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Exploring the role of geothermal energy consumption in achieving carbon neutrality and environmental sustainability

Heliyon 2024年第23期10卷 e40709页

作者： Idroes, Ghalieb Mutig Afjal, Mohd Khan, Mohsin Haseeb, Mohammad Hardi, Irsan Noviandy, Teuku Rizky Idroes, Rinaldi Department of Economics Faculty of Economics and Business Universitas Syiah Kuala Banda Aceh 23111 Indonesia Energy and Green Economics Unit Graha Primera Saintifika Aceh Besar 23371 Indonesia VIT Business School Vellore Institute of Technology Vellore 632014 India Department of Commerce School of Social Sciences and Language Vellore Institute of Technology Vellore Vellore 632014 India School of Economics and Management and Center for Industrial Economics Wuhan University Wuhan 430072 China Economic Modeling and Data Analytics Unit Graha Primera Saintifika Aceh Besar 23371 Indonesia Interdisciplinary Innovation Research Unit Graha Primera Saintifika Aceh Besar 23771 Indonesia Department of Chemistry Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala Banda Aceh 23111 Indonesia School of Mathematics and Applied Sciences Universitas Syiah Kuala Banda Aceh 23111 Indonesia

While there is extensive research on the environmental impacts of renewable energy sources, a notable gap remains in understanding the specific environmental effects of geothermal energy. This knowledge shortfall is particularly critical for Indonesia, which holds the world's second-largest geothermal potential but has yet to effectively harness these valuable resources. This study aims to address this gap by providing a preliminary evaluation of the dynamic impact of geothermal energy consumption and various macroeconomic variables on carbon dioxide (CO₂) emissions in Indonesia from 1995 to 2020. The study employs the Fully Modified Ordinary Least Squares (FMOLS) method and conducts robustness checks using the Dynamic Ordinary Least Squares (DOLS) method. Our findings reveal that geothermal energy consumption, military expenditure, and technology innovation contribute to a reduction in CO₂ emissions, while non-renewable energy consumption increases CO₂ emissions. Additionally, the pairwise Granger causality test results indicate a unidirectional causality running from geothermal energy consumption to CO₂ emissions, suggesting that trends in geothermal energy use can reliably predict changes in CO₂ levels. This study provides valuable insights for policymakers and stakeholders in formulating sustainable policies for carbon reduction in Indonesia. © 2024 The Authors

关键词： Carbon neutrality CO2 emissions DOLS Environmental sustainability FMOLS Geothermal energy consumption Indonesia

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An immersed interface method for incompressible flows and geometries with sharp features

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Journal of Computational Physics 2025年 537卷

作者： Michael J. Facci Ebrahim M. Kolahdouz Boyce E. Griffith Department of Mathematics University of North Carolina Chapel Hill NC USA Flatiron Institute Simons Foundation New York NY USA Department of Biomedical Engineering University of North Carolina Chapel Hill NC USA Carolina Center for Interdisciplinary Applied Mathematics University of North Carolina Chapel Hill NC USA Computational Medicine Program University of North Carolina School of Medicine Chapel Hill NC USA McAllister Heart Institute University of North Carolina School of Medicine Chapel Hill NC USA

The immersed interface method (IIM) for models of fluid flow and fluid-structure interaction imposes jump conditions that capture stress discontinuities generated by forces that are concentrated along immersed boundaries. Most prior work using the IIM for fluid dynamics applications has focused on smooth interfaces, but boundaries with sharp features such as corners and edges can appear in practical analyses, particularly on engineered structures. The present study builds on our work to integrate finite element-type representations of interface geometries with the IIM. Initial realizations of this approach used a continuous Galerkin (CG) finite element discretization for the boundary, but as we show herein, these approaches generate large errors near sharp geometrical features. To overcome this difficulty, this study introduces an IIM approach using a discontinuous Galerkin (DG) representation of the jump conditions. Numerical examples explore the impacts of different interface representations on accuracy for both smooth and sharp boundaries, particularly flows interacting with fixed interface configurations. We demonstrate that using a DG approach provides accuracy that is comparable to the CG method for smooth cases. Further, we identify a time step size restriction for the CG representation that is directly related to the sharpness of the geometry. In contrast, time step size restrictions imposed by DG representations are demonstrated to be nearly insensitive to the presence of sharp features.

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Prediction and Prevention of Pandemics via Graphical Model Inference and Convex programming

arXiv

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arXiv 2021年

作者： Krechetov, Mikhail Sikaroudi, Amir Mohammad Esmaieeli Efrat, Alon Polishchuk, Valentin Chertkov, Michael Skolkovo Institute of Science and Technology Moscow121205 Russia University of Arizona Department of Computer Science TucsonAZ85721 United States University of Arizona Program in Applied Mathematics TucsonAZ85721 United States Linköping Univeristy Communications and Transport Systems Norrkoping60174 Sweden University of Arizona Department of Mathematics TucsonAZ85721 United States

Hard-to-predict bursts of COVID-19 pandemic revealed significance of statistical modeling which would resolve spatio-temporal correlations over geographical areas, for example spread of the infection over a city with census tract granularity. In this manuscript, we provide algorithmic answers to the following two inter-related public health challenges of immense social impact which have not been adequately addressed (1) Inference Challenge: assuming that there are N census blocks (nodes) in the city, and given an initial infection at any set of nodes, e.g. any N of possible single node infections, any N(N-1)=2 of possible two node infections, etc, what is the probability for a subset of census blocks to become infected by the time the spread of the infection burst is stabilized? (2) Prevention Challenge: What is the minimal control action one can take to minimize the infected part of the stabilized state footprint? To answer the challenges, we build a Graphical Model of pandemic of the attractive Ising (pair-wise, binary) type, where each node represents a census tract and each edge factor represents the strength of the pairwise interaction between a pair of nodes, e.g. representing the inter-node travel, road closure and related, and each local bias/field represents the community level of immunization, acceptance of the social distance and mask wearing practice, etc. Resolving the Inference Challenge requires finding the Maximum-A-Posteriory (MAP), i.e. most probable, state of the Ising Model constrained to the set of initially infected nodes. (An infected node is in the +1 state and a node which remained safe is in the-1 state.) We show that almost all attractive Ising Models on dense graphs result in either of the two possibilities (modes) for the MAP state: either all nodes which were not infected initially became infected, or all the initially uninfected nodes remain uninfected (susceptible). This bi-modal solution of the Inference Challenge allows us to re-sta

关键词： Ising model

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Enhanced Gaussian Process Regression for Diagnosing Wind Energy Conversion Systems

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IFAC-PapersOnLine 2022年第6期55卷 673-678页

作者： Majdi Mansouri Radhia Fezai Mohamed Trabelsi Hajji Mansour Hazem Nounou Mohamed Nounou Department of Mathematics and Sciences Prince Sultan University Riyadh 11586 Saudi Arabia Research Laboratory of Automation Signal Processing and Image National Engineering School of Monastir 5019 Tunisia Electronic and Communications Engineering Department Kuwait College of Science and Technology P. O. Box 27235 Kuwait Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Tunisia Chemical Engineering Program Texas A&M University at Qatar Doha Qatar

Fault detection and diagnosis techniques are increasingly important to ensure robust and resource efficient operation of Wind Energy Conversion (WEC) systems. In this context, this paper presents a Reduced Enhanced Gaussian Process Regression (REGPR)-based Random Forest (RF) technique (REGPR-RF) to identify and diagnose faults occurring in a nonlinear WEC systems. The proposed technique uses REGPR technique for features extraction and selection from raw sensor data. Then, these selected features are fed to RF classifier to reliably detect and classify faults. The use of REGPR to learn features avoid the dimension problems and improves the classification performance significantly with a small number of training data. The results obtained by REGPR-RF are compared to those obtained with other conventional classifiers (Support Vector Machines (SVM), Naive Bayes (NB), ...). The results show that the developed REGPR-RF technique achieve higher accuracy (99.99%) with small data sets.

关键词： Gaussian Process Regression (GPR) Noise-Signal-Dependence Random Forest (RF) Feature Extraction Selection Fault Detection Diagnosis (FDD) Wind Energy Conversion (WEC) Systems

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Cosmology with Galaxy Photometry Alone

arXiv

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arXiv 2023年

作者： Hahn, ChangHoon Villaescusa-Navarro, Francisco Melchior, Peter Teyssier, Romain Department of Astrophysical Sciences Princeton University Peyton Hall PrincetonNJ08544 United States Center for Computational Astrophysics Flatiron Institute 162 5th Avenue New YorkNY10010 United States Center for Statistics & Machine Learning Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University Fine Hall Washington Road PrincetonNJ08544-1000 United States

We present the first cosmological constraints using only the observed photometry of galaxies. Villaescusa-Navarro et al. (2022b) recently demonstrated that the internal physical properties of a single simulated galaxy contain a significant amount of cosmological information. These physical properties, however, cannot be directly measured from observations. In this work, we present how we can go beyond theoretical demonstrations to infer cosmological constraints from actual galaxy observables (e.g. optical photometry) using neural density estimation and the CAMELS suite of hydrodynamical simulations. We find that the cosmological information in the photometry of a single galaxy is limited. However, we combine the constraining power of photometry from many galaxies using hierarchical population inference and place significant cosmological constraints. With the observed photometry of ∼20,000 NASA-Sloan Atlas galaxies, we constrain (formula presented) and (formula presented). © 2023, CC BY.

关键词： Galaxies

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Probing the hadron mass spectrum in dense two-color QCD with the linear sigma model

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Physical Review D 2023年第5期107卷 054001-054001页

作者： Daiki Suenaga Kotaro Murakami Etsuko Itou Kei Iida Strangeness Nuclear Physics Laboratory RIKEN Nishina Center Wako 351-0198 Japan Research Center for Nuclear Physics Osaka University Ibaraki 567-0048 Japan Yukawa Institute for Theoretical Physics Kyoto University Kyoto 606-8502 Japan Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS) RIKEN Wako 351-0198 Japan Department of Physics and Research and Education Center for Natural Sciences Keio University 4-1-1 Hiyoshi Yokohama Kanagawa 223-8521 Japan Department of Mathematics and Physics Kochi University 2-5-1 Akebono-cho Kochi 780-8520 Japan

We investigate modifications of hadron masses at finite quark chemical potential in two-flavor and two-color QCD, data of which are available from lattice simulations, within a linear sigma model based on approximate Pauli-Gursey SU(4) symmetry. The model describes not only ground-state scalar diquarks and pseudoscalar mesons but also the excited pseudoscalar diquarks and scalar mesons; each ground-state diquark (meson) has the corresponding excited diquark (hadron) with opposite parity as a chiral partner. Effects of chiral symmetry breaking and diquark condensates are incorporated by a mean-field treatment. We show that various mixings among the hadrons, which are triggered by the breakdown of baryon number conservation in the superfluid phase, lead to a rich hadron mass spectrum. We discuss the influence of U(1)A anomaly on the density dependence of the mass spectrum and also manifestations of the chiral partner structures as the density increases in the superfluid phase. The predicted hadron masses are expected to provide future lattice simulations with useful information on such symmetry properties in dense two-color QCD.

关键词： Lattice QCD Nuclear matter in neutron stars Sigma models Hadrons Superfluids Chiral symmetry

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Trends in bimetallic nanomaterials and methods for fourth-generation glucose sensors

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TrAC - Trends in Analytical Chemistry 2023年 162卷

作者： Naikoo, Gowhar A. Arshad, Fareeha Hassan, Israr U. Omar, Fatima Ba Tambuwala, Murtaza M. Mustaqeem, Mujahid Saleh, Tawfik A. Department of Mathematics and Sciences College of Arts and Applied Sciences Dhofar University SalalahPC 211 Oman College of Engineering Dhofar University SalalahPC 211 Oman Lincoln Medical School University of Lincoln Brayford Pool Campus LincolnLN6 7TS United Kingdom Department of Chemistry National Taiwan University No. 1 Section 4 Roosevelt Road Taipei10617 Taiwan Nano-Science and Technology Program Taiwan International Graduate Program Institute of Physic Academia Sinica Taipei106 Taiwan Department of Chemistry King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Diabetes mellitus is one of the most rapidly rising chronic diseases around the globe. To combat these rising numbers, active research has been done in recent years to enhance the sensitivity, selectivity, and specificity of glucose biosensors. Despite the easy availability of these enzymatic glucose sensors, these have demonstrated limitations like low stability, short shelf-life, and being easily altered by physical and chemical environmental factors. To overcome these limitations, non-enzymatic sensors, also called fourth-generation glucose sensors, have been developed. These sensors use nanomaterials and do not employ any enzymes therefore exhibit better shelf life, improved sensitivity, and allow for quick detection of glucose without needing additional steps. In recent years, bimetallic nanocomposite-based fourth-generation glucose sensors have gained widespread attention from the scientific community. Owing to the synergistic effect of two distinct metal nanomaterials, these sensors show enhanced reproducibility and excellent stability as compared to monometallic nanomaterials. In this review, we aim to represent a comprehensive overview of the recent developments in the development of bimetallic nanocomposite-based glucose sensors for rapid and accurate glucose detection. After a brief introduction, we discussed the various bimetallic nanomaterials developed in recent years, followed by a discussion on the mechanism of action of such sensors. We then briefly discussed the industrial challenges of developing such sensors and discussed the advantages and disadvantages of bimetallic nanomaterial-based sensors. We finally concluded the review by highlighting the future perspectives of this new and upcoming field. © 2023 Elsevier B.V.

关键词： Glucose sensors

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Assessment of Gamma and Neutron Dose of Optimizing the Radiation Protection in TRIGA 2000 BATAN Bandung. 6

Assessment of Gamma and Neutron Dose of Optimizing the Radia...

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6th International Conference on Advances in Nuclear Science and Engineering, ICANSE 2018

作者： Oméga, Rakotovao Lovanantenaina Permana, Sidik Oetami, Rini Heroe Rasito Master Program of Physics Department Faculty of Mathematics and Natural Science Bandung Institute of Technology Jalan Ganesha 10 Bandung40132 Indonesia Nuclear Physics Laboratory Physics Department Faculty of Mathematics and Natural Science Bandung Institute of Technology Jalan Ganesha 10 Bandung40132 Indonesia Center for Science and Applied Nuclear Technology National Nuclear Energy Agency Jl. Tamansari 71 Bandung Indonesia

The TRIGA 2000 reactor is a nuclear installation which is operated at 1000 kW. Gamma and neutron radiation exposure will contribute to occupational doses that will be received by operators and other radiation workers. To maintain the safety of radiation workers, assessment of gamma and neutron radiation is needed in the TRIGA 2000 reactor. survey in the hall reactor using the RTD 30 and RadEye NL detectors. In addition to the direct measurement, calculations are also carried out with simulation data dose using Monte Carlo MCNPX for the calculation and followed by PHITS to get the graphical dose distribution of gamma and neutron. The study will focus on the assessment of the diffusion Gamma and neutron doses by doing the measurement and simulation data collect for the aim of the optimization of the Radiation protection policy in the reactor TRIGA 2000 Bandung. The result of the measurement is shown with graph in different power taken during the measurement and the simulation is the graphical distribution dose of gamma and neutron using PHITS code. A preventive dose reduction technique, worker and publics protection elaborate to optimise the Radiation protection program. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Monte Carlo methods

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Exploring the business aspects of digital pathology, deep learning in cancers

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Intelligence-Based Medicine 2024年 10卷 100172-100172页

作者： Reddy, Arjun Adrian Williams, Darnell K. Graifman, Gillian Hussain, Nowair Amiel, Maytal Priscilla, Tran Haider, Ali Kavitesh, Bali Kumar Li, Austin Alishahian, Leael Perera, Nichelle Efros, Corey Babu, Myoungmee Tharakan, Mathew Etienne, Mill Babu, Benson A. Applied Mathematics & Statistics Stony Brook University LabelMD New York United States Medical Scientist Training Program Albert Einstein College of Medicine Bronx New York United States New York Medical College Valhalla New York United States Department of Internal Medicine Overlook Medical Center NJ United States Artificial Intelligence Yeshiva University New York United States Centre for Frontier AI Research (CFAR) Agency for Science Technology and Research (A∗STAR) Singapore Artificial Intelligence and Mathematics New York City Department of Education New York United States Stonybrook Medical Center New York United States Department of Neurology New York Medical College Valhalla NY United States Department of Hospital Medicine Wyckoff Medical Center Brooklyn NY United States

Introduction: Cancer remains one of the leading causes of morbidity and mortality worldwide. Deep learning in digital pathology has the potential to improve operational efficiency, costs, and care. Methods: We searched Web of Science, Arxiv, MedRxiv, Embase, PubMed, DBLP, Google Scholar, IEEE Xplore, and Cochrane databases for whole slide imaging and deep learning articles published between 2019 and 2023. The final six articles were selected from 776 articles identified through an inclusion criterion. Conclusion: Digital pathology services that utilize deep learning have the potential to enhance clinical workflow efficiencies and can have a positive impact on business activities. We anticipate cost reductions as deep learning technology advances and more companies enter the digital pathology ecosystem. However, the limited availability of business use cases, primarily due to publication bias, poses a challenge in medicine without clear examples to learn from. © 2024

关键词： Artificial intelligence Business development Cancer Computational pathology Deep learning Digital pathology Whole slide image

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Dynamic Measure of Hyperuniformity and Nonhyperuniformity in Heterogeneous Media via the Diffusion Spreadability

arXiv

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arXiv 2021年

作者： Wang, Haina Torquato, Salvatore Department of Chemistry Princeton University PrincetonNJ08544 United States Department of Chemistry Department of Physics Princeton Center for Theoretical Science Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States

Time-dependent interphase diffusion processes in multiphase heterogeneous media arise ubiquitously in physics, chemistry and biology. Examples of heterogeneous media include composites, geological media, gels, foams and cell aggregates. The recently developed concept of spreadability, S(t), provides a direct link between time-dependent diffusive transport and the microstructure of two-phase media across length scales [Torquato, S., Phys. Rev. E., 104 054102 (2021)]. To investigate the capability of S(t) to probe microstructures of real heterogeneous media, we explicitly compute S(t) for well-known two-dimensional and three-dimensional idealized model structures that span across nonhyperuniform and hyperuniform classes. Among the former class, we study fully penetrable spheres and equilibrium hard spheres, and in the latter class, we examine sphere packings derived from "perfect glasses", uniformly randomized lattices (URL), disordered stealthy hyperuniform point processes and Bravais lattices. Hyperuniform media are characterized by an anomalous suppression of volume fraction fluctuations at large length scales compared to that of any nonhyperuniform medium. We further confirm that the small-, intermediate- and long-time behaviors of S(t) sensitively capture the small-, intermediate- and large-scale characteristics of the models. In instances in which the spectral density (Equation presented)V (k) has a power-law form B|k|α in the limit |k| → 0, the long-time spreadability provides a simple means to extract the value of the coefficients α and B that is robust against noise in (Equation presented)V (k) at small wavenumbers. For typical nonhyperuniform media, the intermediate-time spreadability is slower for models with larger values of the coefficient B = (Equation presented)V (0). Interestingly, the excess spreadability S(∞) - S(t) for URL packings has nearly exponential decay at small to intermediate t, but transforms to a power-law decay at large t, and the time f

关键词： Diffusion

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