Set Expansion Research Articles

Multiple attribute decision making based on Pythagorean fuzzy Aczel-Alsina average aggregation operators.

A useful expansion of the intuitionistic fuzzy set (IFS) for dealing with ambiguities in information is the Pythagorean fuzzy set (PFS), which is one of the most frequently used fuzzy sets in data science. Due to these circumstances, the Aczel-Alsina operations are used in this study to formulate several Pythagorean fuzzy (PF) Aczel-Alsina aggregation operators, which include the PF Aczel-Alsina weighted average (PFAAWA) operator, PF Aczel-Alsina order weighted average (PFAAOWA) operator, and PF Aczel-Alsina hybrid average (PFAAHA) operator. The distinguishing characteristics of these potential operators are studied in detail. The primary advantage of using an advanced operator is that it provides decision-makers with a more comprehensive understanding of the situation. If we compare the results of this study to those of prior strategies, we can see that the approach proposed in this study is more thorough, more precise, and more concrete. As a result, this technique makes a significant contribution to the solution of real-world problems. Eventually, the suggested operator is put into practise in order to overcome the issues related to multi-attribute decision-making under the PF data environment. A numerical example has been used to show that the suggested method is valid, useful, and effective.

Spruce giga-genomes: structurally similar yet distinctive with differentially expanding gene families and rapidly evolving genes.

Spruces (Picea spp.) are coniferous trees widespread in boreal and mountainous forests of the northern hemisphere, with large economic significance and enormous contributions to global carbon sequestration. Spruces harbor very large genomes with high repetitiveness, hampering their comparative analysis. Here, we present and compare the genomes of four different North American spruces: the genome assemblies for Engelmann spruce (Picea engelmannii) and Sitka spruce (Picea sitchensis) together with improved and more contiguous genome assemblies for white spruce (Picea glauca) and for a naturally occurring introgress of these three species known as interior spruce (P. engelmannii × glauca × sitchensis). The genomes were structurally similar, and a large part of scaffolds could be anchored to a genetic map. The composition of the interior spruce genome indicated asymmetric contributions from the three ancestral genomes. Phylogenetic analysis of the nuclear and organelle genomes revealed a topology indicative of ancient reticulation. Different patterns of expansion of gene families among genomes were observed and related with presumed diversifying ecological adaptations. We identified rapidly evolving genes that harbored high rates of non-synonymous polymorphisms relative to synonymous ones, indicative of positive selection and its hitchhiking effects. These gene sets were mostly distinct between the genomes of ecologically contrasted species, and signatures of convergent balancing selection were detected. Stress and stimulus response was identified as the most frequent function assigned to expanding gene families and rapidly evolving genes. These two aspects of genomic evolution were complementary in their contribution to divergent evolution of presumed adaptive nature. These more contiguous spruce giga-genome sequences should strengthen our understanding of conifer genome structure and evolution, as their comparison offers clues into the genetic basis of adaptation and ecology of conifers at the genomic level. They will also provide tools to better monitor natural genetic diversity and improve the management of conifer forests. The genomes of four closely related North American spruces indicate that their high similarity at the morphological level is paralleled by the high conservation of their physical genome structure. Yet, the evidence of divergent evolution is apparent in their rapidly evolving genomes, supported by differential expansion of key gene families and large sets of genes under positive selection, largely in relation to stimulus and environmental stress response.

From Infancy to Modern Day: The History of Mother and Baby Units in the United Kingdom

AimsMother and baby units (MBUs) are inpatient units where women with severe acute postpartum psychiatric problems can be cared for alongside their babies. This is currently considered to be gold-standard care, recognising the importance of early childhood bonding and family-centered care. Great Britain has spearheaded the development of the MBU, however the history of MBUs in the United Kingdom (UK) has never been published.MethodsThrough a narrative review of published and grey literature, we explore the development of the MBU in the UK, from its infancy to modern day.ResultsWe outline the history of the MBU model of care, from its early conception to current state. We also examine factors contributing towards the expansion of MBUs and more broadly, the expansion of perinatal mental health services throughout the UK. We also briefly describe the approach to MBUs worldwide, taking into consideration sociocultural differences and approaches to caring for the mother-baby dyad.ConclusionSince its conception, there has been considerable investment in and expansion of perinatal mental health services, both in community and inpatient settings. Sustained research and continued advocacy is required to expand provision of care.

Calculating bond dissociation energies of X-H (X=C, N, O, S) bonds of aromatic systems via density functional theory: a detailed comparison of methods.

In this study, the performance of 17 different density functional theory functionals was compared for the calculation of the bond dissociation energy (BDE) values of X−H (X=C, N, O, S) bonds of aromatic compounds. The effect of the size of the basis set (expansions of 6-31(G)) was also assessed for the initial geometry and zero-point energy calculations, followed by the single-point BDE calculations with different model chemistries with the 6-311 + (3df,2p) basis set. It was found that the size of the basis set for geometry optimization has a much smaller effect on the accuracy of BDE than the choice of functional for the following single-point calculations. The M06-2X, M05-2X and M08−HX functionals yielded highly accurate BDE values compared to experimental data (with the average mean unsigned error MUE = 1.2–1.5 kcal mol−1), performing better than any of the other functionals. The results suggest that geometry optimization may be performed with B3LYP functional and a small basis set, whereas the M06-2X, M05-2X and M08-HX density functionals with a suitably large basis set offer the best method for calculating BDEs of ArX−H (X=C, N, O, S) bonds.

A Meta Path Based Method for Entity Set Expansion in Knowledge Graph

Entity Set Expansion (ESE) is the problem that expands a small set of seed entities into a more complete set, entities of which have common traits. As a popular data mining task, ESE has been widely used in many applications, such as dictionary construction, query suggestion and new brand identification. Existing ESE methods mainly utilize text and Web information. That is, the intrinsic relation among entities is inferred from their occurrences in text or Web. With the surge of knowledge graph in recent years, it is possible to extend entities according to their occurrences in knowledge graph. In this paper, we consider the knowledge graph as a heterogeneous information network (HIN) that contains different types of objects and links, and propose a novel method, called MP_ESE, to extend entities in the HIN. The MP_ESE employs meta paths, a relation sequence connecting entities, in HIN to capture the implicit common traits of seed entities. In addition, an automatic meta path generation method, called SMPG, has been designed to exploit the potential relations among entities. Heuristic learning and PU learning methods are employed to learn the weights of extracted meta paths. With these generated and weighted meta paths, the MP_ESE can effectively extend entities. Comprehensive experiments on real datasets show the effectiveness and efficiency of MP_ESE.

An infrared image based state evaluation method for cable incipient faults

Temperature measurement is frequently used to determine the state of power cable, which is critical for the safety of urban distribution system. This paper combines deep learning and state evaluation, proposing infrared image state evaluation method for cable based on deep learning. First, the concepts of transfer learning and deconvolution are introduced to design the down-sampling and up-sampling networks of the model respectively. Then combined with the data set expansion method, 4000 infrared images of 10 kV cables and their accessories are used for training. Finally, the infrared images of cables and their accessories are statistically analyzed by the method of temperature gradient. The experimental results show that the identification accuracy rate of the model reaches 95.68%. The temperature gradient is distributed in the range of 0–4.2 under normal conditions, and the temperature gradient is greater than or equal to 4.9 in the case of crimping defects. The statistical analysis results show that this method can effectively realize the status of the cable infrared image identity.

Local knowledge distance for rough approximation measure in multi-granularity spaces

As the significant expansion of classical rough sets, local rough sets(LRS) is an effective model for processing large-scale datasets with finite labels. However, the process of establishing a category of monotonic uncertainty measure with strong distinguishing ability for LRS remains ambiguous. To construct this model, both the monotonicity of local lower approximation set and the local structure of granularity should be considered. First, the monotonicity of local rough approximation measure(LRAM) is established by the local lower and upper approximation sets. Subsequently, the local knowledge distance(LKD) is proposed to measure the uncertainty derived from the disparities between local upper and lower approximation sets. The more rational uncertainty measure associated LRAM with LKD is designed as a feature selection algorithm. Eventually, the experiments reflect the feasibility of the developed uncertainty measure.

The Transformation of the Meaning and Concept of Zoku 俗

Abstract This paper clarifies why the characters sei 聖 and zoku 俗 were adopted as the Japanese translations for the terms “sacred” and “profane,” respectively, as well as the circumstances in which these characters took on a diversity of meanings that go beyond those terms in contemporary society at large. While the character zoku has acquired various meanings over its long history, its fundamental nuance is the “general,” in contrast to the particular; accordingly, it only acquires a concrete meaning when contrasted with something else. This ambiguity led to its adoption as the translation of “profane,” as well as the continued expansion of the sei-zoku set’s meaning, resulting in a paired set that can refer to “something mysterious and something not mysterious.” When discussing the above, I also engage with international scholarship on how the cultural background of non-Western societies influenced the meanings and interpretations of imported modern Western concepts.

Quadratic Optimization based Clique Expansion for overlapping community detection

Community detection is a crucial task in the field of network analysis. A community is a collection of tightly connected nodes only have sporadic external connections. In many real-world networks, communities naturally overlap, and prior knowledge about them is usually unavailable, such as the number of ground-truth communities in the network. In this work, we present the QOCE (Quadratic Optimization based Clique Expansion), an overlapping community detection method that does not require any prior knowledge. QOCE follows the popular seed set expansion strategy and regards each high-quality maximal clique as the initial seed set. For seed set expansion, QOCE uses a fast short random walk to sample a subgraph from a clique seed set, then adopts a quadratic optimization to approximate the Cheeger cut on the sampled subgraph. Finally, a local minimum of conductance determines the boundary of the community. We extensively evaluate our method by comparing it with four state-of-the-art baseline algorithms on synthetic and real-world networks in various domains and scales. Empirical results demonstrate the competitive performance of our method in terms of detection accuracy and efficiency.

Quasi-Relativistic Calculation of EPR g Tensors with Derivatives of the Decoupling Transformation, Gauge-Including Atomic Orbitals, and Magnetic Balance

We present an exact two-component (X2C) ansatz for the EPR g tensor using gauge-including atomic orbitals (GIAOs) and a magnetically balanced basis set expansion. In contrast to previous X2C and four-component relativistic ansätze for the g tensor, this implementation results in a gauge-origin-invariant formalism. Furthermore, the derivatives of the relativistic decoupling matrix are incorporated to form the complete analytical derivative of the X2C Hamiltonian. To reduce the associated computational costs, we apply the diagonal local approximation to the unitary decoupling transformation (DLU). The quasi-relativistic X2C and DLU-X2C Hamiltonians accurately reproduce the results of the parent four-component relativistic theory when accounting for two-electron picture-change effects with the modified screened nuclear spin-orbit approximation in the respective one-electron integrals and integral derivatives. According to our benchmark studies, the uncontracted Dyall and segmented-contracted Karlsruhe x2c-type basis sets perform well when compared to large even-tempered basis sets. Moreover, (range-separated) hybrid density functional approximations such as LC-ωPBE and ωB97X-D are needed to match the experimental findings. The impact of the GIAOs depends on the distribution of the spin density, and their use may change the Δg shifts by 10-50% as shown for [(C5Me5)2Y(μ-S)2Mo(μ-S)2Y(C5Me5)2]-. Routine calculations of large molecules are possible with widely available and comparably low-cost hardware as demonstrated for [Pt(C6Cl5)4]- with 3003 basis functions and three spin-(1/2) La(II) and Lu(II) compounds, for which we observe good agreement with the experimental findings.

Expansion and Reduction of Soft Sets and their Applications in Decision Making

In this study, we first define expansion and reduction of the soft sets that are based on the linguistic modifiers. By using these new notions, we then construct a decision-making method called soft reduction method, which selects a set of optimum alternatives. We finally present an example which shows that the methods can be successfully applied to many problems containing uncertainties.

Quasipolynomial Multicut-mimicking Networks and Kernels for Multiway Cut Problems

We show the existence of an exact mimicking network of k O ( log k ) edges for minimum multicuts over a set of terminals in an undirected graph, where k is the total capacity of the terminals, i.e., the sum of the degrees of the terminal vertices. Furthermore, using the best available approximation algorithm for Small Set Expansion , we show that a mimicking network of k O ( log3 k ) edges can be computed in randomized polynomial time. As a consequence, we show quasipolynomial kernels for several problems, including Edge Multiway Cut , Group Feedback Edge Set for an arbitrary group, and Edge Multicut parameterized by the solution size and the number of cut requests. The result combines the matroid-based irrelevant edge approach used in the kernel for s -Multiway Cut with a recursive decomposition and sparsification of the graph along sparse cuts. This is the first progress on the kernelization of Multiway Cut problems since the kernel for s -Multiway Cut for constant value of s (Kratsch and Wahlström, FOCS 2012).

A Comprehensive Analysis of Passenger Alighting and Boarding Rates

The understanding and management of station stops continues to be a key issue in the operation of urban railways. This paper reports a statistical meta-analysis of passenger alighting and boarding rates from an expansion of a real-life worldwide data set which includes 34 different variables reflecting characteristics of passenger flow, rolling stock design, infrastructure and management actions. This has enabled the authors to identify, test hypotheses about, and quantify the impact of, previously-untested variables. A stepwise regression method using the R statistical package was proposed and developed into a more tractable model with fewer variables. This process eliminated those variables shown to provide no statistical explanation (including the presence of platform edge doors). Of the remaining 18 hypothesised variables, all provided some form of statistical explanation at the 90% level (or more) in one model or another. The results will help railways and transport authorities around the world manage station stops, through timetabling and appropriate investment.

Face Dataset Augmentation with Generative Adversarial Network

Face recognition is a widely used scene of artificial intelligence technology. However, some face occlusions cause the face to be unable to be effectively detected in a specific environment. Although many algorithms have been proposed to solve this problem, in essence, a large number of face image data containing occlusion elements is needed to train to improve the detection ability of the algorithm. In recent years, this problem can be effectively solved by using the image generation ability of generative adversarial network. This paper proposes an improved Generative Adversarial Networks (GAN), which improves the effect of occluded face image generation by adding coding module. Through the expansion of data set, the detection accuracy of several classic face detection models for occluded faces is improved by more than 3%. At the moment when the epidemic has not been over, occlusion face data is of great significance to improve the performance of face detection systems in specific public places such as customs security inspection and medical centers.

Comparison of Marginal Fit of Metal Copings in Two Different States with and without Using Ring Liners: An In Vitro Study

To compare the marginal fit of Ni-Cr copings made by casting in two different states (dry and wet) with either cellulose ring liners or no ring liners. An in vitro study was conducted on 40 patterns which were invested, and a burnout casting procedure was used, using a Ni-Cr alloy to obtain the cast copings. The cast copings were divested, cleaned, and run through a finishing procedure. The copings were seated on the stainless steel die and microscopically evaluated for marginal discrepancies at two predetermined reference points using an optical microscope (Rieichert, Austria). Statistical analysis was done using Pearson's correlation coefficient and Chi-square test, keeping 95% confidence intervals and having a p value of less than 0.05 statistically significant. Our results identified marginal discrepancies in all the Ni-Cr cast copings, which significantly differed from each other. The copings obtained from casting with a cellulose ring liner in a wet state showed a significantly higher value followed by casting using a cellulose ring liner in a dry state. The mean marginal discrepancy values were within the clinically acceptable range for all the Ni-Cr cast copings included in two groups. Our study concludes that the copings cast with a cellulose ring liner in dry states had the least amount of vertical marginal discrepancies as compared to those that were cast with a ring liner in two different forms or those cast without a ring liner, suggesting that the use of cellulose ring liner in a dry state is favorable for all casting procedures. Measuring gaps or discrepancies at margins is the commonly used method to determine the fit of Ni-Cr copings. In order to minimize marginal inaccuracies, various authors have suggested different methods to improve the marginal adaptation of cast restorations. There are compensation methods like setting expansion, hygroscopic expansion, and thermal expansion of the investment, which are used to assess metal shrinkage during cooling.

Novel Aczel–Alsina operations-based hesitant fuzzy aggregation operators and their applications in cyclone disaster assessment

As a fuzzy set (FS) expansion, the hesitant fuzzy set (HFS) is successfully employed to demonstrate circumstances where it is admissible to ascertain a few potential membership degrees (MDs) of a component in a set because of the uncertainty between various values. Considering that there is still no research on Aczel–Alsina triangular norms and conorms in a hesitant fuzzy (HF) environment, in this article we introduce for the first time, Aczel–Alsina operations on HFSs. Then, based on these operations, we originate a few new aggregation operators for aggregating HF information namely HF Aczel–Alsina weighted averaging (HFAAWA) operator, HF Aczel–Alsina ordered weighted averaging (HFAAOWA) operator, HF Aczel–Alsina hybrid averaging (HFAAHA) operator, HF Aczel–Alsina weighted geometric (HFAAWG) operator, HF Aczel–Alsina ordered weighted geometric (HFAAOWG) operator, HF Aczel–Alsina hybrid geometric (HFAAHG) operator and HF Aczel–Alsina weighted Bonferroni mean (HFAAWBM) operator. Some essential characteristics of those suggested operators are shown, and the interrelatedness between them is displayed exhaustively. Then, we take advantage of those operators to produce a methodology to interpret the HF multiple attribute decision making (MADM) difficulties. We present a functional model for cyclone disaster assessment to certify the produced approaches and to establish their effectiveness and practicality. Further, we conduct comparison analysis for the legitimacy of our produced methodologies.

Transverse wave dynamics in short tubes with axisymmetric headwall injection

This work describes both traveling and standing vorticoacoustic waves in circular tubes that are driven by axisymmetric headwall injection. In this process, perturbation tools, field decomposition, and boundary-layer theory are jointly used. First, perturbation expansions are initiated to linearize the Navier–Stokes equations. Second, a Helmholtz decomposition of the first-order disturbances is pursued to identify a suitable set of acoustic wave equations. The last step consists of solving for the vortical mode using boundary-layer theory and a viscous expansion of the unsteady rotational set. At the outset, an explicit formulation for arbitrary headwall injection is obtained and confirmed both numerically and through limiting process verifications; the latter take into account special cases involving uniform and bell-shaped injection profiles. The resulting formulation is then described using both laminar and turbulent injection patterns. Using four canonical cases, the characteristics of the evolving vorticoacoustic wave, including its penetration depth, spatial wavelength, and overshoot factor, are systematically explored and discussed. Several fundamental flow features are also unraveled including the radial, tangential, and axial velocities of the time-dependent vortical field. Most rotational flow features are found to depend on the penetration number, the Strouhal number, and the distance from the centerline. The corresponding standing modes are expressed in closed form and shown to be appreciable in view of their amplitudes that twice exceed those associated with strictly traveling waves. Finally, by extending the boundary-layer analysis from the headwall to the sidewall, a uniformly valid wave approximation is achieved, which remains observant of the no-slip requirement everywhere.

Evaluation and Genetic Analysis of Parthenocarpic Germplasms in Cucumber.

Parthenocarpy is an important agronomic trait in cucumber (Cucumis sativus L.) production. However, the systematic identification of parthenocarpic germplasms from national gene banks for cucumber improvement remains an international challenge. In this study, 201 cucumber lines were investigated, including different ecotypes. The percentages of parthenocarpic fruit set (PFS) and parthenocarpic fruit expansion (PFE) were evaluated in three experiments. In natural populations, the PFS rates fit a normal distribution, while PFE rates showed a skewed distribution, suggesting that both PFS and PFE rates are typical quantitative traits. Genetic analysis showed that parthenocarpy in different ecotypes was inherited in a similar incompletely dominant manner. A total of 5324 single nucleotide polymorphisms (SNPs) associated with parthenocarpy were detected in a Genome-wide association study (GWAS) of parthenocarpy in the 31 cucumber lines, from which six parthenocarpic loci, including two novel loci (Pfs1.1 and Pfs4.1), were identified. Consequently, fifteen of the elite lines that were screened presented relatively stronger parthenocarpy ability (PFS > 90%, PFE > 50%), among which six cucumber lines (18007s, 18008s, 18022s, 18076s, 18099s, and 18127s) exhibited weak first-fruit inhibition. Three lines (18011s, 18018s, and 18019s) were screened for super ovary parthenocarpy, which showed more attractive performance. Four low-temperature-enhanced parthenocarpy lines (18018s, 18022s, 18029s, and 18012s) were identified, which were suited for breeding for counter-season production. Our approaches could help increase efficiency and lead to parthenocarpy improvements for modern cucumber cultivars.

Coherence-based sensor set expansion for optimal sensor placement in active road noise control

Road noise is the dominant noise source that directly affects the vehicle noise, vibration, and harshness (NVH) performance. The active noise control (ANC) technology is a promising solution for reducing interior noise from tire–road interactions. Active road noise control (ARNC) systems include a set of acceleration sensors, which acquire vibrations that cause road noise, and the locations of the sensors is important to accurately determine the vibration transfer path. Therefore, it is necessary to find an optimal sensor set among the candidate sensors to maximize the performance of the ARNC system. The trial-and-error method for simulating all possible sensor combinations requires a high computational cost. This paper proposes a method that determines the set of reference sensor locations with the coherence analysis and the Fisher information matrix. The methodology starts with one sensor highly correlated with the output sound pressure level signals. The initial sensor set is iteratively expanded to the desired number of sensors by maximizing the determinant of the coherence information matrix. This approach can significantly reduce the amount of computation and subsequently shorten the time required. In addition, the obtained local optimal results indicated a reduction in broadband road noise of approximately 7 dBA and showed an error within 0.2 △dBA of the targeted noise reduction results in all driving conditions.

Extended kinetic lattice grand canonical Monte Carlo simulation method for transport of multicomponent ion mixtures through a model nanopore system

AbstractAn extended version of the original kinetic lattice grand canonical Monte Carlo simulation method combined with mean field theory (KLGCMC/MF) (J. Chem. Phys. 2007, 127, 024706) is presented for the study of transport of multicomponent ion mixtures through a model nanopore. Comparison of the extended KLGCMC/MF (eKLGCMC/MF) simulation results with Poisson–Nernst–Planck (PNP) calculations is also made to confirm the validity of the extended simulation approach. Unlike the original version of KLGCMC/MF simulation method that treats only a binary ionic solution with one cation and one anion species, this extended version can deal with a system that includes ternary ion mixtures. A diffusion probability algorithm is also added to the extended version of the simulation method to describe the inhomogeneous diffusivity of ions that is often observed in the ion permeation through nanopores. Both Legendre and Chebyshev polynomials of the second kind were tested as a basis set for the basis set expansion (BSE) method with which to calculate the reaction field energy in the eKLGCMC/MF simulation. It turned out that the Legendre polynomials perform better than the Chebyshev polynomials, and as a result, the Legendre polynomials were implemented in the current version of eKLGCMC/MF simulation algorithm. The presented eKLGCMC/MF simulation method with new features finds its potential applications in nanopore systems where the correlation between ion species with the same sign of charges plays a key role such as oscillating ion currents or anomalous mole fraction effects.