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Fabrication of PSf/P84-blended membranes with low P84 content: Characteristics and gas separation performance

Case Studies in Chemical and Environmental Engineering 2024年 10卷

作者： Kayadoe, Victor Widiastuti, Nurul Gunawan, Triyanda Salleh, Wan Norhayati Wan Fansuri, Hamzah Romadiansyah, Taufik Qodar Pratama, Agus Wedi Department of Chemistry Faculty of Science and Data Analytical Institut Teknologi Sepuluh Nopember Chemistry Education Study Program Faculty of Teacher Training and Educational Pattimura University School of Chemical and Energy Engineering Faculty of Engineering Universiti Teknologi Malaysia Advanced Membrane Technology Research Center (AMTEC) Universiti Teknologi Malaysia

The demand for efficient and sustainable gas separation technologies is ever-increasing in various industries, including petrochemicals, natural gas processing, and carbon capture. Polymer-based membranes offer a promising solution due to their potential for high selectivity and energy efficiency. However, achieving optimal gas separation performance often requires overcoming the limitations of individual polymers through modifications such as polymer blending. In this study, PSf/P84 blended membranes with low P84 content were fabricated using the phase separation technique with a mixture of N-methyl-2-pyrrolidone and tetrahydrofuran as solvents. The effect of varying the mass ratio of PSf to P84 on membrane characteristics and gas separation performance was investigated. Characterization techniques included FTIR, XRD, SEM, TGA, Water Contact Angle (WCA) measurement, and mechanical property testing. Gas permeation tests were conducted with single gases at room temperature and 1 bar pressure. The results revealed that the addition of P84 increased membrane thickness, Young's modulus, and thermal stability, while decreasing d-spacing, dense layer thickness, hydrophilicity, tensile strength, and elongation. The F_index values, indicating the competence of the blended membranes compared to the ideal quality, demonstrated the positive impact of P84 addition on gas separation performance compared to pure PSf membranes. The optimal gas separation selectivity was achieved with the PSf/P84 1:0.025 blended membrane for H₂/CH₄ (11.28, 58 % increase), H₂/CO₂ (6.25, 193 % increase), and H₂/N₂ (5.25, 35 % increase). The highest N₂/CH₄ selectivity (6.29, 253 % increase) was observed with the 1:0.10 composition. Regarding commercially relevant separations based on the 2008 Robeson curve, the PSf/P84 1:0.20 blend showed promise for H₂/CO₂ separation, while the 1:0.

关键词： Findex Low P84 content PSf/P84 blended membrane Robeson curve

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Ecology, Spatial Structure, and Selection Pressure Induce Strong Signatures in Phylogenetic Structure

arXiv

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arXiv 2024年

作者： Moreno, Matthew Andres Rodriguez-Papa, Santiago Dolson, Emily Department of Ecology and Evolutionary Biology University of Michigan Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data Science University of Michigan Ann Arbor United States Department of Computer Science and Engineering Michigan State University East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

Evolutionary dynamics are shaped by a variety of fundamental, generic drivers, including spatial structure, ecology, and selection pressure. These drivers impact the trajectory of evolution, and have been hypothesized to influence phylogenetic structure. For instance, they can help explain natural history, steer behavior of contemporary evolving populations, and influence efficacy of application-oriented evolutionary optimization. Likewise, in inquiry-oriented artificial life systems, these drivers constitute key building blocks for open-ended evolution. Here, we set out to assess (1) if spatial structure, ecology, and selection pressure leave detectable signatures in phylogenetic structure, (2) the extent, in particular, to which ecology can be detected and discerned in the presence of spatial structure, and (3) the extent to which these phylogenetic signatures generalize across evolutionary systems. To this end, we analyze phylogenies generated by manipulating spatial structure, ecology, and selection pressure within three computational models of varied scope and sophistication. We find that selection pressure, spatial structure, and ecology have characteristic effects on phylogenetic metrics, although these effects are complex and not always intuitive. Signatures have some consistency across systems when using equivalent taxonomic unit definitions (e.g., individual, genotype, species). Further, we find that sufficiently strong ecology can be detected in the presence of spatial structure. We also find that, while low-resolution phylogenetic reconstructions can bias some phylogenetic metrics, high-resolution reconstructions recapitulate them faithfully. Although our results suggest potential for evolutionary inference of spatial structure, ecology, and selection pressure through phylogenetic analysis, further methods development is needed to distinguish these drivers' phylometric signatures from each other and to appropriately normalize phylogenetic metrics. With s

关键词： Abiotic

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Functional Connectome-Based Predictive Modeling in Autism

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BIOLOGICAL PSYCHIATRY 2022年第8期92卷 626-642页

作者： Horien, Corey Floris, Dorothea L. Greene, Abigail S. Noble, Stephanie Rolison, Max Tejavibulya, Link O'Connor, David McPartland, James C. Scheinost, Dustin Chawarska, Katarzyna Lake, Evelyn M. R. Constable, R. Todd Interdepartmental Neuroscience Program Yale School of Medicine New Haven Connecticut MD-PhD Program Yale School of Medicine New Haven Connecticut Methods of Plasticity Research Department of Psychology University of Zürich Zurich Switzerland Donders Center for Brain Cognition and Behavior Radboud University Nijmegen Nijmegen The Netherlands Department of Radiology and Biomedical Imaging Yale School of Medicine New Haven Connecticut Yale Child Study Center New Haven Connecticut Department of Biomedical Engineering Yale University New Haven Connecticut Department of Psychology Yale University New Haven Connecticut Department of Statistics and Data Science Yale University New Haven Connecticut Department of Pediatrics Yale School of Medicine New Haven Connecticut Department of Neurosurgery Yale School of Medicine New Haven Connecticut

Autism is a heterogeneous neurodevelopmental condition, and functional magnetic resonance imaging-based studies have helped advance our understanding of its effects on brain network activity. We review how predictive modeling, using measures of functional connectivity and symptoms, has helped reveal key insights into this condition. We discuss how different prediction frameworks can further our understanding of the brain-based features that underlie complex autism symptomatology and consider how predictive models may be used in clinical settings. Throughout, we highlight aspects of study interpretation, such as data decay and sampling biases, that require consideration within the context of this condition. We close by suggesting exciting future directions for predictive modeling in autism.

关键词： Clinical translation Development Fingerprinting Individual differences Machine learning Resting-state fMRI

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Herb compounds screening as meningitis inhibitor candidates using neural network and random forest methods

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AIP Conference Proceedings 2025年第1期3250卷

作者： Riska Aprilia Mohammad Hamim Zajuli Al Faroby Muhammad Adib Kamali Muhammad Dzulfikar Fauzi Information Technology Study Program Faculty of Information Technology and Business Institut Teknologi Telkom Surabaya Surabaya Indonesia Data Science Study Program Faculty of Information Technology and Business Institut Teknologi Telkom Surabaya Surabaya Indonesia Informatics Study Program Faculty of Information Technology and Business Institut Teknologi Telkom Surabaya Surabaya Indonesia

Meningitis is an inflammation of the meninges that occurs in the protective lining of the brain and spinal cord caused by bacterial, viral, or fungal infections. This disease is difficult to recognize because it has initial symptoms like the flu where the patient has a fever and headache. Current efforts to prevent the disease by strengthening antibodies. Meanwhile, drug candidates for the treatment of this disease still have not found optimal results in reducing mortality due to meningitis. This study aims to find and analyses herbal compound candidates that might be inhibitors of meningitis. Compound data was acquired from a validated open database. The data acquired are smiles of the chemical bond structure of the compounds. In the data processing process, compound feature extraction is required by applying the concept of molecular fingerprint. The results of feature extraction are used as datasets to build classification models by applying the Multilayer Perceptron (MLP) and Random Forest algorithms. The two models are compared, and a more robust model is selected to be used as a prediction model for herbal compound search. The MLP model has a better accuracy of 0.97 compared to the Random Forest model. The results of screening using the MLP learning model obtained Symphytine, cis-Linalool oxide, and 3-O-Methylcalopocarpin compounds have the highest probability compared to thousands of other herbal compounds. This candidate compound can be used as a recommendation for drug discovery to treat patients who contract Meningitis.

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Testing the Inference Accuracy of Accelerator-Friendly Approximate Phylogeny Tracking

Testing the Inference Accuracy of Accelerator-Friendly Appro...

引用

Computational Intelligence in Artificial Life and Cooperative Intelligent Systems (ALIFE-CIS), IEEE Symposium on

作者： Matthew Andres Moreno Anika Ranjan Emily Dolson Luis Zaman Department of Ecology and Evolutionary Biology University of Michigan Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data and AI in Society University of Michigan Ann Arbor United States Undergraduate Research Opportunities Program University of Michigan Ann Arbor United States Department of Computer Science and Engineering Michigan State University East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

ISBN: (数字)9798331508371

ISBN: (纸本)9798331508388

Computer simulations are an important tool for studying the mechanics of biological evolution. In particular, agent-based approaches provide an opportunity to collect high-quality records of ancestry relationships. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Previous work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, challenges exist in scaling direct ancestrytracking approaches to highly-distributed, many-processor evolution in silico. An alternative approach is to estimate phylogenetic history via noncoding annotations on digital genomes, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recent work has extended this “hereditary stratigraphy” approach to support powerful hardware accelerator platforms, such as the Cerebras Wafer-Scale Engine. Although these second-generation “surface”-based hereditary stratigraphy algorithms have demonstrated order-ofmagnitude speedups over first-generation “column”-based algorithms, it remains unknown how they impact the accuracy of reconstructed phylogenies. To address this question, we assessed reconstruction accuracy under alternative configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. Encouragingly, we find that the second-generation approaches provide higher reconstruction quality across most surveyed conditions.

关键词： Surface reconstruction Accuracy Annotations Heuristic algorithms Computational modeling Biological system modeling Stratigraphy Phylogeny History Bars

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CNEReg Interprets Ruminant-specific Conserved Non-coding Elements by Developmental Gene Regulatory Network

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Genomics, Proteomics & Bioinformatics 2023年第3期21卷 632-648页

作者： Xiangyu Pan Zhaoxia Ma Xinqi Sun Hui Li Tingting Zhang Chen Zhao Nini Wang Rasmus Heller Wing Hung Wong Wen Wang Yu Jiang Yong Wang Key Laboratory of Animal Genetics Breeding and Reproduction of Shaanxi ProvinceCollege of Animal Science and TechnologyNorthwest A&F UniversityYangling 712100China Department of Medical Research Guangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesGuangzhou 510080China Guangdong Cardiovascular Institute Guangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesGuangzhou 510080China Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing 100190China School of Mathematics University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources College of Animal Science and TechnologyGuangxi UniversityNanning 530005China Section for Computational and RNA Biology Department of BiologyUniversity of CopenhagenCopenhagen DK-2100Denmark Department of Statistics Department of Biomedical Data ScienceBio-X ProgramStanford UniversityStanfordCA 94305USA Center for Ecological and Environmental Sciences Northwestern Polytechnical UniversityXi’an 710072China State Key Laboratory of Genetic Resources and Evolution Kunming Institute of ZoologyChinese Academy of SciencesKunming 650223China Center for Excellence in Animal Evolution and Genetics Chinese Academy of SciencesKunming 650223China Key Laboratory of Systems Health Science of Zhejiang Province School of Life ScienceHangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 310024China

The genetic information coded in DNA leads to trait innovation via a gene regulatory network(GRN)in ***,we developed a conserved non-coding element interpretation method to integrate multi-omics data into gene regulatory network(CNEReg)to investigate the ruminant multi-chambered stomach *** generated paired expression and chromatin accessibility data during rumen and esophagus development in sheep,and revealed 1601 active ruminantspecific conserved non-coding elements(active-RSCNEs).To interpret the function of these activeRSCNEs,we defined toolkit transcription factors(TTFs)and modeled their regulation on rumenspecific genes via batteries of active-RSCNEs during *** developmental GRN revealed 18 TTFs and 313 active-RSCNEs regulating 7 rumen functional ***,6 TTFs(OTX1,SOX21,HOXC8,SOX2,TP63,and PPARG),as well as 16 active-RSCNEs,functionally distinguished the rumen from the *** study provides a systematic approach to understanding how gene regulation evolves and shapes complex traits by putting evo-devo concepts into practice with developmental multi-omics data.

关键词： Trait innovation Gene regulatory network Conserved non-coding element Toolkit transcription factor Ruminant

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Multivariate Adaptive Regression Splines Segmentation Based on Latent Variable Score in Partial Least Square-Structural Equation Modeling (Case study: Nutrition Status of Toddlers in Southeast Sulawesi)

SSRN

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SSRN 2024年

作者： Otok, Bambang Widjanarko Purhadi Purnomo, Jerry Dwi Trijoyo Mulyanto, Angga Dwi Sriningsih, Riry Fadmi, Fitri Rachmillah Khair, Abdul Department of Statistics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya60111 Indonesia Department of Mathematics Faculty of Mathematics and Natural Sciences Universitas Negeri Padang West Sumatera Indonesia Public Health Study Program Faculty of Health Sciences Universitas Mandala Waluya Southeast Sulawesi Kendari Indonesia Department of Environmental Health Poltekkes Kemenkes Banjarmasin South Kalimantan Indonesia

Nutrition is a crucial factor that profoundly influences children's growth and development, facilitating proper maturation. Child malnutrition, encompassing stunting, underweight, and severe wasting, constitutes a significant issue in Indonesia. The WHO categorizes the nutritional status of children under-five in Indonesia as chronic, especially in Southeast Sulawesi Province. This research examines the factors influencing the nutritional status of children under-five in Southeast Sulawesi by Partial Least Square Structural Equation Modeling (PLS-SEM). Furthermore, Multivariate Adaptive Regression Splines (MARS) was employed to classify the nutritional condition of children under-five according to factor scores. The findings indicated that the nutritional status of children under-five was affected by parenting, illness, and healthcare factors. The MARS Segmentation Model utilizing basis functions yields more accurate outcomes in evaluating the nutritional condition of children under-five. The models generated for each sub-district vary according to the basis function employed. The nutritional status of children under-five, encompassing stunting, wasting, and underweight, comprises seven areas. This study comprises three significant components: MARS is a novel way for assessing route coefficients in PLS-SEM modelsMARS is a spatial model constructed using basis functionsApplication of MARS-SEM in evaluating the nutritional condition of children under-five in Southeast Sulawesi Province. © 2024, The Authors. All rights reserved.

关键词： Least squares approximations

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Kohonen-SOM LOF Approach for Anomaly Detection

Kohonen-SOM LOF Approach for Anomaly Detection

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Information Technology International Seminar (ITIS)

作者： Amri Muhaimin Kartika Fithriasari Data Science Program Study Universitas Pembangunan Nasional &#x201C Veteran&#x201D Jawa Timur Surabaya Indonesia Department of Statistics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (纸本)9781665408080

Water loss in distribution is a serious problem for water companies, as it can result in a loss of profit. Machine learning has a method for dealing with this, for example, anomaly detection. Anomalies in the study were detected using data from March 2017 to February 2018. The data used is water consumption, and the variables such as mean water use, maximum water use, and standard deviation of water usage were obtained. Kohonen Self-Organizing Map (SOM) and Local Outlier Factor (LOF) are used because they can create a good cluster, and LOF can detect which one of them is filled with anomalies. The Kohonen SOM algorithm produced 45 groups that were considered anomalous because their silhouette width criteria were less than the group's average silhouette width. There are 45 different groups of unexpected anomalies. The LOF generated 1229 unusual consumption events, with each incident involving 579 households or customers. The frequency calculation yields 41 customers who are suspected of being anomalous. While the baseline method from the company only captures 16 anomalies out of 41 customers. This is because the company method fails to capture unusual consumption behaviors, such as monthly consumption. The anomaly detected the customer's characteristic of having an average of more than average usage of classes and sub-zones.

关键词： Self-organizing feature maps Seminars Clustering algorithms Companies Machine learning Behavioral sciences Information technology

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A Guide to Tracking Phylogenies in Parallel and Distributed Agent-based Evolution Models

arXiv

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arXiv 2024年

作者： Moreno, Matthew Andres Ranjan, Anika Dolson, Emily Zaman, Luis Department of Ecology and Evolutionary Biology University of Michigan Ann Arbor United States Center for the Study of Complex Systems University of Michigan Ann Arbor United States Michigan Institute for Data and AI in Society University of Michigan Ann Arbor United States Undergraduate Research Opportunities Program University of Michigan Ann Arbor United States Department of Computer Science and Engineering Michigan State University East Lansing United States Program in Ecology Evolution and Behavior Michigan State University East Lansing United States

Computer simulations are an important tool for studying the mechanics of biological evolution. In particular, in silico work with agent-based models provides an opportunity to collect high-quality records of ancestry relationships among simulated agents. Such phylogenies can provide insight into evolutionary dynamics within these simulations. Existing work generally tracks lineages directly, yielding an exact phylogenetic record of evolutionary history. However, direct tracking can be inefficient for large-scale, many-processor evolutionary simulations. An alternate approach to extracting phylogenetic information from simulation that scales more favorably is post hoc estimation, akin to how bioinformaticians build phylogenies by assessing genetic similarities between organisms. Recently introduced "hereditary stratigraphy" algorithms provide means for efficient inference of phylogenetic history from non-coding annotations on simulated organisms' genomes. A number of options exist in configuring hereditary stratigraphy methodology, but no work has yet tested how they impact reconstruction quality. To address this question, we surveyed reconstruction accuracy under alternate configurations across a matrix of evolutionary conditions varying in selection pressure, spatial structure, and ecological dynamics. We synthesize results from these experiments to suggest a prescriptive system of best practices for work with hereditary stratigraphy, ultimately guiding researchers in choosing appropriate instrumentation for large-scale simulation studies. Copyright © 2024, The Authors. All rights reserved.

关键词： Biotic

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Dual template using P123-gelatin for synthesized large mesoporous silica for enhanced adsorption of dyes

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South African Journal of Chemical Engineering 2023年 43卷 312-326页

作者： Safitri, Widiya Nur Habiddin, Habiddin Ulfa, Maria Trisunaryanti, Wega Bahruji, Hasliza Holilah, Holilah Rohmah, Alya Awinatul Sholeha, Novia Amalia Jalil, Aishah Abdul Santoso, Eko Prasetyoko, Didik Department of Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember East Java Keputih Sukolilo Surabaya 60111 Indonesia Chemistry Education Study Program Faculty of Teacher Training and Education Universitas Sebelas Maret Jl. Ir. Sutami 36A Surakarta 57126 Indonesia Department of Chemistry Faculty of Science Universitas Gadjah Mada Sekip Utara Sleman Indonesia Centre for Advanced Material and Energy Sciences Universiti Brunei Darussalam Bandar Seri Begawan BE 1410 Brunei Darussalam Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN) Cibinong 16911 Indonesia Department of Food Science and Technology Faculty of Agriculture Halu Oleo University Indonesia College of Vocational Studies Bogor Agricultural University (IPB University) Jalan Kumbang No. 14 Bogor 16151 Indonesia Department of Chemical Engineering Faculty of Chemical and Energy Engineering Universiti Teknologi Malaysia 81310 UTM Johor Skudai Johor Bahru Malaysia

Mesoporous Silica with a large mesopore channel was synthesized using the sol-gel method for dye adsorption. A dual template consisting of P123 and gelatin at different ratios was employed in the sol-gel synthesis to enhance surface area and mesopore diameter. Gelatin is a green pore directing agent that strengthens P123 thermal stability during the calcination of silicate gel. Optimization of P123:gelatin ratio to 1:0.02 increased the surface area of mesoporous silica to 561.9 m²/g and expanded the pore diameter to 10 nm. The large pore diameter and surface area enhanced methylene blue adsorption capacity to 363.63 mg/g. The adsorption of methylene blue on mesoporous silica follows the pseudo second-order kinetic and the Langmuir model. Regeneration studies using thermal and chemical treatments exhibited the potential of mesoporous silica as an adsorbent for multiple adsorption-desorption of dyes. © 2022 The Author(s)

关键词： Dual template Dye Gelatin Mesoporous silica Pluronic P123

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