Data Processing Techniques Research Articles

Research on data processing technology based on 6LiF/ZnO:Ga fiber neutron detector array

The 6LiF/ZnO:Ga fiber neutron detector, consisting of a 6LiF/ZnO:Ga scintillator, optical fiber, and PMT, has distinct advantages such as compactness, strong real-time online measurement capabilities, and resistance to electromagnetic interference. These characteristics make it highly suitable for neutron detection in confined spaces with significant electromagnetic interference. However, its small size limits the neutron detection efficiency of a single detector, necessitating the use of multiple fibers to construct a fiber array and enhance sensitivity. The goal is to study data processing techniques for the 6LiF/ZnO:Ga fiber detector array. Inside the FPGA, three digital signal processing channels are used to extract multiple information from signal such as the amplitude, timestamp, and measurement channel ID. The fast channel adopts triangular shaping to provide real-time baselines and timestamps for the discrimination channel and the slow channel. The slow channel adopt cascade shaping method to extract the amplitude of nuclear signal. The discrimination channel method adopt cascade discrimination method to realize the real-time discrimination between gamma signals, neutron signals and interference signals. The method has been successfully applied to 6LiF/ZnO:Ga fiber neutron detector arrays with different mass ratios, providing new solution for digital data processing in fiber neutron detector arrays.

Multiview Multistatic vs. Multimonostatic Three-Dimensional Ground-Penetrating Radar Imaging: A Comparison

The availability of multichannel ground-penetrating radar systems capable of gathering multiview, multistatic, multifrequency data provides novel chances to improve subsurface imaging results. However, customized data processing techniques and smart choices of the measurement setup are needed to find a trade-off between image quality and acquisition time. In this paper, we adopt a Born Approximation-based full 3D approach, which can manage multiview-multistatic, multifrequency data and faces the imaging as a linear inverse scattering problem. The inverse problem is solved by exploiting the truncated singular value decomposition as a regularization scheme. The paper presents a theoretical study aimed at assessing how the reconstruction capabilities of the imaging approach depend on the adopted measurement configuration. In detail, the performance achievable in the standard case of multimonostatic, multifrequency data is compared with that provided by a multiview-multistatic, multifrequency configuration, where the data are gathered by considering a progressively increasing number of transmitting antennas. The comparison of the achievable imaging performance is carried out by exploiting the spectral content and the point spread function, which are general tools to foresee the achievable reconstruction capabilities. Reconstruction results related to a numerical experiment based on full-wave data are also provided.

Profil kemampuan komunikasi peserta didik materi SPLDV

This research aims to find out and describe how high the mathematical communication skills of Grade IX Middle School (SMP) students are on the material Systems of Linear Equations in Two Variables (SPLDV). This study involved students in class IX-D at one of the State Middle Schools in Karawang Regency for the 2023/2024 academic year as research subjects. Purposive sampling technique was used to select subjects and obtained 32 students. Qualitative descriptive research was used. The data collection method was carried out using a mathematical communication ability evaluation tool which consisted of three descriptive questions. The data processing technique used descriptive statistics to determine the high, medium and low categories of all students in classes IX-D. It is proven from the result of the students’ mathematical communication skills test that they are in the low classification because they show an average score of 32,00957.

Implementation of Agent Systems in Big Data Management: Integrating Artificial Intelligence for Data Mining Optimization

Background: The rapid growth of data generated across various domains necessitates advanced methodologies for effective data management and extraction of meaningful insights. Traditional data processing techniques often struggle with the volume, variety, and velocity of big data. The integration of Agent Systems and Artificial Intelligence (AI) presents a promising approach to address these challenges by enhancing the efficiency and effectiveness of data mining processes. Objective: This study aims to explore the implementation of Agent Systems in big data management, focusing on how the integration of AI can optimize data mining operations. By leveraging the capabilities of intelligent agents, we seek to improve the accuracy, speed, and scalability of data analysis. Methods: A hybrid research methodology was employed, combining a systematic literature review with an empirical case study. The literature review analyzed previous research on Agent Systems, AI, and big data management to identify key trends and challenges. The empirical case study involved deploying an AI-integrated Agent System within a large-scale data environment to evaluate its performance. Key performance indicators (KPIs) such as processing time, accuracy, and scalability were measured and analyzed. Results: The findings indicate that the integration of AI within Agent Systems significantly enhances the data mining process. The system demonstrated a reduction in processing time by 40%, an increase in data analysis accuracy by 25%, and improved scalability, handling larger datasets more efficiently compared to traditional methods. These improvements were attributed to the autonomous and adaptive nature of agent systems, which enabled dynamic data processing and real-time decision-making. Conclusion: The study concludes that the implementation of AI-integrated Agent Systems in big data management offers substantial benefits, including optimized data mining performance. This integration facilitates more efficient and effective data analysis, which is crucial for organizations dealing with large volumes of data. Future research should focus on further refining these systems and exploring their application across different sectors.

Analisis Pemanfaatan Pinjaman Dana Bergulir Program Kota Tanpa Kumuh di Kecamatan Gayungan Kota Surabaya

This research is a quantitative study with data collection techniques using a questionnaire. The population used was all members of the self help group in Gayungan District, Surabaya City as many as 95 people. This study uses a sample with the census method. The census sample is the determination of the sample if all members of the population are used as the sample (Sugiyono, 2012). The data processing technique uses Partial Least Square (PLS) analysis, Outer Model, Inner Model, and Hypothesis Testing. The results of this study concluded: 1)Financial Literacy can contribute to the Utilization of Revolving Fund Loans; 2) Financial Experience can contribute to the Utilization of Revolving Fund Loans; 3) Income can contribute to the Utilization of Revolving Fund Loans; 4) Attitude of Managers Finance as a moderation of the Financial Literacy variable cannot contribute to the Utilization of Revolving Loans; 5) The attitude of the Financial Managers as a moderation of the Financial Experience variable cannot contribute to the Utilization of Revolving Loans; dan 6) The attitude of the Financial Manager as a moderation of the Income variable doesn’t contribute on the Utilization of Revolving Loans.

Feedback system for reactor process analysis

The article discusses an approach to analyzing processes in industrial reactors using advanced systems with feedback. The authors describe the system preparation and image reconstruction algorithms of ultrasonic tomographs (USTs) that have applications in industry. The research presented in this paper focuses on developing effective strategies for controlling and monitoring chemical reaction processes in reactors. Using advanced data processing techniques, the authors propose systems with feedback that enable accurate analysis and optimization of reactor operating conditions. A key aspect is ultrasonic tomographic imaging, which provides precise data on the state of the process in the reactor. In summary, the paper presents a state-of-the-art approach to analyzing chemical reaction processes in industrial reactors using advanced feedback systems and ultrasonic imaging technologies. The proposed solutions are essential for improving efficiency and process control in the chemical industry.

Enhancing Generalizability of a Machine Learning Model for Infrared Thermographic Defect Detection by Using 3D Numerical Modeling

The paper describes the implementation of 3D numerical simulation in machine learning models used in infrared thermographic nondestructive testing. The enhancement of generalizability of such models emerges as a decisive factor for producing trust-worthy test results. First, it is demonstrated that the models trained on datasets with fixed parameters yield limited defect detection capabilities. The concept of training datasets, which include subtle variations in material thickness, thermal conductivity, as well as various combinations of material density and heat capacity, provides the best learning results and a noticeable ability to identify defects in all test datasets. Second, the model robustness in respect to noise is explored to demonstrate its ability to withstand additive and multiplicative random noise. Third, potentials of some known techniques of thermographic data processing, such as Thermographic Signal Reconstruction, Fast Fourier Transform and Temperature Contrast, are examined. In particular, the use of the Temperature Contrast data ensured sensitivity (True Positive Rate) better than 98% across all test datasets.

Improving Ocean Management Using Insights from Space.

Advancements in space-based ocean observation and computational data processing techniques have demonstrated transformative value for managing living resources, biodiversity, and ecosystems of the ocean. We synthesize advancements in leveraging satellite-derived insights to better understand and manage fishing, an emerging revolution of marine industrialization, ocean hazards, sea surface dynamics, benthic ecosystems, wildlife via electronic tracking, and direct observations of ocean megafauna. We consider how diverse space-based data sources can be better coupled to modernize and improve ocean management. We also highlight examples of how data from space can be developed into tools that can aid marine decision-makers managing subjects from whales to algae. Thoughtful and prospective engagement with such technologies from those inside and outside the marine remote sensing community is, however, essential to ensure that these tools meet their full potential to strengthen the effectiveness of ocean management.

A rapid imaging approach for transient electromagnetic data based on gradient features

ABSTRACT Data processing techniques in transient electromagnetic (TEM) methods face numerous challenges, such as the problem of 3-D imaging and the demand for new data processing techniques arising from the development of small-loop TEM devices. We have developed an imaging algorithm based on gradient features derived from TEM data to address these challenges. We found that normalising the gradient field of TEM responses under appropriate background conditions enables imaging of the target. Numerical experiments conducted under theoretical models have validated the feasibility of this approach and highlighted the significance of background fields. We further designed more complex models and achieved 3-D imaging of synthetic data within seconds, demonstrating the method's high efficiency and applicability to high-dimensional data. Subsequently, we applied the algorithm to challenging small-loop TEM data and demonstrated its superior performance compared to the inversion method, which presents a novel approach for processing data from small-loop TEM devices. This study demonstrates that the algorithm possesses characteristics of high efficiency and wide applicability, suitable for TEM data from various dimensions and types of devices. Furthermore, it holds promising potential in assisting the enhancement of the efficiency of the three-dimensional inversion of TEM data.

Pengaruh Latihan Tactical Attacking Options for Three Players terhadap Menyerang Tim Futsal Putra UNJ

This study aims to determine the effect of tactical training on attacking options for three players on the men's futsal team at UNJ. The research method used in this study is a quasi-experimental research design employing a one-group pretest and posttest design. Data collection was conducted at the Achievement Sports Club Futsal FIK UNJ. The population of this study consisted of 80 active members of the Achievement Sports Club Futsal FIK UNJ. The sample selected for this study comprised 17 athletes using proportional sampling technique. The research instrument used in this study employed assessment criteria. Data processing techniques included descriptive statistical analysis and hypothesis testing, showing a calculated t-value. The calculated t-value of 5,49 exceeds the critical t-value of 2.16 at a significance level of α = 0.05. Therefore, it can be concluded that there is a significant effect of tactical training on attacking options for three players on the men's futsal team at UNJ.

Pengaruh Leader Member Exchange (LMX) dan Efikasi Diri terhadap Kinerja Karyawan

The relationship between the leader and team members takes place in a one-on-one relationship, where each team member has a unique relationship with the leader. Transparency and fairness are increasingly appreciated, leaders who apply the Leader Member Exchange (LMX) principle can create a fair work environment and increase self-efficacy. A level of self-efficacy that is too high can cause individuals to become overconfident and not calculate risks adequately, resulting in a decline in employee performance. This research aims to determine the influence of Leader Member Exchange (LMX) and Self-Efficacy on Employee Performance at Dian Husada Hospital, Mojokerto. This research was conducted by distributing questionnaires online using Google Form to employees of Dian Husada Hospital, Mojokerto. The population of this study was 50 people with a sample of 50 people using a saturated sample. Data processing techniques were carried out using the SPSS 29.0 program. with the research results showing that there is a positive and significant influence of the LMX variable on performance, the Self-Efficacy variable on Performance, then the LMX and Self-Efficacy variables on employee performance at Dian Husada Hospital Mojokerto.

Raman spectroscopy - a visit to the literature on plant, food, and agricultural studies.

Raman spectroscopy, a fast, non-invasive, and label-free optical technique, has significantly advanced plant and food studies and precision agriculture by providing detailed molecular insights into biological tissues. Utilizing the Raman scattering effect generates unique spectral fingerprints that comprehensively analyze tissue composition, concentration, and molecular structure. These fingerprints are obtained without chemical additives or extensive sample preparation, making Raman spectroscopy particularly suitable for in-field applications. Technological enhancements such as surface-enhanced Raman scattering, Fourier-transform-Raman spectroscopy, and chemometrics have increased Raman spectroscopy sensitivity and precision. These and other advancements enable real-time monitoring of compound translocation within plants and improve the detection of chemical and biological contaminants, essential for food safety and crop optimization. Integrating Raman spectroscopy into agronomic practices is transformative and marks a shift toward more sustainable farming activities. It assesses crop quality - as well as the quality of the food that originated from crop production - early plant stress detection and supports targeted breeding programs. Advanced data processing techniques and machine learning integration efficiently handle complex spectral data, providing a dynamic and detailed view of food conditions and plant health under varying environmental and biological stresses. As global agriculture faces the dual challenges of increasing productivity and sustainability, Raman spectroscopy stands out as an indispensable tool, enhancing farming practices' precision, food safety, and environmental compatibility. This review is intended to select and briefly comment on outstanding literature to give researchers, students, and consultants a reference for works of literature in Raman spectroscopy mainly focused on plant, food, and agronomic sciences. © 2024 Society of Chemical Industry.

Comparative analysis of data driven rainfall-runoff models in the Kolar river basin

To effectively tackle the challenges posed by climate change, it is crucial to enhance the accuracy of rainfall-runoff models to ensure reliability amidst changing climatic conditions. Neural networks, renowned for their ability to capture complex patterns and relationships within uncertain input and output data, offer valuable tools in this pursuit. This study aims to evaluate the efficacy of two neural network (NN) models: the Radial Basis Function Neural Network (RBFNN) and the Model Tree M5 Neural Network (MTM5NN). These models are assessed both individually and in combination with the Wavelet (WT) data processing technique for rainfall-runoff modeling in the Kolar River watershed located in Madhya Pradesh, India. Fifteen models were developed employing four algorithms: RBFNN models, WRBFNN (RBF model integrating wavelet components of rainfall as inputs), MTM5NN, and WMTM5NN (MT model incorporating wavelet components of rainfall as inputs). Initially, rainfall and runoff data underwent normalization and were applied to RBFNN and MTM5NN networks. Subsequently, time series data for both rainfall and runoff were decomposed using wavelet transforms, resulting in various sub-time series signals such as approximations and decompositions. These derived signals were then utilized as input data for RBFNN and MTM5NN, specifically designated as WRBFNN and WMTM5NN. The most effective model identified was Model 8 of WMTM5NN, which demonstrated R2 values close to 0.97, outperforming the other models. These results underscore the superior performance of the WMTM5NN model, highlighting its effectiveness in achieving heightened accuracy in predicting runoff for this specific watershed.

Crucial future observations and directions for unveiling magnetopause dynamics and their geospace impacts

The dynamics of Earth’s magnetopause, driven by several different external/internal physical processes, plays a major role in the geospace energy budget. Given magnetopause motion couples across many space plasma regions, numerous forms of observations may provide valuable information in understanding these dynamics and their impacts. In-situ multi-point spacecraft measurements measure the local plasma environment, dynamics and processes; with upcoming swarms providing the possibility of improved spatiotemporal reconstruction of dynamical phenomena, and multi-mission conjunctions advancing understanding of the “mesoscale” coupling across the geospace “system of systems.” Soft X-ray imaging of the magnetopause should enable boundary motion to be directly remote sensed for the first time. Indirect remote sensing capabilities might be enabled through the field-aligned currents associated with disturbances to the magnetopause; by harnessing data from satellite mega-constellations in low-Earth orbit, and taking advantage of upgraded auroral imaging and ionospheric radar technology. Finally, increased numbers of closely-spaced ground magnetometers in both hemispheres may help discriminate between high-latitude processes in what has previously been a “zone of confusion.” Bringing together these multiple modes of observations for studying magnetopause dynamics is crucial. These may also be aided by advanced data processing techniques, such as physics-based inversions and machine learning methods, along with comparisons to increasingly sophisticated geospace assimilative models and simulations.

Analisis Keputusan Pembelian Produk Wardah melalui Celebrity Endorsement dan Kualitas Produk

The pace of the cosmetics/beauty product industry in Indonesia increases every year. In Indonesia there are several beauty product brands such as: Wardah, Sariayu, MS Glow, Emina, Somethinc, Avoskin, Lacoco, Azarine, PS Glow, Dear Me Beauty, Y.O.U Beauty, Purbasari, Scarlett Whitening, Madame Gie, Make Over. This research aims to analyze celebrity endorsement and product quality on purchasing decisions for Wardah products. The data used is primary and secondary data. The data collection method uses a questionnaire via Google Form. The analysis technique uses the Structural Equation Modeling-Part Least Square (SEM-PLS) method. The sampling technique uses a non-probability technique with purposive sampling type. A sample of 97 respondents was obtained. The data processing technique uses the SmartPLS 4.1 program. The variables in this research are the independent variables, namely celebrity endorsement and product quality, and the dependent variable, namely purchasing decisions. The research results show that celebrity endorsement has a positive and significant effect on purchasing decisions, product quality has a positive and significant effect on purchasing decisions.

Student Performance Analysis: A Systematic Research

In the rapidly evolving landscape of education, understanding and improving student performance is of paramount importance. This research paper explores the application of Machine Learning (ML) technologies within the realm of Big Data Analytics to comprehensively analyze and enhance student performance. Leveraging the vast amount of data generated within educational institutions, this study demonstrates how ML algorithms and techniques can be harnessed to gain insights into student learning patterns, predict academic outcomes, and develop data-driven strategies for educational improvement. The paper begins by highlighting the significance of student performance analysis in modern education and the challenges faced by institutions in managing and interpreting the growing volume of educational data. It then presents a comprehensive review of the state-of-the- art ML algorithms and data processing techniques relevant to student performance analysis. Furthermore, the research outlines a novel framework for student performance analysis, integrating various ML models such as regression, classification, and clustering, alongside advanced data preprocessing techniques. The proposed framework is designed to handle diverse educational datasets, including academic records, attendance records, socio-demographic information, and learning resources utilization[1] . Through a series of experiments and case studies, this paper demonstrates the practical application of ML in predicting student performance accurately, identifying at-risk students, and personalizing educational interventions. It also delves into the ethical considerations and data privacy concerns associated with the use of student data in ML-based educational analytics. The results and insights from this research offer valuable implications for educational institutions, policymakers, and researchers alike. By harnessing the power of Big Data and ML technologies, institutions can make data-driven decisions to enhance teaching methodologies, improve student support systems, and ultimately elevate student success rates. Additionally, this study contributes to the ongoing discourse on the responsible use of data in education, emphasizing the importance of transparency, fairness, and ethical considerations. In conclusion, this research paper presents a robust framework that showcases the potential of Machine Learning technologies within the realm of Big Data Analytics for student performance analysis. It offers a roadmap for educational institutions to harness the power of data to foster better learning outcomes and contributes to the ongoing dialogue on responsible data usage in education.

A Multiform Optimization Framework for Multiobjective Feature Selection in Classification

Feature selection in machine learning as a key data processing technique has two conflicting goals: minimizing the classification error rate and minimizing the number of features selected. However, most of the existing multi-objective feature selection methods face the problems of easily falling into local optima and slow convergence by virtue of their problem characteristics, such as partially conflicting objectives and highly discontinuous Pareto fronts. To solve these problems, this article proposes a multiform optimization framework to solve a multi-objective feature selection task together with several auxiliary single-objective feature selection tasks in a multitask environment. The proposed framework uses the problem-solving experience of single-objective tasks to assist the multi-objective feature selection task in exploring more promising regions and accelerating the convergence speed. Specifically, a knowledge transfer strategy based on the search experience of different tasks is developed to accomplish multiform optimization. In addition, a diversity enhancement mechanism is presented to improve search ability in promising decision space areas by considering historical information about the population. In most cases, the experiment results on 27 datasets demonstrate that the proposed technique can uncover more diversified feature subsets on the Pareto front in less time than existing state-of-the-art methods.

Penerapan Flipped Classroom untuk Meningkatkan Kemampuan Berpikir Kritis Siswa pada Pembelajaran Agama Katolik

Purpose – The critical thinking ability of students forms the foundation for developing competent learners. This critical thinking ability can be facilitated through the application of the Flipped Classroom learning model. The purpose of this research is to understand the implementation of the Flipped Classroom model to enhance students' critical thinking skills in Catholic religious education.Method – This research method used by researchers is qualitative. Data collection techniques were carried out through observation, interviews, and documentation. Data processing techniques are done through data reduction, data display, and conclusion drawing.Findings – The findings of this study indicate that improving students' critical thinking skills in Catholic religion learning can be done in two ways, namely the application of the Flipped Classroom learning model refers to discussion preparation and discussion activities as well as through interpretation, analysis, evaluation and inference. Research Implications – This research has relevant implications for the Flipped Classroom learning model on improving students' critical thinking skills. However, this research is limited by its focus on a conceptual approach without empirical testing so further research is needed to test the applicability of the learning model theory in actual practice in an educational setting.

Pengawasan Badan Permusyawaratan Desa (BPD) terhadap Kinerja Kepala Desa Balokang Kecamatan Banjar Kota Banjar

This research is motivated by a lack of knowledge regarding the main tasks and functions, as well as a lack of effectiveness in monitoring the performance of the village head, so that monitoring the village head does not go according to what the community expects and there is a lot of chaos that occurs due to factors caused by village government and the absence of sharper movements in resolving problems that occur in society. The aim of this research is to determine the supervision of the Village Consultative Body (BPD) on the performance of the Head of Balokang Village, Banjar District, Banjar City. The method used in this research is a descriptive qualitative research method. Data collection techniques include: Interview observation and documentation. The data processing techniques in this research are: Data collection, data reduction, data presentation and conclusion drawing/verification. The informants in this research were 3 (seven) people. Based on the research results, it is known that the Village Government Performance Monitoring by the Village Consultative Body (BPD) in Balokang Village, Banjar District, Banjar City in carrying out work direction has not carried out its role well. So it is difficult for the village government to get input and direction regarding needs that are considered priority needs for the village. In determining the work standards of the Village Consultative Body (BPD) it has not been implemented effectively, this is proven by the lack of understanding by members of the BPD of Balokang village, Banjar sub-district, Banjar city of the existing guidelines for setting work standards carried out by the village head. Lack of supervision in observing the course of operational activities on a regular basis, this has resulted in irregularities committed by unscrupulous village officials. The ability of BPD members to report regularly is still lacking. Because understanding of the main duties and functions of each member has not been implemented properly, the performance of the Balokang village government tends to be slow.

Problematic Land Buying and Selling Transactions

Introduction: Land ownership has become a primary need, but in the process of land ownership there are still problems. Basically, the acquisition of land rights according to the Decree of the Minister of Agrarian Affairs Number 21 of Article 1 Point 1, the acquisition of land is carried out by transferring land rights or through the delivery of release of land rights followed by granting rights. But this activity is not carried out in its entirety so that it will cause problems in granting rights to the community. Purposes of the Research: This research was conducted in Sanahu Village, Elpaputih District, West Seram Regency. This village prioritizes and gives importance to matters relating to customary land. However, based on the dinamics of the community, transactions carried out by the community should be carried out in accordance with the provisions of the legislation governing land registration in order to obtain legal certainty in the transactions carried out and aim for orderly administration in land sector. Methods of the Research: This research method is empirical juridical research that uses primary and secondary data. The legal materials used are primary legal materials, secondary legal materials with questionnaire and interview methods, data collection techniques with literature studies and field studies. Data processing and analysis techniques use descriptive analytical data analysis by identifying, classifying, then analyzing and describing according to the hierarchy. Results of the Research: Based on have not carried out the first land the results of the interviews, it is known that some people in Sanahu village do not know about the sale and purchase practices that should be taken in order to obtain legal certainty from the sale and purchase transactions carried out. This should be done based on the provisions of Article 19 paragraph 2 of the UUPA, Article 37 of PP 24 of 1997 concerning land registration. As a result, in the event of problems related to previous land transactions, the community had to resolve themselves in a family manner, either without or with assistance from local officials. As a result, the people of Sanahu village have not carried out the initial registration and maintenance of land registration data properly. Because land administration is not orderly, if there are land issues in transactions carried out either by transferring rights or encumbering rights, there will be problems in the future and limit land owners in carriying out legal actions.