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arXiv 2025年

作者： Chung, Philip Swaminathan, Akshay Goodell, Alex J. Kim, Yeasul Reincke, S. Momsen Han, Lichy Deverett, Ben Sadeghi, Mohammad Amin Ariss, Abdel-Badih Ghanem, Marc Seong, David Lee, Andrew A. Coombes, Caitlin E. Bradshaw, Brad Sufian, Mahir A. Hong, Hyo Jung Nguyen, Teresa P. Rasouli, Mohammad R. Kamra, Komal Burbridge, Mark A. McAvoy, James C. Saffary, Roya Ma, Stephen P. Dash, Dev Xie, James Wang, Ellen Y. Schmiesing, Clifford A. Shah, Nigam Aghaeepour, Nima Department of Anesthesiology Perioperative and Pain Medicine Stanford Medicine United States Department of Biomedical Data Science Stanford Medicine United States Department of Pediatrics Stanford Medicine United States Department of Medicine Stanford Medicine United States Department of Emergency Medicine Stanford Medicine United States Immunology Program Stanford Medicine United States Medical Scientist Training Program Stanford Medicine United States Clinical Excellence Research Center Stanford Medicine United States Technology and Digital Solutions Stanford Health Care United States

Methods to ensure factual accuracy of text generated by large language models (LLM) in clinical medicine are lacking. VeriFact is an artificial intelligence system that combines retrieval-augmented generation and LLM-as-a-Judge to verify whether LLM-generated text is factually supported by a patient’s medical history based on their electronic health record (EHR). To evaluate this system, we introduce VeriFact-BHC, a new dataset that decomposes Brief Hospital Course narratives from discharge summaries into a set of simple statements with clinician annotations for whether each statement is supported by the patient’s EHR clinical notes. Whereas highest agreement between clinicians was 88.5%, VeriFact achieves up to 92.7% agreement when compared to a denoised and adjudicated average human clinician ground truth, suggesting that VeriFact exceeds the average clinician’s ability to fact-check text against a patient’s medical record. VeriFact may accelerate the development of LLM-based EHR applications by removing current evaluation bottlenecks. © 2025, CC BY.

关键词： Electronic health record

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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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Operative estimation of global horizontal irradiance using transfer functions through network types of artificial neural network in some selected sites in North-East Ethiopia: assessment and comparison

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Heliyon 2025年第10期11卷

作者： Woldegiyorgis, Tegenu Argaw Assamnew, Abera Debebe Benti, Natei Ermias Desalegn, Gezahegn Assefa Anose, Fikru Abiko Mossie, Sentayehu Yigzaw Department of Physics College of Natural Sciences Wollo University P.O. Box 1145 Dessie Ethiopia Computational Data Science Program College of Natural and Computational Sciences Addis Ababa University P. O. Box 1176 Addis Ababa Ethiopia Department of Physics College of Natural and Computational Sciences Wolkite University Wolkite Ethiopia

The estimation of global horizontal irradiance (GHI) for a specific location is crucial to the design, modeling, and effective operation of photovoltaic (PV) and solar thermal energy conversion systems. This study aims to predict the monthly average daily and monthly average GHI for three locations in Ethiopia (Kemissie, Haik, and Woldiya) using artificial neural networks (ANNs) with various activation functions. Days of the year, elevation, and average values (maximum and minimum temperatures, surface pressure, wind speed, relative humidity, wind direction, and precipitation) were fed into the ANN during training. For the prediction of monthly average daily GHI, the CFBP-tansig, FFBP-tansig, and FFBP-logsig networks proved to be the most accurate. Likewise, for monthly average GHI prediction, the FFBP-tansig, CFBP-tansig, and LR-tansig networks exhibited the best performance. The daily and monthly GHI predictions achieved RMSE values ranging from 0.00058 to 0.04042, with correlation coefficients (r) between 0.95417 and nearly 1.0. In comparison, traditional methods showed RMSE values between 0.1 and 0.2, indicating that the ANN models provided up to 80 % better accuracy. The EBP-purelin, CFBP-purelin, and LR-purelin models were less effective, with RMSE values between 0.13815 and 0.17307, and r from 0.91153 to 0.96719. Among the studied locations, Kemissie exhibited the highest GHI potential, emphasizing the region's significant capability for solar energy generation in Ethiopia. © 2025 The Authors

关键词： Activation function Artificial neural network Global horizontal irradiance Network types Prediction

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Cortical network reconfiguration aligns with shifts of basal ganglia and cerebellar influence

arXiv

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arXiv 2024年

作者： Nestor, Kimberly Rasero, Javier Betzel, Richard Gianaros, Peter J. Verstynen, Timothy Department of Psychology Carnegie Mellon University PittsburghPA United States Center for the Neural Basis of Cognition PittsburghPA United States Carnegie Mellon Neuroscience Institute PittsburghPA United States Department of Biomedical Engineering Carnegie Mellon University PittsburghPA United States School of Data Science University of Virginia CharlottesvilleVA United States Department of Psychological and Brain Sciences Indiana University BloomingtonIN United States Cognitive Science Program Indiana University BloomingtonIN United States Indiana University Network Science Institute BloomingtonIN United States Department of Neuroscience University of Minnesota MinneapolisMN55455 United States Department of Psychology University of Pittsburgh PittsburghPA United States

Mammalian functional architecture flexibly adapts, transitioning from integration where information is distributed across the cortex, to segregation where information is focal in densely connected communities of brain regions. This flexibility in cortical brain networks is hypothesized to be driven by control signals originating from subcortical pathways, with the basal ganglia shifting the cortex towards integrated processing states and the cerebellum towards segregated states. In a sample of healthy human participants (N=242), we used fMRI to measure temporal variation in global brain networks while participants performed two tasks with similar cognitive demands (Stroop and Multi-Source Inference Task (MSIT)). Using the modularity index, we determined cortical networks shifted from integration (low modularity) at rest to high modularity during easier i.e. congruent (segregation). Increased task difficulty (incongruent) resulted in lower modularity in comparison to the easier counterpart indicating more integration of the cortical network. Influence of basal ganglia and cerebellum was measured using eigenvector centrality. Results correlated with decreases and increases in cortical modularity respectively, with only the basal ganglia influence preceding cortical integration. Our results support the theory the basal ganglia shifts cortical networks to integrated states due to environmental demand. Cerebellar influence correlates with shifts to segregated cortical states, though may not play a causal role. © 2024, CC BY-NC-SA.

关键词： Integration

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A Position Control System for a Single-port Laparoscopic Manipulating Robot

A Position Control System for a Single-port Laparoscopic Man...

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2022 International Electrical Engineering Congress, iEECON 2022

作者： Sonsilphong, Amornthep Sonsilphong, Suphachoke Soemphol, Chaiyong Hormdee, Daranee Khampitak, Kovit Rajamangala University of Technology Isan Department of Electrical Engineering Khon Kean Thailand Khon Kaen University Data Science Health Informatics Program Faculty of Medicine Khon Kaen Thailand Mahasarakham University Faculty of Engineering Department of Electrical Engineering Mahasarakham Thailand Khon Kaen University Embedded System RD Group Computer Engineering Faculty of Engineering Khon Kaen Thailand Khon Kean University Faculty of Medicine Department of Obstetrics and Gynecology Khon Kean Thailand

ISBN: (纸本)9781665402064

The research herein proposes a position control system, designed for a single-port surgical robot, based on the lockup table method. The programmable logic controller (PLC) was implemented to move the single- port surgical robot and handle laparoscope and surgical instruments with feedback control. The output responses of the desired angle were investigated under several conditions. The rotating angle allows movement of the laparoscope in the range of -10o to +10o. The error of the desired position of the designed system was less than approximately ± 0.1o. Moreover, the output data from several experiments may be employed to improve the open-loop control systems. This system is also capable of providing computer vision task implementation, enabling semi- automatic or automatic surgery in the future. © 2022 IEEE.

关键词： Laparoscopy

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FincGAN: A Gan Framework of Imbalanced Node Classification on Heterogeneous Graph Neural Network

FincGAN: A Gan Framework of Imbalanced Node Classification o...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hung Chun Hsu Ting-Le Lin Bo-Jun Wu Ming-Yi Hong Che Lin Chih-Yu Wang Graduate Program of Data Science National Taiwan University and Academia Sinica Taiwan Department of Accounting and Statistics National ChengChi University Taiwan Department of Risk Management and Insurance National ChengChi University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan

Graph Neural Networks (GNNs) frequently face class imbalance issues, especially in heterogeneous graphs. Existing GNNs often assume balanced class sizes, which isn’t true in many cases. Applying them directly to imbalanced data can lead to sub-optimal performance. Traditional oversampling methods, while effective, risk overfitting and face difficulties in reintegrating synthetic samples into the original graph. In this study, we introduce Framework of Imbalanced Node Classification on heterogeneous graph neural network with GAN (FincGAN), a new framework that utilizes oversampling techniques to address class imbalance in heterogeneous graphs. Instead of duplicating existing samples, FincGAN employs a Generative Adversarial Network (GAN) to create synthetic samples and uses deep learning-based edge generators to connect them back to the original graph. Our evaluations on spam user detection in the Amazon and Yelp Review datasets show that FincGAN outperforms baseline models in all essential metrics, including F-score and AUC-PRC score, showing its effectiveness in addressing class imbalance.

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087 Basal-to-inflammatory transition and tumor resistance via crosstalk with a pro-inflammatory stromal niche

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Journal of Investigative Dermatology 2024年第8期144卷 S16-S16页

作者： N. Li W. Zhang D. Haensel A. Jussila C. Pan S. Gaddam S. Plevritis A. Oro Program of Epithelial Biology Stanford University Stanford California United States Department of Biomedical Data Science Stanford University Stanford California United States

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Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities

arXiv

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arXiv 2024年

作者： Liu, Guangyuan Van Huynh, Nguyen Du, Hongyang Hoang, Dinh Thai Niyato, Dusit Zhu, Kun Kang, Jiawen Xiong, Zehui Jamalipour, Abbas In Kim, Dong The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The Department of Electrical Engineering and Electronics University of Liverpool Liverpool L69 3GJ United Kingdom The School of Electrical and Data Engineering University of Technology Sydney Australia The School of Computer Science and Engineering Nanyang Technological University Singapore College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Electrical and Information Engineering University of Sydney Australia The College of Information and Communication Engineering Sungkyunkwan University Korea Republic of

With recent advances in artificial intelligence (AI) and robotics, unmanned vehicle swarms have received great attention from both academia and industry due to their potential to provide services that are difficult and dangerous to perform by humans. However, learning and coordinating movements and actions for a large number of unmanned vehicles in complex and dynamic environments introduce significant challenges to conventional AI methods. Generative AI (GAI), with its capabilities in complex data feature extraction, transformation, and enhancement, offers great potential in solving these challenges of unmanned vehicle swarms. For that, this paper aims to provide a comprehensive survey on applications, challenges, and opportunities of GAI in unmanned vehicle swarms. Specifically, we first present an overview of unmanned vehicles and unmanned vehicle swarms as well as their use cases and existing issues. Then, an in-depth background of various GAI techniques together with their capabilities in enhancing unmanned vehicle swarms are provided. After that, we present a comprehensive review on the applications and challenges of GAI in unmanned vehicle swarms with various insights and discussions. Finally, we highlight open issues of GAI in unmanned vehicle swarms and discuss potential research directions. © 2024, CC BY.

关键词： Metadata

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Spin-tunable thermoelectric performance in monolayer chromium pnictides

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Physical Review Materials 2022年第6期6卷 064010-064010页

作者： Melania S. Muntini Edi Suprayoga Sasfan A. Wella Iim Fatimah Lila Yuwana Tosawat Seetawan Adam B. Cahaya Ahmad R. T. Nugraha Eddwi H. Hasdeo Department of Physics Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia Research Center for Quantum Physics National Research and Innovation Agency Tangerang Selatan 15314 Indonesia Center of Excellence on Alternative Energy Research and Development Institution Sakon Nakhon Rajabhat University Sakon Nakhon 47000 Thailand Program of Physics Faculty of Science and Technology Sakon Nakhon Rajabhat University Sakon Nakhon 47000 Thailand Department of Physics Faculty of Mathematics and Natural Sciences Universitas Indonesia Depok 16424 Indonesia Department of Physics and Materials Science University of Luxembourg L-1511 Luxembourg

Historically, finding two-dimensional (2D) magnets is well known to be a difficult task due to the instability against thermal spin fluctuations. Metals are also normally considered poor thermoelectric (TE) materials. Combining intrinsic magnetism in two dimensions with conducting properties, one may expect to get the worst for thermoelectrics. However, we will show this is not always the case. Here, we investigate the spin-dependent TE properties of monolayer chromium pnictides (CrX, where X=P, As, Sb, and Bi) using first-principles calculations of electron- and phonon-energy dispersion, along with the Boltzmann transport formalism under an energy-dependent relaxation time approximation. All the CrX monolayers are dynamically stable and they also exhibit half metallicity with ferromagnetic ordering. Using the spin-valve setup with an antiparallel spin configuration, the half metallicity and ferromagnetism in monolayer CrX enable the manipulation of spin degrees of freedom to tune the TE figure of merit (ZT). At an optimized chemical potential and operating temperature of 500 K, the maximum ZT values (≈0.22, 0.12, and 0.09) with the antiparallel spin-valve setup in CrAs, CrSb, and CrBi improve up to almost twice the original values (ZT≈0.12, 0.08, and 0.05) without the spin-valve configuration. Only in CrP, which is the lightest species and less spin polarized among CrX, the maximum ZT (≈0.34) without the spin-valve configuration is larger than that (≈0.19) with the spin-valve one. We also find that, at 500 K, all the CrX monolayers possess exceptional TE power factors of about 0.02–0.08 W/m K2, which could be one of the best values among 2D conductors.

关键词： First-principles calculations Thermopower Monolayer films Electronic structure

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Deep Learning with Greedy Layer-Wise Compound Scaling for Temperature and Humidity Prediction in Solar Dryer Dome

SSRN

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SSRN 2022年

作者： Cenggoro, Tjeng Wawan Elwirehardja, Gregorius Natanael Dominic, Nicholas Setiawan, Karli Eka Rahutomo, Reza Djuana, Endang Gunawan, Fergyanto E. Budiman, Arief S. Romeli, Sugiarto Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States PT. Impack Pratama Industri Jakarta Indonesia

Drying has been an eco-friendly and cost-efficient method to reduce post-harvest losses of agricultural crops. In various countries, the technique has been widely utilized in the form of Solar Dryer Dome (SDD) buildings. Accurate indoor microclimate forecasting is very important to implement smart control systems in SDDs that can maintain the ideal conditions, as the availability of solar energy is limited only at daytimes and the temperature may drop significantly during nighttimes in remote areas. The microclimate variables that play an important role in maintaining ideal environmental conditions include temperature and relative humidity. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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