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Cyber-physical infrastructures for advancing pyrolysis conversion process: A case study of biochar production

Cyber-physical infrastructures for advancing pyrolysis conve...

ASME 2020 International Design engineering Technical Conferences and computers and Information in engineering Conference, IDETC-CIE 2020

作者： Hersh, Benjamin Mohajeri, Amir Mirkouei, Amin Xian, Min Department of Mechanical Engineering University of Idaho ID United States Industrial Technology Program University of Idaho ID United States Industrial Technology Program Department of Mechanical Engineering University of Idaho ID United States Department of Computer Science University of Idaho ID United States

ISBN: (纸本)9780791883983

Biomass-based products (bioproducts) have been introduced as a means to address food-energy-water nexus challenges. However, the existing approaches have not been integrated to convert biomass into market-competitive bioproducts (e.g., biochar and bio-oil). Pretreatment and conversion processes represent the most substantial portion of the total bioproduct cost. This study proposes a portable conversion process for high-quality biochar and bio-oil production, using mixed reactors and advanced cyber-physical infrastructures to promote cross-cutting technological and commercialization opportunities. The proposed portable process converts biomass to bioproducts near the collection sites, which can address collection, staging, and logistics challenges. The fundamental novelty of this study lies in utilizing cyber-physical technologies for advancing pre-/postconversion processes and crossing the boundaries to meet the market needs. A case study for biochar production from various biomass feedstocks is used to demonstrate and verify the application of the conversion process and the cyber-based advances. The results indicate that the integrated cyber-physical conversion pathway can increase process yield and quality. It is also found that biomass properties and conversion process configurations play a crucial role in determining the process yield and biochar quality in terms of the physical-chemical structure. Copyright © 2020 ASME.

关键词： Biomass

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The Pairing Analysis Improvement of Magnetized Structure in Electromagnetic Bulging Process in Case of Tube with Field Shaper

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Journal of Electromagnetic Analysis and Applications 2024年第4期16卷 43-67页

作者： Blaise Kevine Lenz Soronga Zhanqian Zhan Jing Liu Jonas Asamoah Mohamed Bangali Bangura Matala Makiese N’kodia Kambani Chrionie Md Jobayer Hossain Yongdong Chen Program Debugging and Design Department Guangdong Lyric Robot Automation Co. Ltd Huizhou China Electrical and Automation Department China Three Gorges University Yichang China Electronic Department Power Electronic Research Center University of Kent Giles Ln Canterbury UK Program Debugging Department Guangdong Lyric Robot Automation Co. Ltd. Huizhou China Electrical and Computer Engineering Department South China University of Technology Guangzhou China World Standardization Certification and Testing Group (Shenzhen) Co. Ltd. Shenzhen China Electrical Engineering-Power System Mapon University Kindu Democratic Republic of Congo Department of Economy Managment School of Management Computer and Telecomunication Rabbat Marocco Materials Visual Calibration Department Guangdong Lyric Robot Automation Co. Ltd. Huizhou China Program Design Department Guangdong Lyric Robot Automation Co. Ltd. Huizhou China

In the current practical science, the accuracy in the formability of metal alloys being the goal when using electromagnetic forming (EMF) technology, which is a high-speed processing technology that uses Lorentz forces to achieve plastic deformation of sheet metal;according to the previous analysis, the results have shown that in most cases, the Lorentz force acting on the workpiece (metal) is not uniform, there are uneven axial deformations of the metal plates which prevent the rapid advancement of today’s technology. In this article, we presented some advanced analyzes which will lead us to improve the technical solution for the problems of non-uniform axial deformations of the metals in the traditional tube electromagnetic forming technology (EMF). A field shaper is used as a practical forming tool to influence the magnetic field and magnetic pressure distribution, thereby improving the forming ability and result during the electromagnetic forming (EMF) process and we see that induced eddy current control is realized by changing the structural parameters of the magnetic field shaper;which improves the strength and controllability of the magnetic force that acts on the workpiece;thereby a greater radial magnetic pressure can be achieved with field shaper than the case without it;the field shaper regulates the electromagnetic force, the distribution of the magnetic pressure decreases, and the uniform force area of the tube increases which effectively enhances the uniform range of the pipe electromagnetic bulging and the electromagnetic induction coupling between the coil and the metallic workpiece is generally required to produce the Lorentz forces. Using COMSOL Multiphysics® simulation software helped us to accurately represent the real world, simulating multiple physical effects that happened in this model during the process.

关键词： Electromagnetic Forming Field Shaper Lorentz Force Magnetic Pressure Distribution

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k-mer-based approaches to bridging pangenomics and population genetics

arXiv

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arXiv 2024年

作者： Roberts, Miles D. Davis, Olivia Josephs, Emily B. Williamson, Robert J. Genetics and Genome Sciences Program Michigan State University East LansingMI48824 United States Department of Computer Science and Software Engineering Rose-Hulman Institute of Technology Terre HauteIN47803 United States Department of Biology and Biomedical Engineering Rose-Hulman Institute of Technology Terre HauteIN47803 United States Department of Plant Biology Michigan State University East LansingMI48824 United States Ecology Evolution and Behavior Program Michigan State University East LansingMI48824 United States Plant Resilience Institute Michigan State University East LansingMI48824 United States

Many commonly studied species now have more than one chromosome-scale genome assembly, revealing a large amount of genetic diversity previously missed by approaches that map short reads to a single reference. However, many species still lack multiple reference genomes and correctly aligning references to build pangenomes is challenging, limiting our ability to study this missing genomic variation in population genetics. Here, we argue that k-mers are a crucial stepping stone to bridging the reference-focused paradigms of population genetics with the reference-free paradigms of pangenomics. We review current literature on the uses of k-mers for performing three core components of most population genetics analyses: identifying, measuring, and explaining patterns of genetic variation. We also demonstrate how different k-mer-based measures of genetic variation behave in population genetic simulations according to the choice of k, depth of sequencing coverage, and degree of data compression. Overall, we find that k-mer-based measures of genetic diversity scale consistently with pairwise nucleotide diversity (π) up to values of about π = 0.025 (R2 = 0.97) for neutrally evolving populations. For populations with even more variation, using shorter k-mers will maintain the scalability up to at least π = 0.1. Furthermore, in our simulated populations, k-mer dissimilarity values can be reliably approximated from counting bloom filters, highlighting a potential avenue to decreasing the memory burden of k-mer based genomic dissimilarity analyses. For future studies, there is a great opportunity to further develop methods to identifying selected loci using k-mers. © 2024, CC BY.

关键词： Genome

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A Deep Learning Approach for Nerve Injury Classification in Brachial Plexopathies Using Magnetic Resonance Neurography with Modified Hiking Optimization Algorithm

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Academic Radiology 2025年

作者： Dahou, Abdelghani Elaziz, Mohamed Abd Khattap, Mohamed G. Hassan, Hend Galal Eldeen Mohamed Ali School of Computer Science and Technology Zhejiang Normal University Jinhua 321004 China Mathematics and Computer Science department University of Ahmed DRAIA Adrar 01000 Algeria Department of Mathematics Faculty of Science Zagazig University Zagazig 44519 Egypt Faculty of Computer Science and Engineering Galala University Suez 435611 Egypt Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Diagnostic Interventional Radiology and Molecular Imaging Faculty of Medicine Ain Shams University Cairo 11591 Egypt Technology of Radiology and Medical Imaging Program Faculty of Applied Health Sciences Technology Galala University Suez 435611 Egypt

Rationale and Objectives: Brachial plexopathies (BPs) encompass a complex spectrum of nerve injuries affecting motor and sensory function in the upper extremities. Diagnosis is challenging due to the intricate anatomy and symptom overlap with other neuropathies. Magnetic Resonance Neurography (MRN) provides advanced imaging but requires specialized interpretation. This study proposes an AI-based framework that combines deep learning (DL) with the modified Hiking Optimization Algorithm (MHOA) enhanced by a Comprehensive Learning (CL) technique to improve the classification of nerve injuries (neuropraxia, axonotmesis, neurotmesis) using MRN data. Materials and Methods: The framework utilizes MobileNetV4 for feature extraction and MHOA for optimized feature selection across different MRI sequences (STIR, T2, T1, and DWI). A dataset of 39 patients diagnosed with BP was used. The framework classifies injuries based on Seddon's criteria, distinguishing between normal and abnormal conditions as well as injury severity. Results: The model achieved excellent performance, with 1.0000 accuracy in distinguishing normal from abnormal conditions using STIR and T2 sequences. For injury severity classification, accuracy was 0.9820 in STIR, outperforming the original HOA and other metaheuristic algorithms. Additionally, high classification accuracy (0.9667) was observed in DWI. The proposed framework outperformed traditional methods and demonstrated high sensitivity and specificity. Conclusion: The proposed AI-based framework significantly improves the diagnosis of BP by accurately classifying nerve injury types. By integrating DL and optimization techniques, it reduces diagnostic variability, making it a valuable tool for clinical settings with limited specialized neuroimaging expertise. This framework has the potential to enhance clinical decision-making and optimize patient outcomes through precise and timely diagnoses. © 2025 The Association of University Radiologists

关键词： Brachial Plexopathy (BP) Comprehensive Learning (CL) Deep Learning (DL) Feature Selection (FS) Hiking Optimization Algorithm (HOA) Magnetic Resonance Neurography (MRN) MobileNetV4

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Analyzing Biomedical Datasets with Symbolic Tree Adaptive Resonance Theory

TechRxiv

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TechRxiv 2023年

作者： Petrenko, Sasha Hier, Daniel B. Obafemi-Ajayi, Tayo Bone, Mary Timpson, Erik J. Speight, Michael Marsh, William E. Wunsch, Donald C. Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO United States Department of Neurology and Rehabilitation University of Illinois at Chicago ChicagoIL United States University of Southeastern Norway Kongsberg Norway Engineering Program Missouri State University SpringfieldMO United States Honeywell Federal Manufacturing & Technologies Kansas CityMO United States

Biomedical datasets distill many mechanisms of human diseases, linking diseases to genes and phenotypes (signs and symptoms of disease), genetic mutations to altered protein structures, and altered proteins to changes in molecular functions and biological processes. It is desirable to gain new insights from these data, especially with regard to the uncovering of hierarchical structures relating disease variants. However, analysis to this end has proven difficult due to the complexity of the connections between multicategorical symbolic data. This article proposes Symbolic Tree Adaptive Resonance Theory (START), with additional supervised, Dual-Vigilance (DV-START), and Distributed Dual-Vigilance (DDV-START) formulations, for the clustering of multicategorical symbolic data from biomedical datasets by demonstrating its utility in clustering variants of Charcot-Marie-Tooth disease using genomic, phenotypic, and proteomic data. © 2023, CC BY.

关键词： Ontology

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BENCHMARKING COMMUNITY DRUG RESPONSE PREDICTION MODELS: DATASETS, MODELS, TOOLS, AND METRICS FOR CROSS-DATASET GENERALIZATION ANALYSIS

arXiv

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arXiv 2025年

作者： Partin, Alexander Vasanthakumari, Priyanka Narykov, Oleksandr Wilke, Andreas Koussa, Natasha Jones, Sara E. Zhu, Yitan Overbeek, Jamie C. Jain, Rajeev Fernando, Gayara Demini Sanchez-Villalobos, Cesar Garcia-Cardona, Cristina Mohd-Yusof, Jamaludin Chia, Nicholas Wozniak, Justin M. Ghosh, Souparno Pal, Ranadip Brettin, Thomas S. Weil, M. Ryan Stevens, Rick L. Division of Data Science and Learning Argonne National Laboratory LemontIL United States Frederick National Laboratory for Cancer Research Cancer Data Science Initiatives Cancer Research Technology Program FrederickMD United States Department of Statistics University of Nebraska–Lincoln LincolnNE United States Department of Electrical & Computer Engineering Texas Tech University LubbockTX United States Division of Computer Computational and Statistical Sciences Los Alamos National Laboratory Los AlamosNM United States Department of Computer Science The University of Chicago ChicagoIL United States

Deep learning (DL) and machine learning (ML) models have shown promise in drug response prediction (DRP), yet their ability to generalize across datasets remains an open question, raising concerns about their real-world applicability. Due to the lack of standardized benchmarking approaches, model evaluations and comparisons often rely on inconsistent datasets and evaluation criteria, making it difficult to assess true predictive capabilities. In this work, we introduce a benchmarking framework for evaluating cross-dataset prediction generalization in DRP models. Our framework incorporates five publicly available drug screening datasets, six standardized DRP models, and a scalable workflow for systematic evaluation. To assess model generalization, we introduce a set of evaluation metrics that quantify both absolute performance (e.g., predictive accuracy across datasets) and relative performance (e.g., performance drop compared to within-dataset results), enabling a more comprehensive assessment of model transferability. Our results reveal substantial performance drops when models are tested on unseen datasets, underscoring the importance of rigorous generalization assessments. While several models demonstrate relatively strong cross-dataset generalization, no single model consistently outperforms across all datasets. Furthermore, we identify CTRPv2 as the most effective source dataset for training, yielding higher generalization scores across target datasets. By sharing this standardized evaluation framework with the community, our study aims to establish a rigorous foundation for model comparison, and accelerate the development of robust DRP models for real-world applications. © 2025, CC BY.

关键词： Prediction models

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Alternative Role of Motif B in Template Dependent Polymerase Inhibition

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Chinese Journal of Chemical Physics 2022年第3期35卷 407-412,I0001页

作者： Xueying Luo Tiantian Xu Xin Gao Lu Zhang State Key Laboratory of Structural Chemistry Fujian Institute of Research on the Structure of MatterChinese Academy of SciencesFuzhou 350002China University of Chinese Academy of Sciences Beijing 100049China Computer Science Program ComputerElectrical and Mathematical Sciences and Engineering(CEMSE)DivisionKing Abdullah University of Science and TechnologyThuwal 23955-6900King-dom of Saudi Arabia KAUST Computational Bioscience Research Center(CBRC) King Abdullah University of Science and TechnologyThuwal 23955-6900Kingdom of Saudi Arabia Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry Xiamen 361005China

Severe acute respiratory syndrome coronavirus 2(SARS-Co V-2) relies on the central molecular machine RNA-dependent RNA polymerase(Rd Rp) for the viral replication and transcription. Remdesivir at the template strand has been shown to effectively inhibit the RNA synthesis in SARS-Co V-2 Rd Rp by deactivating not only the complementary UTP incorporation but also the next nucleotide addition. However, the underlying molecular mechanism of the second inhibitory point remains unclear. In this work, we have performed molecular dynamics simulations and demonstrated that such inhibition has not directly acted on the nucleotide addition at the active site. Instead, the translocation of Remdesivir from +1 to-1 site is hindered thermodynamically as the posttranslocation state is less stable than the pre-translocation state due to the motif B residue G683. Moreover, another conserved residue S682 on motif B further hinders the dynamic translocation of Remdesivir due to the steric clash with the 1′-cyano substitution. Overall,our study has unveiled an alternative role of motif B in mediating the translocation when Remdesivir is present in the template strand and complemented our understanding about the inhibitory mechanisms exerted by Remdesivir on the RNA synthesis in SARS-Co V-2 Rd Rp.

关键词： SARS-CoV-2 RNA-dependent RNA polymerase Inhibitory mechanism Nu-cleotide analog Molecular dynamics simulation

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Energy Preservation in MANET Using Self-Adaptive CH Selection with Advanced Genetic Algorithm

Energy Preservation in MANET Using Self-Adaptive CH Selectio...

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Advanced engineering and Technologies (ICONNIC), International Conference on

作者： Bonifacius Vicky Indriyono Natalinda Pamungkas Fatima Hashim Abbas Ery Mintorini Mohammed Almohamadi Sarmad Nozad Mahmood Sameer Alani Ahmed H. Alkhayyat Eko Hari Rachmawanto Information System University of Dian Nuswantoro Kediri Indonesia Medical Laboratories Techniques Department Al-Mustaqbal University College Hillah Babil Iraq Visual Communication Design University of Dian Nuswantoro Kediri Indonesia College of Information Technology Imam Ja'afar Al-Sadiq University Al-Muthanna Iraq Computer Technology Engineering College of Engineering Technology Al-Kitab University Iraq Medical Laboratories Techniques University of Mashreq Research center Baghdad Iraq College of technical engineering University of Mashreq The Islamic University Najaf Iraq Study Program in Informatics Engineering University of Dian Nuswantoro Semarang Indonesia

Mobile Adhoc Networks (MANETs) is an emerging technology in both the industrial and academic research. The major drawback in MANETs is improving the battery capacity. MANETs are dynamic in nature therefore during communication it consumes more energy that reduces the overall energy efficiency of the network. Many past and present researches are concern about this problem. In this paper, Energy Preservation in MANETs using Self-Adaptive Cluster Head Selection with Advanced Genetic Algorithm (EPMSA-CHAG) approach is proposed where the CH selection is performed using two segments; they are initial parameter based on CH selection and Advanced Genetic Algorithm (AGA) based CH selection. At the initial stage the parameters which are considered for the CH selection are node degree, node stability, distance, residual energy, and speed and delivery rate. Using these parameters are the best fit for CH selection is chosen then in order to find the optimal best fit from the best fit calculation, Advanced Genetic Algorithm (AGA). The proposed EPMSA-CHAG approach is simulated using NS2 and the parameters which are considered for the performance analysis are packet delivery rate, energy efficiency, end to end delay, routing overhead and throughput. The methods that are taken for the comparative analysis are HLSPM-CHSR and HAMBO-CHLD. From the results calculated it is proven that the proposed EPMSA-CHAG approach achieved high packet delivery rate, energy efficiency and throughput as well as lower end to end delay and routing overhead when compared with the earlier methods HLSPM-CHSR and HAMBO-CHLD.

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Gumbel-Softmax Flow Matching with Straight-Through Guidance for Controllable Biological Sequence Generation

arXiv

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arXiv 2025年

作者： Tang, Sophia Zhang, Yinuo Tong, Alexander Chatterjee, Pranam Department of Biomedical Engineering Duke University United States Management and Technology Program University of Pennsylvania United States Center of Computational Biology Duke-NUS Medical School Singapore Mila Quebec AI Institute Canada Université de Montréal Canada Department of Computer Science Duke University United States Department of Biostatistics and Bioinformatics Duke University United States

Flow matching in the continuous simplex has emerged as a promising strategy for DNA sequence design, but struggles to scale to higher simplex dimensions required for peptide and protein generation. We introduce Gumbel-Softmax Flow and Score Matching, a generative framework on the simplex based on a novel Gumbel-Softmax interpolant with a time-dependent temperature. Using this interpolant, we introduce Gumbel-Softmax Flow Matching by deriving a parameterized velocity field that transports from smooth categorical distributions to distributions concentrated at a single vertex of the simplex. We alternatively present Gumbel-Softmax Score Matching which learns to regress the gradient of the probability density. Our framework enables high-quality, diverse generation and scales efficiently to higher-dimensional simplices. To enable training-free guidance, we propose Straight-Through Guided Flows (STGFlow), a classifier-based guidance method that leverages straight-through estimators to steer the unconditional velocity field toward optimal vertices of the simplex. STGFlow enables efficient inference-time guidance using classifiers pre-trained on clean sequences, and can be used with any discrete flow method. Together, these components form a robust framework for controllable de novo sequence generation. We demonstrate state-of-the-art performance in conditional DNA promoter design, sequence-only protein generation, and target-binding peptide design for rare disease treatment. © 2025, CC BY-NC-ND.

关键词： Velocity distribution

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Analysis of temperature in an air-cooled combustion motorcycle engine using fiber Bragg grating sensors

Analysis of temperature in an air-cooled combustion motorcyc...

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Optics and Photonics Conference (SBFoton IOPC), SBFoton International

作者： Henrique Ferazza Rodrigo Fiorin Valmir de Oliveira Ilda Abe Hypolito José Kalinowski Graduate Program in Electrical and Computer Engineering Federal University of Technology - Paraná (UTFPR) Curitiba Brazil Universidade Federal Fluminense RJ Brazil

A fiber Bragg grating sensor is used to monitor the temperature of an air-cooled combustion motorcycle engine in this study. The sensor is attached to one of the aluminum cylinders fins of the motorcycle to characterize the heat transfer process. We present the results of the heating and cooling curves during tests with the motorcycle at rest and in motion. A better understanding of the air-cooling process can lead to the optimization of designs in this area.

关键词： Temperature sensors Temperature measurement Heating systems Motorcycles Optics Combustion Engines

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