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HYDROCARBON VAPOR DIFFUSION IN INTACT CORE SLEEVES

GROUND WATER MONITORING AND REMEDIATION 1993年第1期13卷 139-150页

作者： OSTENDORF, DW MOYER, EE XIE, YF RAJAN, RV David W. Ostendorf (Civil Engineering Department University of Massachusetts Amherst MA 01003) is an associate professor in the Environmental Engineering Program of the Civil Engineering Department of the University of Massachusetts at Amherst. His research interests include unconfined aquifer contamination hazardous waste site remediation and analytical modeling of problems in environmental fluid mechanics. Ostendorf is a Registered Professional Engineer in Massachusetts and a member of the American Geophysical Union American Society of Civil Engineers Soil Science Society of America Water Pollution Control Federation and Association of Environmental Engineering Professors as well as the National Ground Water Association. Ellen E. Moyer (Civil Engineering Department University of Massachusetts Amherst MA 01003) is a doctoral candidate in the Environmental Engineering Program of the Civil Engineering Department of the University of Massachusetts at Amherst with an M.S. degree in environmental engineering from that institution. Her research interests include subsurface investigation soil venting bioremediation and analytical modeling of subsurface contamination. She has six years of professional experience managing hazardous waste site investigation and cleanup projects and is a member of the National Ground Water Association and the American Society of Civil Engineers. Yuefeng Xie (Civil Engineering Department University of Massachusetts Amherst MA 01003) is a postdoctoral research associate in the Environmental Engineering Program of the Civil Engineering Department of the University of Massachusetts at Amherst. His research interests include environmental analyses drinking water treatment and the chemical characterization and removal of disinfection by-products. A graduate with a Ph.D. and an M.S. in environmental engineering and a B.S. in chemistry and chemical engineeering from Tsinghua University Beijing China Xie is a member of the American Water Works Association and the Water Poll

The diffusion of 2,2,4-trimethylpentane (TMP) and 2,2,5-trimethylhexane (TMH) vapors out of residually contaminated sandy soil from the U.S. Environmental Protection Agency (EPA) field research site at Traverse City, Michigan, was measured and modeled. The headspace of an intact core sleeve sample was swept with nitrogen gas to simulate the diffusive release of hydrocarbon vapors from residual aviation gasoline in and immediately above the capillary fringe to a soil-venting air flow in the unsaturated zone. The resulting steady-state profile was modeled using existing diffusivity and air porosity estimates in a balance of diffusive flux and a first order source term. The source strength, which was calibrated with the observed flux of 2,2,4-TMP leaving the sleeve, varied with the residual gasoline remaining in the core, but was independent of the headspace sweep flow rate. This finding suggested that lower soil-venting air flow rates were in principle as effective as higher air flow rates in venting LNAPL vapors from contaminated soils. The saturated vapor concentration ratio of 2,2,4-TMP to 2,2,5-TMH decreased from 6.6 to 3.5 over the duration of the experiments in an expression of distillation effects. The vertical profile model was tested against sample port data in four separate experiments for both species, yielding mean errors ranging from 0 to -24 percent in magnitude.

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Life cycle assessment and leather production

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Journal of Leather science and engineering 2020年第1期2卷 321-333页

作者： Diego Navarro Jianhui Wu Wei Lin Pere Fullana-i-Palmer Rita Puig Environmental Engineering doctoral program Universitat Politècnica de Catalunya(UPC)C/Jordi Girona 1-308034 BarcelonaSpain Curtidos Badia SAU c/Sol5208700 IgualadaSpain Department of Biomass and Leather Engineering Key Laboratory of Leather Chemistry and Engineering of Ministry of EducationSichuan UniversityChengdu 610065China UNESCO Chair in Life Cycle and Climate Change ESCI-UPF Pompeu Fabra UniversityPg.Pujades 108003 BarcelonaSpain Department of Computer Science and Industrial Engineering Universitat de Lleida(UdL)Pla de la Massa808700 IgualadaSpain

Leather industry is facing new trends on production and consumption patterns due to society *** economy is proposing a transition from the current economic model to a more sustainable one,in which waste is designed out and resources will be reused and recycled as long as *** this transition,Life Cycle Assessment(LCA)is an important tool to help *** the present review,39 English-written peer-reviewed papers related to LCA and leather have been found,30 of which were published in the last 6 years,meaning LCA is nowadays an important *** are presented within 4 types,focused on:1)the whole leather production process,2)a single step in the production process(e.g,new technologies for unhairing),3)waste treatment and recycling,and 4)life cycle thinking with ideas on long-term strategies for leather *** discussed in the literature review,leather industry has important challenges to address:increasing sustainability and transparency on the supply chain,and strengthening the beauty of *** up these challenges from a life cycle perspective will help leather industry flourish in the coming future.

关键词： Circular economy Life cycle thinking Inventory data Tanning processes Leather sustainability

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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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Deep learning-based real time detection for cardiac objects with fetal ultrasound video

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Informatics in Medicine Unlocked 2023年 36卷

作者： Iriani Sapitri, Ade Nurmaini, Siti Naufal Rachmatullah, Muhammad Tutuko, Bambang Darmawahyuni, Annisa Firdaus, Firdaus Rini, Dian Palupi Islami, Anggun Intelligent System Research Group Faculty of Computer Science Universitas Sriwijaya Palembang 30139 Indonesia Doctoral Program Faculty of Engineering Universitas Sriwijaya Palembang 30139 Indonesia Department of Informatics Engineering Faculty of Computer Science Universitas Sriwijaya Palembang 30139 Indonesia

Fetal cardiac anatomical structure interpretation by ultrasound (US) is a key part of prenatal assessment. Unfortunately, the numerous speckles in US video, the small size of fetal cardiac structures, and unfixed fetal positions make manual detection processes difficult. To alleviate such problems, a deep learning model was developed to fully automate the processing of fetal echocardiographs with 2-dimensional cross-sectional images. The weakness of such an approach is that a physician immediately interprets US video with 3-dimensional objects to diagnose patients in clinical practice. To improve fetal cardiac anatomical structure interpretation via US for accurate and real time diagnosis, this paper proposes a real-time fetal cardiac substructure detection using US video with the You Only Look Once (YOLO) framework. In YOLO, an end-to-end neural network makes predictions for cardiac substructure objects, boxes, and class probabilities simultaneously. To achieve reliable performance, 40 fetal echocardiography videos were trained with the new YOLOv7 architecture and fine-tuned to work optimally and run efficiently. We conducted with nine fetal cardiac substructure objects such as the left atrium, right atrium, left ventricle, right ventricle, tricuspid valve, pulmonary valve, mitral valve, aortic valve, and aorta. The results yielded the highest mean average precision of 82.10%, reaching 17 frames per second (FPS) for nine cardiac substructure objects in 0.3 ms. The main finding of our study is that with even a limited number of US videos, YOLOv7 can detect fetal cardiac substructure objects in real-time. Such a network can work efficiently to detect small fetal cardiac objects automatically in a rapid phase with the help of proper fine-tuning. This work mainly assists medical experts in the fetal cardiac anatomy diagnostic process. © 2022 The Authors

关键词： Computer vision Fetal echocardiography Object detection YOLOv7

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Multi Task Classification Using Deep Learning Approaches for Big Five Personality Traits Prediction: A Review

Multi Task Classification Using Deep Learning Approaches for...

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International Conference on Information Technology, Computer and Electrical engineering (ICITACEE)

作者： Salamun Rohman Nudin Rahmat Gernowo Maman Somantri Adi Wibowo Doctoral Program of Information System School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Electrical Engineering Faculty of Engineering Diponegoro University Semarang Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia

ISBN: (数字)9798350389302

ISBN: (纸本)9798350389319

This paper presents a comprehensive review of deep learning methods for the Big Five personality traits prediction using multi-task classification. The purpose of the review is to determine the performance of models developed in previous research to describe research opportunities and challenges in the future. The Big Five model is a framework in psychology that categorizes human personality into five dimensions: openness, conscientiousness, extraversion, agreeableness, and neuroticism. The review begins by analyzing 22 articles from the science Direct, IEEE Xplore, and Scopus databases by applying screening using the Big Five Personality keywords. Then study the concepts of deep learning for model development such as: input, architecture, method, dataset, and performance to identify the strengths and limitations of each approach. The review results of the highest average accuracy by modality such as unimodal text with essay dataset (80.98 % ), unimodal image with ImageNet dataset (95.36%), unimodal audio with SSPNet dataset (79.48%), bimodal image and audio with Chalearn Video First Impression dataset (91.67 % ), multimodal text, image, and audio with Chalearn Video First Impression dataset (91.88%). The average accuracy of the Big Five Personality traits was highest (95.36 % ) and lowest (59 % ). This highlights the need for standard benchmarks, interpretable models and interdisciplinary collaboration to improve the accuracy, reliability and ethical implications of personality prediction. Future research is expected to pave the way for advancements in understanding human personality through deep learning approaches using other modalities such as EEG, GSR, and PPG.

关键词： Deep learning Accuracy Reviews Psychology Predictive models Brain modeling Multitasking Reliability Information technology Standards

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A Study on Text Feature Selection Using Ant Colony and Grey Wolf Optimization

A Study on Text Feature Selection Using Ant Colony and Grey ...

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International Conference on Informatics and Computing (ICIC)

作者： Joan Angelina Widians Retantyo Wardoyo Sri Hartati Doctoral Program dept. of Computer Science and Electronics Universitas Gadjah Mada Dept. of Informatics Faculty of Engineering Universitas Mulawarman Samarinda Indonesia Dept. of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9798350345728

Text classification (TC) is widely used for organizing digital documents. The issues in TC are numerous characteristics and high-element dimensions. Many pattern classification issues require feature selection (FS), which is pertinent. FS removes unneeded and redundant data from the dataset. The Ant Colony Optimization (ACO) and Grey Wolf Optimizer (GWO) for FS are the main topics of our thorough assessment of the literature on the Swarm Intelligence (SI) algorithm. Furthermore, it illustrates how the hybrid SI technique is used in FS across various sectors. The hybrid SI technique uses applicable data from various FS methods to find feature subsets with smaller sizes and better classification performance than those found by regular FS algorithms.

关键词： Ant colony optimization Text categorization Pattern classification Filtering algorithms Feature extraction Classification algorithms Particle swarm optimization

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Improving student's complex problem solving through LAPS-Talk-Ball learning integrated with interactive games 10

Improving student's complex problem solving through LAPS-Tal...

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10th International Conference and Workshop on High Dimensional Data Analysis, ICW-HDDA 2020

作者： Asfar, A.M.I.T. Asfar, A.M.I.A. Sulastri, S. Department of Mathematics Education STKIP Muhammadiyah Bone Abu Dg.Pasolong Street No.62 Watampone Indonesia Doctoral Program Science Education Universitas Negeri Makassar Bonto Langkasa street Makassar Indonesia Department of Chemical Engineering State Polytechnic of Ujung Pandang Tamalanrea Makassar Indonesia Department of Economics Education STKIP Muhammadiyah Bone Abu Dg.Pasolong Street No.62 Watampone Indonesia

One of the competencies needed currently is non-routine competency in solving a complex problem. Students with complex problem solving will be able to identify the complex problem, understand to review related information, and create solutions for the problems. However, the improvement of complex problem-solving abilities lacks attention. This phenomenon occurs when students are given problem-solving, students have not been able to understand the problem, plan solutions, and provide solutions to problems. To overcome these problems, the LAPS-Talk-Ball learning model integrated with interactive games that guide the students in solving problems was implemented by using interactive games and speech balloons to stimulate each student to express their ideas. This research is quantitative experimental research to determine the success of the LAPS-Talk-Ball learning model integrated with android-based interactive games in training complex problem-solving students using saturated sampling and non-equivalent control group research designs. From the results of gain test results high improvement category, which means that the LAPS-Talk-Ball learning model integrated with android-based interactive games can train the complex problem-solving abilities of students. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Students

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Extraction of ultradian rhythm component reflecting REM-NREM sleep cycle from core body temperature

Extraction of ultradian rhythm component reflecting REM-NREM...

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IEEE Global Conference on Life sciences and Technologies (LifeTech)

作者： Hiroko Sawai Tetsuo Kurahashit Maki Matsumoto Emi Koyama Human Science Research Domain Toyota Central R&D Labs. Inc. Aichi Japan Doctoral Program of Engineering Design Kyoto Institute of Technology Kyoto Japan Faculty of Information and Human Science Kyoto Institute of Technology Kyoto Japan

ISBN: (数字)9781728170633

ISBN: (纸本)9781728170640

Rapid eye movement - non rapid eye movement (REM-NREM) sleep and daytime sleepiness vary with ultradian rhythm (U rhythm) of approximately 1-2 hour period. U rhythm component was also found in suprachiasmatic nucleus which is a master clock of circadian rhythm (C rhythm). However, the interaction between U and C rhythms is not fully understood. To find a measure to reveal the interaction between U and C rhythms, we focused on core body temperature (CBT) which shows C rhythm. U rhythm of CBT recorded using rectal temperature was analyzed comparing to REM-NREM sleep cycle. As results, U rhythm components reflecting REM-NREM sleep cycle were extracted from CBT by filtering. In addition, U rhythm components of CBT through a day were also extracted. Therefore, it is suggested that CBT can be used as a measure to evaluate U and C rhythms. This finding might contribute to revealing the interaction between U and C rhythms.

关键词： ultradian rhythm circadian rhythm core body temperature REM-NREM sleep cycle body core temperature Ultradian Rhythm Uranium Rhythms Biorhythms sleep pattern reference clock

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Design and synthesis of hydroxychloroquine-sugar conjugates as promising antimalarial agents

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Bioorganic & Medicinal Chemistry Letters 2025年 125-126卷

作者： Emil Salim Hilkatul Ilmi Aty Widyawaruyanti Natsuhisa Oka Department of Chemistry and Biomolecular Science Faculty of Engineering Gifu University 1-1 Yanagido Gifu 501-1193 Japan Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Andalas Padang 25163 Indonesia Doctoral Program at the Faculty of Pharmacy Universitas Airlangga Surabaya 60115 Indonesia Natural Product Medicine Research and Development Institute of Tropical Disease Universitas Airlangga Surabaya 60286 Indonesia Department of Pharmaceutical Sciences Faculty of Pharmacy Universitas Airlangga Surabaya 60115 Indonesia Institute for Glyco-core Research (iGCORE) Gifu University 1-1 Yanagido Gifu 501-1193 Japan Center for One Medicine Innovative Translational Research (COMIT) Institute for Advanced Study Gifu University 1-1 Yanagido Gifu 501-1193 Japan

Novel hydroxychloroquine (HCQ)-sugar conjugates were evaluated for their antimalarial activity against chloroquine-sensitive ( Pf 3D7) and multi-resistant ( Pf Dd2) strains of Plasmodium falciparum , cytotoxicity, and hydrolytic stability. HCQ-galactose conjugate with a 1,2-orthioester linkage exhibited the highest activity against Pf 3D7 (IC 50 value of 0.018 ± 0.001 μg/mL). Per- O -acetyl-β- d -glucopyranosyl-HCQ exhibited the highest activity against Pf Dd2 (IC 50 = 0.20 ± 0.02 μg/mL), while also maintaining comparable activity against Pf 3D7. Orthoester-linked conjugates were gradually hydrolyzed in human serum, resulting in HCQ release, which may contribute to their high activity. Glycosidic bond-linked conjugates showed high hydrolytic stability, retaining a certain level of activity, possibly without releasing HCQ. Toxicity assessments using the BHK21 cell line indicated significantly lower cytotoxicity for all HCQ-sugar conjugates compared to HCQ.

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In-silico structural and functional analysis of nonsynonymous single nucleotide polymorphisms in human FOLH1 gene

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In silico pharmacology 2025年第1期13卷 32页

作者： Abtin Tondar Muhammad Irfan Sergio Sánchez-Herrero Hafsa Athar Aleena Haqqi Asim Kumar Bepari Laura Calvet Liñán David Hervás Marin Department of Computer Science Multimedia and Telecommunication Interuniversity Doctoral Program in Bioinformatics Universitat Oberta de Catalunya Barcelona (UOC) Spain. Stanford Deep Data Research Center Stanford University Stanford USA. Atta-Ur-Rahman School of Applied Biosciences (ASAB) National University of Sciences and Technology (NUST) Islamabad Punjab Pakistan. School of Medical Laboratory Technology Minhaj University Lahore (MUL) Lahore Punjab Pakistan. Department of Pharmaceutical Sciences North South University (NSU) Dhaka Bangladesh. Telecommunications and Systems Engineering Department Universitat Autònoma de Barcelona (UAB) Sabadell Spain. Department of Applied Statistics and Operational Research and Quality Alcoy Universitat Politècnica de València (UPV) Alcoy Spain.

Non-synonymous single nucleotide polymorphisms (nsSNPs), also known as missense SNPs, can seriously affect an individual’s vulnerability to numerous diseases, including cancer. In this study, we conducted a comprehensive in-silico analysis to examine the structural and functional implications of nsSNPs within the Folate Hydrolase 1(FOLH1) gene, which encodes the Prostate-Specific Membrane Antigen (PSMA). A total of 504 SNPs were retrieved, and after filtering, 15 pathogenic nsSNPs were identified using five different in-silico tools. Three of these SNPs—R255H (rs375565491), R255C (rs201789325), and G168E (rs267602926)—were consistently predicted to be pathogenic across all in-silico tools. MutPred2 was used to predict the structural and functional consequences of the identified mutations. The analysis revealed multiple alterations in the PSMA protein, including changes in helical conformations, glycosylation patterns, transmembrane properties, and solvent accessibility. Furthermore, I-Mutant 2.0 analysis demonstrated a decrease in protein stability for most nsSNPs, except for rs267602926 (G168E), which was predicted to increase stability. Conservation analysis using ConSurf revealed varying degrees of amino acid conservation, with R255H and R255C identified as highly conserved residues, indicating their potential functional and structural significance. Additionally, post-translational modification (PTM) analysis indicated that while phosphorylation and methylation sites remained unchanged, specific glycosylation sites were lost in two pathogenic mutant variants (R255H and R255C), potentially affecting PSMA function and adversely impacting prostate cancer. Our findings highlight the importance of in silico studies to investigate the structural and functional impacts of FOLH1 nsSNPs on the PSMA protein. Such in silico studies can deepen our understanding of the roles of nsSNPs in prostate cancer onset, progression, and drug resistance.

关键词： FOLH1 In-silico structural and functional analyses PSMA PTM nsSNPs

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