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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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IoT for Water Quality Categorization

IoT for Water Quality Categorization

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Hermantoro Sastrohartono Andreas Wahyu Krisdiarto Arief Ika Uktoro Reza Rahutomo Teddy Suparyanto Bens Pardamean Agricultural Engineering Department Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

In agricultural water research, the adoption of Internet of Things (IoT) technology has emerged as a pivotal approach for large-scale data collection. Water availability in the context of water quality is very important, both for domestic and industrial purposes. For domestic purposes, drinking water and bathing water are separated. Meanwhile, for the palm oil industry, boiler filler is differentiated from additional process water (dilution water). Water quality parameters can be assessed from turbidity and Total Dissolve Solid (TDS). Measurements using measuring instruments separately and repeatedly require significant energy, time, and costs. This research was conducted with the primary objective of presenting a novel method for categorizing water quality with the approach of IoT sensor technology. The research methodology entailed the utilization of an integrated IoT water sensors system in conjunction with manual water categorization. The methods consist of (1) system design, (2) design and installation of sensor and IoT-based microcontrollers, and (3) accuracy and precision testing compared with laboratory measurements. The precision of the integrated IoT water sensors was assessed through a dedicated sensor precision test, resulting in an accuracy rate of 94.4% for the turbidity sensor and 97.5% for the TDS sensor. Notably, this approach successfully discriminated drinking water with valid categorization, while other water types, including groundwater, water with tea, and water with coffee, yielded null categorization results.

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Recommendation system based on recognition of prior learning to support curriculum design in online higher education 6

Recommendation system based on recognition of prior learning...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Murad, Dina Fitria Hassan, Rosilah Heryadi, Yaya Wijanarko, Bambang Dwi Titan Bina Nusantara University BINUS Online Learning Information Systems Department Jakarta11480 Indonesia Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science Technology UKM Bangi Selangor Malaysia Bina Nusantara University BINUS Graduate Program- Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Online Learning Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Based on the policy of independent study and independent campus, where students can participate in part of the study period and load from within the campus, as well as others outside the campus, so that they can complete all periods and study loads as needed qualification is determined by the study program conducted through assessors. and this requires a variety of processes and takes a lot of time. For this reason, an alternative technique is needed to conduct an automatic Recognition of Prior Learning (RPL) assessment through the application of a recommendation system. The purpose of this research is to find out how the recommendation system approach is used to predict RPL assessments and provide support for curriculum development in tertiary institutions that can meet the learning needs of the digital community. This study has succeeded in classifying learning outcomes based on independent assessment data of prospective students and can provide recommendations for curriculum development in colleges that organize online learning. This study proves that the use of a recommendation system using deep learning in the RPL assessment has an accuracy (97, 24%) and is relatively the same as the assessor's assessment. © 2021 IEEE.

关键词： Recommender systems

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New Frontiers for Wave Engineering Using Metamaterials

New Frontiers for Wave Engineering Using Metamaterials

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2022年全国微波毫米波会议

作者： Andrea Alù Photonics Initiative Advanced Science Research Center City University of New York Physics Program Graduate Center City University of New York Department of Electrical and Computer Engineering City College of New York

Metamaterials are engineered materials with properties that go well beyond what offered by nature, providing unprecedented opportunities to tailor and enhance the control of waves. In this talk, I discuss our recent activity at microwaves and THz frequencies, showing how suitably tailored meta-atoms and their arrangements open exciting avenues for wave manipulation, including metasurfaces with enhanced wavefront manipulation, and magnet-free nonreciprocity and topological phenomena. Insights into the underlying physics and new devices based on these concepts will be presented.

关键词： New Frontiers for Wave Engineering Using Metamaterials

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Robust prediction of personalized in vivo response to unseen drugs from in vitro screens using a novel context-aware deconfounding autoencoder

Research Square

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Research Square 2021年

作者： He, Di Liu, Qiao Xie, Lei PhD program in Computer Science Graduate Center City University of New York New York City10016 United States Department of Computer Science Hunter College City University of New York New York City10065 United States

Accurate and robust prediction of patient-specific responses to drug treatments is critical for drug development and personalized medicine. However, patient data are often too scarce to train a generalized machine learning model. Although many methods have been developed to utilize cell line data, few of them can reliably predict individual patient clinical responses to new drugs due to data distribution shift and confounding factors. We have developed a novel Context-aware Deconfounding Autoencoder (CODE-AE) that can extract intrinsic biological signals masked by context-specific patterns and confounding factors. Extensive comparative studies demonstrated that CODE-AE effectively alleviated the out-of-distribution problem for the model generalization, significantly improved accuracy and robustness over state-of-the-art methods in predicting patient-specific in vivo drug responses purely from in vitro screens. Using CODE-AE, we screened 59 drugs for 9,808 cancer patients. Our results are consistent with existing clinical observations, suggesting the potential of CODE-AE in developing personalized anti-cancer therapies and drug-response biomarkers. © 2021, CC BY.

关键词： Cell culture

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Platform for Evanescent Wave Sensor Fabrication Based on 3D-Printed Fiber Optic Holder

Platform for Evanescent Wave Sensor Fabrication Based on 3D-...

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Optical Fiber Sensors (LAWOFS), Latin American Workshop on

作者： Afonso Henrique Henschel Baptista Pedro Bertemes-Filho Aleksander Paterno Raquel Bettega Jose Pedro Mansueto Serbena Nathalia Prediger Ricardo Kamikawachi Department of Electrical Engineering Center for Science and Technology - Santa Catarina State University – CCT-UDESC Joinville Brazil Group of Organic Optoelectronic Devices (GOOD) Federal University of Parana – UFPR Curitiba Brazil Graduate Program in Electrical and Computer Engineering Federal University of Technology - Parana – UTFPR Curitiba Brazil

ISBN: (数字)9786589532026

ISBN: (纸本)9798350362725

This paper describes the low-cost manufacturing process of an evanescent wave fiber sensor platform that allows the etching of the fiber in hydrofluoric acid with the proposed 3D-printed fiber holder in an acid resistant material. The fiber holder is practical for allowing several processing procedures during its use and sensor fabrication, allowing sensor optical and electrical operation. In this work, some aspects of its characterization are presented in the optical domain showing the excitation of surface plasmons in the visible range, and also indication of its electrical operation of the thin-film as electrode.

关键词： Optical fiber polarization Optical fiber sensors Gold Optical polarization Biomedical optical imaging Optical device fabrication Optical films

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CODE-AE: A coherent de-confounding autoencoder for predicting patient-specific drug response from cell line transcriptomics

arXiv

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arXiv 2021年

作者： He, Di Xie, Lei PhD program in Computer Science Graduate Center City University of New York New York City10016 United States Department of Computer Science Hunter College City University of New York New York City10065 United States

Accurate and robust prediction of patient’s response to drug treatments is critical for developing precision medicine. However, it is often difficult to obtain a sufficient amount of coherent drug response data from patients directly for training a generalized machine learning model. Although the utilization of rich cell line data provides an alternative solution, it is challenging to transfer the knowledge obtained from cell lines to patients due to various confounding factors. Few existing transfer learning methods can reliably disentangle common intrinsic biological signals from confounding factors in the cell line and patient data. In this paper, we develop a Coherent Deconfounding Autoencoder (CODE-AE) that can extract both common biological signals shared by incoherent samples and private representations unique to each data set, transfer knowledge learned from cell line data to tissue data, and separate confounding factors from them. Extensive studies on multiple data sets demonstrate that CODE-AE significantly improves the accuracy and robustness over state-of-the-art methods in both predicting patient drug response and deconfounding biological signals. Thus, CODE-AE provides a useful framework to take advantage of in vitro omics data for developing generalized patient predictive models. The source code is available at https://***/XieResearchGroup/CODE-AE. © 2021, CC BY.

关键词： Learning systems

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Demonstration of a polarization-agnostic geometric phase in nonlocal metasurfaces

Demonstration of a polarization-agnostic geometric phase in ...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Adam C. Overvig Y. Kasahara Andrea Alù Photonics Initiative Advanced Science Research Center City University of New York New York NY USA Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX USA Physics Program Graduate Center of the City University of New York New York NY USA

We theoretically propose and experimentally demonstrate nonlocal metasurfaces rationally designed using symmetry-breaking principles to manipulate the optical wavefront with a geometric phase selective to custom ellip... 详细信息

ISBN: (纸本)9781957171258

关键词： Circular polarization Constructive interference Fano resonance Polarization Polarized light Thermal emission

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Strategic Planning and Knowledge Management Approaches as input to remote area strategic plans: A Case study in Aru Islands Regency Maluku Province 8

Strategic Planning and Knowledge Management Approaches as in...

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8th International Conference on ICT for Smart Society, ICISS 2021

作者： Irena Gutandjala, Ilona Harso Supangkat, Suhono Lumban Gaol, Ford Rianti, Benny Bina Nusantara University BINUS Graduate Program-Computer Science Computer Science Department Jakarta11480 Indonesia Bandung Institute of Technology Smart City and Community Innovation Center School of Electrical and Informatics Engineering Bandung Indonesia Information Systems Department BINUS Online Learning Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781665416979

Knowledge is an important asset in an organization. Aru Islands District is one of the districts in Maluku Province. The Government of Aru Islands District Maluku has a vision and mission as outlined in the Regional Strategic Plan or commonly referred to as RENSTRA. This vision and mission are an activity that requires a policy direction that is planned and integrated with the strategic environment of all related aspects. The strategic plan is then realized with the implementation of the government with regional autonomy which is the modern governance in realizing quality public services, fast, easy, fair and unlimited for the welfare and progress of the region. In this research will be used a strategic approach as input from the analysis with knowledge management and assessment of the condition of Aru district. © 2021 IEEE.

关键词： Strategic planning

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Improved camptothecin encapsulation and efficacy by environmentally sensitive block copolymer/chitosan/fucoidan nanoparticles for targeting lung cancer cells

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International Journal of Biological Macromolecules 2024年第Part2期277卷 133901页

作者： Don, Trong-Ming Hong, Yu-Ting Jheng, Pei-Ru Chuang, Er-Yuan Huang, Yi-Cheng Department of Chemical and Materials Engineering Tamkang University New Taipei City Taiwan College of Life Sciences Department of Food Science National Taiwan Ocean University Keelung Taiwan Graduate Institute of Biomedical Materials and Tissue Engineering International PhD Program in Biomedical Engineering Taipei Medical University Taipei Taiwan Cell Physiology and Molecular Image Research Center Taipei Medical University–Wan Fang Hospital Taipei Taiwan

In this study, thermo-sensitive poly(N-isopropyl acrylamide) (PNP) was polymerized with pH-sensitive poly(acrylic acid) (PAA) to prepare a PAA-b-PNP block copolymer. Above its cloud point, the block copolymer self-assembled into nanoparticles (NPs), encapsulating the anticancer drug camptothecin (CPT) in situ. Chitosan (CS) and fucoidan (Fu) further modified these NPs, forming Fu-CPT-NPs to enhance biocompatibility, drug encapsulation efficiency (EE), and loading content (LC), crucially facilitating P-selectin targeting of lung cancer cells through a drug delivery system. The EE and LC reached 82 % and 3.5 %, respectively. According to transmission electron microscope observation, these Fu-CPT-NPs had uniform spherical shapes with an average diameter of ca. 250 nm. They could maintain their stability in a pH range of 5.0–6.8. In vitro experimental results revealed that the Fu-CPT-NPs exhibited good biocompatibility and had anticancer activity after encapsulating CPT. It could deliver CPT to cancer cells by targeting P-selectin, effectively increasing cell uptake and inducing cell apoptosis. Animal study results showed that the Fu-CPT-NPs inhibited lung tumor growth by increasing tumor cell apoptosis without causing significant tissue damage related to generating reactive oxygen species in lung cancer cells. This system can effectively improve drug-delivery efficiency and treatment effects and has great potential for treating lung cancer. © 2024 Elsevier B.V.

关键词： Nanoparticles

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