Data Reduction Methods Research Articles

Implementation Of The School Literacy Movement (GLS) In Growing Reading Interest Of The Students

According to some studies, reading is not very important for children in Indonesia. Students' reading interest is low, particularly in elementary schools. This is evidenced by children's lack of reading habits. The objective of this study was to determine: (1) the effectiveness of the School Literacy Movement (GLS) in fostering students' reading interest at SD Anugrah Denpasar; (2) the development of reading interest through GLS at SD Anugrah Dennpasar; and (3) the supporting and inhibiting factors of GLS in fostering students' reading interest. This study was qualitative method. Methods of data collection included observation, interviews, literature reviews, and documented studies. Data collection, data reduction, data presentation, inference, and verification methods were used to analyze the collected data. The findings of this study showed that (1) The stages of habituation, development, and learning were discussed in the context of GLS implementation. (2) Through GLS on indicators of reading interest, such as the need for reading material, actions to find reading materials, pleasure of reading material, interest in reading, and the desire to always read, students' reading interest can grow. (3) GLS supporting factors included: active participation of school members, planned programs, students, parental support, school environment, and facilities. The inhibiting factors included coordinators' consistency, the condition of the books, different interests, time allocation, and socialization.

Tamtsil Al Marah Fi Majmuati Qashaid “Anta-Lii” Li Nizar Qabaani Bi Nadzariyah Michael Riffatere

This research aims to know the indicative reading, the hermeneutic reading, and the indirect expression of the representation of women in the collection of poems "You are mine" by Nizar Qabbani by Michael Rifter. The research questions are. 1) What is the indicative reading (Heuristik) and hermeneutic reading (Hermeneutik) in Nizar Qabbani’s collection of poems ”Anti Lii” according to Nizar Qabbani? 2) What is the indirect expression of the representation of women in the collection of Nizar Qabbani's poems "Anti Lii" by Michael Riffatere? Semiotics is the science of signs in a context in which people's lives are discussed directly and pictorially. Semiotics can also be the study of signs found about human life. Poetry is in fact mainly created by the author according to his life experience and social environment. The type of research in this research is desk study. The research method used in this research is qualitative and descriptive. And the way to put data in a way to read and write. To analyze the data, the method used is Mills and Huberman, which consists of three stages, namely: the method of data collection, data reduction, and drawing conclusions. The result of this research is in a collection of poems you are mine entitled, clothes, blue swimsuits and he in each stanza contains elements of heuristic and heurmenetic meanings. Among the indirectness of expression in the poem entitled clothes there are 5 words, the pink nightgown poem is in 5 words and in the poem entitled swimsuit there are 7 words which indicate the indirectness of expression and describe representasion about woman

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.

Rate-dependency analysis of mode I delamination by means of different data reduction strategies for the GDCB test method

The loading-rate mechanical response of the mode I delamination in composites and adhesively bonded joints was investigated. The tests were carried out using a recently developed Guided Double Cantilever Beam test method. Three different data reduction methods were proposed and assessed: a displacement-based formulation, a near-crack-tip displacement formulation, and a numerical assessment using the Virtual Crack-Closure Technique method. The methods account for the dynamic effects which may be present. While small differences between the three different methods can be seen, no rate-dependency higher than the uncertainty for the materials and the loading rates considered have been evidenced.

Kinerja Birokrasi dalam Mempersiapkan Bahan Administrasi Pembuatan Daftar Urut Kepangkatan dan Analisis Jabatan Sekretariat DPRK Langsa

This study aims to identify and analyze the performance of the bureaucracy in preparing administrative materials for making rank order lists and position analysis for the Langsa City DPR Secretariat and to find out the factors that support and hinder bureaucratic performance in preparing administrative materials for making rank order lists and position analysis for the Langsa City DPR Secretariat. The method used is a qualitative method with an inductive approach. Data collection techniques include interviews, observations, and documentation. This research was conducted at the Langsa DPRK Secretariat Office from October 2022 – September 2022. This research used the method of data collection, data reduction, data presentation, and drawing conclusions. The results of the study revealed that there were 5 stages in knowing the performance of the bureaucracy in preparing administrative materials for making rank order lists and analysis of program and financial positions of the DPRK Langsa Secretariat consisting of (1) Productivity found a discrepancy between the position map structure of the DPRK Langsa Secretariat and Job Analysis and Workload Analysis, (2) Responsiveness found the position analysis document of the DPRK Langsa Secretariat last made in 2018 which was not in accordance with the Organizational Structure, (3) Service Quality found that there were no problems and was counted well, (4) Responsibility found that the head of the General Administration Subdivision and Personnel do not know the main tasks and functions of the position before taking office and after taking office, (5) Accountability is known in the General Administration and Personnel Sub-Division that has not been able to meet accountability criteria such as being result-oriented and improving work.

Strategi Marketing Mix pada Bisnis Tanaman Hias untuk Mencapai Keunggulan Bersaing Ditinjau dari Etika Bisnis Islam

The ornamental plant business has good profit prospects so that there are more and more enthusiasts day by day, so new strategies are needed in order to survive and respond to challenges in the midst of increasingly fierce competition. One way for Kebon Godonk's business to achieve competitive advantage is to understand and apply the concept of a marketing mix strategy. The aspect used is the marketing mix with 4P which consists of product, price, place, promotion. The purpose of this study is to evaluate the effectiveness of implementing the marketing mix strategy in achieving competitive advantage, taking into account the principles of Islamic business ethics. Researchers will evaluate the extent to which the marketing strategy has been implemented properly, therefore companies must be able to analyze to meet consumer needs so as to create consumer awareness and interest in related goods or services. The research method used is descriptive qualitative method. Methods of data collection, data reduction, data presentation, and drawing conclusions are also applied. Data validity will be tested using triangulation techniques. From the research that has been done, it shows that the implementation of the marketing mix strategy in Kebon Godonk is very influential in increasing this business so that it can develop and be able to compete with other entrepreneurs. The strategy carried out has also implemented the concepts of Islamic business ethics such as divinity, honesty, responsibility and justice.

PENERAPAN STANDAR PELAPORAN KEUANGAN SERTA HUBUNGANNYA DENGAN ETIKA DAN TATA KELOLA PERUSAHAAN

This study aims to describe and analyze the application of financial reporting standards and their relationship with ethics and corporate governance. The method used is qualitative research data reduction method with the type of research that is descriptive statistics and the data used is secondary data. The results of this study indicate that the application of General SAK has been applied to companies, especially companies listed on the IDX, but the implementation of SAK ETAP, SAK EMKM, and SAK Syariah has not been maximized. There are still many entities that do not comply with applicable standards in compiling company performance reports. This is due to limitations and what stands out is due to limited quality of human resources in the entity. Meanwhile, in the implementation of financial recording and reporting, there is ethics from an accountant that needs to be considered because accountants will often be faced with problems in the form of ethical dilemmas. As in making reporting decisions, accountants will be faced with choices that create a dilemma so that accountants need to have a reference in the form of a code of ethics to be accountable for the decisions they have taken. The existence of ethical dilemmas that cannot be resolved will cause an imbalance in the company and make the management of a company not run optimally.

Operational Performance of Roundabouts at High-Speed and Rural Locations

A recent research project investigated the operational benefits of modern roundabouts for high-speed locations that serve heavy vehicles, while developing examples of best practices for design. The research team compiled proven results from designs in other states and collected and analyzed data from within and outside of Texas to develop updated design guidance, which can be used to implement roundabout designs that accommodate oversize/overweight (OSOW) vehicles in rural areas. This paper summarizes activities and findings from that project. Project activities indicate that the drone-based data collection effort, along with a computer algorithm-based method for data reduction, was an effective and efficient alternative to traditional data collection and reduction methods for this project. Findings from field data and simulation confirmed practices in other states that roundabouts can be designed to accommodate not only heavy vehicles but also OSOW vehicles in rural locations with high-speed approaches. The roundabout with the largest diameter studied (180 ft) had the best performance, but the smallest roundabout (120-ft diameter) was also able to accommodate larger vehicles at lower volumes, and roundabouts of all sizes generally outperformed two-way stop-controlled intersections in simulation results.

A New Data Dimension Reduction Method Based On Convolution In The Application Of Authenticity Identification Of Traditional Chinese Medicine LongGu

As a commonly used Chinese herbal medicine in clinic, LongGu is often used as a powder, which is difficult to identify manually. In this study, unsupervised learning method was used to model and identify LongGu, calcining LongGu and LongGu counterfeits by infrared spectroscopy. After preprocessing the original data, the current commonly used principal component analysis method, which was used to reduce the data dimension, and then the KMeans algorithm was used to realize the classification and identification of the samples. The classification results were better in the theoretical samples, but not in the actual samples. Based on this problem, this paper proposes a convolution-based machine learning feature dimension reduction method for spectral data. Comparing with the data method of principal component analysis, the partial classification effect is obviously better on the theoretical data. The effect of identifying calcining LongGu and LongGu counterfeits in actual data has been significantly improved, and it also provides a reference for the application of machine learning technology in the field of spectral identification of traditional Chinese medicine.

Film condensation with high heat fluxes and scaled experiments using pure steam for reactor containment cooling

Condensation tests were performed using a newly developed test facility for scaling the passive containment cooling system (PCCS) to a small modular reactor (SMR). The PCCS of the SMR plays a pivotal role in ensuring greater safety, reliability, and compactness than what is afforded by traditional reactors. Therefore, a well-designed PCCS is essential to SMRs. However, previous studies and test data were unsuitable for scaling, due to high variation in the test geometry and operating conditions. This study intends to close this research gap by using a novel designed scaled test facility consisting of vertical condensing test sections featuring 1-, 2-, and 4-inch-diameter condensing tubes with annular water cooling, and by applying superheated and saturated steam with different steam mass flow ranges of 5–25 g/s. The primary test data, including axial temperatures, mass flow rates, and pressures, were used in conjunction with a standard data reduction method to estimate critical parameters such as heat fluxes, heat transfer coefficients, and condensation rates. These scaled test data would support improving empirical correlations and validating condensation models to identify scaling distortion for SMR PCCSs.

Full-frame Data Reduction Method: A Data Mining Tool to Detect the Potential Variations in Optical Photometry

A Synchronous Photometry Data Extraction (SPDE) program, performing indiscriminate monitoring of all stars appearing in the same field of view of an astronomical image, is developed by integrating several Astropy affiliated packages to make full use of time series observed by traditional small/medium aperture ground-based telescopes. The complete full-frame stellar photometry data reductions implemented for the two time series of cataclysmic variables: RX J2102.0+3359 and Paloma J0524+4244 produce 363 and 641 optimal light curves, respectively. A cross-identification with SIMBAD finds 23 known stars, of which 16 are red giant-/horizontal-branch stars, 2 W UMa-type eclipsing variables, 2 program stars, an X-ray source and 2 Asteroid Terrestrial-impact Last Alert System variables. Based on the data products from the SPDE program, a follow-up light curve analysis program identifies 32 potential variable light curves, of which 18 are from the time series of RX J2102.0+3359, and 14 are from that of Paloma J0524+4244. They are preliminarily separated into periodic, transient, and peculiar types. By querying for the 58 VizieR online data catalogs, their physical parameters and multi-band brightness spanning X-ray to radio are compiled for future analysis.

COORDINATION STRATEGY OF GOVERNMENT AGENCIES IN THE DEVELOPMENT OF BORDER AREA IN MOROTAI ISLAND REGENCY FISHERIES AND TOURISM SECTOR 2015-2019

The poverty rate for the people of Morotai Island Regency from 2015 to 2019 has increased. In addition, the poverty depth and poverty indices have increased indicating that the poverty condition of the people in Morotai Island Regency is getting worse. This study discusses the issue of coordination mechanisms in the development of borders in Morotai Island Regency in the leading sectors, namely the fisheries and tourism sectors carried out by the KKP and the Ministry of Tourism and Creative Economy in 2015 - 2019 within the BNPP container as well as between the Tourism Office and DKP and BPPD in the Regions as well as functionally between ministries with Technical OPD and Coordination between BNPP and BPPD. This research was conducted qualitatively where data sources were obtained from observation, interviews, documentation, and visual materials. In determining informants, they were selected purposively to obtain in-depth information from one informant and then move on to other informants. Meanwhile, researchers conducted a literature review and searched other documents to collect supporting data, and were evaluated using the data reduction method. Research shows that the formulation of the strategy needed to overcome the problem of coordinating the development of border areas in Morotai Island Regency in the fisheries and tourism sectors in 2015 - 2019 consists of Anticipatory Coordination, Adaptive Coordination, Cultural Strategy, Communicative Strategy, Functional Strategy, Cooperation Strategy, Informational Strategy, Control Strategy, Structural Strategy, Political Strategy.

Development of a Unified Specimen for Adhesive Characterisation-Part 1: Numerical Study on the Mode I (mDCB) and II (ELS) Fracture Components.

Adhesives are increasingly being employed in industrial applications as a replacement for traditional mechanical joining methods, since they enable improvements in the strength-to-weight ratio and lower the cost of the overall structures. This has led to a need for adhesive mechanical characterisation techniques that can provide the data needed to build advanced numerical models, allowing structural designers to expedite the adhesive selection process and grant precise optimisation of bonded connection performance. However, mechanically mapping the behaviour of an adhesive involves numerous different standards resulting in a complex network of various specimens, testing procedures and data reduction methods that concern techniques which are exceedingly complex, time-consuming, and expensive. As such, and to address this problem, a novel fully integrated experimental characterisation tool is being developed to significantly reduce all the issues associated with adhesive characterisation. In this work, a numerical optimisation of the unified specimen's fracture toughness components, comprising the combined mode I (modified double cantilever beam) and II (end-loaded split) test, was performed. This was achieved by computing the desired behaviour as a function of the apparatus' and specimens' geometries, through several dimensional parameters, and by testing different adhesives, widening the range of applications of this tool. In the end, a custom data reduction scheme was deduced and set of design guidelines was defined.

Optimal Cluster-Based Topology and Deep LSTM-Based Prediction Method for Data Reduction in IoT

In the Wireless Sensor Network (WSN), the data prediction approach is needed to attain data effectively by diminishing node energy consumption. Hence, in this research, Water cycle Fire Fly Optimization and Deep Long Short-Term Memory (WCFO+ Deep LSTM) approach is employed for aggregation and reduction of data. The processes involved in the developed method are node simulation, cluster-based topology construction, routing tree construction, and data aggregation. Initially, IoT nodes are simulated in the network environment. The cluster-based topology construction is made using the WCFO algorithm. The WCFO is developed by the integration of the Firefly Optimization Algorithm (FOA) and Water cycle algorithm (WCA). The cluster-based topology is constructed by considering the objective function that includes the parameters, including distance, delay, link quality, and energy. After that, the routing process is performed using developed WCFO approach for constructing a routing tree and estimating the optimal path. Finally, the Deep LSTM is trained by the proposed WCFO algorithm, which is utilized for executing data reduction and data aggregation process with minimum energy consumption. The devised WCFO+ Deep LSTM approach achieved better performance in terms of prediction error, delay, energy, and Packet delivery ratio (PDR) with values 0.029, 0.001[Formula: see text]s, 0.161[Formula: see text]J and 99.054%, respectively.

Dimension reduction method of high-dimensional fault datasets based on C_M_t-SNE under unsupervised background

The unlabeled fault datasets often contain much non-sensitive redundant, and uncertain information. This study designs a novel interpretable and unsupervised dimension reduction method for unlabeled data containing redundancy and uncertainty. Firstly, a fuzzy-based way for pseudo-label generation is given, and feature cloud models under pseudo labels are established; Secondly, this study takes the expectation, entropy, and hyper entropy of the cloud models representing uncertainty in features as spatial vectors. The difference degree between vectors is treated as the evaluation standard to filter out non-sensitive features based on the maximum initial difference; Moreover, redundant elements are fused by t-SNE, and lower dimensional feature components conducive for fault classification are obtained; Finally, the effectiveness of the method is demonstrated by comparative experiments. The results show that this method has a higher factor, which means that the method can better mine the difference among different faults and improve the performance of fault identification.

Big Data Preprocessing Approach for Smart Grid Based on Partial Order Reduction

In the age of big data, power grid data is characterized by large amount, fast speed and variable types. Traditional attribute reduction methods can no longer meet the needs of big data preprocessing. Therefore, this paper proposes a partial order attribute reduction method for power big data based on rough set. This method deeply analyzes the characteristics of the decision table, uses the parallelization algorithm to improve the attribute reduction algorithm, and effectively solves the efficiency problem in the computing process in the big data environment. Then it generates a series of MapReduce tasks corresponding to the generated Hive command, and obtains the decision rules through reduction. Finally, based on Hadoop platform, the incremental attribute reduction calculation of power fault data in certain area is designed according to the actual demand. The results show that the method has good performance in processing big data, and it can effectively process massive continuous power grid equipment monitoring data.

Machine-learning-assisted automation of single-crystal neutron diffraction

Neutron scattering is a powerful but expensive technique to study materials and discover new matter. Advanced detector technology has significantly improved the efficiency of neutron experiments, increasing the complexity of neutron data reduction and analysis. Machine learning (ML) brings new directions for neutron diffraction data reduction and experiment operation. This work presents an ML-assisted data reduction and analysis method for precise recognition of Bragg peaks and the corresponding regions of interest; it can then automatically screen and align a measured crystal using the recognized peaks, and subsequently plan and optimize the data collection with user-provided information and uncertainty quantification values of detected peaks. This method shows robust performance in different complex sample environments and enables automated single-crystal neutron diffraction.

Surfing to the political extremes: Digital media, social media, and policy attitude polarization

AbstractBackgroundCable news makes it possible for consumers to avoid information that challenges their predispositions, and the Internet makes it even easier. Cable news consumption is related to attitude extremity, but the extant literature is absent empirical examination of how the two may work together.ObjectivesThis study aims to contribute to our understanding of the relationship between media consumption and policy attitude extremity. In particular,, it asks how digital media consumption may combine with cable or traditional mass media to drive higher levels of polarization and attitude extremity.MethodsWe use American National Election Studies and data reduction methods to model and measure the gathering of political information to compare the relationship between different forms of media consumption and policy attitude extremity.ResultsOur results indicate that, independently, the consumption of both “partisan” cable news and “non‐partisan” political digital information have the same positive relationship to attitude extremity across four major issue domains (economy, federal spending, social issues, and foreign affairs) and that when combined into a single index, the relationship is stronger than their independent relationships. Finally, the results suggest that the combined effect is strongest among those who consume more liberal cable news.ConclusionOur high choice media environment contributes to the polarized climate in the United States today.

A new reproducing kernel‐based nonlinear dimension reduction method for survival data

AbstractBased on the theories of sliced inverse regression (SIR) and reproducing kernel Hilbert space (RKHS), a new approach RDSIR (RKHS‐based Double SIR) to nonlinear dimension reduction for survival data is proposed. An isometric isomorphism is constructed based on the RKHS property, then the nonlinear function in the RKHS can be represented by the inner product of two elements that reside in the isomorphic feature space. Due to the censorship of survival data, double slicing is used to estimate the weight function to adjust for the censoring bias. The nonlinear sufficient dimension reduction (SDR) subspace is estimated by a generalized eigen‐decomposition problem. The asymptotic property of the estimator is established based on the perturbation theory. Finally, the performance of RDSIR is illustrated on simulated and real data. The numerical results show that RDSIR is comparable with the linear SDR method. Most importantly, RDSIR can also effectively extract nonlinearity from survival data.

Distributed data filtering and modeling for fog and networked manufacturing

Fog Manufacturing applies both Fog and Cloud Computing collaboratively in Smart Manufacturing to create an interconnected network through sensing, actuation, and computation nodes. Fog Manufacturing has become a promising research component to be integrated into the existing Smart Manufacturing paradigm and provides reliable and responsive computation services. However, Fog nodes' relatively limited communication bandwidth and computation capabilities call for reduced data communication load and computation time latency for modeling. There has long been a lack of an integrated framework to automatically reduce manufacturing data and perform computationally efficient modeling/machine learning. This research direction is increasingly important as both the computational demands and Fog/networked Manufacturing become prevalent. This paper proposes an integrated and distributed framework for data reduction and modeling of multiple systems in a Smart Manufacturing network considering the system similarities. A simulation study and a Fog Manufacturing testbed for ingot growth manufacturing validated that the proposed framework significantly reduces the sample size used for improved computational runtime metrics while outperforming various other data reduction methods in modeling performance.