Set Expansion Research Articles

From electrons to phase diagrams with machine learning potentials using pyiron based automated workflows

AbstractWe present a comprehensive and user-friendly framework built upon the integrated development environment (IDE), enabling researchers to perform the entire Machine Learning Potential (MLP) development cycle consisting of (i) creating systematic DFT databases, (ii) fitting the Density Functional Theory (DFT) data to empirical potentials or MLPs, and (iii) validating the potentials in a largely automatic approach. The power and performance of this framework are demonstrated for three conceptually very different classes of interatomic potentials: an empirical potential (embedded atom method - EAM), neural networks (high-dimensional neural network potentials - HDNNP) and expansions in basis sets (atomic cluster expansion - ACE). As an advanced example for validation and application, we show the computation of a binary composition-temperature phase diagram for Al-Li, a technologically important lightweight alloy system with applications in the aerospace industry.

Nonorthogonal basis adapted parameter-free complex absorbing potential and its application to open-boundary cluster model

Abstract This study extends the method of determining the complex absorbing potential (CAP) required for the open-boundary cluster model (OCM) based on the surface Green's function theory [Imamura K, Yasuike T, Sato H. Open-boundary cluster model with a parameter-free complex absorbing potential. 2024. J. Chem. Phys. 160:034103] to allow calculations using nonorthogonal basis sets. We devised a novel scheme that avoids the need to find adiabatically connected solutions for a large number of virtual energy levels, caused by the basis set expansion, and succeeded in constructing an optimal CAP. We generated basis sets with plane wave-like characteristics solely from localized functions, thereby eliminating the numerical instability in calculations of the surface Green's function. Application of the obtained CAP to the OCM calculation of a model 1D system confirms that the density of states of solid and localized states is well reproduced.

Study on the anchoring properties of new water glass-modified alkali-activated portland cement materials

This study developed a novel water glass-modified alkali-activated portland cement (WGSC) anchoring agent, characterized by low cost, rapid setting, early strength, and slight expansion. It is suitable for fully bonded anchor rod anchoring in soft rock tunnels. Using orthogonal experiments and range analysis, the optimal composition of WGSC was determined: silicate cement as the matrix, water-cement ratio of 0.3, 20 % sodium silicate with a modulus of 3.3, 2.5 % early strength agent, and 9 % UEA. The initial/final setting times were 6.72/8.63 minutes, linear expansion rates at 3 days/7 days were 0.32 %/0.54 %, and compressive strengths at 1 hour/24 hours were 16.4/28.2 MPa. Pull-out model tests demonstrated a 47.95–92.85 % increase in 24 h tensile bond strength compared to conventional silicate cement anchoring agents. Corrosion resistance tests indicated WGSC maintained a stable appearance compared to traditional agents. SEM analysis revealed a dense structure and clear network in WGSC, while XRD analysis identified microcomponents mainly as CSH, active silica (SiO2), and CASH, contributing to its dense structure and ideal expansion properties, facilitating superior anchoring and durability. This research elucidates the mechanism of WGSC, confirming its rapid setting, compressive strength, and corrosion resistance advantages, making it economically feasible and advantageous for application and promotion, thereby providing insights for the development of anchoring materials in highly deformed soft rock tunnels.

A prestressed anchor-grouting reinforcement method based on the novel HFQSME grouting material for deep high-stress fractured rock mass

In order to control the surrounding rock of deep high-stress roadways, a new prestressed anchor-grouting reinforcement method was proposed with an engineering background of the 1780 ventilation roadway of Shiyakou Coal Mine. A high-fluidity quick-setting micro-expansion (HFQSME) grouting material had been developed through superplasticizer, accelerator, and expansion agent. The results showed that the optimal ratio of HFQSME grouting material was superfine Portland cement of 95%, superplasticizer of 0.5%, accelerator agent of 0.5%, expansion agent of 5.0%, and water-binder ratio of 0.4. The fluidity, initial setting time and expansion rate of the grouting material were 47cm, 41minutes and 0.12%, respectively. The grouting reinforcement effect of superfine slurry and HFQSME grouting material on rock mass with different degrees of fragmentation was investigated. The peak strength of rock mass with two degrees of fragmentation reinforced by HFQSME grouting material was 166% and 233% higher than that of superfine slurry. The macro reinforcement effect of HFQSME grouting material on the surrounding rock of deep high-stress roadway was evaluated through industrial testing. The prestressed anchor grouting method was used to improve the original support design scheme, and data monitoring and analysis were conducted on site. Compared with the original supporting scheme, the deformation of the 1780 ventilation was significantly reduced. Therefore, HFQSME grouting material had good adaptability for the surrounding rock of deep high-stress roadways.

Transforming Social Policies and Institutions in a Capability Perspective: Agency, Voice and the Capability to Aspire

ABSTRACT The article suggests that putting the enhancement of capability to aspire and capability for voice centre stage of the design and implementation of social policies would entail a deep-seated transformation of such policies. Based on the receiver-doer-judge framework we explore what conditions are to be fulfilled to implement such a transformation, emphasising how the expansion of objective opportunity and entitlement sets should go hand in hand with an increased subjective sense of opportunity and entitlement. We then turn to examine what such transformed institutions would imply in terms of enhancing individual aspirations and capabilities on the one hand and increasing capacity for collective action and social change on the other one, emphasising the key role of capability for voice in both respects. We show how institutions and policies transformed in such a way would differ from dominant approaches to social policies such as social investment, which endorse a more adaptive approach to social policy. It is also suggested how Freire, Dewey and Khader’s ideas could be useful reference points towards the design and implementation of such transformed institutions.

PTPN11 Mutation Clonal Hierarchy in Acute Myeloid Leukemia.

Mutations in protein tyrosine phosphatase non-receptor type 11 ( PTPN11 ) have been considered late acquired mutations in acute myeloid leukemia (AML) development. To interrogate the ontogeny of PTPN11 mutations, we utilized single-cell DNA sequencing and identified that PTPN11 mutations can occur as initiating events in some AML patients when accompanied by strong oncogenic drivers, commonly NPM1 mutations. The co-driver role of PTPN11 mutations was confirmed in a novel murine model that exhibits an AML phenotype with early expansion of a diverse set of variably differentiated myeloid cells that engrafted into immunodeficient and immunocompetent mice. This immune diversity was reconstituted from early precursor cells when engrafted into immunodeficient mice. Moreover, immune diversity was also observed in the blast component of patient samples with NPM1 and PTPN11 mutations, providing novel antigen targets for immune based approaches in this subset of AML that is resistant to multiple targeted therapies.

Estimating Leaf Area Index in Apple Orchard by UAV Multispectral Images with Spectral and Texture Information

The Leaf Area Index (LAI) strongly influences vegetation evapotranspiration and photosynthesis rates. Timely and accurately estimating the LAI is crucial for monitoring vegetation growth. The unmanned aerial vehicle (UAV) multispectral digital camera platform has been proven to be an effective tool for this purpose. Currently, most remote sensing estimations of LAIs focus on cereal crops, with limited research on economic crops such as apples. In this study, a method for estimating the LAI of an apple orchard by extracting spectral and texture information from UAV multispectral images was proposed. Specifically, field measurements were conducted to collect LAI data for 108 sample points during the final flowering (FF), fruit setting (FS), and fruit expansion (FE) stages of apple growth in 2023. Concurrently, UAV multispectral images were obtained to extract spectral and texture information (Gabor transform). The Support Vector Regression Recursive Feature Elimination (SVR-REF) was employed to select optimal features as inputs for constructing models to estimate the LAI. Finally, the optimal model was used for LAI mapping. The results indicate that integrating spectral and texture information effectively enhances the accuracy of LAI estimation, with the relative prediction deviation (RPD) for all models being greater than 2. The Categorical Boosting (CatBoost) model established for FF exhibits the highest accuracy, with a validation set R2, root mean square error (RMSE), and RPD of 0.867, 0.203, and 2.482, respectively. UAV multispectral imagery proves to be valuable in estimating apple orchard LAIs, offering real-time monitoring of apple growth and providing a scientific basis for orchard management.

Hamacher Aggregation Operators for Pythagorean Fuzzy Set and its Application in Multi-Attribute Decision-Making Problem

Pythagorean fuzzy set is a useful expansion of intuitionistic fuzzy set for dealing with ambiguities, which mostly occur in real-life problems. Hamacher t-norm also has important and compatible norms that incorporate a parameter that offers various options to decision-makers during the information fusion process, thereby enhancing their ability to model decision-making problems effectively compared to alternative methods. In this study, Hamacher operators are being used to introduce several Pythagorean fuzzy Hamacher interactive weighted averaging (PFHIWA), Pythagorean fuzzy Hamacher interactive ordered weighted averaging (PFHIOWA), Pythagorean fuzzy Hamacher interactive weighted geometric (PFHIWG), and Pythagorean fuzzy Hamacher interactive ordered weighted geometric (PFHIOWG) operators. The properties of these operators are examined in detail. The benefit of using progressive operators is that they deliver more understanding of the scenario to the decision-makers. Proposed operators are utilized to elaborate multi-attribute decision-making (MADM). By showing the sensitivity analysis, our proposed operator has high stability related to multi-attribute decision-making (MADM) under the Pythagorean fuzzy data set.

Optimal control set expansion method for three‐phase voltage source converter with slip window selection and online parameter modification

AbstractFinite control set model predictive control (FCS‐MPC) has been widely used in the control of three‐phase voltage source converter (VSC), but its control performance declines sharply at low sampling frequencies. This paper proposes an optimal control set expansion method with slip window selection and online parameter modification to improve the performance and robustness of FCS‐MPC. First, the performance of different control set expansion methods is compared. Then, an optimal control set expansion method that carries 12 virtual vectors is proposed. Last, a slip window selection strategy and an online parameter modification algorithm are proposed to reduce the computational burden and improve the controller's robustness respectively. Experimental result show that the proposed method significantly improves the control accuracy and carries good robustness and anti‐disturbance ability. Compared with existing typical MPC strategies, the proposed method obtains better current quality with lower switching frequency and requires less calculation. Moreover, the design of the proposed method is simple and there is no weighting factor to be tuned, which increases the method's practicality.

Revolutionizing diabetes care with innovative decision-making using cubic intuitionistic fuzzy Schweizer and Sklar power aggregation operators

The cubic intuitionistic fuzzy set is an expansion of the cubic fuzzy set that displays massive information to demonstrate interval-valued intuitionistic fuzzy sets and intuitionistic fuzzy sets. This increment informs limitations essential in existing frameworks, primarily focusing on the significance of embracing our access for more accurate decisions in compound and unresolved structures. The Schweizer and Sklar (SS) operations are engaged in promoting strong aggregation operators for cubic intuitionistic fuzzy sets through this research. Operators such as cubic intuitionistic fuzzy Schweizer and Sklar power weighted average (CIFSSPWA) and cubic intuitionistic fuzzy Schweizer and Sklar power weighted geometric (CIFSSPWG) are offered that enhance the workability of data aggregation within the cubic intuitionistic fuzzy (CIF) environment when compared to surviving methods. The proposed operators may assist in patient treatment and handling by upgrading decision-making in medical sectors like diabetes care. Moreover, to determine the stability and reliance of the outcomes, sensitivity and comparison studies are richly absorbed by this approach.

Picture fuzzy normed linear space

Picture fuzzy set (PFS) is a recent advancement tool to deal with vulnerability. It is an immediate expansion of intuitionistic fuzzy set that can display vulnerability in such circumstances including more responses of these kinds: indeed, decline, no. In this manuscript the idea of Picture fuzzy normed linear space (PFNLS) is discussed for the first time. Naturally PFNLS is an hybrid concept of PFS and normed linear space. Also Convergence in PFNLS are shown. Later on Completeness property on PFNLS are explored. Finally boundedness of Cauchy sequence in PFNLS is analysed.

Fourier-Hankel-Abel Nyquist-limited tomography: A spherical harmonic basis function approach to tomographic velocity-map image reconstruction.

A new method for the fully generalized reconstruction of three-dimensional (3D) photoproduct distributions from velocity-map imaging (VMI) projection data is presented. This approach, dubbed Fourier-Hankel-Abel Nyquist-limited TOMography (FHANTOM), builds on recent previous work in tomographic image reconstruction [C. Sparling and D. Townsend, J. Chem. Phys. 157, 114201 (2022)] and takes advantage of the fact that the distributions produced in typical VMI experiments can be simply described as a sum over a small number of spherical harmonic functions. Knowing the solution is constrained in this way dramatically simplifies the reconstruction process and leads to a considerable reduction in the number of projections required for robust tomographic analysis. Our new method significantly extends basis set expansion approaches previously developed for the reconstruction of photoproduct distributions possessing an axis of cylindrical symmetry. FHANTOM, however, can be applied generally to any distribution-cylindrically symmetric or otherwise-that can be suitably described by an expansion in spherical harmonics. Using both simulated and real experimental data, this new approach is tested and benchmarked against other tomographic reconstruction strategies. In particular, the reconstruction of photoelectron angular distributions recorded in a strong-field ionization regime-marked by their extensive expansion in terms of spherical harmonics-serves as a key test of the FHANTOM methodology. With the increasing use of exotic optical polarization geometries in photoionization experiments, it is anticipated that FHANTOM and related reconstruction techniques will provide an easily accessible and relatively low-cost alternative to more advanced 3D-VMI spectrometers.

Weakly supervised classification through manifold learning and rank-based contextual measures

Over the last decade, significant advances have been achieved by machine learning approaches, notably in supervised learning scenarios. Supported by the advent of deep learning and comprehensive training sets, the accuracy achieved on classification tasks has improved significantly. Simultaneously, we have experienced massive growth in multimedia data and applications, which have become ubiquitous in several domains. However, with the increase in multimedia data collections, significant bottlenecks associated with the lack of labeled data emerged. To surpass this critical issue, developing methods capable of exploiting the unlabeled data and operating under weak supervision has become imperative. This work proposes a rank-based model capable of using contextual information encoded in the unlabeled data to perform weakly supervised classification. We evaluated the proposed weakly supervised approach on multimedia classification tasks with and without manifold learning algorithms, considering several combinations of rank correlation measures and classifiers. An experimental evaluation was conducted on 6 public image datasets considering different features, including convolutional neural networks and visual transformers. Positive gains were achieved compared to supervised and semi-supervised baselines for the same amount of labeled data. For instance, the proposed approach with manifold learning enhanced the accuracy of the Optimum-Path Forest (OPF) classifier from 71.77% to 83.24% when applied to the Flowers dataset and ResNet features. Among the conclusions, this work reveals that rank-based correlation measures and manifold learning can be used for a more effective labeled set expansion.

Effects of vehicles on the physical properties and biocompatibility of premixed calcium silicate cements.

Premixed calcium silicate cements (pCSCs) contain vehicles which endow fluidity and viscosity to CSCs. This study aimed to investigate the effects of three vehicles, namely, polyethylene glycol (PEG), propylene glycol (PG), and dimethyl sulfoxide (DMSO), on the physicochemical properties and biocompatibility of pCSCs. The setting time, solubility, expansion rate, and mechanical strength of the pCSCs were evaluated, and the formation of calcium phosphate precipitates was assessed in phosphate-buffered saline (PBS). The effects of pCSC extracts on the osteogenic differentiation of mesenchymal stem cells (MSCs) were investigated. Finally, the tissue compatibility of pCSCs in rat femurs was observed. CSC containing PEG (CSC-PEG) exhibited higher solubility and setting time, and CSC-DMSO showed the highest expansion rate and mechanical strength. All pCSCs generated calcium phosphate precipitates. The extract of CSC-PG induced the highest expressions of osteogenic markers along with the greatest calcium deposites. When implanted in rat femurs, CSC-PEG was absorbed considerably, whereas CSC-PG remained relatively unaltered inside the femur.

MESED: A Multi-Modal Entity Set Expansion Dataset with Fine-Grained Semantic Classes and Hard Negative Entities

The Entity Set Expansion (ESE) task aims to expand a handful of seed entities with new entities belonging to the same semantic class. Conventional ESE methods are based on mono-modality (i.e., literal modality), which struggle to deal with complex entities in the real world such as (1) Negative entities with fine-grained semantic differences. (2) Synonymous entities. (3) Polysemous entities. (4) Long-tailed entities. These challenges prompt us to propose novel Multi-modal Entity Set Expansion (MESE), where models integrate information from multiple modalities to represent entities. Intuitively, the benefits of multi-modal information for ESE are threefold: (1) Different modalities can provide complementary information. (2) Multi-modal information provides a unified signal via common visual properties for the same semantic class or entity. (3) Multi-modal information offers robust alignment signals for synonymous entities. To assess model performance in MESE, we constructed the MESED dataset which is the first multi-modal dataset for ESE with large-scale and elaborate manual calibration. A powerful multi-modal model MultiExpan is proposed which is pre-trained on four multimodal pre-training tasks. The extensive experiments and analyses on MESED demonstrate the high quality of the dataset and the effectiveness of our MultiExpan, as well as pointing the direction for future research. The benchmark and code are public at https://github.com/THUKElab/MESED.

Promoting open science in test-driven software experiments

A core principle of open science is the clear, concise and accessible publication of empirical data, including “raw” observational data as well as processed results. However, in empirical software engineering there are no established standards (de jure or de facto) for representing and “opening” observations collected in test-driven software experiments — that is, experiments involving the execution of software subjects in controlled scenarios. Execution data is therefore usually represented in ad hoc ways, often making it abstruse and difficult to access without significant manual effort. In this paper we present new data structures designed to address this problem by clearly defining, correlating and representing the stimuli and responses used to execute software subjects in test-driven experiments. To demonstrate their utility, we show how they can be used to promote the repetition, replication and reproduction of experimental evaluations of AI-based code completion tools. We also show how the proposed data structures facilitate the incremental expansion of execution data sets, and thus promote their repurposing for new experiments addressing new research questions.

A Mini-Review of the Kinetic Energy Partition Method in Quantum Mechanics

Based on the idea of adiabatic symmetry, we present a novel basis set expansion method—the kinetic energy partition (KEP) method—for solving quantum eigenvalue problems. Broken symmetry is responsible for quantum entanglement in many-body systems via parametric non-adiabatic corrections. Starting from simple one-particle-in-one-dimension problems, we gradually increase the complexity in the number of particles and the interaction patterns. Our goal in the mini-review is to advocate for the utility of the KEP method in front-line research, in particular for research beginners in quantum many-body problems.

From Neutrosophic Soft Set to Effective Neutrosophic Soft Set Generalizations and Applications

The Neutrosophic Soft Set (NSS) is an advanced and highly effective expansion of soft sets, specifically designed to handle parameterized values of alternatives. As an enhanced version of fuzzy soft sets, it provides a novel mathematical framework that offers significant advantages in dealing with uncertain information. This model is created by merging soft sets and neutrosophic sets, providing a robust approach to uncertainty management. Various algorithms have been proposed for making neutrosophic decisions using NSSs. However, these algorithms neglect external effective that influence the Decision-Making (DM) process, focusing solely on parameters. To address this issue, the article introduces the concept of Effective Neutrosophic Soft Sets (ENSSs). Additionally, we extend and generalize the innovative concept of Effective Fuzzy Soft Sets (EFSSs) to accommodate three independent membership criteria, aiming to enhance effectiveness and realism. We also introduce operations on ENSSs, including subset, complement, union, intersection, AND, and OR, which are defined along with illustrative examples. Furthermore, we examine some of its properties. Moreover, we present applications of this concept in DM problems and Medical Diagnosis (MD)

Evaluation of the Yttrium Oxide Effect on Some Mechanical Properties of (Type III) Dental Stone

For many years, gypsum compounds have been used in dentistry. They're used to make casts for various dental laboratory operations. Gypsum products have important characteristics such as "quality, abrasion resistance, compressive strength, hardness, linear setting expansion during setting, and detail replication". Additionally, the use of additives such as gum arabic, ferric oxide, calcium oxide, and calcium carbonate has greatly improved the compressive strength of gypsum products. The purpose of this study was to investigate the effects of yttrium oxide (Y2O3) addition on the hardness and compressive strength of dental stones. Thirty dental stone specimens of (type III) were prepared and separated into (3) groups: group (A) control group, no additives, group (B) 3% Y2O3, and group (C) 5% Y2O3. The control and experimental groups' mechanical parameters (surface hardness and compressive strength) were measured, and a Shore D hardness tester was used to indent the specimens. An electronic universal testing apparatus with computer control was used to conduct the compressive strength test. The Tukey-HSD test found no difference in surface hardness between the control and experimental groups, a non-significant difference in compressive strength between the control group and dental stone +3% Y2O3, and a significant difference in compressive strength between the control group and dental stone +5% Y2O3. Between dental stones with 3% and 5% Y2O3, there was a substantial difference in compressive strength. The addition of yttrium oxide at weight ratios of 3% and 5% to dental stone did not affect the surface hardness. The addition of 5% yttrium oxide decreased the compressive strengths.

Vulnerability Assessment of Prefabricated Component Suppliers in Prefabricated Buildings Based on the Controlled Interval and Memory Method Extended under the Probability Language Terminology Set

Accurately evaluating the vulnerability of prefabricated components in prefabricated buildings is the basis of scientific management of its supply chain. Thus, a novel vulnerability evaluation method for the suppliers of prefabricated components in the context of prefabricated buildings was proposed in this study. First, a vulnerability evaluation index system was identified and constructed by the pressure–state–response (PSR) model. With the clear logic and strong applicability of PSR, this system, including 18 indexes, was easy to understand and exhibited strong practicability. The weights were then determined via the multiplicative analytic hierarchy process with the expansion of a probabilistic language terminology set (PLTS). PLTS could describe decision information more comprehensively, so the evaluation result is more scientific. Subsequently, the probability of the vulnerability level was superimposed in a parallel bottom-to-top method via the controlled interval and memory (CIM) models, and these were extended by the PLTS. This set handled the uncertain information in the decision-making process more efficiently and accurately, and finally determined the vulnerability distribution. Ultimately, the probability of each vulnerability level was determined by combining the weight information. The reconstruction and resettlement housing construction project of urban shanty towns in the Taohua South Road Community in Xihu District, Nanchang City, was selected as a case study. The case study proves the effectiveness of this method, which has higher resolution and is more convenient for managers to make decisions.