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Production and characterization of polymeric nanocomposite foams based on starch and cellulose nanofibers from oil palm mesocarp fiber

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Applied Nanoscience (Switzerland) 2025年第2期15卷 1-11页

作者： Souza, Mariana Costa Gonçalves, Ana Paula Bispo da Silva Oliveira, Emanoel Igor dos Santos, Juscivaldo Passos Lazzari, Lídia Kunz Zattera, Ademir José José, Nadia Mamede Chemical Engineering Graduate Program Polytechnic School Federal University of Bahia Bahia Salvador Brazil Department of Education Federal Institute of Education Science and Technology Feira de Santana Bahia Brazil Postgraduate Program in Process Engineering and Technologies University of Caxias Do Sul Rio Grande Do Sul Caxias Do Sul Brazil

The demand for sustainable and eco-friendly materials has promoted studies over the years to explore different polymeric materials that meet requirements such as biodegradability and sustainability. In this context, biopolymer materials based on cellulose nanoparticles and starch from different botanical sources have been investigated, aiming to achieve satisfactory performance. The present study aims to develop and characterize cassava starch foams based on density, scanning electron microscopy (SEM), and three-point flexure tests, and to evaluate the effect of cellulose nanofibers obtained from palm mesocarp fibers using the ultrafine friction grinding method after different processing times, characterized by X-ray diffractometry (XRD), microscopy, and chemical composition, as a reinforcing filler. The chemical composition and scanning electron microscopy revealed the effectiveness of the cellulose isolation process, with the analysis of the chemical composition revealing a cellulose content of 55.70% in the fibers after pulping and bleaching, in addition to changes in the visual characteristics of the material after the process aimed at isolating the cellulose. After grinding, XRD showed an increase in crystallinity (76.1% at the maximum grinding time), along with typical microscopy images of cellulose nanofibers. The analysis of the obtained nanocomposites provided insights into the role of these nanostructures in the thermo-expanded starch matrix, indicating that the nanofibers promoted changes such as an increase in mechanical properties and crystallinity, which contributed to improving overall mechanical performance. A 67.48% increase in flexural strength was achieved for the formulation with cellulose nanofibers that underwent 150 min of grinding, without causing major variations in density. © King Abdulaziz City for science and Technology 2025.

关键词： Starch

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Fetal Health Risk Classification Using Important Feature Selection and Cart Model on Cardiotocography Data

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第1期49卷 193-206页

作者： Ilham, Ahmad Nagara, Thahta Ardhika Prabu Kamaruddin, Mudyawati Khikmah, Laelatul Mantoro, Teddy Department of Informatics Universitas Muhammadiyah Semarang Semarang Indonesia Intelligent Data Science Research Group Universitas Muhammadiyah Semarang Semarang Indonesia Postgraduate Program of Medical Laboratory Science Universitas Muhammadiyah Semarang Semarang Indonesia Interdisciplinary Research Laboratory for Experimental Plasma Medicine Universitas Muhammadiyah Semarang Semarang Indonesia Department of Statistics Institut Teknologi Statistika dan Bisnis Muhammadiyah Semarang Semarang Indonesia School of Computer Science Nusa Putra University West Java Indonesia

Fetal mortality and newborn health issues require urgent attention because of high maternal and infant mortality rates during labor, highlighting the critical need for accurate fetal condition monitoring to reduce complications. This study proposes the development of a fetal health risk classification model based on Important Feature Selection (IFS) and a Classification and Regression Tree (CART) using cardiotocography (CTG) data from the UCI Machine Learning Repository. The IFS method was used to select the most relevant features, reduce model complexity, and increase generalization to prevent overfitting. The IFS-CART model was tested with 10-fold cross-validation and showed an accuracy of 94.50%, superior to the conventional CART, which only reached 93.83%. In addition, the average values of the True Positive Rate (TPR) and True Negative Rate (TNR) also increased, indicating that this model is effective in distinguishing normal, suspected, and pathological fetal conditions. Evaluation using the area under the curve receiver operating characteristic (AUC-ROC) showed that the model had high performance in detecting at-risk conditions, with an AUC of 0.981 for the"suspect" class. This finding confirmed that IFS-CART is not only accurate but also has high interpretability, making it easy for medical personnel to use for clinical decision support. The results of this study show that IFS-CART can serve as a reliable decision support system for real-time fetal health monitoring. Further implementation is expected to improve diagnostic accuracy and prevent complications during pregnancy and labor © 2025 Slovene Society Informatika. All rights reserved.

关键词： Fetal monitoring

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A Two-stage Reconfiguration in Network Function Virtualization: Toward Service Function Chain Optimization

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IEEE Transactions on Network and Service Management 2025年

作者： Assis, Karcius D. R. Almeida, Raul C. Baghban, Hojjat Santos, Alex F. Boutaba, Raouf Electrical and Computer Engineering Department Salvador Brazil University of Waterloo David R. Cheriton School of Computer Science Canada Department of Electronics and Systems Recife Brazil Chang Gung University Department of Artificial Intelligence Taiwan Chang Gung University College of Intelligent Computing Taiwan Center of Science and Technology in Energy and Sustainability Brazil Postgraduate Program in Computer Science Bahia Brazil

Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service Function Chain (SFC) which consists of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within a dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF instances. In this paper, we assume that each deployed VNF is used by multiple SFCs and deal with the optimal placement for the contemporaneous migration of VNFs based on the actual network situation. We formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the VNF migration between nodes or the total number of core changes per node. The approach is a two-stage MILP based on optimal order to solve the reconfiguration. Extensive evaluation shows that the proposed approach can reduce the change in terms of location or number of cores per node in a 6-node and 14-node networks while ensuring network latency compared with the model without reconfiguration. © 2004-2012 IEEE.

关键词： Network function virtualization

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Social learning: Defining learning objects from social tool

Social learning: Defining learning objects from social tool

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IADIS International Conference WWW/Internet 2012, ICWI 2012

作者： Menolli, André Luís Andrade Reinehr, Sheila Malucelli, Andreia Polytechnic School Postgraduate Program in Computer Science Computer Science Department State University of North Paraná PPGIa Pontifical Catholic University of Paraná PUCPR Brazil Postgraduate Program in Computer Science Polytechnic School PPGIa Pontifical Catholic University of Paraná PUCPR Curitiba Brazil

ISBN: (纸本)9789898533098

Social learning is focused for that learning occurs within a social context, in a place where people can work and learn collaboratively. New technologies, such as social network, wiki, blogs, among others social tools, enable collaborative work and are important facilitators of social learning process. These tools provide an easy mechanism for people to communicate and collaborate, which help in the creation of knowledge. However, collaboration is one of the several necessary components for learning. It is important that all acquired knowledge be organized to be reused faster, easily and efficiently. This paper aims to propose an approach to generate learning objects from social tool, in order to organize the information to be easily reused, improving social learning.

关键词： Semantic Web

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Influence of submerged arc welding slag content variation on cementitious materials: A sustainable alternative

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Construction and Building Materials 2025年 483卷

作者： Castro-Lopes, S. Rodrigues, V.D. Marden, Sandro Peña-Garcia, R. Padrón-Hernández, E. Berenguer, Romildo Federal Institute of Education Science and Technology of Pará PA Altamira Brazil Laboratory of Composite Materials and Structural Integrity Corrosion and Biocorrosion - CompoLab/LBC Federal University of Pernambuco PE Recife Brazil Federal University of Paraiba Department of Mechanical Engineering PB Joao Pessoa Brazil Federal Rural University of Pernambuco Postgraduate Program in Physics Engineering Academic Unit of Cabo de Santo Agostinho Cabo de Santo PE Agostinho Brazil Federal University of Pernambuco Department of Physics PE Recife Brazil Polytechnic School University of Pernambuco PE Recife Brazil Federal University of Pernambuco Department of Mechanical Engineering PE Recife Brazil

This study evaluates the effects of submerged arc welding (SAW) slag as a partial replacement for Portland cement in cementitious materials. Cement pastes with 5 %, 10 %, 15 %, 20 %, 25 %, and 30 % SAW slag were tested for compressive strength, tensile strength, carbonation resistance, phase formation, and environmental impact. The results revealed that up to 10 % SAW slag significantly enhanced compressive strength, with the 5 % replacement showing a 20.10 % increase in strength at 28 days compared to the control. The mechanical improvement was attributed to the formation of the CSH phase, which was confirmed by X-ray diffraction (XRD) and thermogravimetric analysis (TGA). Carbonation tests indicated that the 5 % SAW samples reduced carbonation by 5 % compared to the control, while the 10 % SAW samples showed a 1 % reduction. This suggests that SAW slag enhances the resistance of cementitious materials to CO2 attack, improving long-term durability. XRD analysis also identified an increase in amorphous phases in the SAW samples, particularly in the 5 % addition, further supporting the pozzolanic activity of SAW slag. Pozzolanic effectiveness coefficient analysis revealed synergistic effects for SAW slag blends, with optimal performance observed at 5 % replacement, in line with microstructural evidence of enhanced CSH gel formation. Environmental analysis demonstrated that replacing 10 % of Portland cement with SAW slag could reduce CO2 emissions by approximately 4564 kg annually, while a 15 % replacement could lead to a reduction of 6846 kg of CO2. These results underscore the potential of SAW slag as a sustainable and performance-enhancing alternative to conventional cement in concrete production. © 2025 Elsevier Ltd

关键词： Compressive strength

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Predicting Relegation Clubs in Italian Serie A with Method based C4.5 Decision Tree Algorithm 1

Predicting Relegation Clubs in Italian Serie A with Method b...

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1st Bukittinggi International Conference on Education, BICED 2019

作者： Syukmana, F. Wahyudi, E. Gata, W. Wahono, H. Febianto, N.I. Kuntoro, A.Y. Nitra, R.O. Effendi, L. Saputra, D.D. Sulaeman, O.R. Computer Science Postgraduate Program STMIK Nusa Mandiri Indonesia Graduate School Master's Degree Computer Science STMIK Nusa Mandiri Indonesia

The purpose of this study is to help small clubs from Italian Serie A in finding the minimum targets to avoid relegation into Serie B competition (below Serie A league). Relegation will reduce the club's income from TV revenues and the decline of enthusiastic supporters. Based on the data from the final standings (seasons 2006 until 2018), this can be explained by the Decision Tree method using the C4.5 algorithm. The methods used in this study are data collection, data pre-processing, model proposal, model testing, and model validation. In this study, it is expected that the value of accuracy exceeds 85% to achieve a proper classification. © Published under licence by IOP Publishing Ltd.

关键词： Decision trees

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Development of Financial Planner Application Software Based on Waterfall Model 9

Development of Financial Planner Application Software Based ...

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9th International Conference on ICT for Smart Society, ICISS 2022

作者： Imanuel, Jason Kintanswari, Lusia Vincent Anggreainy, Maria Susan Yusuf, Sablin Sembiring Kembaren, Sula Yosege School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Postgraduate Program Bakrie University Jakarta12940 Indonesia

ISBN: (数字)9781665471350

ISBN: (纸本)9781665471350

Financial planning is necessary, especially for those who started to have savings. It can be achieved through thorough planning, organizing, directing, and controlling financial activities. To overcome these problems, we developed and designed the financial planner app. It can help all our users to control their money flow according to their budgets every month. In developing this project, Business Model Canvas is used to assess the key points and Waterfall Model is used to define the process. All requirements of the financial planner app are modeled using Use Case Diagram. To cope with the reduplication of coding for the app development, we used the Object-Oriented approach and made a Class Diagram to portray the relation between objects involved. Besides that, we also use Sequence Diagram to develop the flow process of our system. We also developed a prototype of the User Interface to give a picture of how the app's visuals. Finally, we used the Black Box Method for testing the 'Financially' app and the results are given in the form of tables representing every page. We hope that this 'Financially' app will be helpful and the impacts on our users align with the purpose of the creation of the app. © 2022 IEEE.

关键词： Application programs

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Norepinephrine changes behavioral state through astroglial purinergic signaling

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science (New York, N.Y.) 2025年第6748期388卷 769-775页

作者： Chen, Alex B. Duque, Marc Rymbek, Altyn Dhanasekar, Mahalakshmi Wang, Vickie M. Mi, Xuelong Tocquer, Loeva Narayan, Sujatha Legorreta, Emmanuel Marquez Eddison, Mark Yu, Guoqiang Wyart, Claire Prober, David A. Engert, Florian Ahrens, Misha B. Howard Hughes Medical Institute Janelia Research Campus Ashburn VA United States Department of Molecular and Cellular Biology Harvard University Cambridge MA United States Graduate Program in Neuroscience Harvard Medical School Boston MA United States Tianqiao and Chrissy Chen Institute for Neuroscience Division of Biology and Biological Engineering California Institute of Technology Pasadena CA United States Sorbonne Université Paris Brain Institute (Institut du Cerveau Institut National de la Santé et de la Recherche Médicale U1127 Centre National de la Recherche Scientifique Unité Mixte de Recherche 7225 Assistance Publique-Hôpitaux de Paris Campus Hospitalier Pitié-Salpêtrière Paris France Bradley Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State University Arlington VA United States Present address: Allen Institute for Brain Science Seattle WA United States Department of Automation Tsinghua University Beijing China

Both neurons and glia communicate through diffusible neuromodulators;however, how neuron-glial interactions in such neuromodulatory networks influence circuit computation and behavior is unclear. During futility-induced behavioral transitions in the larval zebrafish, the neuromodulator norepinephrine (NE) drives fast excitation and delayed inhibition of behavior and circuit activity. We found that astroglial purinergic signaling implements the inhibitory arm of this motif. In larval zebrafish, NE triggers astroglial release of adenosine triphosphate (ATP), extracellular conversion of ATP into adenosine, and behavioral suppression through activation of hindbrain neuronal adenosine receptors. Our results suggest a computational and behavioral role for an evolutionarily conserved astroglial purinergic signaling axis in NE-mediated behavioral and brain state transitions and position astroglia as important effectors in neuromodulatory signaling.

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Enhancing Security Information and Event Management (SIEM) by Incorporating Machine Learning for Cyber Attack Detection 9

Enhancing Security Information and Event Management (SIEM) b...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Ayu, Media Anugerah Erlangga, Dimas Mantoro, Teddy Handayani, Dini Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia Nusa Putra University - Sukabumi Postgraduate Program - School of Computer Science Indonesia

ISBN: (纸本)9798350370126

Network security is a crucial component of Information Technology, yet organizations continue to grapple with meeting established security benchmarks. Given the rise in cyber-attacks and the continuous emergence of new attack types, it's practically infeasible to persistently update attack patterns or signatures within security parameters. Key tools such as Intrusion Detection Systems (IDS) and Security Information and Event Management (SIEM) are instrumental in monitoring network traffic and identifying potential threats. However, these tools face limitations, such as the high volume of alerts produced by IDS and the use of rule-based method, also the inability of SIEM tools to analyze logs comprehensively to identify inappropriate activities. This research has conducted anomaly detection using machine learning process to classify cyber-attacks network flow collected from IDS that installed incident network infrastructure. The analysis of IDS using machine learning, integrated with SIEM. The algorithm used in this research was Random Forest Classifier using CSE-CID-IDS2018 dataset pre-processed with Principal Component Analysis (PCA). Results of the experiments show that Random Forest Classifier Model, when combined with Principal Component Analysis (PCA), yields the most commendable results when applied to a 70/30 training/testing data ratio with accuracy of 0.99953. © 2023 IEEE.

关键词： Network security

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Sentiment Analysis with N-Gram Preprocessing for Online-Shopping Reviews in Indonesian Language 9

Sentiment Analysis with N-Gram Preprocessing for Online-Shop...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Asian, Jelita Putra, Oktavianus Tegar N.K. Ayu, Media Anugerah Mantoro, Teddy Nusa Putra University Postgraduate Program - School of Computer Science Sukabumi Indonesia Sampoerna University Faculty of Engineering and Technology Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350370126

Currently, online Shopping platforms have grown significantly, especially during the COVID-19 pandemic. This condition motivates the need for analyzing how the users/customers' opinions on using such platform. Sentiment analysis, as a process of detecting, extracting, and classifying users' opinions and attitudes toward specific topics, is a good tool for the required analysis. This study aims to evaluate the performance of machine learning approach which combined with N-Gram technique in doing sentiment analysis. The dataset used in this study comes from scraping reviews in Bahasa Indonesia regarding the Shopee Apps. In this study, N=2 for the N-Gram was employed in the preprocessing process. Our main goal is to investigate whether the performance of machine learning in doing sentiment analysis can be improved by adding the N-Gram technique in its preprocessing. This work applied the Naive Bayes Classifier and k-Nearest Neighbor with K=11 as the machine learning algorithms. The best accuracy in this study was achieved by Naive Bayes Classifier after applying N-Gram Terms (N=2) with Split Validation (8:2), which is 97.26%. © 2023 IEEE.

关键词： Sentiment analysis

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