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14th International Conference on Graphics and Image Processing, ICGIP 2022

作者： Ul Rehman, Sami Hawbani, Ammar Wang, Xingfu Hamza, Muhammad Zhao, Liang H. Alsamhi, Saeed Al-Qatf, Majjed Computer Science and Technology University of Science and Technology of China JinZhai Anhui Hefei230026 China Shenyang Aerospace University China Insight Center for Data Analytics University of Galway Galway Ireland Faculty of Engineering Ibb University Ibb70270 Yemen

ISBN: (纸本)9781510666313

The visual quality of nighttime photographs diminishes greatly due to low contrast and high noise. We need a robust image enhancement methodology to improve such low-light images close to standard daylight images. Due to deteriorated conditions of uneven light and noise, this image enhancement problem becomes ill-posed. Our paper has proposed a Densely Residual Attention Network (DRANet), an end-to-end attention base densely residual network. The architecture of DRANet consists of sub-modules convolution block (CB) and densely residual feature - convolutional block attention module (DRF-CBAM). DFR-CBAM also has sub-modules, deep residual feature block (DRFB), and convolutional block attention module (CBAM). Using the most recent results from attention and deep residual-based convolution networks in number of computer vision problems, we have used DRFB to enhance the features in-depth by using its dense and residual skip connections. Similarly, features in both spatial and channel axis have been extracted by using a lightweight CBAM attention module. Contrast, luminosity, and noise of the enhanced images have been balanced by additionally implementing a color balancing function at the end of the proposed network. Furthermore, we have used a combination of LLab, LSSIM and LMAE loss functions to make the proposed network stable and recover both contextual and local details while training. MAE, PSNR, SSIM, MS-SSIM, FSIM, Cosine Similarity, and deltaE2000 have been used as referenced while NIQE as nonreferenced based image quality assessment (IQA) metric. Experiment results showed that our proposed methodology is very effective with higher referenced and lower non-referenced image IQA metrics values. Furthermore, the effectiveness of our method has also demonstrated by the visual and perceptual quality of enhanced images. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Image enhancement

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The New Measures of Lorenz Curve Asymmetry: Formulation and Hypothesis Testing

Decision Making: Applications in Management and Engineering

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Decision Making: Applications in Management and Engineering 2024年第1期7卷 99-130页

作者： Fajar, Muhammad Setiawan, Setiawan Iriawan, Nur Department of Statistics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya Indonesia Badan Pusat Statistik-Statistics Indonesia Jakarta Indonesia

The existence of an asymmetric empirical Lorenz curve requires a measure of asymmetry that directly involves the geometry of the Lorenz curve as a component of its formulation. Therefore, establishing hypothesis testing for Lorenz curve asymmetry is necessary to conclude whether the Lorenz curve exhibits symmetry in actual data. Consequently, this study aims to construct a measure of Lorenz curve asymmetry that utilizes the area and perimeter elements of the inequality subzones as its components and establish a procedure for hypothesis testing the symmetry of the Lorenz curve. This study proposes two types of asymmetry measures, RA and RP, constructed based on the ratio of area and perimeter obtained from the inequality subzone. These measures effectively capture the asymmetric phenomenon of the Lorenz curve and provide an economic interpretation of the values of RA and RP. The Lorenz curve symmetry hypothesis testing, based on RA and RP through a nonparametric bootstrap, yields reliable results when applied to actual data. © 2023 by the authors;licensee Learning Gate.

关键词： Asymmetry Bootstrap Hypothesis Income inequality Lorenz curve Measure

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

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

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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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Slip effects on unsteady mixed convection of hybrid nanofluid flow near the stagnation point

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Applied Mathematics and Mechanics(English Edition) 2022年第4期43卷 547-556页

作者： N.A.ZAINAL R.NAZAR K.NAGANTHRAN I.POP Department of Mathematical Sciences Faculty of Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Fakulti Teknologi Kejuruteraan Mekanikal dan Pembuatan Universiti Teknikal Malaysia MelakaHang Tuah JayaDurian Tunggal 76100Malaysia Institute of Mathematical Sciences Faculty of ScienceUniversiti MalayaKuala Lumpur 50603Malaysia Center for Data Analytics Consultancy and ServicesFaculty of ScienceUniversiti MalayaKuala Lumpur 50603Malaysia Department of Mathematics Babes-Bolyai UniversityR-400084 Cluj-NapocaRomania

The unsteady mixed convection of the Al_(2)O_(3)-Cu/H_(2)O hybrid nanofluid flow near the stagnation point past a vertical plate is *** bvp4c technique is used to solve the resulting ordinary differential *** combined effects of the velocity and thermal slip are *** effects of different relevant physical parameters are studied *** results show that the heat transfer rate is reduced when the volume fraction of the nanoparticles increases,while the unsteadiness parameter has an opposite effect in the opposing *** presence of the slip parameter is proven to increase the skin friction coefficient while reduce the local Nusselt number in the buoyancy opposing flow.A contradictory result is observed in the buoyancy assisting ***,the heat transfer rate is reduced in the buoyancy of the assisting and opposing flows when the thermal slip effect is considered.

关键词： hybrid nanofluid mixed convection unsteady flow stagnation point

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Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Learning Approach 12

Estimating Smart Grid Stability with Hybrid RNN+LSTM Deep Le...

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12th International Conference on Smart Grid, icSmartGrid 2024

作者： Oyucu, Saadin Sagiroglu, Seref Aksoz, Ahmet Bicer, Emre Faculty of Engineering Department of Computer Engineering Adiyaman Turkey Gazi University Artificial Intelligence and Big Data Analytics Security R&d Center Ankara Turkey Sivas Cumhuriyet University Mobilers Team Sivas Turkey Sivas University of Science and Technology Faculty of Engineering and Natural Sciences Battery Research Laboratory Sivas Turkey

ISBN: (纸本)9798350361612

Smart grids are faced with a range of challenges, such as the development of communication infrastructure, cybersecurity threats, data privacy, and the protection of user information, due to their complex structure. Another key challenge faced by smart grids is the stability issues arising from variable energy sources and consumption patterns. In these complex grid systems where energy demand and supply need to be balanced instantly, stability predictions play a significant role in foreseeing potential disruptions and optimizing energy flow. Therefore, within the scope of this study, a hybrid structure utilizing Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks is employed for stability classification to predict grid stability. This hybrid model combines the ability of RNN to recognize relationships between consecutive data points with LSTM's capability to preserve long-term dependencies. The results obtained indicate that the model exhibited stable performance with accuracy rates of 98.06% and 98.02% at 50 and 100 epochs, respectively. The findings of this study contribute valuable insights to research on the management and stability of smart grids, enabling energy systems to be operated more reliably and efficiently. © 2024 IEEE.

关键词： Smart power grids

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Structure,Dynamic-Mechanical and Acoustic Properties of Oil Palm Trunk Modified by Melamine Formaldehyde

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Journal of Renewable Materials 2021年第9期9卷 1647-1660页

作者： Tetti Novalina Manik Sulung Apria Nuki Nur Aini Fauziyah Mashuri Mochamad Zainuri Darminto Department of Physics Faculty of Science and Data AnalyticsInstitut Teknologi Sepuluh NopemberSurabaya60111Indonesia Physics Study Program Faculty of Mathematics and Natural SciencesUniversitas Lambung MangkuratBanjarbaru70714Indonesia

The performance of oil palm trunk wastes from Banjarbaru of South Kalimantan was improved with the help of chemical modification in a two-step *** first was formalization with formaldehyde solution with varying pH,and the second was impregnation with melamine-formaldehyde resin under 5 bar pressure for an *** these processes,the samples were cured at 120℃ for 10 min and then dried in an oven at(103±2)℃ in order to attain a moisture content of less than 6%.These treatments improved the physical properties(density,moisture content,and volume swelling),mechanical resistance,dynamic-mechanical and acoustic performance of the *** combination of impregnation and formalization changed the structure and the morphology of the woods such that the surface became flatter and *** was confirmed by results from FTIR,SEM,and *** with alkaline modification displayed the best results for dimensional stability,storage modulus,and damping factor in varied *** treatments in this study also heightened acoustic performances as evidenced by the resulting characteristics of sound absorption coefficient and acoustic impedance.

关键词： Acoustic performances dynamic-mechanical formalization oil palm trunk

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Predicting Antiviral Compounds for Avian Influenza A/H9N2 Using Logistic Regression with RBF Kernel

Predicting Antiviral Compounds for Avian Influenza A/H9N2 Us...

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2023 International Conference on Modeling and E-Information Research, Artificial Learning and Digital Applications, ICMERALDA 2023

作者： Amiroch, Siti Jamhuri, Mohammad Irawan, Mohammad Isa Mukhlash, Imam Nidom, Chairul Anwar Universitas Islam Darul 'Ulum Faculty of Mathematics and Natural Sciences Department of Mathematics Lamongan Indonesia Institut Teknologi Sepuluh Nopember Faculty of Science and Data Analytics Department of Mathematics Surabaya Indonesia Uin Maulana Malik Ibrahim Faculty of Science and Technology Department of Mathematics Malang Indonesia Nidom Foundation Surabaya Indonesia Faculty of Veterinary Medicine Universitas Airlangga Surabaya Indonesia

ISBN: (纸本)9798350369359

Avian Influenza A/H9N2 is a significant threat to the global poultry industry and presents occasional but severe health risks to humans. Given the potential ramifications of an outbreak, the swift and accurate identification of effective antiviral compounds becomes crucial. Traditional methods employed for predicting the efficacy of these compounds often encounter challenges, particularly in maintaining a balance between accuracy and efficiency. Recognizing these limitations, our study introduces an innovative predictive approach. We leverage the combined strengths of Radial Basis Function (RBF) networks and Logistic Regression. This methodology transforms compound features using the RBF network. The changed features are then fed into a Logistic Regression model to make predictions regarding efficacy. Initial findings from our research indicate a remarkable enhancement in prediction accuracy and precision compared to prevalent methods. Furthermore, our study provides a potentially transformative tool for antiviral compound prediction and establishes a precedent, emphasizing the profound potential of hybrid modeling techniques in advancing biomedical research. © 2023 IEEE.

关键词： Forecasting

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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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Molecular interaction of tartaric acid and vinyl pyrrolidone with silver nanoparticle

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Case Studies in Chemical and Environmental Engineering 2025年 11卷

作者： Suprapto, Suprapto Harmami, Harmami Anggraeni, Zalina Nilam Putro, Adityo Pratomo Instrumentation and Analytical Science Laboratory Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia

This study investigated the molecular interactions of tartaric acid and vinylpyrrolidone with silver nanoparticles (AgNPs) using density functional theory (DFT). Tartaric acid showed stronger binding and higher stability than vinylpyrrolidone, which reduced the band gap of AgNPs. The solvent effect was minimal, while temperature slightly increased the energy of the system. The pH value significantly affected the stability, with the ionized form having higher energy due to electrostatic repulsion. These findings provide insights into the stabilization and reactivity of AgNPs relevant to biomedical and catalytic applications. Further experimental validation is needed to confirm these theoretical predictions. © 2025 The Author(s)

关键词： Density functional theory Molecular interaction Silver nanoparticles Tartaric acid Vinylpyrrolidone

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Hierarchical Time Series Forecasting of COVID-19 Cases Using County-Level Clustering data

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Operations Research Forum 2025年第1期6卷 1-28页

作者： Mohanty, Sonaxy Shimamura, Airi Nicholson, Charles D. González, Andrés D. Razzaghi, Talayeh School of Industrial and Systems Engineering University of Oklahoma 202 W. Boyd Norman 73019-1022 OK United States Data Science and Analytics Institute University of Oklahoma 865 Asp Ave. Norman 73019-1022 OK United States Faculty Fellow Data Institute for Societal Challenges (DISC) Five Partners Place 201 Stephenson Pkwy Ste 4600 Norman 73019 OK United States

In light of the far-reaching impact of the COVID-19 pandemic, the accurate estimation of infected cases, fatalities, and recoveries has become crucial. While much research has focused on national-level predictions, it has become evident that an exclusive focus on broader statistics may lead to inadequate preparedness in managing hospital resources in rural and smaller regions. Given the critical role local areas play in the spread of COVID-19 and the hierarchical structure of available data, this study proposes a novel modeling framework using hierarchical time series forecasting (HTSF) tailored to county-level clusters within the USA. This approach aims to provide short-term daily forecasts for every county in the USA, employing bottom-up, top-down, and minimum trace optimal reconciliation techniques. A major barrier to accurate short-term forecasting is the scarcity of COVID-19 hospitalization data in both rural and urban regions across the USA. To address this limitation, the study employs county-level clustering, a method that enables the generation of forecasts even for areas with limited or no available data. The primary aim is to improve the accuracy of COVID-19 case forecasts by combining autoregressive integrated moving average (ARIMA) models as base forecasts with the HTSF approach. The findings reveal that the bottom-up HTSF method offers comparable performance at the county level but significantly outperforms other approaches at the cluster and national levels for 3-week-ahead forecasting. This highlights the vital role of local regions in achieving more precise and effective pandemic prediction strategies. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Clustering COVID-19 Hierarchical time series Time series forecasting

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