing states. Several quantities are provided to quantify the properties of Voronoi cells, including local porosity, reduced surface area, sphericity, and a modified Minkowski tensor. We show that average local porosity, average reduced surface area, and average sphericity are functions of global porosity and mean coordination number during shearing, suggesting a relationship between void and particle networks. Moreover, local porosity and reduced surface area statistically comply with a modified lognormal distribution regardless of particle shape for a similar global porosity. However, the shape of a Voronoi cell is significantly dependent on the particle it encloses. Shear-induced inhomogeneity and anisotropy measured by Voronoi-based quantities are also examined. Increasing shear-induced entropy is observed, which means a more disordered void network during shearing. Furthermore, anisotropy of Voronoi cell orientations shows a comparable trend with anisotropy of contact normals. These findings are useful for developing a better understanding of void networks and their relationship with particle networks for non-spherical assemblies. [ABSTRACT FROM AUTHOR]