Data Reduction Methods Research Articles

Students' Creative Thinking Strategies As High-Level Thinking, Solving Hot Problems In Improving Mathematics Learning

Creative Thinking Strategy of Students as Higher-Order Thinking, Solving Hot Problems Under Mathematics Learning Improvement. In our observation during PLP at SMK 2 Pancasila Jember, all students in one flat improved their' creative thinking strategy To improve higher-order thinking in mathematics learning. Therefore, this condition results in an imbalance in learning practices. Thus, this study fills the imbalance gap by describing students' creative thinking strategies as higher-order thinking skills in improving mathematics learning. Results of the study: This can contribute knowledge for educators to manage teaching strategies that can improve mathematics learning, which refers to higher-order thinking skills for all levels of creative thinking. This study is a qualitative descriptive study. The course is SMK 2 Pancasila Jember data collection, a test, observation, and interview. Data analysis is done using the data reduction method, presenting data, and concluding. The results of this study are descriptions of students' creative thinking strategies as higher-order thinking skills in improving mathematics learning, namely students' problems in planning detailed problems; students take time breaks to make a plan, identify the core of the problem, provide original ideas, provide alternative plan solving problems, combine previous ideas with question problems; operate and implement their plans by creating various solution original.

Noise reduction method for mine wind speed sensor data based on CEEMDAN-wavelet threshold

The mine wind speed sensor is an important intelligent sensing equipment in the mine intelligent ventilation system that can provide accurate and key wind speed parameters for the intelligent ventilation system. The turbulent pulsation characteristics of the airflow in the underground tunnel are a major factor for the inaccurate measurement of mine wind speed. Therefore, according to the random non-stationary characteristics of a turbulent pulsation signal, a denoising method based on adaptive complete ensemble empirical mode decomposition (CEEMDAN) combined with the wavelet threshold is proposed for suppressing the turbulent pulsation noise in the wind speed signal. First, the CEEMDAN algorithm is used for decomposing the wind speed signal into a series of IMF components. Second, the continuous mean square error criterion is used for determining the high-frequency IMF components with more noise. The wavelet threshold denoising method is used for denoising the high-frequency IMF components with more noise. Finally, the denoised IMF components and remaining low-frequency IMF components are reconstructed for obtaining the denoised signal. The results of the denoising analysis of measured turbulent pulsation signals, comparative analysis of denoising of simulated turbulent pulsation signals by different joint denoising methods, and denoising analysis of actual mine wind speed sensor data indicate that the joint denoising method proposed in this study has a higher signal-to-noise ratio and lower root mean square error of the wind speed signal after denoising. Compared with the EMD-wavelet threshold and EEMD-wavelet threshold denoising methods, the denoising method proposed in this study is better and has higher denoising accuracy, which provides a new method for processing actual mine wind speed sensor data.

School Principal Leadership In Improving Teacher Performance In SMPN 1 Banda Aceh And SMPN 8 Banda Aceh

The leadership style of the principal affects how well the school performs as a whole. The general pattern or course of action that the principle takes, which is seen as a tactic for raising teacher professionalism in order to achieve the next objective, which will build a pattern and an integral unity, is known as the principal's leadership. The purpose of this study is to determine how performance is enhanced by the principal's leadership. instructor at Banda Aceh SMPN 1 and Banda Aceh SMPN 8. This study employs a qualitative methodology using a descriptive research design. Techniques for gathering data include documentation, interviews, and observation. Using methods for data reduction, data presentation, and conclusion-making, qualitative descriptive analysis is the data analysis approach applied. According to research findings, it was discovered that: (1) programs designed and implemented to enhance the performance of teachers, such as those that involve training or improving soft skills for teachers, educational seminars, workshops for teachers, MGMP, KKG, awarding excellence in teaching, adding incentives for teachers, and giving them the chance to use IT devices to solve problems that arise during teaching and learning activities; (2) The principal's plan to raise teacher performance includes: offering incentives, recognizing exceptional teachers with prizes, introducing teacher incentives, educational seminars, MGMP, and teacher workshops; (3) The following are barriers to better teacher performance that school administrators must overcome: insufficient funds, time, or motivation; moreover, they lack the assistance of professionals who can conduct workshops or offer training in a language that is easily understood by teachers. The provision of suitable infrastructure that facilitates engaging, successful, and efficient teaching and learning activities is one of the supporting variables in the interim.

Analysis Of Room Promotion Activities At Kambaniru Beach Hotel & Resort, East Sumba

This research evaluates the impact of room promotions at Kambaniru Beach Hotel & Resort, East Sumba, on increasing competitiveness and occupancy rates. Using a qualitative approach and case study method, the research focuses on five promotion elements: advertising, sales promotion, public relations, direct marketing, and digital marketing. Data was collected through observation, in-depth interviews, literature reviews, and documentation analysis, then analyzed using data reduction, presentation, and conclusion drawing methods, with validation through triangulation. The research results show that using social media and digital platforms for promotions successfully attracts millennial customers, while sales promotions and public relations have a minor impact. The main findings reveal that bundled package offers and seasonal discounts are the most effective promotion strategies, significantly increasing occupancy rates. However, the main challenges are seasonal fluctuations, budget limitations, and a need for more staff training in digital tools. Based on these findings, practical implications include enhancing digital promotions, refining advertising messages, and regularly assessing service quality. This research concludes that integrated and diverse promotion strategies are crucial for consistently increasing hotel occupancy and revenue and strengthening marketing strategies in the competitive hospitality industry.

Implementasi SAK EMKM Terhadap UMKM di Kota Pontianak (Studi Kasus Pada FT Laundry)

The majority of businesses in Indonesia are micro, small and medium enterprises (MSMEs). Due to their presence in various sectors, MSMEs play an important role in driving the country's economy. Nonetheless, MSMEs face obstacles and lack of understanding in following the Financial Accounting Standard for Micro, Small, and Medium Entities (SAK EMKM) which highlights the importance of preparing financial statements. This study aims to investigate how FT Laundry MSMEs prepare their financial statements whether they are in accordance with SAK EMKM standards through a qualitative research approach involving document analysis, interviews, and observations. Data was analyzed using the methods of information collection, data reduction, data presentation, and conclusion drawing. Based on the research findings, it appears that owners of laundry businesses still rely on traditional accounting methods for their financial records and do not adopt the SAK EMKM financial reporting system. The main reasons why these businesses do not adopt SAK EMKM include a lack of understanding of the system, a lack of manpower who can prepare reports in accordance with SAK EMKM guidelines, and a lack of awareness of the importance of preparing reports in accordance with the standard.

A Novel Data Reduction Method for Arabic Online Handwriting Recognition on Mobile Devices

A Novel Data Reduction Method for Arabic Online Handwriting Recognition on Mobile Devices

Discerning sediment provenance in the Outer Banks (USA) through detrital zircon geochronology

Detrital zircon data from modern barrier island and estuarine environments in the Outer Banks (Atlantic Coast, USA) were statistically compared to sands from nearby rivers to assist in determining source-to-sink pathways. Fluvial samples, collected from near the Fall Line contact between the Appalachian Orogen and sediments of the coastal plain, all have age unique distributions, making them ideal for tracing provenance. Three samples from the Atlantic foreshore showed high similarities to one another, as well as to three samples from the estuarine (back-barrier) Pamlico and Albemarle Sounds. Mixture modeling with multiple data reduction methods and three different statistical tests for similarity consistently indicated that the nearby Potomac River was the primary source for all Atlantic foreshore and estuarine zircons, followed by minor contributions from the James River in some models. The models indicate little or no sediment contribution from the Susquehanna, Roanoke, Tar, Neuse, Cape Fear, and Peedee Rivers. Both Atlantic foreshore and estuarine sands are therefore interpreted to have initially originated from Appalachian bedrock to the north of their present-day location, and subsequently to have been transported southward through the Chesapeake Bay watershed before deposition in Virginia and North Carolina. Prior to barrier island formation in the last several thousand years, differing geomorphology of the Chesapeake Bay facilitated southward movement of sediments from its constituent rivers via longshore drift, where they were deposited in coastal settings on the mainland. The modern barrier islands, formed during the most recent post-glacial transgression, may be reworked from these deposits, but may also include a contribution from sediments that were derived more recently from relict deposits on the shelf. Oceanographic and sedimentological evidence suggests that movement of sand-sized grains from southern rivers across the back-barrier sounds is unlikely. These findings can assist with coastal resilience planning and resource management in a region under severe threat from climate change and rising sea levels.

Reducing neural network complexity via optimization algorithms for fault diagnosis in renewable energy systems

This article discusses the challenges involved in diagnosing faults in renewable energy systems. A significant challenge is the high computational demands of the artificial intelligence algorithms needed for grid-connected photovoltaic (GCPV) and wind energy conversion (WEC) systems in fault diagnosis. To address this issue, several methods are proposed to reduce computation time, minimize memory requirements, and improve efficiency. First, optimization algorithms such as the Salp Swarm Algorithm (SSA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), combined with machine learning classifiers for feature selection, are suggested to reduce computational time and memory space requirements. This approach notably decreases computation time but has a limited impact on memory space. Secondly, further reduction in memory space requirements is recommended by using the variogram method for data reduction. This method can leverage the outputs of preceding algorithms to optimize renewable energy systems, making their operations cost-effective and efficient. Finally, the outputs of the variogram are used to train neural network (NN), recurrent neural network (RNN), and long short-term memory (LSTM) classifiers to differentiate between various modes of operation in GCPV and WEC systems. Experimental results demonstrate the effectiveness and robustness of the proposed methods, showing that memory space can be reduced by 1.3 to 5.6 times and CPU time by 1.1 to 11 times while maintaining accuracy and improving efficiency compared to conventional algorithms.

Warping-Driven Greedy Method for Data Reduction in Planar Near-Field Antenna Measurements

Warping-Driven Greedy Method for Data Reduction in Planar Near-Field Antenna Measurements

Metabolomic Changes Associated with AGXT2 Genotype Variants and Stone Formation in a Colony of Cats.

The objective of this study was to assess serum chemistries and metabolomic parameters in cats with genetic variants of the alanine-glyoxylate aminotransferase 2 (AGXT2) gene to determine abnormalities associated with urolith formation and better understand effective approaches for the treatment of cats with uroliths. AGXT2 genotypes of 445 cats in the colony at Hill's Pet Nutrition, Inc. (Topeka, KS, USA) were assessed in a genome-wide association study. Additionally, the serum chemistries and metabolic profiles of each cat were determined, along with their lifetime history of stone incidence. Factor analysis was used as a data-reduction method for metabolites in order to perform statistical hypothesis testing and to select significant metabolites from the more than 600 serum metabolites identified. Of the 82 cats forming stones in the colony (18.4%), the majority were calcium oxalate. Results showed that approximately one third of the cats with the AA variant of the AGXT2 gene have stones, that chronic kidney disease (CKD) is more common in cats with stones, and that having stones results in a shorter lifespan. A discriminant variable selection process was performed to determine the complete blood count, serum biochemistries, and serum metabolomic factors that best discriminated among the three genotypes (AA, AG, GG) and between cats forming stones and non-stone formers. Several of the highly ranked discriminating factors included metabolites related to decreased aminotransferase activity in cats with the AA variant of the AGXT2 gene. Another factor that ranked highly for discriminating between stone formers and non-stone formers contained lipid metabolites, consisting of multiple sphingomyelin species and cholesterol. These findings support the results of feeding studies in cats, whereby CKD cats fed food supplemented with betaine and prebiotics have experienced an increase in total body mass, reduced uremic toxins, and altered sphingomyelin concentrations.

An in-depth analysis of data reduction methods for sustainable deep learning.

In recent years, deep learning has gained popularity for its ability to solve complex classification tasks. It provides increasingly better results thanks to the development of more accurate models, the availability of huge volumes of data and the improved computational capabilities of modern computers. However, these improvements in performance also bring efficiency problems, related to the storage of datasets and models, and to the waste of energy and time involved in both the training and inference processes. In this context, data reduction can help reduce energy consumption when training a deep learning model. In this paper, we present up to eight different methods to reduce the size of a tabular training dataset, and we develop a Python package to apply them. We also introduce a representativeness metric based on topology to measure the similarity between the reduced datasets and the full training dataset. Additionally, we develop a methodology to apply these data reduction methods to image datasets for object detection tasks. Finally, we experimentally compare how these data reduction methods affect the representativeness of the reduced dataset, the energy consumption and the predictive performance of the model.

Hybrid optimization‐based topology construction and DRNN‐based prediction method for data reduction in IoT

SummaryThe Internet of Things (IoT) acts as a prevalent networking setup that plays a vital role in everyday activities due to the increased services provided through uniform data collection. In this research paper, a hybrid optimization approach for the construction of heterogeneous multi‐hop IoT wireless sensor network (WSN) network topology and data aggregation and reduction is performed using a deep learning model. Initially, the IoT network is stimulated and the network topology is constructed using Namib Beetle Spotted Hyena Optimization (NBSHO) by considering different network parameters and encoding solutions. Moreover, the data aggregation and reduction in the IoT network are performed using a Deep Recurrent Neural Network (DRNN)‐based prediction model. In addition, the performance improvement of the designed NBSHO + DRNN approach is validated. Here, the designed NBSHO + DRNN method achieved a packet delivery ratio (PDR) of 0.469, energy of 0.367 J, prediction error of 0.237, and delay of 0.595 s.

Theoretical analysis of mode I fracture of adhesively bonded bi-material DCB joints

There has been a disputation about how to achieve a pure mode I fracture and thus measure its toughness for an adhesively bonded bi-material double cantilever beam (DCB) specimen. This paper therefore develops a theoretical methodology to calculate the normal and shear stress distributions in the adhesive layer and to perform data reduction and mode partitioning for generic bi-material DCB specimens. The theoretical model is validated using experimental data. The adhesive layer in a generic bi-material DCB joint is loaded both in normal and shear based on the predicted stress distributions in the adhesive layer, resulting in a mixed mode fracture behavior. The prediction results substantiate that a strain-based design principle eliminates the shear stresses and thus leads to pure mode I fracture in bi-material DCB specimens. This paper justifies that the established data reduction method is applicable for measuring mode I fracture toughness when a bi-material DCB is designed according to the strain-based design criterion.

Macro-Nutrient Prediction of Paddy Field Soil Using Artificial Neural Network and NIR Spectroscopy

Understanding soil fertility, influenced by macronutrients like nitrogen, phosphorus, and potassium, is essential for adaptive agriculture implementation based on various soil conditions. Near-infrared spectroscopy technology provides non-destructive, rapid soil property measurements without chemicals, applicable both in-field and in-laboratory. However, the wide NIR spectrum range and neural network complexities can hinder Artificial Neural Network (ANN) training and inference, leading to time and resource inefficiency, especially without sophisticated computing devices. This study examines data reduction methods to enhance ANN performance in predicting soil macronutrients using NIR spectra. Multiple Linear Regression (MLR) and Principal Component Analysis (PCA) were applied to select wavelengths from the 1000–2500 nm for ANN input, comparing their performance. About 237 NIR reflectance data from paddy soil were transformed into absorbance data. MLR used forward selection to identify wavelengths with correlations higher than 0.9, while PCA selected wavelengths corresponding to the loading factor peaks for each principal component. These selected wavelengths served as inputs for the ANN model. The ANN’s performance was assessed using correlation and determination coefficients, RMSE, RPD, and model consistency. For nitrogen, the PCA+ANN model with reflectance spectra performed better (RPD 2.4-4.8) than the MLR+ANN model (RPD 2.2-2.6) using fewer wavelengths (5-9 for PCA+ANN vs. 9-12 for MLR+ANN). For phosphorus estimation, the PCA+ANN model also excelled (RPD 2.3-7.0 vs. 2.3-2.4) with fewer wavelengths (4-7 vs. 7). For potassium estimation, the PCA+ANN model showed superior performance (RPD 4.3-9.5 vs. 4.2-4.4), using the same number of wavelengths (4-8 vs. 4-6).

A comparative study of principal component analysis and kernel principal component analysis for photogrammetric shape-based turbine blade damage analysis

Photogrammetry is popular as a non-contact full-field data acquisition technique for machine condition monitoring purposes. Two popular implementations thereof are Digital Image Correlation (DIC) and 3D Point Tracking (3DPT). Since these two approaches require surface preparation in the form of applying speckle patterns or discrete markers, their use is negated in several industrial applications, such as the online condition monitoring and/or assessment of horizontal axis wind turbines. A shape-based photogrammetric approach that focuses on analysing changes in the form of a rotating blade’s boundary contour is a viable alternative that does not require any surface preparation. 3D shapes of a blade at a particular instance can be extracted from a pair of stereoscopic images of the blade. Shape Principal Component Descriptors (SPCDs) determined from applying Principal Component Analysis (PCA) to Fourier coefficients of chain-code shape signatures of the blade contour can be determined. These can be regarded as indicators of the form of the shape, and as the blade rotates, variations in the SPCDs can be investigated to better understand the dynamics of the blades. This paper focuses on the application and performance evaluation of data reduction and classification techniques for a novel shape-based photogrammetry condition monitoring technique. Investigations are conducted to show that applying data reduction methods to raw time domain SPCDs can result in successful classification of differently damaged blades. Post-processing strategies are developed to ensure a more robust shape based condition monitoring tool for analysing turbine blades. Kernel Principal Component Analysis (KPCA) is implemented and it is shown that it out-performs the conventional PCA in terms of classifying different blade damage modes. The feasibility of using multi-domain statistical features as feature vectors to which PCA or KPCA is applied for classification purposes is also investigated. Results indicating how well differently damaged blades can be distinguished are provided, and it is clearly illustrated that multi-domain statistical features are more robust to noise contamination in the signals compared to using the raw SPCDs data.

ETHNOMATHEMATICS EXPLORATION OF THE ISTIQLAL MOSQUE JAKARTA

Ethnomathematics is a branch of mathematical study that was developed in a cultural context. This research aims to explore the concept of ethnomathematics in the Istiqlal Mosque in Jakarta. The method used is descriptive exploratory with an ethnographic approach, which aims to explore information thoroughly to describe and analyze cultural elements in society. Data collection techniques include two types, namely library data collection through literature studies and field data collection through observation and documentation. The collected data is then analyzed using the data reduction method, which involves sorting, centralizing, and simplifying information. The results of the study show that the structure and ornaments of the Istiqlal Mosque Jakarta contain various flat and spatial structures, such as half-spheres, tubes, rectangles, and circles. Keywords: Ethnomathematics, Geometry, Istiqlal Mosque Jakarta

Improving the Quality of Islamic Education through Diversity-Informed Pedagogy at Pesantren Ar-Raudlatul Hasanah Medan

This research aims to explore and understand other aspects of improving the quality of education at Pesantren Ar-Raudlatul Hasanah Medan beyond the pedagogy of classroom learning. The focus of this research is on the development of exemplary pedagogy and pedagogy with diversity and social insight in the context of pesantren education. This research uses a qualitative approach. Data were collected through in-depth interviews, participatory observation, and documentation studies. Data analysis was conducted using data reduction, data presentation, and conclusion-drawing methods. The results showed that improving the quality of education in Pesantren Ar-Raudlatul Hasanah Medan is achieved through pedagogical coaching that focuses on teacher exemplars. Discipline in teaching and ubudiyyah activities also have a positive impact on the learning process. In addition, this study found that diversity- and social-minded pedagogy is critical in the context of pesantren that have diverse santri backgrounds. The implementation of this pedagogy helps to create an inclusive and harmonious learning environment. This research makes a significant contribution to the field of Islamic education studies, especially in the context of pesantren. The findings emphasize the importance of developing exemplary pedagogy and diversity- and social-minded pedagogy to improve the quality of pesantren education. In addition, this study also highlights the need for a holistic approach in pesantren education that not only focuses on academic aspects but also on developing students' character and social skills

The Impact of Using Sevima Edlink E-Learning as A Media For Indonesian Language Learning

The aim of the current research is to describe and examine the impact of using Sevima Edlink e-learning as a medium for learning Indonesian at the junior high school level. The research method used in the current research is a qualitative method using a case study approach.. The data collected used observation and interviews with the aim of obtaining information about teacher and students' experiences regarding digital-based learning media. The primary data used is a description of the use of the Sevima Edlink application in Indonesian language learning activities. Secondary data is in the form of literature studies originating from books, articles, proceedings, theses and other media. The data that has been collected will be analyzed according to the discussion of related topics using data reduction, presentation and conclusion methods. The research results show that students prefer the learning process using digital media compared to conventional learning. One way is to use the Sevima Edlink application as a learning medium in class. There are advantages and disadvantages of the Sevima Edlink application from the perspective of teachers and students.

The Impact of Using Digital Comic as A Media for Learning Short Stories

The aim of this research is to describe the impact of using digital comic as a learning media of Indonesian language subjects for junior high school students. The research method used qualitative method with a case study approach. Data collection uses questionnaires at the beginning of the research process, observations, and interviews with the aim of obtaining information on students' experiences regarding digital-based learning media. The primary data used is an overview of the use of webtoon applications in learning activities and secondary data in the form of literature studies originating from books, articles, proceedings, theses, and other media that are able to support the availability of information related to related topics. The data that has been collected is analyzed according to the discussion of related topics using data reduction, data display, and conclusion methods. The results of the research show that the majority of grade 7th of junior high school students prefer digital comics compared to using text in conveying short story material. Webtoon-based digital comics are considered more effective and interesting compared to text only. The majority of students understand the story presented using the webtoon application and can answer questions related to the story correctly.

A community of practice approach to the management of metal resources, metalworking and hoarding in Bronze Age societies

The burial of metals in hoards is a trademark phenomenon of prehistoric Europe that may be counterintuitive to perceptions of value nowadays. For the first time here, we establish detailed biographies of a large corpus of hoarded metal objects, providing new insights into how societies in the second millennium BC engaged with their convertible material wealth. We move beyond previous research on prehistoric hoarding commonly focussing on separate questions such as what was placed in hoards, who selected the objects, what were the origins of materials, and where and when they were buried. Analysing ca. 200 metal tools and weapons, we use data reduction methods to define technological pathways in the long biographies of hoarded objects extending across the sourcing of materials, production, use, decommissioning, and deposition in the Carpathian Basin. We show how the differential treatment of materials and objects was strongly biased by social decisions across artefact types. We identify shared, standardised signature treatments that crossed over social-spatial boundaries. Our findings bring new insights on the interface between communal and elite wealth management at the intersection of technological reasoning and cultural beliefs in prehistoric communities.