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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Aldian Nurcahyo Yaya Heryadi Lukas Wayan Suparta Ilvico Sonata Compuer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research Group (CERG) Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia Compuer Science Departement Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia

Crop Yield Analysis and Prediction is a fast-expanding discipline that is critical for optimizing agricultural methods. A lack of trustworthy data is one of the challenges in estimating crop yields. We develop predictive models for 22 different fruits and vegetables data. The goals of this study are to create accurate and interpretable crop recommendation models. We used multiple machine learning (ML) models for multi-class crop production prediction to fulfill our research goal. We thoroughly examined the influence of climate and nutrient factors on crop yield, considering their complex interactions. To improve the dataset, augmented data techniques were applied. Configuring the parameters and fine-tuning the hyperparameters is our technique to increase the model performance. Furthermore, we employ explainable artificial intelligence (XAI) techniques and interpretability tools like Shapley Additive exPlanations (SHAP) to improve the interpretability of our prediction model. Our findings reveal that the XGBoost model has the best performance model with 99.86% accuracy, followed by SVM Poly Kernel with 99.32% and Random Forest with 98.82%. Feature selection and analysis are emphasized, particularly in regional agricultural contexts. This study contributes to the creation of accurate and interpretable crop recommendation models while also addressing the issue of untrustworthy data, providing useful insights for optimizing agricultural practices.

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Double-Emulsion Droplet Digital Crispr/Cas12a for Amplification-Free, Absolute Quantification of Nucleic Acids at Attomole Levels

SSRN

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SSRN 2025年

作者： Zhang, Yang Liu, Hangrui Tang, Shi-Yang Yalikun, Yaxiaer Barber, Tracie J. Goda, Keisuke Li, Ming SydneyNSW2052 Australia Department of Bioengineering and Therapeutic Sciences University of California San Francisco San FranciscoCA94158 United States School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom Division of Materials Science Nara Institute of Science and Technology Ikoma630-0192 Japan Department of Chemistry The University of Tokyo Tokyo113-0033 Japan Department of Bioengineering University of California Los AngelesCA90095 United States Institute of Technological Sciences Wuhan University Hubei430072 China Macquarie University Australia Department of Electronic Electrical and Systems Engineering

The quantification of nucleic acids is of prominent importance for biology and medicine sciences. Droplet digital polymerase chain reaction (ddPCR) provides an absolute measure of target nucleic acid molecules with unrivalled sensitivity and accuracy, but suffers from limitations inherent to PCR amplification, droplet partition, and signal detection. Here, we present an ultrasensitive, rapid, and high-throughput technique for the absolute quantification of nucleic acids without the need for amplification, by combining the double-emulsion (DE) droplet digital platform with CRISPR/Cas12a system (d3CRISPR). We demonstrate the developed approach by accurately quantifying various DNA molecules, such as target human papillomavirus (HPV) 18, HPV16, and E. coli DNA, at concentrations down to attomole levels. This represents an over 1,000-fold improvement in the limit of detection (LOD) compared to existing amplification-free Cas12a assays. Given the versatility and generality of the CRISPR system, we believe that this approach has great potential in the detection and measurements of diverse nucleic acid molecules for many biomedical, clinical, and environmental applications. © 2025, The Authors. All rights reserved.

关键词： DNA

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High-Order Cardiomyopathy Human Heart Model and Mesh Generation

High-Order Cardiomyopathy Human Heart Model and Mesh Generat...

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2021 Computing in Cardiology, CinC 2021

作者： Mohammadi, Fariba Shontz, Suzanne M Linte, Cristian A Department of Mechanical Engineering University of Kansas LawrenceKS United States Department of Electrical Engineering and Computer Science University of Kansas LawrenceKS United States Bioengineering Program University of Kansas LawrenceKS United States Information and Telecommunication Technology Center University of Kansas LawrenceKS United States Chester F Carlson Center for Imaging Science Rochester Institute of Technology RochesterNY United States Department of Biomedical Engineering Rochester Institute of Technology RochesterNY United States

ISBN: (纸本)9781665479165

Faithful, accurate, and successful cardiac biomechanics and electrophysiological simulations require patient-specific geometric models of the heart. Since the cardiac geometry consists of highly-curved boundaries, the use of high-order meshes with curved elements would ensure that the various curves and features present in the cardiac geometry are well-captured and preserved in the corresponding mesh. Most other existing mesh generation techniques require computer-aided design files to represent the geometric boundary, which are often not available for biomedical applications. Unlike such methods, our technique takes a high-order surface mesh, generated from patient medical images, as input and generates a high-order volume mesh directly from the curved surface mesh. In this paper, we use our direct high-order curvilinear tetrahedral mesh generation method [1] to generate several second-order cardiac meshes. Our meshes include the left ventricle myocardia of a healthy heart and hearts with dilated and hypertrophic cardiomyopathy. We show that our high-order cardiac meshes do not contain inverted elements and are of sufficiently high quality for use in cardiac finite element simulations. © 2021 Creative Commons.

关键词： Heart

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A novel rule-based expert system for early diagnosis of bipolar and Major Depressive Disorder

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Smart Health 2025年 35卷

作者： Zolfagharnasab, Mohammad Hossein Damari, Siavash Soltani, Madjid Ng, Artie Karbalaeipour, Hengameh Haghdadi, Amin Saghayan, Masood Hamed Matinfar, Farzam Department of Statistics Mathematics and Computer Science University of Allameh Tabataba'i Tehran Iran Department of Mechanical Engineering K. N. Toosi University of Technology Tehran Iran Centre for Sustainable Business International Business University Toronto Canada Department of Electrical and Computer Engineering University of Waterloo Waterloo Canada Centre for Biotechnology and Bioengineering (CBB) University of Waterloo Waterloo Canada Department of Integrative Oncology BC Cancer Research Institute Vancouver Canada Department of Psychology Science and Research Branch Islamic Azad University Tehran Iran

A confident and timely diagnosis of mental illnesses is one of the primary challenges practitioners repeatedly encounter when they start treating new patients. However, diagnosing can quickly become problematic as the subjects expose comparative symptoms among mental illnesses. Due to influencing a broad populace among mental ailments, an adjusted differentiation between Major Depressive Disorder, Mania Bipolar Disorder, Depressive Bipolar Disorder, and ordinary individuals with mild symptoms is one of the critical subjects for community health. This study responded to the described problem by proposing a novel rule-based Expert System, which evaluates the impact of disorder symptoms on the Certainty Factor concerning each mental status. The semantic rules are developed based on the recommendation of experts, and the implementation is carried out using Prolog and C# languages. Furthermore, an easy-to-use user interface is considered to facilitate the system workflow. The consistency of the developed framework is established by performing rigorous tests by expert psychiatrists as well as 120 clinical samples collected from private samples. Based on the results, the current model classifies mental disorder cases with a success rate of 93.33% using only the 17 symptoms specified in the ontology model. Furthermore, a questionnaire that measures user satisfaction after the test also achieves a mean score of 3.56 out of 4, which indicates a high degree of user acceptance. As a result, it is concluded that the current framework is a reliable tool for achieving a solid diagnosis in a shorter period. © 2024

关键词： Bipolar disorder Clinical Decision Support computer science Expert system Healthcare Informatics Rule-based Reasoning

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No-boundary thinking: a viable solution to ethical data-driven AI in precision medicine

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AI and ethics 2022年第4期2卷 635-643页

作者： Tayo Obafemi-Ajayi Andy Perkins Bindu Nanduri Donald C Wunsch Ii James A Foster Joan Peckham Engineering Program Missouri State University Springfield MO USA. Department of Computer Science and Engineering Mississippi State University Starkville MS USA. Department of Comparative Biomedical Sciences College of Veterinary Medicine Mississippi State University Starkville MS USA. Electrical & Computer Engineering Department Missouri University of Science and Technology Rolla MO USA. Biological Sciences Department University of Idaho Moscow ID USA. Computer Science & Statistics Department University of Rhode Island Kingston RI USA.

Today Artificial Intelligence (AI) supports difficult decisions about policy, health, and our personal lives. The AI algorithms we develop and deploy to make sense of information, are informed by data, and based on models that capture and use pertinent details of the population or phenomenon being analyzed. For any application area, more importantly in precision medicine which directly impacts human lives, the data upon which algorithms are run must be procured, cleaned, and organized well to assure reliable and interpretable results, and to assure that they do not perpetrate or amplify human prejudices. This must be done without violating basic assumptions of the algorithms in use. Algorithmic results need to be clearly communicated to stakeholders and domain experts to enable sound conclusions. Our position is that AI holds great promise for supporting precision medicine, but we need to move forward with great care, with consideration for possible ethical implications. We make the case that a no-boundary or convergent approach is essential to support sound and ethical decisions. No-boundary thinking supports problem definition and solving with teams of experts possessing diverse perspectives. When dealing with AI and the data needed to use AI, there is a spectrum of activities that needs the attention of a no-boundary team. This is necessary if we are to draw viable conclusions and develop actions and policies based on the AI, the data, and the scientific foundations of the domain in question.

关键词： Artificial intelligence Data science Ethics Precision medicine

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Full-Duplex Beamforming in the Sub-Terahertz Regime

Full-Duplex Beamforming in the Sub-Terahertz Regime

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International Conference on Infrared and Millimeter Waves

作者： Subhajit Karmakar Atsutse Kludze Jacques Doumani Andrey Baydin Junichiro Kono Yasaman Ghasempour Department of Electrical and Computer Engineering Princeton University Princeton NJ USA Department of Electrical and Computer Engineering Rice University Houston TX USA Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Smalley-Curl Institute Rice University Houston TX USA Department of Physics and Astronomy Rice University Houston TX USA Department of Material Science and NanoEngineering Rice University Houston TX USA

We propose a novel architecture that realizes full-duplex directional beamforming in the sub-terahertz regime. Our architecture leverages the frequency-controlled beam steering of leaky-wave antenna together with the polarization-dependent response of aligned single-wall carbon nanotubes. We evaluate the performance of this architecture via preliminary experiments.

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Low-Loss, Nonvolatile Tuning of Bragg Structures with Sb2Se3

Low-Loss, Nonvolatile Tuning of Bragg Structures with Sb2Se3

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Conference on Lasers and Electro-Optics (CLEO)

作者： Nicholas A. Nobile Chuanyu Lian Hongyi Sun Yi-Siou Huang Brian Mills Cosmin Constantin Popescu Dennis Callahan Juejun Hu Carlos A. Ríos Ocampo Nathan Youngblood Electrical & Computer Engineering Department University of Pittsburgh Pittsburgh PA USA Department of Materials Science & Engineering University of Maryland College Park MD USA Institute for Research in Electronics and Applied Physics University of Maryland College Park MD USA Department of Materials Science & Engineering Massachusetts Institute of Technology Cambridge MA USA Draper Scholar Program The Charles Stark Draper Laboratory Inc Cambridge MA USA The Charles Stark Draper Laboratory Inc Cambridge MA USA

Sb 2 Se 3 is used to switch between broadband transparency and enhanced index contrast in two device types leveraging Bragg gratings for tunable stop-and pass-band functionalities. Experimental results highlight fabr... 详细信息

ISBN: (纸本)9798350369311

关键词： Bragg gratings Waveguide transitions Phase change materials Optical filters Optical switches Refractive index Fabry-Perot Indexes Passband Optical waveguides

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A Flexible Origami Opto-Electro Array For In Vivo Optogenetic Neurostimulation and Neurophysiology Recording

A Flexible Origami Opto-Electro Array For In Vivo Optogeneti...

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IEEE International Conference on Micro Electro Mechanical Systems

作者： Yan Gong Xiang Liu Yifan Liu Zhen Qiu Arthur Weber Wen Li Department of Electrical and Computer Engineering Michigan State University East Lansing MI USA Institute for Quantitative Health Science and Engineering Michigan State University East Lansing Michigan USA Department of Physiology Neuroscience Program Michigan State University East Lansing MI USA Department of Biomedical Engineering Michigan State University East Lansing Michigan USA Department of Physiology Michigan State University East Lansing MI USA

This paper reports a thin-film, three-dimensional (3D) opto-electro array with four individually addressable microscale light-emitting diodes (μ-LEDs) capable of surface illumination of the cortex and nine penetrating electrodes for simultaneous recording of light-evoked neural activities. Inspired by the origami concept, a carefully designed "bridge + trench" structure facilitates the conversion of the array from two-dimensional (2D) to 3D while avoiding mechanical damage to thin film metal. Before device transformation, the shape and dimensions of the 2D array can be customized, making it versatile for a variety of applications. The array is packaged using polyimide (PI) and polydimethylsiloxane (PDMS) to ensure the device’s mechanical flexibility and biocompatibility. The efficacy of the device is characterized both in vitro and in vivo.

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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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Large Fluorescence Enhancement via Lossless All-Dielectric Spherical Mesocavities

arXiv

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arXiv 2023年

作者： Zakomirnyi, Vadim I. Moroz, Alexander Bhargava, Rohit Rasskazov, Ilia L. Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States *** Department of Bioengineering Electrical & Computer Engineering Mechanical Science & Engineering Chemical and Biomolecular Engineering and Chemistry Cancer Center at Illinois Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States SunDensity Inc. RochesterNY14604 United States

Nano- and microparticles are popular media to enhance optical signals, including fluorescence from a dye proximal to the particle. Here we show that homogeneous, lossless, all-dielectric spheres with diameters in the mesoscale range, between nano- ( 100 nm) and micro- ( 1 µm) scales, can offer surprisingly large fluorescence enhancements, up to F ∼ 104. With the absence of nonradiative Ohmic losses inherent to plasmonic particles, we show that F can increase, decrease or even stay the same with increasing intrinsic quantum yield q0, for suppressed, enhanced or intact radiative decay rates of a fluorophore, respectively. Further, the fluorophore may be located inside or outside the particle, providing additional flexibility and opportunities to design fit for purpose particles. The presented analysis with simple dielectric spheres should spur further interest in this less-explored scale of particles and experimental investigations to realize their potential for applications in imaging, molecular sensing, light coupling, and quantum information processing. © 2023, CC BY.

关键词： Spheres

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