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2021 2nd International Symposium on Automation, Information and Computing, ISAIC 2021

作者： Budiman, Arief S Gunawan, Fergyanto Djuana, Endang Pardamean, Bens Romeli, Sugiarto Putri, Dianing Nn Aji, Daisman Pb Rahardjo, Kuat Stevanus Ilham Wibowo, M. Daffa, Nadiev Owen, Raymond Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia PT. Impack Pratama Industri Tbk Jakarta Indonesia Mechanical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

Many economically essential crops in Indonesia (such as coffee, tea, chocolate, or copra) require storage or drying under certain environmental conditions, especially temperature and humidity. The solar dryer dome, typically used for agricultural purposes in Indonesia, produces a sufficient amount of heat to increase the evaporation rate inside the dome and reduce the moisture content of the commodity. A hybrid solar dryer accompanied by a photovoltaic panel, fan, and ventilation system is generally suitable. The system can provide an optimum environment with minimum control. However, as the outdoor temperature and humidity change dramatically, such as at night time, more control is required. Based on Industry 4.0 technologies, we have developed a new kind of hybrid solar dryer that provides an optimum environment 24/7. The system, called Smart Dome 4.0, is an intelligent, low-cost, self-sufficient drying and storage system to support Indonesia Agriculture 4.0. The system has a local power generation unit to self-sustain the required energy and operate without connecting to the electricity grid. The system utilizes a machine-learning algorithm to predict the environmental condition and optimally uses self-generated electric power. The developed Smart Dome 4.0 technology is critical to producing a sustainable solar dome under drastic environmental dynamics. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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Corrigendum to "Effect of cell imprinting on viability and drug susceptibility of breast cancer cells to doxorubicin" [Acta Biomaterialia, 2020, 113, 119-129] (Acta Biomaterialia (2020) 113 (119–129), (S1742706120303287), (10.1016/***.2020.06.007))

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Acta Biomaterialia 2025年 198卷 571-572页

作者： Shahriyari, Fatemeh Janmaleki, Mohsen Sharifi, Shahriar Hesar, Milad Eyvazi Hoshian, Sasha Taghiabadi, Reza Razaghian, Ahmad Ghadiri, Majid Peirovi, Afshin Mahmoudi, Morteza Nezhad, Amir Sanati Khademhosseini, Ali Division of Engineering in Medicine Department of Medicine Brigham and Women's Hospital Harvard Medical School CambridgeMA United States Division of Health Sciences & Technology Harvard-Massachusetts Institute of Technology Massachusetts Institute of Technology CambridgeMA United States Department of Materials Engineering Faculty of Engineering Imam Khomeini International University Qazvin Iran Biomedical Engineering Graduate Program University of Calgary Canada Precision Health Program and Department of Radiology Michigan State University East LansingMI48824 United States RWTH Aachen University Templergraben 55 Aachen52062 Germany Department of Chemistry and Materials Science Aalto University School of Chemical Engineering Espoo Finland Department of Mechanical Engineering Faculty of Engineering Imam Khomeini International University Qazvin Iran Nano-Biotechnology Department Pasteur Institute of Iran Tehran Iran Departments of Bioengineering Chemical and Biomolecular Engineering and Radiological Sciences Center for Minimally Invasive Therapeutics California NanoSystems Institute University of California – Los Angeles Los AngelesCA United States Terasaki Institute for Biomedical Innovation Los AngelesCA United States

The authors regret to report that the images for panels h (Plain PDMS) and I (MCF7-imprinted PDMS) in Fig. 3 were mistakenly taken from the same dataset, resulting in the omission of the correct image in panel h for the Plain PDMS day 7 image. We sincerely apologize for this error. For an accurate representation, please see the corrected Fig. 3 provided here along with its original figure caption. This does not affect the validity of the statistical analyses or the overall conclusions drawn from the study. © 2025

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Application of level-set type topology optimization for defect topology identification analysis using hammering response data

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Materials Today: Proceedings 2023年

作者： Kurahashi, Takahiko Koike, Towa Kishida, Masayuki Murakami, Yuki Ikeda, Fujio Department of Mechanical Engineering Institute of GIGAKU Nagaoka University of Technology Niigata Nagaoka Japan Mechanical Engineering Master's Program Graduate School of Engineering Nagaoka University of Technology Niigata Nagaoka Japan Science of Technology Innovation 5-year Integrated Doctoral Program Graduate School of Engineering Nagaoka University of Technology Niigata Nagaoka Japan Department of Civil Engineering National Institute of Technology Nagaoka College Niigata Nagaoka Japan Department of Mechanical Engineering National Institute of Technology Nagaoka College Niigata Nagaoka Japan

In this study, we presented the defect identification analysis based on the level-set type topology optimization using hammering testing data. To simulate the oscillation behavior of concrete structure, the equation of motion in three dimensions is introduced as the governing equation. The performance function is defied by the time integral of the squared residual between the computed and observed displacements. The problem is to find the defect topologies so as to minimize the performance function. In the model with a small number of observation points, it was found that nonexistent defects disappeared by using the weighted sensitivity method. In addition, it was confirmed that the model with a large number of observation points did not show the same tendency as the model with a small number of observation points because the accuracy of defect identification increased as the number of observation points increased. © 2023

关键词： Defects

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DROPLET DIGITAL IMMUNO-PCR FOR PROFILING PROTEIN-SPECIFIC EXTRACELLULAR VESICLES 26

DROPLET DIGITAL IMMUNO-PCR FOR PROFILING PROTEIN-SPECIFIC EX...

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26th International Conference on Miniaturized Systems for Chemistry and Life sciences, MicroTAS 2022

作者： Lix, Chxnchen Lin, Hxixian Yao, Shxhxai Department of Laboratory Medicine Nanfang Hospital Southern Medical University Guangzhou China Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Bioengineering Graduate Program Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong

ISBN: (纸本)9781733419048

We construct a versatile droplet digital immuno-PCR ddiPCR assay which integrates immuno-PCR and droplet digital PCR ddPCR for quantifying protein-specific extracellular vesicle EV subpopulations at the single-vesicle level. We applied the ddiPCR assays for profiling the EV proteins of CD/CD63/CD81, HER2, EpCAM in a breast cancer cohort. The results demonstrated that the counting of multi-subpopulation EVs could significantly distinguish patients with breast tumor at different stages or hepatocellular carcinoma from healthy controls. This study provides a promising strategy to count multi-subpopulation EVs at the single-vesicle level for cancer diagnosis. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized Systems for Chemistry and Life sciences. All rights reserved.

关键词： Drops

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Effective Dimension Aware Fractional-Order Stochastic Gradient Descent for Convex Optimization Problems

arXiv

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arXiv 2025年

作者： Partohaghighi, Mohammad Marcia, Roummel Chen, YangQuan Electrical Engineering and Computer Science graduate program University of California Merced United States Department of Applied Mathematics University of California Merced MercedCA United States Lab Department of Mechanical Engineering School of Engineering University of California MercedCA95343 United States

Fractional-order stochastic gradient descent (FOSGD) leverages a fractional exponent to capture long-memory effects in optimization, yet its practical impact is often constrained by the difficulty of tuning and stabilizing this exponent. In this work, we introduce 2SED Fractional-Order Stochastic Gradient Descent (2SEDFOSGD), a novel method that synergistically combines the Two-Scale Effective Dimension (2SED) algorithm with FOSGD to automatically calibrate the fractional exponent in a data-driven manner. By continuously gauging model sensitivity and effective dimensionality, 2SED dynamically adjusts the exponent to curb erratic oscillations and enhance convergence rates. Theoretically, we demonstrate how this dimension-aware adaptation retains the benefits of fractional memory while averting the sluggish or unstable behaviors frequently observed in naive fractional SGD. Empirical evaluations across multiple benchmarks confirm that our 2SED-driven fractional exponent approach not only converges faster but also achieves more robust final performance, suggesting broad applicability for fractional-order methodologies in large-scale machine learning and related domains. © 2025, CC BY.

关键词： Convex optimization

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Evaluation of Single and Duplex Plasma Treatment Applied to Aisi 409 Stainless Steel

SSRN

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SSRN 2024年

作者： do Monte e Silva, André Felipe Soares de Almeida, Larissa Solano Monteiro, Leandro Rossino, Luciana Sgarbi Nunes, Maelson Sousa Libório, Maxwell Santana de Carvalho Costa, Thércio Henrique Bandeira, Rafael Marinho de Sousa, Rômulo Ribeiro Magalhães Post-Graduate Materials Science and Engineering Program Federal University of Piauí PI Teresina Brazil Post-Graduate Program in Materials Science Federal University of São Carlos São Carlos Brazil Post-Graduate Program in Chemistry Federal University of Piauí PI Teresina Brazil School of Science and Technology Federal University of Rio Grande do Norte RN Natal Brazil Brazil Department of Mechanical Engineering Federal University of Piauí PI Teresina Brazil Brazil

The industrial landscape has witnessed a growing demand for components exhibiting superior performance and extended lifespan, especially in parts made of stainless steels like AISI 409 steel, widely used in automotive exhaust systems, heat exchangers, and lightweight structural components. This study investigates the enhancement of surface properties of AISI 409 stainless steel through cathodic cage plasma nitriding (CCPN) and cathodic cage plasma deposition (CCPD) processes, focusing on the novel implementation of duplex treatments. The research aims to improve the mechanical strength, wear resistance, and corrosion resistance of the steel by forming nitrides and vanadium nitride films on its surface. The duplex treatments, specifically N400D450 and N450D450, were found to significantly enhance the hardness of AISI 409 stainless steel, with the N450D450 sample achieving a maximum hardness of 1100 HV—a 461.22% increase compared to the untreated steel. This enhancement is attributed to the formation of a thick, uniform nitrided layer followed by a vanadium nitride film. The critical role of processing order was highlighted, with the sequence of nitriding followed by deposition proving more effective than the reverse. Corrosion resistance was markedly improved in the duplex-treated samples. The N450D450 sample exhibited an Ecorr of -198 mV and reduced jcorr values, indicating superior resistance to corrosive environments. These results underscore the synergistic effect of combining nitriding and vanadium deposition, which optimizes the formation of protective surface layers. Overall, this study demonstrates that duplex treatments significantly enhance the surface properties of AISI 409 stainless steel, offering the potential for extended component lifespan and reduced maintenance in industrial applications. The findings suggest that careful control of process parameters and treatment sequence is essential for achieving optimal results. © 2024, The Authors. All rights

关键词： Tribology

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A Genetic Algorithm approach for optimization of geothermal power plant production: Case studies of direct steam cycle in Kamojang

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South African Journal of Chemical engineering 2023年 45卷 1-9页

作者： Rudiyanto, Bayu Birri, Mochammad Syahrul Widjonarko Avian, Cries Kamal, Dianta Mustofa Hijriawan, Miftah Energy Engineering Laboratory Department of Renewable Energy Engineering Politeknik Negeri Jember Jl. Mastrip No.164 Krajan Timur Jember 68121 Indonesia Graduate School of Department of Renewable Energy Engineering Politeknik Negeri Jember Jl. Mastrip No.164 Krajan Timur Jember 68121 Indonesia Department of Electrical Engineering Universitas Jember Jl. Kalimantan 37 Tegalboto Jember 68121 Indonesia Departement of Electronic and Computer Engineering National Taiwan University of Science and Technology No.43 Keelung Rd. Sec.4 Da'an Dist. Taipei City 106335 Taiwan Department of Mechanical Engineering Politeknik Negeri Jakarta Jl. Prof. Dr. G. A. Siwabessy Depok 16425 Indonesia Graduate Program of Mechanical Engineering Universitas Sebelas Maret Jl. Ir. Sutami 36 Surakarta 57126 Indonesia

Indonesia has enormous geothermal potential, but it only contributes 5% to Indonesia's energy matrix. During 37 years of operation, PT. Pertamina Geothermal Energy Kamojang area has been operating to produce electricity and is currently capable of supplying and distributing electricity to the Java-Bali area with a capacity of 60 MWe. However, this can run into a decrease in the efficiency and effectiveness of system performance due to energy losses in several geothermal power plant components during energy conversion. In this case, exergy analysis at PT. Pertamina Geothermal Energy Kamojang area Unit 4 direct-dry steam cycle was done on each component and state. This aims to know the energy and exergy stream and where it happened irreversibly at the component. The biggest irreversibility value occurred at the turbine and main condenser, with a value of 21,693.890 kW and 21,688.148 kW. The total irreversibility of all systems is 58,326.201 kW, while the total exergy inlet systems is 119,308.457 kW, so the value efficiency exergy obtained is 51.13%. Based on the environment as dead state analysis, an efficiency exergy value is inversely proportional to the irreversibility value and ascending environment temperature. System optimization was done with the genetic algorithm method, with variable values at the pressure wellhead and inlet turbine for the overall exergy efficiency value. The value obtained from optimization is 11.98 bar at the wellhead and 10.023 bar at the inlet turbine, and the overall efficiency exergy increased by 51.22%. © 2023 The Author(s)

关键词： Dry steam cycle Exergy Genetic Algorithm Geothermal power plant Optimization

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Piezoelectric electrostatic superlattices in monolayer MoS2

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Physical Review Materials 2024年第1期8卷 014002-014002页

作者： Ashwin Ramasubramaniam Doron Naveh Department of Mechanical and Industrial Engineering University of Massachusetts Amherst 01003 MA United States Materials Science and Engineering Graduate Program University of Massachusetts Amherst 01003 MA United States Faculty of Engineering Bar-Ilan University Ramat Gan 52900 Israel Institute for Nanotechnology and Advanced Materials Bar-Ilan University Ramat Gan 52900 Israel

Modulation of electronic properties of materials by electric fields is central to the operation of modern semiconductor devices, providing access to complex electronic behaviors and greater freedom in tuning the energy bands of materials. Here, we explore one-dimensional superlattices induced by a confining electrostatic potential in monolayer MoS2, a prototypical two-dimensional semiconductor. Using first-principles calculations, we show that periodic potentials applied to monolayer MoS2 induce electrostatic superlattices in which the response is dominated by structural distortions relative to purely electronic effects. These structural distortions reduce the intrinsic band gap of the monolayer substantially while also polarizing the monolayer through piezoelectric coupling, resulting in spatial separation of charge carriers as well as Stark shifts that produce dispersive minibands. Importantly, these minibands inherit the valley-selective magnetic properties of monolayer MoS2, enabling fine control over spin-valley coupling in MoS2 and similar transition-metal dichalcogenides.

关键词： Electronic structure Piezoelectricity Valley degrees of freedom

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A Cascade Deep Neural Network Approach for Oil Spills Classification and Detection

A Cascade Deep Neural Network Approach for Oil Spills Classi...

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International Conference on Electric Power and Energy Conversion Systems (EPECS)

作者： Mohammad Rashwan Mohammed Alhadi Mohammad A. Jaradat Mohammad I. Daoud Mechatronics Graduate Program American University of Sharjah Sharjah United Arab Emirates Mechanical Engineering Department American University of Sharjah Sharjah United Arab Emirates Computer Science and Engineering Department American University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9798331516963

ISBN: (纸本)9798331516970

Oil spills represent a growing environmental challenge that poses a significant threat to living organisms. Moreover, the treatment of oil spills, especially in severe cases, has serious economic repercussions and requires substantial labor and time. Therefore, the effective detection of oil spills has become an important research problem. Traditional methods for detecting oil spills, such as manual patrolling and dynamic sensors, are often limited in accuracy and coverage. As a result, the automation of oil spills detection has emerged as a critical global imperative in scientific research. The aim of this paper is to employ deep learning technology to achieve effective detection of oil spills based on aerial images. Our approach is composed of two phases. In the first phase, a Deep Convolutional Neural Network (DCNN), namely ResNet50, is trained on a large dataset containing images showing oil spills at a seaport. The trained DCNN is used to classify the input image as "Oil Spill" or "No Oil Spill". In the second phase, the images classified as "Oil Spill" are analyzed using a deep learning detection model, namely You-Only-Look-Once (YOLOv4), to localize the oil spills. The results indicate the capability of the proposed method to achieve effective oil spill detection. In particular, the classification accuracy obtained by the ResNet50 model is equal to 98%. Moreover, the YOLOv4 model was able to obtain effective localization of the oil spills with mean-average precision of 62%.

关键词： Deep learning Accuracy Oils Manuals Seaports Organisms Sensors Power systems Convolutional neural networks Residual neural networks

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CVIT: CONTINUOUS VISION TRANSFORMER FOR OPERATOR LEARNING

arXiv

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arXiv 2024年

作者： Wang, Sifan Seidman, Jacob H. Sankaran, Shyam Wang, Hanwen Pappas, George J. Perdikaris, Paris Institution for Foundation of Data Science Yale University United States Graduate Program in Applied Mathematics and Computational Science University of Pennsylvania United States Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania United States Department of Electrical and Systems Engineering University of Pennsylvania United States

Operator learning, which aims to approximate maps between infinite-dimensional function spaces, is an important area in scientific machine learning with applications across various physical domains. Here we introduce the Continuous Vision Transformer (CViT), a novel neural operator architecture that leverages advances in computer vision to address challenges in learning complex physical systems. CViT combines a vision transformer encoder, a novel grid-based coordinate embedding, and a query-wise cross-attention mechanism to effectively capture multi-scale dependencies. This design allows for flexible output representations and consistent evaluation at arbitrary resolutions. We demonstrate CViT’s effectiveness across a diverse range of partial differential equation (PDE) systems, including fluid dynamics, climate modeling, and reaction-diffusion processes. Our comprehensive experiments show that CViT achieves state-of-the-art performance on multiple benchmarks, often surpassing larger foundation models, even without extensive pretraining and roll-out fine-tuning. Taken together, CViT exhibits robust handling of discontinuous solutions, multi-scale features, and intricate spatio-temporal dynamics. Our contributions can be viewed as a significant step towards adapting advanced computer vision architectures for building more flexible and accurate machine learning models in the physical sciences. All data and code are publicly available at https://***/PredictiveIntelligenceLab/cvit. © 2024, CC BY-NC-SA.

关键词： Computer vision

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