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JACC: Advances 2023年第3期2卷 100294-100294页

作者： Cenko, Edina Manfrini, Olivia Yoon, Jinsung van der Schaar, Mihaela Bergami, Maria Vasiljevic, Zorana Mendieta, Guiomar Stankovic, Goran Vavlukis, Marija Kedev, Sasko Miličić, Davor Badimon, Lina Bugiardini, Raffaele Laboratory of Epidemiological and Clinical Cardiology Department of Medical and Surgical Sciences University of Bologna Bologna Italy IRCCS Azienda Ospedaliero-Universitaria di Bologna Sant'Orsola Hospital Bologna Italy Google Cloud AI Sunnyvale CA United States Department of Electrical and Computer Engineering University of California Los Angeles Los Angeles CA United States Department of Applied Mathematics and Theoretical Physics and Department of Population Health Cambridge Centre for Artificial Intelligence in Medicine University of Cambridge Cambridge United Kingdom Medical Faculty University of Belgrade Belgrade Serbia Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC) Madrid Spain Clinic of Cardiology University Clinical Centre of Serbia Belgrade Serbia University Clinic for Cardiology Skopje North Macedonia Faculty of Medicine Ss. Cyril and Methodius University in Skopje Skopje North Macedonia Department for Cardiovascular Diseases University Hospital Center Zagreb University of Zagreb Zagreb Croatia Cardiovascular Research Program ICCC IR-IIB Sant Pau Hospital de la Santa Creu i Sant Pau CiberCV-Institute Carlos III Barcelona Spain

Background: There have been conflicting reports regarding outcomes in women presenting with an acute coronary syndrome (ACS). Objectives: The objective of the study was to examine sex-specific differences in 30-day mortality in patients with ACS and acute heart failure (HF) at the time of presentation. Methods: This was a retrospective study of patients included in the International Survey of Acute Coronary Syndromes-ARCHIVES (ISACS-ARCHIVES;NCT04008173). Acute HF was defined as Killip classes ≥2. Participants were stratified according to ACS presentation: ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation ACS (NSTE-ACS). Differences in 30-day mortality and acute HF presentation at admission between sexes were examined using inverse propensity weighting based on the propensity score. Estimates were compared by test of interaction on the log scale. Results: A total of 87,812 patients were included, of whom 30,922 (35.2%) were women. Mortality was higher in women compared with men in those presenting with STEMI (risk ratio [RR]: 1.65;95% CI: 1.56-1.73) and NSTE-ACS (RR: 1.18;95% CI: 1.09-1.28;P_interaction <0.001). Acute HF was more common in women when compared to men with STEMI (RR: 1.24;95% CI: 1.20-1.29) but not in those with NSTE-ACS (RR: 1.02;95% CI: 0.97-1.08) (P_interaction <0.001). The presence of acute HF increased the risk of mortality for both sexes (odds ratio: 6.60;95% CI: 6.25-6.98). Conclusions: In patients presenting with ACS, mortality is higher in women. The presence of acute HF at hospital presentation increases the risk of mortality in both sexes. Women with STEMI are more likely to present with acute HF and this may, in part, explain sex differences in mortality. These findings may be helpful to improve sex-specific personalized risk stratification. © 2023 The Authors

关键词： acute heart failure outcomes sex differences

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Mahakala: a Python-based Modular Ray-tracing and Radiative Transfer Algorithm for Curved Space-times

arXiv

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arXiv 2023年

作者： Sharma, Aniket Medeiros, Lia Chan, Chi-Kwan Halevi, Goni Mullen, Patrick D. Stone, James M. Wong, George N. Indian Institute of Science Education and Research Mohali Sector 81 SAS Nagar PO Manauli Punjab Mohali140306 India School of Natural Sciences Institute for Advanced Study 1 Einstein Drive PrincetonNJ08540 United States Steward Observatory Department of Astronomy University of Arizona 933 N. Cherry Ave. TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Ave. TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 N. Santa Rita TucsonAZ85721 United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08544 United States CCS-2 Los Alamos National Laboratory Los AlamosNM United States Center for Theoretical Astrophysics Los Alamos National Laboratory Los AlamosNM United States Princeton Gravity Initiative Princeton University PrincetonNJ08544 United States

We introduce Mahakala, a Python-based, modular, radiative ray-tracing code for curved space-times. We employ Google’s JAX framework for accelerated automatic differentiation, which can efficiently compute Christoffel symbols directly from the metric, allowing the user to easily and quickly simulate photon trajectories through non-Kerr metrics. JAX also enables Mahakala to run in parallel on both CPUs and GPUs and achieve speeds comparable to C-based codes. Mahakala natively uses the Cartesian Kerr-Schild coordinate system, which avoids numerical issues caused by the "pole" of spherical coordinates. We demonstrate Mahakala’s capabilities by simulating the 1.3 mm wavelength images (the wavelength of Event Horizon Telescope observations) of general relativistic magnetohydrodynamic simulations of low-accretion rate supermassive black holes. The modular nature of Mahakala allows us to easily quantify the relative contribution of different regions of the flow to image features. We show that most of the emission seen in 1.3 mm images originates close to the black hole. We also quantify the relative contribution of the disk, forward jet, and counter jet to 1.3 mm images. Copyright © 2023, The Authors. All rights reserved.

关键词： Python

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Dynamic Mode Decomposition and Sparse Measurements for Characterization and Monitoring of Power System Disturbances

arXiv

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arXiv 2019年

作者： Ramos, J. Jorge Kutz, J. Nathan Graduate Program in Electrical Engineering Cinvestav Unidad Guadalajara Guadalajaram Jal Mexico Department of Applied Mathematics University of Washington SeattleWA98195 United States

We introduce the dynamics mode decomposition for monitoring wide-area power grid networks from sparse measurement data. The mathematical framework fuses data from multiple sensors based on multivariate statistics, providing accurate full state estimation from limited measurements and generating data-driven forecasts for the state of the system. Our proposed data-driven strategy, which is based on energy metrics, can be used for the analysis of major disturbances in the network. The approach is tested and validated using time domain simulations in the IEEE 118 bus system under various disturbance scenarios and under different sparse observations of the system. In addition to state reconstruction, the minimal number of sensors required for monitoring disturbances can be evaluated. Visualization techniques are developed in order to aid in the analysis and characterization of the system after disturbance. Copyright © 2019, The Authors. All rights reserved.

关键词： Time domain analysis

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Low-energy astrophysics with KamLAND 37

Low-energy astrophysics with KamLAND

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37th International Cosmic Ray Conference, ICRC 2021

作者： Kawada, Nanami Obara, Shuhei Ishidoshiro, Koji Grant, C. O’Donnell, T. Dell’Oro, S. Abe, S. Asami, S. Gando, A. Gando, Y. Gima, T. Goto, A. Hachiya, T. Hata, K. Hayashida, S. Hosokawa, K. Ichimura, K. Ieki, S. Ikeda, H. Inoue, K. Kamei, Y. Kishimoto, Y. Kinoshita, T. Koga, M. Maemura, N. Mitsui, T. Miyake, H. Nakamura, K. Nakamura, K. Nakamura, R. Ozaki, H. Sakai, T. Sambonsugi, H. Shimizu, I. Shirai, J. Shiraishi, K. Suzuki, A. Suzuki, Y. Takeuchi, A. Tamae, K. Ueshima, K. Wada, Y. Watanabe, H. Yoshida, Y. Ichikawa, A.K. Kozlov, A. Chernyak, D. Takemoto, Y. Yoshida, S. Umehara, S. Fushimi, K. Nakamura, K.Z. Yoshida, M. Berger, B.E. Fujikawa, B.K. Learned, J.G. Maricic, J. Axani, S.N. Winslow, L.A. Fu, Z. Ouellet, J. Efremenko, Y. Karwowski, H.J. Markoff, D.M. Tornow, W. Li, A. Detwiler, J.A. Enomoto, S. Decowski, M.P. Research Center for Neutrino Science Tohoku University Sendai980-8578 Japan Frontier Research Institute for Interdisciplinary Sciences Tohoku University Sendai980-8578 Japan Institute for the Physics and Mathematics of the Universe The University of Tokyo Kashiwa277-8568 Japan Graduate Program on Physics for the Universe Tohoku University Sendai980-8578 Japan Department of Physics Tohoku University Sendai980-8578 Japan Graduate School of Science Osaka University Osaka Toyonaka560-0043 Japan Osaka University Osaka Ibaraki567-0047 Japan Graduate School of Advanced Technology and Science Tokushima University Tokushima770-8506 Japan Department of Physics Kyoto University Kyoto606-8502 Japan Nuclear Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Physics and Astronomy University of Hawaii at Manoa HonoluluHI96822 United States Massachusetts Institute of Technology CambridgeMA02139 United States Department of Physics and Astronomy University of Tennessee KnoxvilleTN37996 United States Triangle Universities Nuclear Laboratory DurhamNC27708 United States The University of North Carolina at Chapel Hill Chapel HillNC27599 United States North Carolina Central University DurhamNC27701 United States Physics Department Duke University DurhamNC27705 United States Center for Experimental Nuclear Physics and Astrophysics University of Washington SeattleWA98195 United States Nikhef The University of Amsterdam Science Park Amsterdam Netherlands Boston University BostonMA02215 United States Center for Neutrino Physics Virginia Polytechnic Institute State University BlacksburgVA24061 United States

We present two results of a search for MeV-scale neutrino and anti-neutrino events correlated with gravitational wave events/candidates and large solar flares with KamLAND. The KamLAND detector is a large-volume neutrino detector using liquid scintillator, which is located at 1 km underground under the top of Mt. Ikenoyama in Kamioka, Japan. KamLAND has multiple reaction channels to detect neutrinos. Electron antineutrino can be detected via inverse-beta decay with 1.8 MeV neutrino energy threshold. All flavors of neutrinos can be detected via neutrino-electron scattering without neutrino energy threshold. KamLAND has continued the neutrino observation since 2002 March. We use the data set of 60 gravitational waves provided by the LIGO/Virgo collaboration during their second and third observing runs and search for coincident electron antineutrino events in KamLAND. We find no significant coincident signals within a ±500 s timing window from each gravitational wave and present 90% C.L. upper limits on the electron antineutrino fluence between 108–1013 cm−2 for neutrino energies of 1.8–111 MeV. For a solar-flare neutrino search at KamLAND, we determine the timing window using the solar X-ray data set provided by the GOES satellite series from 2002 to 2019 and search for the excess of coincident event rate on the all-flavor neutrinos. We find no significant event rate excess in the flare time windows and get 90% C.L. upper limits on the fluence of neutrinos of all flavors (electron anti-neutrinos) between 1010–1013 cm−2 (108–1013 cm−2) for neutrino energies in the energy range of 0.4–35 MeV. © Copyright owned by the author(s).

关键词： Gravity waves

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A fully adaptive multilevel stochastic collocation strategy for solving elliptic PDEs with random data

arXiv

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arXiv 2019年

作者： Lang, Jens Scheichl, Robert Silvester, David Technische Universität Darmstadt Department of Mathematics Dolivostraße 15 Darmstadt64293 Germany Ruprecht-Karls-Universität Heidelberg Institute for Applied Mathematics and Interdisciplinary Center for Scientific Computing Im Neuenheimer Feld 205 Heidelberg69120 Germany University of Manchester Department of Mathematics ManchesterM13 9PL United Kingdom

We propose and analyse a fully adaptive strategy for solving elliptic PDEs with random data in this work. A hierarchical sequence of adaptive mesh refinements for the spatial approximation is combined with adaptive anisotropic sparse Smolyak grids in the stochastic space in such a way as to minimize the computational cost. The novel aspect of our strategy is that the hierarchy of spatial approximations is sample dependent so that the computational effort at each collocation point can be optimised individually. We outline a rigorous analysis for the convergence and computational complexity of the adaptive multilevel algorithm and we provide optimal choices for error tolerances at each level. Two numerical examples demonstrate the reliability of the error control and the significant decrease in the complexity that arises when compared to single level algorithms and multilevel algorithms that employ adaptivity solely in the spatial discretisation or in the collocation *** Codes 65C20, 65C30, 65N35, 65M75 Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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Isotope effects on molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on SCAN functional

arXiv

引用

arXiv 2020年

作者： Xu, Jianhang Zhang, Chunyi Zhang, Linfeng Chen, Mohan Santra, Biswajit Wu, Xifan Department of Physics Temple University PhiladelphiaPA19122 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States CAPT HEDPS College of Engineering Peking University Beijing100871 China Institute for Computational Molecular Science Temple University PhiladelphiaPA19122 United States

Feynman path-integral deep potential molecular dynamics (PI-DPMD) calculations have been employed to study both light (H2O) and heavy water (D2O) within the isothermal–isobaric ensemble. In particular, the deep neural network is trained based on ab initio data obtained from the strongly constrained and appropriately normed (SCAN) exchange-correlation functional. Because of the lighter mass of hydrogen than deuteron, the properties of light water is more influenced by nuclear quantum effect than those of heavy water. Clear isotope effects are observed and analyzed in terms of hydrogen-bond structure and electronic properties of water that are closely associated with experimental observables. The molecular structures of both liquid H2O and D2O agree well with the data extracted from scattering experiments. The delicate isotope effects on radial distribution functions and angular distribution functions are well reproduced as well. Our approach demonstrates that deep neural network combined with SCAN functional based ab initio molecular dynamics provides an accurate theoretical tool for modeling water and its isotope effects. Copyright © 2020, The Authors. All rights reserved.

关键词： Electronic properties

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Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations

arXiv

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arXiv 2019年

作者： Kobak, Dmitry Linderman, George Steinerberger, Stefan Kluger, Yuval Berens, Philipp Institute for Ophthalmic Research University of Tübingen Germany Applied Mathematics Program Yale University New Haven United States Department of Mathematics Yale University New Haven United States Department of Pathology Yale School of Medicine New Haven United States

T-distributed stochastic neighbour embedding (t-SNE) is a widely used data visualisation technique. It differs from its predecessor SNE by the low-dimensional similarity kernel: the Gaussian kernel was replaced by the heavy-tailed Cauchy kernel, solving the "crowding problem" of SNE. Here, we develop an efficient implementation of t-SNE for a t-distribution kernel with an arbitrary degree of freedom v, with v → ∞ corresponding to SNE and v = 1 corresponding to the standard t-SNE. Using theoretical analysis and toy examples, we show that v Copyright © 2019, The Authors. All rights reserved.

关键词： Data visualization

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GLOBAL SOLUTIONS TO THE SUPERCOOLED STEFAN PROBLEM WITH BLOW-UPS: REGULARITY AND UNIQUENESS

arXiv

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arXiv 2019年

作者： Delarue, François Nadtochiy, Sergey Shkolnikov, Mykhaylo Université Côte d’Azur CNRS Laboratoire J.A. Dieudonné Nice France Department of Applied Mathematics Illinois Institute of Technology ChicagoIL60616 United States ORFE Department Bendheim Center for Finance and Program in Applied & Computational Mathematics Princeton University PrincetonNJ08544 United States

We consider the supercooled Stefan problem, which captures the freezing of a supercooled liquid, in one space dimension. A probabilistic reformulation of the problem allows to define global solutions, even in the presence of blow-ups of the freezing rate. We provide a complete description of such solutions, by relating the temperature distribution in the liquid to the regularity of the ice growth process. The latter is shown to transition between (i) continuous differentiability, (ii) Hölder continuity, and (iii) discontinuity. In particular, in the second regime we rediscover the square root behavior of the growth process pointed out by Stefan in his seminal paper [Ste89] from 1889 for the ordinary Stefan problem. In our second main theorem, we establish the uniqueness of the global solutions, a first result of this kind in the context of growth processes with singular self-excitation when blow-ups are *** Codes 80A22, 35B44, 60H30, 35B05 Copyright © 2019, The Authors. All rights reserved.

关键词： Supercooling

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Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021

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The Lancet 2023年第10397期402卷 203-234页

作者： Ong, Kanyin Liane Stafford, Lauryn K. McLaughlin, Susan A. Boyko, Edward J. Vollset, Stein Emil Smith, Amanda E. Dalton, Bronte E. Duprey, Joe Cruz, Jessica A. Hagins, Hailey Lindstedt, Paulina A. Aali, Amirali Abate, Yohannes Habtegiorgis Abate, Melsew Dagne Abbasian, Mohammadreza Abbasi-Kangevari, Zeinab Abbasi-Kangevari, Mohsen ElHafeez, Samar Abd Abd-Rabu, Rami Abdulah, Deldar Morad Abdullah, Abu Yousuf Md Abedi, Vida Abidi, Hassan Aboagye, Richard Gyan Abolhassani, Hassan Abu-Gharbieh, Eman Abu-Zaid, Ahmed Adane, Tigist Demssew Adane, Denberu Eshetie Addo, Isaac Yeboah Adegboye, Oyelola A. Adekanmbi, Victor Adepoju, Abiola Victor Adnani, Qorinah Estiningtyas Sakilah Afolabi, Rotimi Felix Agarwal, Gina Aghdam, Zahra Babaei Agudelo-Botero, Marcela Arriagada, Constanza Elizabeth Aguilera Agyemang-Duah, Williams Ahinkorah, Bright Opoku Ahmad, Danish Ahmad, Rizwan Ahmad, Sajjad Ahmad, Aqeel Ahmadi, Ali Ahmadi, Keivan Ahmed, Ali Ahmed, Luai A. Ahmed, Syed Anees Ajami, Marjan Akinyemi, Rufus Olusola Al Hamad, Hanadi Al Hasan, Syed Mahfuz AL-Ahdal, Tareq Mohammed Ali Alalwan, Tariq A. Al-Aly, Ziyad AlBataineh, Mohammad T. Alcalde-Rabanal, Jacqueline Elizabeth Alemi, Sharifullah Ali, Hassam Alinia, Tahereh Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Amare, Firehiwot Ameyaw, Edward Kwabena Amiri, Sohrab Amusa, Ganiyu Adeniyi Andrei, Catalina Liliana Anjana, Ranjit Mohan Ansar, Adnan Ansari, Golnoosh Ansari-Moghaddam, Alireza Anyasodor, Anayochukwu Edward Arabloo, Jalal Aravkin, Aleksandr Y. Areda, Demelash Arifin, Hidayat Arkew, Mesay Armocida, Benedetta Arnlov, Johan Artamonov, Anton A. Arulappan, Judie Aruleba, Raphael Taiwo Arumugam, Ashokan Aryan, Zahra Asemu, Mulu Tiruneh Asghari-Jafarabadi, Mohammad Askari, Elaheh Asmelash, Daniel Astell-Burt, Thomas Athar, Mohammad Athari, Seyyed Shamsadin Atout, Maha Moh'd Wahbi Avila-Burgos, Leticia Awaisu, Ahmed Azadnajafabad, Sina Darshan, B.B. Babamohamadi, Hassan Badar, Muhammad Badawi, Alaa Badiye, Ashish D. Baghcheghi, Nayereh Baghe Institute for Health Metrics and Evaluation School of Medicine General Medicine Service Department of Veterans Affairs Seattle WA United States Department of Health Metrics Sciences School of Medicine Faculty of Medicine France Department of Clinical Governance and Quality Improvement Aleta Wondo Hospital Aleta Wondo Ethiopia Department of Nursing Woldia University Woldia Ethiopia Department of Nursing Department of Orthopedic Surgery Orthopaedic Surgery Social Determinants of Health Research Center Non-communicable Diseases Research Center Epidemiology Department Evidence-Based Practice Center Community and Maternity Nursing Unit University of Duhok Duhok Iraq School of Planning Faculty of Environment Department of Public Health Sciences Penn State College of Medicine Pennsylvania State University Hershey PA United States Biocomplexity Institute Virginia Tech Blacksburg VA United States College of Medicine Department of Public Health Laboratory Technology Sciences Department Department of Family and Community Health University of Health and Allied Sciences Ho Ghana Research Center for Immunodeficiencies Department of Biosciences and Nutrition Karolinska University Hospital Huddinge Sweden Clinical Sciences Department Department of Surgery Department of Surgery Department of Obstetrics & Gynecology College of Graduate Health Sciences University of Tennessee Memphis TN United States Department of Obstetrics & Gynecology Department of Clinical and Psychosocial Epidemiology Department of Anesthesia and Critical Care Centre for Social Research in Health Quality and Systems Performance Unit Cancer Institute NSW Sydney NSW Australia Department of Public Health and Tropical Medicine HIV and Infectious Diseases Department Jhpiego Abuja Nigeria Adolescent Research and Care Department Adolescent Friendly Research Initiative and Care Ado Ekiti Nigeria Department of Epidemiology Department of Epidemiology and Medical Statistics Department of Family Medicine McMaster University

Background: Diabetes is one of the leading causes of death and disability worldwide, and affects people regardless of country, age group, or sex. Using the most recent evidentiary and analytical framework from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD), we produced location-specific, age-specific, and sex-specific estimates of diabetes prevalence and burden from 1990 to 2021, the proportion of type 1 and type 2 diabetes in 2021, the proportion of the type 2 diabetes burden attributable to selected risk factors, and projections of diabetes prevalence through 2050. Methods: Estimates of diabetes prevalence and burden were computed in 204 countries and territories, across 25 age groups, for males and females separately and combined;these estimates comprised lost years of healthy life, measured in disability-adjusted life-years (DALYs;defined as the sum of years of life lost [YLLs] and years lived with disability [YLDs]). We used the Cause of Death Ensemble model (CODEm) approach to estimate deaths due to diabetes, incorporating 25 666 location-years of data from vital registration and verbal autopsy reports in separate total (including both type 1 and type 2 diabetes) and type-specific models. Other forms of diabetes, including gestational and monogenic diabetes, were not explicitly modelled. Total and type 1 diabetes prevalence was estimated by use of a Bayesian meta-regression modelling tool, DisMod-MR 2.1, to analyse 1527 location-years of data from the scientific literature, survey microdata, and insurance claims;type 2 diabetes estimates were computed by subtracting type 1 diabetes from total estimates. Mortality and prevalence estimates, along with standard life expectancy and disability weights, were used to calculate YLLs, YLDs, and DALYs. When appropriate, we extrapolated estimates to a hypothetical population with a standardised age structure to allow comparison in populations with different age structures. We used the comparative r

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Machine learning-based health risk assessment of fluoride and nitrate in indian drinking water: A systematic review

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Journal of Food Composition and Analysis 2025年 145卷

作者： Shahjad Ali Rajesh Kumar Deolia Azhar Shadab Raisul Islam Mohammad Usama Salman Ahmed Athar Hussain Kamlesh Deshmukh Ali Akbar Mohammadi Department of Environmental Science Anand School of Engineering and Technology Sharda University Agra Keetham Agra 282007 India Department of Applied Science (Mathematics) G.L. Bajaj Group of Institutions Mathura India Department of Electronics and Communication Engineering G. L. Bajaj Institute of Technology and Management Greater Noida Uttar Pradesh 201306 India Department of Civil Engineering GLA University Mathura India Department of Environmental Science Integral University Lucknow India Interdisciplinary Department of Remote Sensing and GIS Applications Aligarh Muslim University Aligarh India Department of Civil Engineering Netaji Subhas University of Technology New Delhi India Department of Computer Science and Engineering Anand Engineering College Agra India Workplace health research center Neyshabur University of Medical Sciences Neyshabur Iran Department of Environmental Health Engineering Neyshabur University of Medical Sciences Neyshabur Iran

This systematic review examines the health risk assessment of fluoride and nitrate concentrations in drinking water sources across India. 1250 studies on fluoride and nitrate in Indian waters were examined between 2015 and 2024; following thorough evaluation and exclusion, 55 high-quality, pertinent papers were chosen. The results were shown using a geographic information system (GIS) and corresponding zoning maps. Alarmingly, more than 40 % and 50 % of samples investigated exceeded the allowable level set for fluoride and nitrate concentration in potable water 1.5 mg/L and 50 mg/L, posing potential Health risks. The results indicated that the HQ of fluoride and nitrate across all targeted groups (children, teenagers, and adults) exceeded 1. The HQ values were 3.206, 2.466, 1.9764 for fluoride and 6.487, 4.99, 3.998 for nitrate. Significantly, the order of vulnerability was children > teenagers > adults, with children being the most vulnerable group to fluoride and nitrate consumptions in the investigated locations. This research study shows that a detailed investigation of the hydro geochemistry of fluoride and nitrate can predict groundwater quality zones of the state using machine-learning techniques, viz., artificial neural network (ANN) and extreme gradient boosting (XGBoost) regression. The ANN and XGBoost models, with their high R 2 values of 0.9991 and 0.9992 in training, and 0.9868 and 0.8943 in testing, inspire confidence in their predictive power. The average RMSE values during training were 0.0441 and 0.0067 for fluoride. Similarly, the R 2 values of 0.9998 and 0.9985 in training, and 0.9956 and 0.9448 in testing, along with the average RMSE values of 0.0441 and 0.0067 for nitrate, further reinforce the models' predictive capabilities. Therefore, fluoride and nitrate amounts must be measured in diverse water sources to determine the optimal removal technique and limit human health risks.

关键词： Fluoride Health risk assessment Machine learning models Nitrate Potable water

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