Data Reduction Model Research Articles

Integrated sources model: A new space-learning model for heterogeneous multi-view data reduction, visualization, and clustering

In machine learning, multi-view data involve multiple distinct sets of attributes (“views”) for a common set of observations; when each view has the same attributes considered in different contexts, the data are said to contain multiple views of homogeneous format, which can be conceptualized as a tensor. In this article, we describe a novel approach for integrating multiple views of heterogeneous format into a common latent space using a workflow that involves non-negative matrix and tensor factorization (NMF/NTF). This approach, which we refer to as the integrated sources model (ISM), consists of two main steps: Embedding and analysis. In the embedding step, the views are transformed into matrices with common non-negative components. In the analysis step, the transformed views are combined into a tensor and decomposed using NTF. We also present a variant of ISM; the integrated latent sources model (ILSM), which offers significant advantages over ISM in terms of computational power and in cases where the views are highly unbalanced with regard to the number of attributes per view. Noteworthy, ISM can be extended to process multi-omic and multi-view datasets even in the presence of missing views. We provide a proof-of-concept analysis using five examples, including the UCI Digits (the University of California Irvine Pen-Based Recognition of Handwritten Digits) dataset, a public cell-type gene signatures dataset, and a multi-omic single-cell dataset. These examples demonstrate that, in most cases, multi-view clustering is better achieved with ISM or its variant ILSM than with other latent space approaches. We also show how the non-negativity and sparsity of the ISM model components enable straightforward interpretations, in contrast to other approaches that involve latent factors of mixed signs. Finally, we present potential applications to single-cell multi-omics and spatial mapping, including spatial imaging, spatial transcriptomics, and computational biology, which are currently under evaluation. ISM relies on state-of-the-art algorithms invoked through a simple workflow implemented in Python.

Tinjauan Ekonomi Islam Terhadap Upah Mandor Pada PT. Kartika Hijau Lestari

The aim of the research is to determine the system for determining wages for palm oil plantation foremen and the forms of Islamic economic values at PT Kartika Hijau Lestasi Serafoh plantation, Sangga Lima village, Gebang district. Langkat Regency,. North Sumatra. The method used is descriptive qualitative with a data reduction model, data presentation and drawing conclusions. The research results show that the palm oil plantation wage system in determining wages is in accordance with the principles of Islamic Economics, because the oil palm plantation owner discusses in advance the amount of wages that will be given. Regarding payment procedures, oil palm plantation owners do not delay giving wages, wages are given directly to workers after the workers have finished their work. The wage system for palm oil plantations meets the value of justice, because in providing wages to workers there are differences in wages for each type of work carried out. However, the wages paid do not meet the appropriate value, because the wages set cannot meet the living needs of workers even though the wages paid are in accordance with Langkat Regency UMK standards.

The Kyai’s Strategy to Develop Cadres with Islamic Characters

This article aims to explain the Islamic character regeneration model in Islamic boarding schools and the strategy of the kyai as the highest leader in developing Islamic character regeneration for students. The location of this research, namely Arrisalah Modern Islamic Boarding School Ponorogo, is unique from other sites because of the curriculum or general subjects at the Islamic boarding school, which is famous for its salaf curriculum, which is integrated with Islamic values typical of Islamic boarding schools. This study uses an interpretive paradigm through a qualitative approach, a case study type. The primary data were obtained from caregivers, madrasa heads, teachers, students, and other parties involved, using data collection techniques such as in-depth interviews, observation, and documentation. The analysis technique used is the Miles and Huberman data reduction models, data display, and verification. Checking data validity using triangulation: credibility, transferability, dependability, and confirmability. The study results show that the cadre model at Arrisalah Modern Islamic Boarding School is a reduction or transfer of the Islamic boarding school's Islamic character and values. The kyai's strategy in creating cadres with Islamic character is actualized in several aspects, including the kyai's example, curriculum, 'Ala Manhaj Rabbaniy education system, organization, Pondok discipline, and compulsory service. So, this research provides a lot of valuable insights into the cadre model in Islamic boarding schools, and it cannot be denied that there are still various opportunities for more in-depth and broad future research.

Model-based evaluation of spatiotemporal data reduction methods with unknown ground truth through optimal visualization and interpretability metrics.

Optimizing and benchmarking data reduction methods for dynamic or spatial visualization and interpretation (DSVI) face challenges due to many factors, including data complexity, lack of ground truth, time-dependent metrics, dimensionality bias and different visual mappings of the same data. Current studies often focus on independent static visualization or interpretability metrics that require ground truth. To overcome this limitation, we propose the MIBCOVIS framework, a comprehensive and interpretable benchmarking and computational approach. MIBCOVIS enhances the visualization and interpretability of high-dimensional data without relying on ground truth by integrating five robust metrics, including a novel time-ordered Markov-based structural metric, into a semi-supervised hierarchical Bayesian model. The framework assesses method accuracy and considers interaction effects among metric features. We apply MIBCOVIS using linear and nonlinear dimensionality reduction methods to evaluate optimal DSVI for four distinct dynamic and spatial biological processes captured by three single-cell data modalities: CyTOF, scRNA-seq and CODEX. These data vary in complexity based on feature dimensionality, unknown cell types and dynamic or spatial differences. Unlike traditional single-summary score approaches, MIBCOVIS compares accuracy distributions across methods. Our findings underscore the joint evaluation of visualization and interpretability, rather than relying on separate metrics. We reveal that prioritizing average performance can obscure method feature performance. Additionally, we explore the impact of data complexity on visualization and interpretability. Specifically, we provide optimal parameters and features and recommend methods, like the optimized variational contractive autoencoder, for targeted DSVI for various data complexities. MIBCOVIS shows promise for evaluating dynamic single-cell atlases and spatiotemporal data reduction models.

Multiscale ultra‐short‐term wind power prediction model based on GD‐IFEM‐PSO and VMD‐BP

AbstractWind power prediction enables advance prediction of the future capacity of wind farms to improve production, increase capacity and reduce costs, however, wind power generation data are highly unstable, making it difficult to achieve high‐precision prediction. Signal decomposition methods and optimization algorithms allow for data smoothing and optimization of parameter settings, but the limitations of parameter dependency and the slow optimization search process prevent existing research from being applied in a practical setting. To address these issues, this article proposes a multiscale ultra‐short‐term wind power prediction model based on particle swarm optimization based on greedy dynamic and integrated fitness evaluation method (GD‐IFEM‐PSO) and variational mode decomposition and back propagation network (VMD‐BP). First, this article improves the velocity update formula in the particle swarm optimization algorithm by the proposed GD weight and sets up a fitness function using multiple evaluation metrics by the proposed integrated fitness evaluation method (IFEM), so that the improved particle swarm optimization algorithm achieves high efficiency in the optimization search while ensuring the comprehensiveness of the evaluation. Second, the variable mode decomposition (VMD) decomposition algorithm is used to decompose the historical wind power data to achieve smoothing of the wind power data, and the improved particle swarm optimization algorithm is used to optimize the K and ⍺ values in the VMD decomposition algorithm to improve the comprehensive performance of the decomposition. Then, to achieve data reduction and fast model training, the components are divided into trend components, low‐frequency vibration components, high‐frequency vibration components, and random noise components according to the central frequency, so that the model can better grasp the data trend while reducing the number of components and achieving high prediction accuracy. Finally, the different components are predicted by the BP neural network and the predicted values of the different categories of components are reconstructed into the final wind power prediction values. To demonstrate the rationality and progressiveness of the proposed model, several models are compared on several data sets, and the results show that the proposed model has faster prediction speed and higher prediction performance, and is more suitable for application in real‐world environments.

A Long Short-Term Memory-Based Model for Kinesthetic Data Reduction

This paper proposes a novel mathematical model for teleoperation over communication networks. For teleoperation over a communication network, a high packet rate can result in inefficient data transmission and cross-traffic problems, leading to extra delay and jitter. This paper proposes an LSTM-based mathematical model which focuses on kinesthetic data reduction without loss of transparency during the transmission process through joint training combined with haptic data and perceptual deadband. Since the LSTM network can deal with a time series of haptic data, we further test the system performance through practically collected data. We investigate the packet rate and perceptual transparency of the proposed mathematical model by comparing with the conventional deadband. Additionally, we compare the proposed mathematical model with the perceptual deadband-based codecs. Simulation results show that the proposed solution further reduces the packet rate when dealing with haptic data without noticeable distortion. Also, comparing with the current JND perceptual threshold, the proposed mathematical model helps improve the practicality of the bilateral teleoperation system without losing transparency.

On the sampling strategies and models for measuring diffusion exchange with a double diffusion encoding sequence

Water exchange between the different compartments of a heterogeneous specimen can be characterized via diffusion magnetic resonance imaging (dMRI). Many analysis frameworks using dMRI data have been proposed to describe exchange, often using a double diffusion encoding (DDE) stimulated echo sequence. Techniques such as diffusion exchange weighted imaging (DEWI) and the filter exchange and rapid exchange models, use a specific subset of the full space DDE signal. In this work, a general representation of the DDE signal was employed with different sampling schemes (namely constant b1, diagonal and anti-diagonal) from the data reduction models to estimate exchange. A near-uniform sampling scheme was proposed and compared with the other sampling schemes. The filter exchange and rapid exchange models were also applied to estimate exchange with their own subsampling schemes. These subsampling schemes and models were compared on both simulated data and experimental data acquired with a benchtop MR scanner. In synthetic data, the diagonal and near-uniform sampling schemes performed the best due to the consistency of their estimates with the ground truth. In experimental data, the shifted diagonal and near-uniform sampling schemes outperformed the others, yielding the most consistent estimates with the full space estimation. The results suggest the feasibility of measuring exchange using a general representation of the DDE signal along with variable sampling schemes. In future studies, algorithms could be further developed for the optimization of sampling schemes, as well as incorporating additional properties, such as geometry and diffusion anisotropy, into exchange frameworks.

Dinamika Kebijakan Sistem Zonasi Pendidikan di Indonesia

The education zoning policy in Indonesia is a new breakthrough by the government for equal distribution of the quality of education in all regions in Indonesia. The existence of an educational dichotomy marked by the labeling of favorite schools causes an imbalance in the distribution of quality and students in Indonesia. The education zoning policy has been in effect since 2017 and has undergone several technical changes in its implementation. This study uses a qualitative method, by applying a literature study model. The data collection is taken by data reduction model, data display, verification, and drawing conclusions. Even though the zoning policy has been in effect since 2017, issues regarding the quality of education and students are still an important area of ​​discussion in the field of education in Indonesia. This is indicated by the imbalance in the number of new students in private schools and public schools as well as changes in the model of implementation of learning programs that have occurred since the zoning policy came into force. Therefore, the Indonesian government needs to improve the existing zoning policy system, so that the main objectives of the educational zoning policy can be properly realized.

Data reduction considerations for the burning velocity of spherical constant volume flames of R32 (CH2F2) with air

The present work explores data reduction techniques for the measurement of the laminar burning velocities of R32(CH2F2)-air mixtures using a constant volume combustion device, in which the pressure-time history is the only measured parameter. To allow clear assessment of the accuracy of the data reduction methods, the pressure-time histories used for analysis are synthetically generated via a detailed numerical simulation employing full kinetics and with and without an optically-thin radiation model. Various data reduction models are employed, including a two-zone model and two multi-zone models, and these are compared with the results from the burning velocity obtained from the output of the numerical simulation. The data reduction schemes are shown to be accurate if the same radiation model is employed in the data reduction as was used in the flame simulation to generate the pressure trace used for post-processing. If the incorrect radiation model is employed, however, the errors can be quite large. The effects of stretch, radiation, and different data post-processing methodologies are explored and the errors quantified. Stretch is shown to be important for the early stages and the selected data range that is used for extrapolation has a significant effect on the extrapolated burning velocity. However, with an appropriate choice of data considered for extrapolation, the prediction of the unstretched burning velocity can be quite accurate.

Innovation, Alliances and Management of Halal Products as Strategies to Increase MSME Competitiveness in the Era of the Covid-19 Pandemic

Purpose – This study discusses the efforts made by MSMEs in the culinary field in the aspects of innovation, alliances and halal products to maintain their business during the pandemic, then these efforts are compiled into MSME recommendations to increase the competitiveness of the New Normal Era. Methodology/approach – This writing uses a descriptive qualitative method using a literature study of Miles Hubermen's data reduction model with trigulation validity. The author conducted semi-structured interviews by determining the object of the interview based on the criteria. Findings – The results of the writing are that the MSMEs in the culinary field that the writer interviewed did product innovation, organizational innovation and marketing innovation, some made alliances and all participants carried out halal product management. Novelty/value – This writing contributes to MSMEs to increase the competitiveness of the New Normal Era and improve knowledge, especially the management of product innovation, organizational innovations, marketing innovations, alliances and halal products

Analysis of Mathematical Problem-Solving Ability Based on Self-Efficacy and Self-Confidence of Class VIII Junior High School Students

This study aims to describe students’ mathematical problem-solving ability of Junior High School in terms of students’ self-efficacy (SE) and self-confidence (SC). This typr of research is descriptive qualitative. This research was conducted in class VIII at one of Junior High Schools in Jakarta. Subjects in this research based on the result of questionnaire selected 3 students who had SE and SC from high, medium and low. The research’s instruments are SE and SC questionnaires, mathematical problem-solving ability tests, and interviews.The test and non-test instruments were validated by expert validators before being tested for validity and reliability. The data analysis techniques uses the Miles and Huberman model for data reduction, data presentation, and drawing conclusions. Triangulation techniques was used to test the validity of the research data. The results of this research indicate that students with high SE and SC had the best mathematical problem-solving abilities compared to students with medium to low SE and SC. Students with high SE and SC fulfill 4 indicators of problem-solving ability with maximum scores. Students with medium to low SE and SC only met 3 indicators of problem-solving ability.

How Do Magnetic Field Models Affect Astrophysical Limits on Light Axion-like Particles? An X-Ray Case Study with NGC 1275

Axion-like particles (ALPs) are a well-motivated extension to the standard model of particle physics, and X-ray observations of cluster-hosted AGN currently place the most stringent constraints on the ALP coupling to electromagnetism, g a γ , for very light ALPs (m a ≲ 10−11 eV). We revisit limits obtained by Reynolds et al. using Chandra X-ray grating spectroscopy of NGC 1275, the central AGN in the Perseus cluster, examining the impact of the X-ray spectral model and magnetic field model. We also present a new publicly available code, ALPro, which we use to solve the ALP propagation problem. We discuss evidence for turbulent magnetic fields in Perseus and show that it can be important to resolve the magnetic field structure on scales below the coherence length. We reanalyze the NGC 1275 X-ray spectra using an improved data reduction and baseline spectral model. We find the limits are insensitive to whether a partially covering absorber is used in the fits. At low m a (m a ≲ 10−13 eV), we find marginally weaker limits on g a γ (by 0.1–0.3 dex) with different magnetic field models, compared to Model B from Reynolds et al. (2020). A Gaussian random field (GRF) model designed to mimic ∼50 kpc scale coherent structures also results in only slightly weaker limits. We conclude that the existing Model B limits are robust assuming that β pl ≈ 100, and are insensitive to whether cell-based or GRF methods are used. However, astrophysical uncertainties regarding the strength and structure of cluster magnetic fields persist, motivating high-sensitivity RM observations and tighter constraints on the radial profile of β pl.

Manajemen Kesiswaan Dalam Peningkatan Kualitas Input Peserta Didik di MA. As-Salafiyah Sumber Duko Pakong Pamekasan

This study aims to describe student management in improving the quality of student input in MA. As-Salafiyah Source Duko Pakong Pamekasan. The approach used in this research is qualitative with qualitative descriptive research methods. The data collection technique of this research was done by interview, observation and documentation. Data analysis was carried out by analyzing descriptive data using triangulation techniques, the analysis model using a data reduction model, data display, and verification. So that it can give meaning to the data that has been collected and conclusions can be drawn from the data. The research subjects were Deputy Head of Student Affairs and Deputy Head of Curriculum. The results showed that the implementation of student management in improving the quality of student input in MA. As-Salafiyah Sumber Duko Pakong Pamekasan, namely: 1. Applying the analysis of student needs, 2. Recruiting students, 3. Selection of new students. Based on the analysis of the data sources obtained, it can be concluded that the implementation of student management in improving the quality of student input in MA. As-Salafiyah Sumber Duko Pakong Pamekasan has implemented the needs of new students, recruited new students, and selected new students.

Parents Assistance on Down Syndrome Children: Pastoral Counseling Perspective

Researchers aim to find out how to strengthen and assist parents with strategies for children with Down syndrome to become independent. The method used is qualitative—the procedure used for data collection by observation, interviews, and documentation. Furthermore, the data were analyzed using a data reduction model, data presentation, and verification. The resource persons for this study were ten parents with Down Syndrome children and servants of God involved in pastoral care for families with Down Syndrome children in Jakarta. The results showed that the community for parents with Down Syndrome children is essential because it provides reinforcement and support in mentoring children with down syndrome. After attending formal and informal therapy, Down syndrome children have significant developments that enable DS children to be independent. The primary model of parental assistance is holistically providing whole love and providing therapies, especially music and spiritual therapy.

The Development and Validation of the Christian Environmentalism Scale (CES) from a Philippine Sample

Abstract Responding to emerging ecotheologies, we articulated a new environmental construct distinct from previous environmental measures which largely follow developmental models. In the present study we developed and reviewed the psychometric properties of the Christian Environmentalism Scale (CES) initially in three dimensions from 970 youth participants in the Philippines. Study design included exploratory and confirmatory factor analysis for data reduction and best fit model analysis. CES is a unidimensional 15-item measure with a high internal consistency (Cronbach’s alpha = .95) score and adequate results for best fit model. CES had significant associations with pro-environment and religious attitude scales indicating initial evidence of convergent validity.

THE SURVIVABILITY OF SMALL MICRO ENTERPRISE (SME) IN INDRAGIRI HULU DURING THE EPIDEMIC

The spread of Corona Virus in along 2020 is unpredictable and uncontrolled. According to this condition, a lot of Big Company weakened and got involved in to a worst situation and even collapse. And of course, Small Micro Enterprise (SME) will be or had been bothered by the impact. Indonesian Government has made various efforts to avoid the effects of the COVID-19 epidemic on SME. There are a lot of ways and approaches implemented to overcome it. The objectives of the study is to find win-win solution for SME to keep survive during the COVID-19 epidemic. The population of the study is the Indragiri Hulu SME Entrepreneurs. The Category of five years are selected to be the sample which is limited to 10 successful survival entrepreneurs that represents the descriptive category. Moreover, the data analysis technique used in this study was data analysis with a data reduction model that was obtained from data collection and data display. The results of this study show six strategies based on push and pull factors theories, as social psychology theory which develop the survivalist entrepreneurship in Indragiri Hulu. The strategy also needs to pay attention on opportunity, added value, necessity, overcome and avoiding dissatisfaction of the previous way. Furthermore, it is also suggested that an entrepreneur of SME players needs to play strategy in order to establish and survive the business during the epidemic, because the epidemic is not the end of everything.

Strategi Pembelajaran Guru PAI dalam Mengatasi Kesulitan Kegiatan Belajar Mengajar pada Era Pandemi Covid-19

This research aims to determine the application of learning strategies carried out by schools, especially PAI teachers in overcoming the difficulties of teaching and learning activities in grade 4 SD Dua Mei Ciputat. The method used in this research is qualitative with qualitative descriptive research. Data collection techniques using interviews, observation and documentation. Primary data sources come from school principals, PAI teachers, and 4th grade students, while secondary data sources come from school data, books, journals, articles and theses. The data analysis technique uses a data reduction model, data presentation, and data verification or conclusions. The results of this study indicate that the implementation of contextual learning strategies has been going well. This is seen from several aspects, namely PAI teachers have planned learning strategies in the era of the covid-19 pandemic, minimized difficulties in online teaching and learning activities and collaborated in online teaching and learning activities with parents. The supporting factors in carrying out online learning include learning strategy innovations, assistance with school wifi facilities for teachers and good coordination between parents and teachers. While the inhibiting factors are the internet network which is less stable, there are some parents who do not understand the use of the internet and the competence of students decreases

Nationalist character at Muhammadiyah school

One of the ways to strengthen nationalist character values is through formal education, including in Muhammadiyah schools. The practice needs strengthening in Muhammadiyah schools for deviations in student behavior that do not reflect the values of nationalist character. This article attempts to provide an overview of strengthening the values of nationalist characters through Muhammadiyah school culture. The research method used is qualitative research. The focus of the object in this study is strengthening the value of nationalist character for students in Muhammadiyah schools. The steps of data analysis using data reduction models, data presentation, conclusions. The study results show that there are several school programs to instill the values of nationalist character through school culture, among others, through learning in the classroom and habituation outside the classroom, among others, through extracurricular activities. The supporting factor is that the school establishes with the parties to run the program. The inhibiting factor is that some students are still not optimal in participating in a school program.

IMPLEMENTASI KURIKULUM 2013 (TERPADU) DI RA BAITURRAHMAN BIMA CIREBON

<p align="center"><strong><em>Abstract</em></strong><strong><em></em></strong></p><p><em>The curriculum is an important foundation in the learning process at RA Baiturrahman Bima Cirebon which starts from planning to assessment. Curriculum development tailored to the needs of society and child-oriented. The purpose of this study was to determine the implementation development of the 2013 curriculum. The method used in this study used a descriptive qualitative approach. The data collection techniques used in this study used observation to see firsthand the implementation of the 2013 curriculum, interviews to obtain more valid information, and documentation. The data analysis technique in this observation is using the data reduction model of Miles and Huberman in order to retrieve the actual data and this research uses the triangluation technique to test the validity of the data that has been obtained.</em></p>

An effective data reduction model for machine emergency state detection from big data tree topology structures

An effective data reduction model for machine emergency state detection from big data tree topology structures