Similar Calculations Research Articles

Innovative Path of Music Education Teaching in Colleges and Universities under the Architecture of Disciplinary Knowledge Mapping

Abstract The purpose of this paper is to present a pedagogical model for mapping subject knowledge in the field of music education. The model includes two aspects: the extraction of entities and inter-entity relationships within the subject domain and the construction of a subject ontology. In the construction process of subject knowledge mapping, this paper proposes a remote supervised music subject knowledge extraction method based on a convolutional neural network combined with an attention mechanism, which realizes the extraction of entities in the music subject domain and the relations between corresponding entities. This paper proposes a keyword extraction method for obtaining the set of discipline concepts in ontology construction. To improve performance, this paper proposes a framework structure for knowledge fusion that includes three aspects: data preprocessing, similarity calculation, and knowledge fusion. As a result of the study, the teaching model can improve the performance of music majors on average by 13.3. In terms of self-efficacy, on average reached 3.58 (SD = 0.535), which is at a good level. The results demonstrated the effectiveness of the music teaching model based on subject knowledge mapping.

Uncovering developmental time and tempo using deep learning

During animal development, embryos undergo complex morphological changes over time. Differences in developmental tempo between species are emerging as principal drivers of evolutionary novelty, but accurate description of these processes is very challenging. To address this challenge, we present here an automated and unbiased deep learning approach to analyze the similarity between embryos of different timepoints. Calculation of similarities across stages resulted in complex phenotypic fingerprints, which carry characteristic information about developmental time and tempo. Using this approach, we were able to accurately stage embryos, quantitatively determine temperature-dependent developmental tempo, detect naturally occurring and induced changes in the developmental progression of individual embryos, and derive staging atlases for several species de novo in an unsupervised manner. Our approach allows us to quantify developmental time and tempo objectively and provides a standardized way to analyze early embryogenesis.

Cross-domain open-set fault diagnosis using prototype learning and extreme value theory

The open-set cross-domain fault diagnosis scenario is similar to the practical industrial application. It assumes that a distribution discrepancy between the training (source) and testing (target) datasets exists, and the testing label set contains the training label set. The developed model transfers knowledge from the source domain to the share-class faults of the target domain and simultaneously rejects unknown fault samples. Previous studies have two shortcomings. First, feature alignment strategies only consider the alignment of the source and target-known distributions but ignore the segregation of the source and target-unknown distributions, resulting in unreasonable boundaries between known and unknown classes. Secondly, the similarity calculation for feature alignment relies on overconfident network predictions, resulting in biased similarities. To address these problems, this study employs two domain discriminators to clarify the boundaries between the known and unknown classes; one for aligning the source and target-known, and the other for segregating the source and target-unknown. In addition, an unbiased similarity calculation strategy using prototype learning and extreme value theory was proposed to quantify the contribution of each target sample to the domain adversarial training loss. Extensive experiments were conducted on a public bearing dataset and a laboratory gas-insulated switchgear vibration signals. The results outperformed those of other reported approaches, indicating that the proposed method is a promising model for practical industrial applications.

Counting Spanning Trees in The Ladders

Aims: In order to study the rule of entropy in different graph transformations, this paper introduces and further generalizes the ladder graph from different perspectives, including pinwheel ladders and 3-dimensional ladders. Then the problem of entropy comes down to the calculation of spanning trees in these graphs. Place and Duration of Study: School of Mathematical Sciences, Jiangsu University, between January 2023 and October 2023. Methodology: A method by seeking similar terms was generalized to help study the essential principle in these graphs. By similarity diagonalization and computer aided calculation, the exact expressions for the number of spanning trees in these graphs are obtained.

CASE BASED REASONING UNTUK PENENTUAN JENIS BEASISWA INTERNAL (BSM DAN PPS) UNIVERSITAS ‘AISYIYAH YOGYAKARTA

Education is the most important thing to educate the nation's life, but there are still many students who fail because of constrained tuition fees, in order to overcome this, the University of 'Aisyiyah Yogyakarta provides internal scholarship assistance to students in the form of Student Study Assistance (BSM) scholarships and Scholarship Improvement Scholarships (PPS). Many students apply for scholarships each year. The criteria for submitting scholarships tend to be similar every year, resulting in data being used to assess repetitions or having similarities between old data and new data. Case Based Reasoning (CBR) is a system that aims to solve a new case by adapting solutions found in previous similar cases. To simplify the process of determining the type of scholarship obtained by students, Case Based Reasoning is used. Similarity calculation for the case of determining the type of PPS and BSM scholarship using Euclidean Distance Similarity. The results of the K05 case similarity calculation are similar to the K02 case, the similarity value obtained is 0.86727, so the K05 case will get a PPS scholarship

(2,0) theory on S5 × S1 and quantum M2 branes

The superconformal index Z of the 6d (2,0) theory on S5×S1 (which is related to the localization partition function of 5d SYM on S5) should be captured at large N by the quantum M2 brane theory in the dual M-theory background. Generalizing the type IIA string theory limit of this relation discussed in arXiv:2111.15493 and arXiv:2304.12340, we consider semiclassically quantized M2 branes in a half-supersymmetric 11d background which is a twisted product of thermal AdS7 and S4. We show that the leading non-perturbative term at large N is reproduced precisely by the 1-loop partition function of an “instanton” M2 brane wrapped on S1×S2 with S2⊂S4. Similarly, the (2,0) theory analog of the BPS Wilson loop expectation value is reproduced by the partition function of a “defect” M2 brane wrapped on thermal AdS⊂3 AdS7. We comment on a curious analogy of these results with similar computations in arXiv:2303.15207 and arXiv:2307.14112 of the partition function of quantum M2 branes in AdS×4S7/Zk which reproduced the corresponding localization expressions in the ABJM 3d gauge theory.

Graph neural network-based similarity relationship construction model for geospatial services

ABSTRACT During the development of service-based software systems, Geospatial Service (GS) replacement is often performed, which requires the discovery of functionally similar services in service registries to replace failed services. Compared to real-time similarity computations, direct extraction of similar services from constructed similarity relationships can yield higher replacement efficiency. However, missing and inconsistent service-registry information impedes accurate similarity relationship construction. Here, we propose a Graph Neural Network (GNN)-based model for GS Similarity Relationship construction considering service descriptions and tags, which is named GSSR-GNN. As the sparsity of the service similarity relationship graph constructed based on labeled samples limits the information propagation ability, a graph augmentation method for similarity relationship construction among second-order neighbors is proposed. Considering the differences in the semantic-information feature distributions, such as the service descriptions and tags, a feed-forward neural network-based fusion method is designed to embed them into the same vector space. Pre-trained Bidirectional Encoder Representations from Transformers (BERT) and WordNet models are introduced to enhance the service-representation expressiveness. When an enhanced service representation is input to the GNN, the similarity is calculated and the service similarity relationship is obtained. Experimental results show that the proposed model constructs service similarity relationships with high precision, thus improving the service replacement efficiency and reducing the computational cost of service registry during service replacement.

Strategies for Safeguarding Intangible Cultural Heritage of Yi Ethnic Groups in the Context of Informatization and Its Practice in Regional Culture

Abstract The digitization of intangible cultural heritage provides the basis for its protection and inheritance. In this paper, we extracted the classes and interrelationships corresponding to each element for the content of Yi Intangible Cultural Heritage, constructed the data model and accomplished the relationship abstraction of different classes. Based on the structure of different data classes, the correlation between spatial change and non-heritage status is explored. The data model is proposed to cluster non-heritage data based on SWC-WMD distance, which improves the similarity calculation based on WMD distance. The association analysis of non-heritage item classes was performed using Yi folk dance as an example, and the overall non-heritage data was analyzed by clustering. In Yi folk dance, the association formed a network correlation graph with N2, N3, and N12 as the three main centers, in which the dance class items associated with N2, N3, and N12 were 9, 9, and 8, respectively. Mining out the relationships among NH items helps to better the overall NH protection.

Estimating the effectiveness of electric vehicles braking when determining the circumstances of a traffic accident

In the vast majority of cases, the braking process is used to prevent traffic accidents. The effectiveness of this process depends on the design and functionality of vehicle braking systems (presence of anti-lock braking system, emergency braking system, preventive safety systems, etc.) and is limited by the amount of frictional forces in contact of tires with the road. The improvement of methodical approaches to evaluating the effectiveness of braking of cars contributes to increasing the accuracy and objectivity of establishing the circumstances of the occurrence of emergency situations. The paper analyses existing methods of evaluating the braking parameters of vehicles (including those with an electric drive) and modern methods of evaluating electric vehicle braking parameters and conducting auto-technical investigations of traffic accidents, which relate to using different methodological approaches and digital technologies at all stages of expert research. In contrast to existing models, the proposed mathematical model for estimating the trajectory of two-axle cars during braking allows for considering various types of input parameter uncertainty, reducing the range of possible modeling errors by 39%. Comparing simulation results and experimental data showed that the average relative error is 4.58%, and the maximum error did not exceed 7.82%. The performed study of the stability of the electric vehicles' movement during emergency braking with the help of developed mathematical models in the Mathcad software environment reveals the content of the algorithm of a similar calculation in specialized computer programs of auto technical examination. Conducting such calculations is relevant in the analysis of real accident situations, where specific circumstances and features that cannot be considered during modeling in specialized software must be taken into account. Simultaneously, the probability of type I errors is reduced by 2–19%, and type II errors are reduced by 43–68%.

A Study of Reciprocal Job Recommendation for College Graduates Integrating Semantic Keyword Matching and Social Networking

With the surge in college graduate numbers, a disparity has emerged where the supply of jobs falls short of demand, intensifying employment pressures annually. College graduates, due to their lack of historical employment data compared with job seekers in the broader society, encounter a ‘cold start’ issue in the job recommendation process. Additionally, the nature of job recommendations, which differs fundamentally from unilateral recommendations, requires consideration of reciprocity between both parties involved. This article introduces a new approach to job recommendations using college graduates as the object of study. In the screening stage, a semantic keyword iterative algorithm is applied to compute the similarity between the resume and recruitment texts. This algorithm enhances the intersectionality of keywords in the calculation process, maximizing the utilization of resume information to enhance the accuracy of text similarity calculations. The ranking phase utilizes in-school data to build a social network between college graduates and graduated students and solves the system’s cold-start problem using the social network to recommend jobs for college graduates where graduated students are employed. We introduce a dual-dimensional matching approach that incorporates both specialty and salary, building upon the amalgamated semantic keyword iterative algorithm and the social network job recommendation method, to enhance the reciprocity of job recommendations. The job recommendation method introduced herein outperforms other methods in terms of the average satisfaction rate (AR) and normalized discounted cumulative gain (NDCG), thereby confirming its superior ability to meet the job-seeking preferences of graduates and the recruitment criteria of employers. This job recommendation method offers effective assistance to graduates lacking employment experience and historical employment data, facilitating their search for more suitable job opportunities.

Enhancing semantic text similarity with functional semantic knowledge (FOP) in patents

The semantic text similarity (STS) estimation between patents is a critical issue for the patent portfolio analysis. Current methods such as keywords, co-word analysis and even the Subject-Action-Object (SAO) algorithms, are not quite reasonable for the patent similarity calculation due to the lack of fine-grained semantic knowledge, “property-parameter” features and flexible “functional or non-functional” combinations. In the meanwhile, standardized similarity datasets are also unavailable. In this paper, we have proposed a new kind of functional semantic knowledge (Function-Object-Property, i.e., FOP) instead of SAO triples, which can contribute directly to enhance the patent similarity. Moreover, patent STS datasets, including the matching dataset and the ranking dataset, have firstly been processed and released as benchmarks for the comparative evaluation. Preliminary results have demonstrated that FOP-based methods are more appropriate in the STS tasks incorporated with IPC codes, weights’ assignments and patent pre-trained vectors. To be further, the deep interaction-based models with the averaged FOP embeddings are recommended to be one of the most optimal choices of effectively improving the semantic learning capability. Finally, a new patent similarity calculation framework is summarized and successfully applied in the patent retrieval, which highlight that the proposed methodology serves as a dominant power in diverse patented STS tasks.

Foreign Language Teaching and Learning Behaviour with a Big Data Corpus

Abstract This paper firstly constructs a foreign language subject system according to the foreign language teaching objectives and students’ learning situation in colleges and universities, puts forward a policy of informatization of foreign language teaching, and summarizes the ways in which college students’ foreign language learning behavior in the era of big data. Secondly, on the basis of corpus technology, the word vectorization representation of foreign language utterances is carried out, followed by similarity calculation of the word vectorization representation, judging the type of foreign language learning according to the results of foreign language semantic similarity calculation, and calculating the maximum weight path of the word vector sequence by using dynamic planning algorithm. Then, according to the demand analysis of the foreign language teaching corpus, the foreign language teaching corpus is constructed, and the application analysis of the corpus of foreign language teaching is carried out. The results indicate that the students in both classes have a similar understanding of the meaning and lexical properties of vocabulary. However, there is a certain gap in the collocation and utilization of vocabulary, and the corpus-based vocabulary teaching method is more conducive to students’ mastery of the target vocabulary than the traditional vocabulary teaching method, and the level of vocabulary learning is comparatively higher and more effective. The quality of foreign language teaching in colleges and universities can be improved by reference to this study.

Strategies for improving the industrial carbon emission efficiency in China: an approach based on trend prediction and regional learning mechanism.

It is an important way for mining the characteristics of regional carbon emission efficiency and exploring regional similarity to learn from intergovernmental learning mechanisms and regional industrial low-carbon development experiences. This study proposes a regional learning mechanism of industrial carbon emission efficiency (ICEE) prediction and regional similarity analysis to explore strategies for carbon emission reduction. We first calculated the industrial carbon emission efficiency of 30 provinces in China from 2000 to 2021 using the super-SBM model. Secondly, the spatiotemporal characteristics of industrial carbon emission efficiency were explored through the space-time cube model, time series clustering method, and local outlier analysis. Finally, the screening of regions with low efficiency levels and the search for learning objects were realized by forest regression prediction and regional similarity calculation. The results of the study were as follows: (1) There were significant differences in industrial carbon emission efficiency among different provinces. (2) Based on the time series clustering results, we found that there were similar change characteristics of industrial carbon emission efficiency in different provinces. (3) The industrial carbon emission efficiency of most provinces had significant correlation in space and time, mainly in high-high clustering. (4) The industrial carbon emission efficiency of most regions will maintain a high efficiency level in the next 10 years, but the six provinces of Xinjiang, Qinghai, Gansu, Ningxia, Liaoning, and Heilongjiang will always be at a low efficiency level. It is possible to set appropriate learning targets for each region and to find lessons to be learned from the regions with high similarity by calculating the similarity between each province and the six provinces.

Phenotypic similarity-based approach for variant prioritization for unsolved rare disease: a preliminary methodological report

Rare diseases (RD) have a prevalence of not more than 1/2000 persons in the European population, and are characterised by the difficulty experienced in obtaining a correct and timely diagnosis. According to Orphanet, 72.5% of RD have a genetic origin although 35% of them do not yet have an identified causative gene. A significant proportion of patients suspected to have a genetic RD receive an inconclusive exome/genome sequencing. Working towards the International Rare Diseases Research Consortium (IRDiRC)’s goal for 2027 to ensure that all people living with a RD receive a diagnosis within one year of coming to medical attention, the Solve-RD project aims to identify the molecular causes underlying undiagnosed RD. As part of this strategy, we developed a phenotypic similarity-based variant prioritization methodology comparing submitted cases with other submitted cases and with known RD in Orphanet. Three complementary approaches based on phenotypic similarity calculations using the Human Phenotype Ontology (HPO), the Orphanet Rare Diseases Ontology (ORDO) and the HPO-ORDO Ontological Module (HOOM) were developed; genomic data reanalysis was performed by the RD-Connect Genome-Phenome Analysis Platform (GPAP). The methodology was tested in 4 exemplary cases discussed with experts from European Reference Networks. Variants of interest (pathogenic or likely pathogenic) were detected in 8.8% of the 725 cases clustered by similarity calculations. Diagnostic hypotheses were validated in 42.1% of them and needed further exploration in another 10.9%. Based on the promising results, we are devising an automated standardized phenotypic-based re-analysis pipeline to be applied to the entire unsolved cases cohort.

A quantitative fuzzy-valued intersection matrix for obtaining fuzzy relationships between vague spatial objects

Topological relation between geographical objects is one of the key components of the Geographical Information System (GIS). Considering the objects to be exact with sharp boundaries, four intersection, and nine intersection matrices initiated the study of calculating the topological relations. Due to the extensive existence of vague spatial phenomenons, the study of uncertainty object modeling has grown rapidly. Parallelly, the relationship calculating tools have been upgraded to handle vague concepts. The fuzzy 9-intersection matrix and Egg-Yolk methods are popular tools. Although the geographical objects are uncertain, surprisingly, most of these tools generate certain relations between them. The qualitative nature of these tools overlooks the essence of their uncertainty. We propose a quantitative fuzzy-valued 9-intersection matrix to overcome these drawbacks to obtain a fuzzy relationship between uncertain geographical objects. Then two new similarity computation techniques are introduced to calculate the membership grade between the proposed matrix and the known crisp matrices. These similarity computations allow two spatial objects to have partial membership against the eight established topological relations. The quantitative calculations indicate the strength of the relationship. The superiority of the proposed model is exhibited through comparative studies. Further, certain linguistic variables are linked to the evaluated membership grades to generate an immediate association with the known crisp relations. Examples are provided to demonstrate the applicability and efficacy of this association.

A combustion mechanism reduction method based on entropy production analysis in fuel auto-ignition and laminar flames

This study provides a chemical mechanism reduction strategy based on entropy production analyses in both auto-ignition and laminar flames, which enhances the predictive accuracy for laminar burning velocities. In addition to chemical reactions, other irreversible sources causing entropy generation, such as mass diffusion and heat conduction, are considered in the modified approach. Specifically, initial skeletal mechanisms are first generated based on important reactions that contribute to entropy production in auto-ignition processes. Mechanism patches are then constructed to include important species and reactions, which contribute to entropy production from mass diffusion and heat conduction in laminar premixed flames beyond the pre-defined thresholds, respectively. Finally, the initial skeletal mechanisms and mechanism patches are combined to establish the final skeletal mechanisms. In this way, two final skeletal mechanisms for n-dodecane, consisting of 162 species and 2276 reactions, and 160 species and 1916 reactions, respectively, are developed from the detailed POLIMI mechanism with 451 species and 17,848 reactions. The two final skeletal mechanisms are proven to accurately predict laminar burning velocities and entropy production in n-dodecane flames with insignificant variations in the simulation results compared to the detailed mechanism, while their accuracy in predicting ignition delay times relies on the initial skeletal mechanisms. Specifically, the reduced mechanism with 160 species and 1916 reactions exhibits less satisfactory performance in predicting ignition delay compared to that with 162 species and 2276 reactions, indicating that a lower threshold is required to generate the initial skeletal mechanism through entropy production analysis of homogeneous auto-ignition processes. Additionally, compared with the reduced mechanisms with similar sizes obtained with other mechanism reduction strategies, the two final skeletal mechanisms accurately capture the characteristics of laminar burning velocities and ignition delay times, with similar calculation time required.

Self‐calibrating circuit for phase correction to support phased array antenna systems

AbstractA self‐calibrating circuit system for use within active phased array antennas is presented. The system addresses the issue of unwanted channel phase imbalances and random phase errors which can deteriorate array performance. Basically, unwanted insertion phase offsets that can originate within each RF channel, due to the supporting and active circuit components and possible temperature variations, can be eliminated by our designed, simulated, and experimentally verified self‐calibrating circuit. With existing technologies and calibration approaches, it is practically difficult and time consuming to check the phase feeding accuracy, as typically a large number of data sets are required to achieve similar channel corrections. As an alternative, our proposed circuit system allows for the consecutive phase shift between RF channels to be inspected and self‐corrected within a feedback loop managed by a MATLAB‐Arduino program. The active S‐band feed system can self‐calibrate in about 25 s (or less), using commercially available phase sensors and voltage controlled phase shifters. To the best knowledge of the authors, no similar computer controlled feed‐back circuit to support the self‐correction of active phased arrays has been previously reported.

Multimodal discourse analysis of English reading instruction in colleges and universities based on weighted function algorithm

Abstract This paper proposes a kernel clustering method that uses the weighted function method to address the problem of excessive similarity calculation in the kernel function clustering process. Based on function-based clustering with orthogonal basis expansion, function-based principal component analysis is used to reduce the dimensionality of function-based data and extract the first few principal components that can contain the most information of the original data. The factors of each component are assigned with characteristic weights so that the scores of the principal components replace the original functional data for clustering analysis of English reading teaching in colleges and universities. A systematic functional language framework was used to conduct multimodal discourse analysis based on clustering results. The symbolic annotation of pure language reached 14.5, and the symbolic resources of visual imagery reached 85.7 during the CB stage of English reading instruction. The number of semiotic resources was relatively small, so the complexity of the modality was lower than in other stages.

Proposed modification to a muscle-like acid-base switchable [2](2)rotaxane for improved force delivery

ABSTRACTSwitchable rotaxanes are good candidates for molecular machines because of their ability to undergo reversible and repeatable co-conformational isomerism in response to an external stimulus. An acid-base switchable [2](2)rotaxane reported by Li et al. [J Org Chem. 79(15): [6996–7004] has been reported to exhibit a 48% change in length upon a stimulus-induced change in protonation state, suggesting that it could serve as the fundamental element of a ‘molecular muscle’. In the present work, the mechanical behaviour of this topologically complex system is explored with calculations based on semiempirical electronic structure theory. Our calculations predict that the original system will not work as envisioned. Although the calculations confirm a 48% change in length, they also reveal an energy barrier to ring shuttling upon deprotonation, preventing the immediate delivery of force. Therefore, a modification to the system is proposed. Similar semiempirical calculations were carried out on the modified system and the force calculated. The average force generated from the modified system is predicted to be approximately −86 pN. The results demonstrate that the proposed modification reduces the force generated by ring shuttling by 11% but largely eliminates the energy barrier to ring shuttling so that the force is immediately available.

Calculation of Temperature Fields in a Lithium Ceramic Pebble Bed during Reactor Irradiation in a Vacuum.

Two-phase lithium ceramic Li2TiO3-Li4SiO4 is considered as a tritium multiplier for use in the solid blanket of fusion reactors. To date, the most accurate understanding of the processes of tritium and helium production and release occurring in the breeder blanket materials under neutron irradiation can only be obtained from experiments in fission research reactors. At that, irradiations in vacuum give the possibility to register even very fast gas release processes (bursts) from the ceramics' voids and pores, although it reduces the thermal conductivity of the pebble bed. The purpose of this work was to simulate the heating of mono-sized pebble bed (1 mm in diameter) of two-phase lithium ceramic 25 mol%Li2TiO3+75 mol%Li4SiO4 in an ampoule device during neutron irradiation at the WWR-K research reactor under vacuum conditions, and to determine experimental parameters in order to prevent heating of the lithium ceramics up to the Li4SiO4-Li2SiO3 phase transition temperatures (>900 °C). For the first time, it was obtained that the effective thermal conductivity of a 1 mm mono-sized pebble bed of 25 mol%Li2TiO3+75 mol%Li4SiO4 significantly decreases (four times) when it is irradiated with neutrons in a vacuum (at a helium pressure of approximately 10 Pa), compared to a similar calculation at 100 kPa of helium (when the He sweep is used). It was concluded that it is difficult to evaluate the maximal temperature of the ceramics in the capsule by measuring the temperature of its outer metal wall (according to thermocouple readings) without using the results of thermophysical calculations for each type of ceramic, taking into account its quantity, specific heat release and pebble size(s). To control the temperature of the ceramics during an irradiation experiment in a vacuum, an in-capsule thermocouple should be used, placed in the center of the pebble bed. Measuring the temperature of the pebble bed based on the capsule wall temperature can lead to overheating of the ceramics and phase changes.