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Nonparametric Regression Estimator of Multivariable Truncated Spline For Categorical data

IAENG International Journal of Applied Mathematics 2025年第5期55卷 1357-1368页

作者： Suriaslan, Afiqah Saffa Budiantara, I Nyoman Ratnasari, Vita Statistics Department Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Statistics Department Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia

Recent years have witnessed significant interest in truncated spline estimators for nonparametric regression with quantitative data. However, the applicability of these estimators is limited by the frequent occurrence of categorical response variables in real-world applications. A paucity of nonparametric estimators exists for handling categorical response data. This necessitates a method capable of modeling relationships between variables exhibiting pattern shifts across sub-intervals with categorical outcomes. This article thus introduces a novel multivariate truncated spline nonparametric regression estimator for categorical data, developed through a synthesis of literature and theoretical research. The developed method was applied to Indonesia's 2023 poverty depth index data and East Java's 2020 gender development index data. A comparative analysis of the Truncated Spline nonparametric regression model and the binary logistic regression model for estimating categorical data revealed that the Truncated Spline approach yielded superior estimations. Some of the highlights of the proposed method are: 1) This study employs truncated spline nonparametric regression to model categorical response data, 2) Optimal knot placement is determined using the Akaike Information Criterion (AIC), 3) The method's overall performance is demonstrated through its application to two established datasets, and 4) This study compares truncated spline nonparametric regression and logistic regression. © (2025), (International Association of Engineers). All Rights Reserved.

关键词： Maximum likelihood estimation

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Android Application for Children to Learn Arabic Alphabets and Numbers

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第5期19卷 115-127页

作者： Al Khaldy, Mohammad Ali Shaheen, Ameen Al-Shaikh, Ala’A Alzyadat, Wael Alhroob, Aysh Department of Business Intelligence and Data Analytics University of Petra Amman Jordan Faculty of Science and Information Technology Al-Zaytoonah University Amman Jordan Faculty of Information Technology Zarqa University Zarqa Jordan

Mobile learning has become a new trend in e-learning that uses portable media such as smartphones. This offers a new space for creating kid-friendly educational game applications. Therefore, this helps parents fulfill their responsibility to teach kids. This study aims to develop and integrate an educational application that contains multimedia elements that inspire children and evaluate their trustworthiness. The waterfall model was used to develop the application of this study. The application includes four sections: numbers, alphabets, animals, and fruits. A survey was conducted and distributed among four experts and 20 children to validate our application. A selective sampling technique was used to choose these responders. Thematic analysis was used to analyze the data collected. According to the results, the application is appropriate for beginners who want to learn the Arabic alphabet and numbers can easily inspire them with the multimedia elements used and can be implemented in the classroom to enhance the effectiveness of the educational process. Future research could examine the effectiveness of the application across different learning styles or age groups. © 2025 by the authors of this article.

关键词： Contrastive Learning

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Numerical prediction of ureter stone size using an integrated CFD-ML approach

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Neural Computing and Applications 2025年第7期37卷 5325-5341页

作者： Ashraf, Muhammad Mubashar Kamal, Khurram Fahad, Muhammad Noor, N.F.M. Ratlamwala, Tahir Abdul Hussain Islamabad Pakistan College of Science and Engineering Hamad bin Khalifa University Doha Qatar Institute of Mathematical Sciences Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia Center for Data Analytics Consultancy & Services Faculty of Science Universiti Malaya Kuala Lumpur50603 Malaysia

Ureteral flow parameters provide significant details about its physical attributes. Ureter is a single transport medium for urine transmission from kidney to ureter and its health is very important for a healthy human body. Understanding the fluid flow behavior can contribute toward the ureter health monitoring including estimation of any kind of blockage in the flow. Using ANSYS Fluent, Computational Fluid Dynamics (CFD) analysis and the grid independence study are carried out through iterative simulation process to achieve the solution independence. The CFD modeling provides tools and techniques to observe varying fluid parameters such as pressure, velocity and effect of the flow on smooth walls. Fluid Structure Interaction (FSI), an effective technique to analyze the effects of such flows on the ureter walls is also employed. Although the exact modeling of the ureter wall is not possible due to its complex physical parameters, some of its available physiological properties can be used to visualize the model of the ureter numerically. The present study is intended to predict the ureter stone size by using the FSI analysis. The simulations are carried out by increasing the stone size gradually from 1.7 to 3.4 mm and the input flow parameters are compared with the output flow parameters within the same solution setup and boundary conditions via artificial neural network in MATLAB. The output results obtained from the FSI simulations are then utilized to generate a prediction model for the ureter stone size. It is observed that the increasing stone size has a significant effect on the ureter wall, causing high stress regions in the point of interaction. The findings also revealed that the predicted size of the ureter stone is the closest to the actual size and with the least mean squared error at 80 optimal neurons. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Computational fluid dynamics

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The Inhibition Activity of Fatty Imidazoline Acetate Derived from Waste Cooking Oil on the AISI 1018 Mild Steel Corrosion

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-11页

作者： Cholidah, Tsana Kusumasari, Lutfiah Lila Febriyanti, Tria Firdaus, Achmad Fadjar Maulana Fuadi, Raihana Putri Nariswari Madurani, Kartika A. Chemistry Department Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Sukolilo East Java Surabaya60111 Indonesia Department of Materials and Metallurgical Engineering Faculty of Industrial Technology and Systems Engineering Institut Teknologi Sepuluh Nopember Sukolilo East Java Surabaya60111 Indonesia

Imidazoline and its derivatives have been proposed as effective organic corrosion inhibitors for AISI 1018 mild steel, though challenges remain regarding their production processes and efficiency in CO2/H2S-saturated brine solutions. In this study, fatty imidazoline acetate (FIA) was synthesized from waste cooking oil and evaluated for its ability to protect AISI 1018 steel in corrosive environments. The chemical composition of the synthesized FIA was confirmed through gas chromatography-mass spectroscopy (GC–MS) and Fourier transform infrared (FTIR) spectroscopy. Corrosion inhibition performance was assessed using potentiodynamic polarization at room temperature, with the most promising results further examined via weight loss measurements and contact angle analyses. A commercial inhibitor commonly used in pipelines with CO2/H2S-saturated brine was also evaluated for comparison. Surface characterization of the steel samples, both with and without inhibitors, was conducted using scanning electron microscopy (SEM). The FIA demonstrated an inhibition efficiency (IE%) of 99.89% at a concentration of 40 mg/L, closely matching the performance of the commercial inhibitor (IE% = 99.96%). This study underscores the potential of FIA as a sustainable and effective corrosion inhibitor in CO2/H2S-saturated environments. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Gas chromatography

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Q-Rung Orthopair Fuzzy Sets-Enhanced FMEA for COVID-19 Risk Assessment

Informatica (Slovenia)

引用

Informatica (Slovenia) 2025年第1期49卷 81-92页

作者： Abdullah, Lazim Awang, Noor Azzah Qiyas, Muhammad Special Interest Group of Modelling and Data Analytics Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Kuala Nerus21030 Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Department of Mathematics Riphah International University Failsabad Campus Pakistan

Failure Modes and Effects Analysis (FMEA) is a widely used tool for risk analysis, primarily to identify risk factors affecting system quality. Due to the limitations of the traditional FMEA model, several recent models incorporating advanced fuzzy set extensions have been developed to enhance the reliability of risk assessment outcomes. However, most of these models limit expert flexibility in expressing preferences and often overlook the impact of unequal expert weights and the stability of risk ranking results. This study introduces a new FMEA model based on Q-Rung Orthopair Fuzzy Sets (Q-ROFSs), termed Q-ROFSs-FMEA. Q-ROFSs, an extension of intuitionistic fuzzy sets, introduce a new linguistic term. The Q-ROFSs-FMEA model considers the unequal weights of experts, enabling a dynamic representation of expert preferences. These weights and the linguistic evaluation of risk factors are integrated through an aggregation operator, facilitating consensus among experts. The model is applied to a case study on COVID-19 risk factors, revealing that ‘older age’ (risk priority number 0.000012) is the highest risk factor, while ‘gender’ (risk priority number -0.0037) is the lowest. It is found that the ranking of risk factors determined by the Q-ROFSs-FMEA model is obtained as FM1 FM3 FM6 FM4 FM5 FM7 FM8 FM2 . Furthermore, a comparative analysis indicates consistent ranking results across different models, demonstrating the reliability of the proposed model. The case study and comparative analysis validate the effectiveness and applicability of the Q-ROFSsbased risk assessment model. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Failure modes

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Synthesis of Imidazole Coated Magnetic Particles as a Candidate Material for Polymer Electrolyte Membrane in Fuel Cells 2nd

Synthesis of Imidazole Coated Magnetic Particles as a Candid...

引用

2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Atmaja, Lukman Wafiroh, Siti Nashrullah, Hubbi Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Department of Chemistry Faculty of Sciences and Technology Universitas Airlangga Surabaya60115 Indonesia School of Pharmacy Institut Teknologi Bandung Bandung40132 Indonesia

ISBN: (纸本)9789819787111

The aim of this research is to create a novel conductive material that has the capability to efficiently initiate proton transfer in a fuel cell membrane by conjugating imidazole with magnetic particles (MagP). The alignment of heterocyclic molecules by applying MagP facilitated proton transfer. The imidazole coated magnetic particles (Imid-MagP) was synthesized through several steps including the consecutive synthesis of oleic coated MagP (Oleic-MagP) and epoxy coated MagP (Epoxy-MagP). Elemental analysis of Imid-MagP was carried and the particle size distribution and morphology of Imid-MagP were investigated. It was identified that Fe was the most abundant element in Imid-MagP (87.94 wt %), the particle size distribution of Imid-MagP was 134.16 nm, and the morphology of Imid-MagP exhibited a lump-like material. The FTIR spectra of Imid-MagP exhibited a shift of imidazole at 1550 cm−1, which refers to the N-H deformation band. The ion exchange capacity (IEC), an important property in fuel cells that indicates the amount of proton exchanged in the material, of Imid-MagP was 0.675 mmol.g−1. The Imid-MagP material demonstrated excellent properties for improving proton conductivity, which is very important in fuel cell applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Proton transfer

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Enhancing Organizational Performance: Synergy of Cyber-Physical Systems, Cloud Services, and Crowdsensing

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International Journal of Crowd science 2025年第1期9卷 44-55页

作者： Al-Qerem, Ahmad Ali, Ali Mohd Nabot, Ahmad Jebreen, Issam Alauthman, Mohammad Alangari, Someah Aburub, Faisal Aldweesh, Amjad Computer Science Department Faculty of Information Technology Zarqa University Zarqa13110 Jordan Communications and Computer Engineering Department Faculty of Engineering Al-Ahliyya Amman University Amman19328 Jordan Department of Information Security University of Petra Amman11196 Jordan Department of Computer Science College of Science and Humanities Dawadmi Shaqra University Shaqra11911 Saudi Arabia Department of Business Intelligence and Data Analytics University of Petra Amman11196 Jordan College of Computing and Information Technology Shaqra University Shaqra11911 Saudi Arabia

In the contemporary business landscape, software has evolved into a strategic asset crucial for organizations seeking sustainable competitive advantage. The imperative of ensuring software quality becomes evident as low-quality software systems pose formidable challenges to organizational performance. This study delves into the profound impact of three key dimensions of information system quality on organizational performance—information quality (IQ), quality of service (QoS), and software quality (SQ). Anchored in the DeLone and McLean information system (IS) success model, a quantitative questionnaire was administered to 360 industry experts and academics. Rigorous data analysis, employing exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM), revealed significant positive effects of all three quality dimensions on organizational performance. Among these dimensions, software quality emerged as the most influential, showcasing substantial total effects, closely followed by information and service qualities. The study underscores the tangible value derived from strategic investments in enhancing software, information, and service quality. Elevating these facets manifests as a catalyst for improved organizational performance, empowering decision-makers with accurate and timely information while enhancing user satisfaction with the system. This research contributes significantly to the IS success literature by empirically validating the synergistic relationship between information quality, service quality, software quality, and organizational outcomes. The systematic analysis offered in this study goes beyond theoretical validation, providing actionable insights for managers. The findings guide the prioritization of quality initiatives and resource allocation, enabling organizations to maximize competitive advantage. As a future research direction, investigating moderator influences and exploring alternate qualit

关键词： data quality

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Leveraging Machine Learning to Assess Factors Influencing Undernourishment in Nigeria 1st

Leveraging Machine Learning to Assess Factors Influencing Un...

引用

1st International Conference on Intelligent Systems, Blockchain, and Communication Technologies, ISBCom 2024

作者： Mobark, Nada Elfiky, Noha Ahmedin, Ahmed Ibrahim, Ameera Abdel-Aziz, M. Hamad, Safwat Faculty of Computers and Information South Valley University Qena83523 Egypt Business Analytics and Data Science Department School of Economics and Business Administration Saint Mary’s College of California MoragaCA94575 United States Department of Artificial Intelligence Engineering Faculty of Computer Science and Engineering King Salman International University South Sinai46511 Egypt

ISBN: (纸本)9783031823763

Notwithstanding economic progress, Nigeria continues to countenance significant food security issues. This study investigates the root causes of this problem and attempts to develop predictive models to address hunger in Nigeria. The research covering data from 2000 to 2022, sourced primarily from the World Bank and other institutions. The trend analysis, along with the linear model, indicates that investment in farming is crucial for reducing undernourishment. Additionally, there is a strong relationship between agricultural expenditure, exports, access to electricity, and unemployment in relation to undernourishment. In this paper, a linear model, correlation analysis, ARIMA, and trend analysis are utilized to demonstrate how undernourishment, representing hunger and food insecurity, is influenced by other factors. As a result, it is recommended to maintain the unemployment rate between 3.5% and 3.7%, while ensuring that exceeds 5% of government spending is recommended. In other words, to combat hunger in Nigeria, it is important to focus on investing in agriculture, creating jobs, and improving infrastructure. This aligns with our findings and with another research around the world. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： ARIMA Nigeria predictive model Undernourishment

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Polydopamine-Coated Carbon Nanodots as Filler Candidates for Fuel Cell Applications 2nd

Polydopamine-Coated Carbon Nanodots as Filler Candidates for...

引用

2nd International Conference on Sustainability: Developments and Innovations, ICSDI 2024

作者： Permana, Dian Atmaja, Lukman Priyangga, Arif Kedang, Yohana Ivana Purwanto, Mochammad Santoso, Mardi Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Keputih Sukolilo Surabaya60111 Indonesia Department of Chemistry Education Faculty of Teacher Training and Education Universitas Sembilanbelas November Kolaka Kolaka93517 Indonesia Department of Faculty of Agriculture Universitas Timor Kefamenanu85613 Indonesia Department of Chemical Engineering Institut Teknologi Kalimantan Balikpapan76127 Indonesia

ISBN: (纸本)9789819787111

The polymer electrolyte membrane fuel cell (PEMFC) utilizes a polymer membrane as an electrolyte, possibly needing filler or functionalization for improved cell effectiveness. In this study, carbon nanodots (CDs) were functionalized by polydopamine (PD) to enhance the properties of CDs as potential filler for fuel cell applications. The functionalized carbon nanodots (PD-CDs) were subjected to characterization of Structural groups, building properties, elemental evaluation, thermal properties, and morphological features of the resulting functionalized carbon nanodots (PD-CDs) using FTIR, XRD, thermogravimetric analysis, SEM-EDX, as well as TEM. Ion exchange capacity (IEC) was also conducted to quantify ions transfer to have more insight on the potential use of PD-CDs as filler. The infrared spectra of PD-CDs reveal a broader band at 3400 cm−1 compared to CDs. This peak is attributed to the presence of N-H and O-H groups formed in the PD functionalization process. The diffractogram of PD-CDs exhibits a wide peak at 2θ = 26°, which is a result of the creation of a PD covering on outerly of CDs. However, thermal analysis shows that PD-CDs have lower thermal stability than CDs. Filler morphology does not alter by the coating as shown by microscopy images except increasing in its size across. Micrographs of the filler confirmed size growth after dopamine alteration, crediting a positive PDA layer decoration on CDs. IEC value of the PD-CDs increases from 0.56 to 1.22 mmol g−1 with the increasing temperature. These PD-CDs have great potential as fillers for PEMFC construction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Thermogravimetric analysis

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TERRIER: A DEEP LEARNING REPEAT CLASSIFIER

arXiv

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arXiv 2025年

作者： Turnbull, Robert Young, Neil D. Tescari, Edoardo Skerratt, Lee F. Kosch, Tiffany A. Melbourne Data Analytics Platform The University of Melbourne ParkvilleVIC3010 Australia Faculty of Science The University of Melbourne ParkvilleVIC3010 Australia

Repetitive DNA sequences underpin genome architecture and evolutionary processes, yet they remain challenging to classify accurately. Terrier is a deep learning model designed to overcome these challenges by classifying repetitive DNA sequences using a publicly available, curated repeat sequence library trained under the RepeatMasker schema. Existing tools often struggle to classify divergent taxa due to biases in reference libraries, limiting our understanding of repeat evolution and function. Terrier overcomes these challenges by leveraging deep learning for improved accuracy. Trained on RepBase, which includes over 100,000 repeat families—four times more than Dfam—Terrier maps 97.1% of RepBase sequences to RepeatMasker categories, offering the most comprehensive classification system available. When benchmarked against DeepTE, TERL, and TEclass2 in model organisms (rice and fruit flies), Terrier achieved superior accuracy while classifying a broader range of sequences. Further validation in non-model amphibian and flatworm genomes highlights its effectiveness in improving classification in non-model species, facilitating research on repeat-driven evolution, genomic instability, and phenotypic variation. © 2025, CC BY-NC-SA.

关键词： Invertebrates

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