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Estimating Formant Frequencies of Vowels Sung by Sopranos Using Weighted Linear Prediction

Journal of Voice 2023年

作者： Barrientos, Eduardo Cataldo, Edson Postgraduate Program in Electrical and Telecommunications Engineering (PPGEET) R. Passo da Pátria RJ Niterói Brazil

This study introduces the weighted linear prediction adapted to high-pitched singing voices (WLP-HPSV) method for accurately estimating formant frequencies of vowels sung by lyric sopranos. The WLP-HPSV method employs a variant of the WLP analysis combined with the zero-frequency filtering (ZFF) technique to address specific challenges in formant estimation from singing signals. Evaluation of the WLP-HPSV method compared to the LPC method demonstrated its superior performance in accurately capturing the spectral characteristics of synthetic /u/ vowels and the /a/ and /u/ natural singing vowels. The QCP parameters used in the WLP-HPSV method varied with pitch, revealing insights into the interplay between the vocal tract and glottal characteristics during vowel production. The comparison between the LPC and WLP-HPSV methods highlighted the robustness of the WLP-HPSV method in accurately estimating formant frequencies across different pitches. © 2023 The Voice Foundation

关键词： Formant frequencies LPC method Singing vowels Soprano voice WLP-HPSV method

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Evaluating nitrogen sources for enhanced halophilic bacteria growth, electron transfer, and microbial fuel cell performance

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Chemosphere 2025年 378卷 144397页

作者： Christwardana, Marcelinus Khoirunnisa, K. Asy'ari, Mukhammad Hadiyanto, H. Department of Chemistry Faculty of Science and Mathematics Diponegoro University 50275 Indonesia Master Program of Energy School of Postgraduate Studies Diponegoro University 50241 Indonesia Research Collaboration Center for Electrochemistry BRIN – Diponegoro University 50275 Indonesia Department of Chemical Engineering Faculty of Engineering Diponegoro University 50275 Indonesia

This study investigates the comparative effects of different nitrogen sources—peptone, tryptone, and bovine serum albumin (BSA)—on the growth, electron transport mechanisms, and MFCs performance of halophilic bacteria Bacillus clausii J1G-o%B. The objective is to identify the most effective nitrogen source for optimizing bacterial growth and enhancing MFC efficiency. Comprehensive analysis reveals that tryptone and peptone significantly enhance bacterial growth and stability compared to BSA. Increased concentrations of these nitrogen sources correlate with elevated ammonia production and notable pH changes, indicating heightened metabolic activity. The non-linear relationship between scan rate and current density suggests diffusion-limited redox reactions. Notably, higher tryptone concentrations significantly increase the electron transfer rate constant to 3.66 ± 0.02 s−1 when the concentration increases to 0.1 g/100 mL. Early voltage increases at around the 30th hour to 0.175 V under the T-0.1 condition further support the critical role of tryptone in accelerating bacterial growth and biofilm formation. Cyclic voltammetry experiments demonstrate that nitrogen source type and concentration influence electrical double layer characteristics. These findings underscore the potential of tryptone to optimize Bacillus clausii electrochemical performance, achieving a maximum power density of 36.93 mW/m2 at a current density of 196 mA/m2, paving the way for bioelectrochemical system applications. © 2025 Elsevier Ltd

关键词： Rate constants

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Zeolite functionalized with metal ions: A dual strategy for water purification − removal of sertraline hydrochloride and pathogenic bacteria

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Environmental Nanotechnology, Monitoring and Management 2025年 23卷

作者： Beltran, Laiza Bergamasco Ribeiro, Anna Carla Vidovix, Taynara Basso Wernke, Gessica Cusioli, Luis Fernando Palazzo de Mello, João Carlos de Abreu Filho, Benício Alves Bergamasco, Rosângela Salcedo Vieira, Angélica Marquetotti State University of Maringá Postgraduate Program in Food Science Paraná Maringá Brazil State University of Maringá Department of Biotechnology Genetics and Cell Biology Paraná Maringá Brazil State University of Maringá Department of Chemical Engineering Paraná Maringá Brazil State University of Maringá Department of Food Engineering Paraná Maringá Brazil State University of Maringá Postgraduate Program in Pharmaceutical Sciences Paraná Maringá Brazil

Sertraline hydrochloride (SER) is widely used to treat depression, anxiety, and other disorders, but its presence in global water bodies underscores the need for innovative water treatment solutions. This study introduces a novel adsorbent developed through a sustainable approach, where natural zeolites were functionalized with copper oxide nanoparticles (NZ_NPCuO) to enhance SER removal from contaminated water. Copper oxide nanoparticles were synthesized via a green method using Barbatimão (Stryphnodendron polyphyllum Mart.) stem bark extract. Comprehensive characterization techniques, including scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FTIR), X-Ray Diffraction (XRD), X-Ray Fluorescence (XRF), leaching tests, and zeta potential measurements validated the successful formation and chemical stability of the nanoparticles. SER adsorption tests revealed a maximum capacity (q_max) of 25.19 mg/g at 298 K, achieving equilibrium within 960 min. The Elovich and Langmuir models were determined to best describe the kinetic and isothermal data, respectively. Thermodynamic analyses indicated that the adsorption process is exothermic, spontaneous, and reversible. Potential adsorption mechanisms included hydrogen bonding, π-interactions, and electrostatic attraction. We also evaluated the antibacterial properties of NZ_NPCuO using the disk diffusion method against Gram-positive and Gram-negative bacteria, including Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa, resulting in inhibition zones of 26 mm, 7.5 mm, and 14 mm, respectively. Thus, the proposed composite shows great potential for SER removal from wastewater and offers a promising alternative for developing effective antimicrobial agents for different proposals, particularly for microbiological water treatment. © 2025 Elsevier B.V.

关键词： Adsorption Antibacterial activity Copper oxide nanoparticles Green synthesis Sertraline hydrochloride Water treatment

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Features Reduction with PCA Technique for Malware Detection Using Machine Learning

Features Reduction with PCA Technique for Malware Detection ...

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International Conference on Computing, engineering, and Design (ICCED)

作者： Teddy Mantoro Agung Ginanjar Media Anugerah Ayu Umar Aditiawarman Department of Computer Science Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia Postgraduate Program - School of Computer Science Nusa Putra University Sukabumi Indonesia

Malware had been a problem for quite some times since it spreads easily and can cause various problems. Currently, malware is also one of the big threats for internet users. With a huge number of internet users today, techniques that can automatically detect malware before it infects the system is required. This study aims to develop malware detection using machine learning approach with Principal Component Analysis (PCA) as feature reduction. PCA (Principal Component Analysis) is expected to be able to reduce the number of features which then could also reduce the learning time but do not reduce its accuracy significantly. There were four machine learning classifiers used in this study, i.e. K-Nearest Neighbor, Decision Tree, Naïve bayes, and Random Forest. The n-components used in this study were 20 and 34 and the ratio of test and train in the dataset was 35% for test and 65% for training. The results have shown that the best performance come from the detection using random forest with 34 n-component and 100 n-estimator with the average accuracy was 0.991688.

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

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

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International Conference on Computing, engineering, and Design (ICCED)

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

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, $\mathrm{N}=2$ 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 $\mathbf{97.26\%}$.

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Development of Bacterial Cellulose Membranes Incorporated with Plant Extracts

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Macromolecular Symposia 2022年第1期406卷 2200031-2200031页

作者： Neves, Eduarda Zeni Kumineck Junior, Sandro Rogério Katrucha, Graziéli Paulino Silveira, Victória Fonseca Silveira, Márcia Luciane Lange Pezzin, Ana Paula Testa Schneider, Andréa Lima dos Santos Garcia, Michele Cristina Formolo Apati, Giannini Pasiznick Department of Chemical Engineering University of the Joinville Region – UNIVILLE SC Joinville89219-710 Brazil Postgraduate Program in Process Engineering University of the Joinville Region – UNIVILLE SC Joinville89219-710 Brazil Postgraduate Program in Environment and Health University of the Joinville Region – UNIVILLE SC Joinville89219-710 Brazil

A new generation of functional healing membranes has been researched to avoid infection and improve wound healing, combining wound moisture maintenance properties, exudate removal, antimicrobial activity, and the absence of side effects. Thus, this work aims to synthesize wound dressing membranes of bacterial cellulose (BC) incorporated with different plant extracts with antimicrobial properties. The BC membranes are synthesized by the bacterium Komagataeibacter hansenii and are associated with the extracts through the ex situ incorporation technique. The barbatimão extract presents the most significant total phenolic content and antimicrobial activity of the extracts researched. The determination of tannins and flavonoids confirms the extracts’ incorporation into the samples’ structure. The thermogravimetric analysis of the membranes incorporated with the extracts shows a reduction in the thermal stability relative to BC. The results show a good final extract for developing healing membranes with antimicrobial properties. © 2022 Wiley-VCH GmbH.

关键词： Membranes

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Real-Time State-of-Charge Estimation in Lithium-Ion Batteries Using Extended Kalman Filter

Real-Time State-of-Charge Estimation in Lithium-Ion Batterie...

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IEEE Annual Southern Power Electronics Conference (SPEC)

作者： Suélen Bampi Gierri Waltrich Anderson Vaccari Postgraduate Program in Electrical Engineering Federal University of Santa Catarina Florianópolis Brazil Development and Innovation Sector Vale S.A. Vila Velha Brazil

The autonomy of a battery applied in a system depends on the accurate estimation of its State of Charge (SoC). This paper presents real-time SoC tracking using an ESP32 module, which reads current and voltage sensors and processes an algorithm based on the Extended Kalman Filter (EKF) to estimate the SoC. The data applied to the EKF is based on an equivalent circuit model and obtained from tests conducted on a designed electronic load. The SoC estimation results obtained with LTO-66160H-2.3V40Ah batteries are consistent with expectations using EKF tracking.

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Impact of Filter-Based Feature Selection on Prediction Model Performance for Early Detection of Stunting

Impact of Filter-Based Feature Selection on Prediction Model...

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International Conference on Informatics and Computing (ICIC)

作者： Sri Handani Widiastuti Purwanto Adi Wibowo Ratih Wirapuspita Wisnuwardani Informatics Engineering Study Program Sekolah Tinggi Teknologi Bontang Indonesia Doctoral Program of Information System School of Postgraduate Studies Diponegoro Univercity Indonesia Department of Chemical Engineering Faculty of Engineering Diponegoro Univercity Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro Univercity Semarang Indonesia Faculty of Public Health Mulawarman Univercity Samarinda Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

This study investigates the impact of feature selection results using the filter method on the performance of predictive models for the nutritional status of children aged 0-23 months. This study aimed to understand how correlation-based feature selection methods affect the efficiency and accuracy of predictive models. The dataset comes from the Basic Health Research survey, which consists of 84 variables and around 130,000 data, including anthropometric, family, socioeconomic, and environmental information. In this study, various filter-based feature selection methods were applied to evaluate and identify features that have significant relationships with children’s nutritional status, both linearly and non-linearly. The mutual information method was used to identify complex relationships between features and target variables, while the Pearson Correlation Coefficient and information gain were used to assess the relevance of numerical and categorical features. Of the 84 variables in the dataset, 37 features were selected after the selection process. The model performance evaluation was carried out using the Random Forest (RF) and Gradient Boosting (GB) algorithms, by comparing the results between the dataset that had been selected and without feature selection. The RF model without feature selection produces accuracy: 0,84600, precision: 0,83958, recall: 0,84600, F1-score: 0,78962, and a computation time of 9 seconds. The GB model without feature selection produces accuracy: 0,86330, precision: 0,84722, recall: 0,86330, F1-score: 0,84925, and a computation time of 2 minutes 50 seconds. After feature selection and cross-validation, the RF model shows accuracy: 0,85701, precision: 0,83599, recall: 0,85701, and F1 score: 0,83484. The GB model shows accuracy at 0,86124, precision at 0,84226, recall at 0,86130, and F1 score at 0,84120.

关键词： Radio frequency Pediatrics Accuracy Correlation Computational modeling Predictive models Aging Feature extraction Prediction algorithms Numerical models

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

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

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International Conference on Computing, engineering, and Design (ICCED)

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

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.

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Evaluation Criteria for Applying Fuzzy Delphi Method to Establish Disaster Prevention Teaching Aids for Autonomous Flood Prevention

Evaluation Criteria for Applying Fuzzy Delphi Method to Esta...

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IEEE International Conference on Architecture, Construction, Environment and Hydraulics (ICACEH)

作者： Su-Hsing Huang Szu-Hsien Peng Department of Spatial Design and Postgraduate Program of Creative Design Chienkuo Technology University Changhua Taiwan Department of Civil Engineering Chienkuo Technology University Changhua Taiwan

This study was carried out to establish evaluation criteria for applying the Fuzzy Delphi Method to establish disaster prevention teaching aids for autonomous flood prevention. First, possible evaluation criteria for disaster prevention teaching aids were selected through a literature review. The Fuzzy Delphi Method was used in the experts’ questionnaire survey to integrate experts’ opinions with dual triangular fuzzy numbers. The evaluation criteria for disaster prevention teaching aids for autonomous flood prevention were eventually established. The evaluation items contained “learning interest”, “disaster prevention literacy”, “environmental awareness, attitude and disaster prevention ability”, and “simulated situation experience and teaching application”. 22 criteria in “disaster risk cognition”, “disaster cause and awareness”, “enhancing learners’ interests”, and “experiential teaching” included the design, communication, and practicability of disaster prevention teaching aids for autonomous flood prevention. The criteria are expected to help communities and local governments better evaluate, choose, and develop disaster prevention teaching aids and enhance community residents’ disaster prevention ability.

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