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Study on triphase of polymorphs TiO2 (anatase/rutile/brookite) for boosting photocatalytic activity of metformin degradation

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Chemosphere 2024年 351卷

作者： Eddy, Diana Rakhmawaty Nur Sheha, Geometry Amal Permana, Muhamad Diki Saito, Norio Takei, Takahiro Kumada, Nobuhiro Irkham Rahayu, Iman Abe, Ikki Sekine, Yuta Oyumi, Tomoki Izumi, Yasuo Department of Chemistry Faculty Mathematics and Natural Sciences Universitas Padjadjaran Sumedang45363 Indonesia Special Educational Program for Green Energy Conversion Science and Technology Integrated Graduate School of Medicine Engineering and Agricultural Sciences University of Yamanashi Kofu400-8511 Japan Center for Crystal Science and Technology University of Yamanashi Kofu400-8511 Japan Department of Chemistry Graduate School of Science Chiba University Chiba263-8522 Japan

The elution of pharmaceutical products such as metformin at higher concentrations than the safe level in aquatic systems is a serious threat to human health and the ecosystem. Photocatalytic technology using TiO2 semiconductors potentially fixes this problem. This study aims to synthesize triphasic anatase–rutile–brookite TiO2 using ultrasound assisted sol–gel technique in the presence of acid and its application to photodegradation of metformin under UV light irradiation. Based on X-ray diffraction analysis, a TiO2 sample consisted of anatase (76%), rutile (7%), and brookite (17%) polymorph (A76R7B17) that was fully crystallized. Scanning electron microscopy (EM)–energy dispersive X-ray spectra results showed agglomerated triphasic A76R7B17 with irregular spherical clusters. Transmission EM results revealed that the crystal size of A76R7B17 was 4–14 nm. The Brunauer–Emmett–Teller analysis showed the sample's specific surface area of 149 m2 g−1. The degradation test of metformin demonstrated that the A76R7B17 exhibited a 75.4% degradation efficiency after 120 min under UV light irradiation, significantly higher than using biphasic and single-phase TiO2 photocatalysts. This difference could be attributed to the heterojunction effect of triphasic materials that effectively reduced electron–hole recombination rate as well as the combination of effective electron transfer from conduction band of brookite and anatase and the utilization of wider range of UV–visible light using rutile. © 2024 Elsevier Ltd

关键词： Titanium dioxide

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COVID-19 Infected Lung Computed Tomography Segmentation and Supervised Classification Approach

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Computers, Materials & Continua 2021年第7期68卷 391-407页

作者： Aqib Ali Wali Khan Mashwani Samreen Naeem Muhammad Irfan Uddin Wiyada Kumam Poom Kumam Hussam Alrabaiah Farrukh Jamal Christophe Chesneau Department of Computer Science Concordia College BahawalpurBahawalpur63100Pakistan Department of Computer Science&IT Glim Institute of Modern StudiesBahawalpur63100Pakistan Institute of Numerical Sciences Kohat University of Science&TechnologyKohat26000Pakistan Institute of Computing Kohat University of Science and TechnologyKohat26000Pakistan Program in Applied Statistics Department of Mathematics and Computer ScienceFaculty of Science and TechnologyRajamangala University of Technology ThanyaburiThanyaburi12110Thailand Departments of Mathematics Faculty of ScienceCenter of Excellence in Theoretical and Computational Science(TaCS-CoE)&KMUTT Fixed Point Research LaboratoryRoom SCL 802 Fixed Point LaboratoryScience Laboratory BuildingKing Mongkut’s University of Technology Thonburi(KMUTT)Bangkok10140Thailand Department of Medical Research China Medical University HospitalTaichung40402Taiwan College of Engineering Al Ain UniversityAl Ain64141United Arab Emirates Department of Mathematics Tafila Technical UniversityTafila66110Jordan Department of Statistics The Islamia University of BahawalpurBahawalpur63100Pakistan 11Department of MathematicsUniversitéde CaenLMNOCaen14032France

The purpose of this research is the segmentation of lungs computed tomography(CT)scan for the diagnosis of COVID-19 by using machine learning *** dataset contains data from patients who are prone to the *** contains three types of lungs CT images(Normal,Pneumonia,and COVID-19)collected from two different sources;the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur,Pakistan,and the second one is a publicly free available medical imaging database known as *** the preprocessing,a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest(ROIs)and acquire 52 hybrid statistical features for each ***,12 optimized statistical features are selected via the chi-square feature reduction *** the classification,five machine learning classifiers named as deep learning J4,multilayer perceptron,support vector machine,random forest,and naive Bayes are deployed to optimize the hybrid statistical features *** is observed that the deep learning J4 has promising results(sensitivity and specificity:0.987;accuracy:98.67%)among all the deployed *** a complementary study,a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.

关键词： COVID-19 machine learning fuzzy c-mean deep learning J4

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Unbiased Estimation using Underdamped Langevin Dynamics

arXiv

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arXiv 2022年

作者： Ruzayqat, Hamza Chada, Neil K. Jasra, Ajay Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this work we consider the unbiased estimation of expectations w.r.t. probability measures that have non-negative Lebesgue density, and which are known point-wise up-to a normalizing constant. We focus upon developing an unbiased method via the underdamped Langevin dynamics, which has proven to be popular of late due to applications in statistics and machine learning. Specifically in continuous-time, the dynamics can be constructed so that as the time goes to infinity they admit the probability of interest as a stationary measure. In many cases, time-discretized versions of the underdamped Langevin dynamics are used in practice which are run only with a fixed number of iterations. We develop a novel scheme based upon doubly randomized estimation as in [17, 19], which requires access only to time-discretized versions of the dynamics. The proposed scheme aims to remove the dicretization bias and the bias resulting from running the dynamics for a finite number of iterations. We prove, under standard assumptions, that our estimator is of finite variance and either has finite expected cost, or has finite cost with a high probability. To illustrate our theoretical findings we provide numerical experiments which verify our theory, which include challenging examples from Bayesian statistics and statistical *** Codes 60J22, 65C05, 65C40, 82C31, 62G08, 35Q56 © 2022, CC BY.

关键词： Dynamics

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Deep potentials for materials science

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Materials Futures 2022年第2期1卷 89-115页

作者： Tongqi Wen Linfeng Zhang Han Wang Weinan E David J Srolovitz Department of Mechanical Engineering The University of Hong KongHong KongHong Kong Special Administrative Region of China DP Technology BeijingPeople’s Republic of China AI for Science Institute BeijingPeople’s Republic of China Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijingPeople’s Republic of China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingPeople’s Republic of China School of Mathematical Sciences Peking UniversityBeijingPeople’s Republic of China Department of Mathematics and Program in Applied and Computational Mathematics Princeton UniversityPrincetonNJUnited States of America International Digital Economy Academy(IDEA) ShenzhenPeople’s Republic of China

To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;*** learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)*** this review,we provide an introduction to DP methods in computational materials *** theory underlying the DP method is presented along with a step-by-step introduction to their development and *** also review materials applications of DPs in a wide range of materials *** DP Library provides a platform for the development of DPs and a database of extant *** discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.

关键词： deep potential atomistic simulation machine learning potential neural network

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Bayesian Parameter Inference for Partially Observed SDEs driven by Fractional Brownian Motion

arXiv

引用

arXiv 2022年

作者： Maama, Mohamed Jasra, Ajay Ombao, Hernando Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Statistics Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

In this paper we consider Bayesian parameter inference for partially observed fractional Brownian motion (fBM) models. The approach we follow is to time-discretize the hidden process and then to design Markov chain Monte Carlo (MCMC) algorithms to sample from the posterior density on the parameters given data. We rely on a novel representation of the time discretization, which seeks to sample from an approximation of the posterior and then corrects via importance sampling;the approximation reduces the time (in terms of total observation time T) by O(T). This method is extended by using a multilevel MCMC method which can reduce the computational cost to achieve a given mean square error (MSE) versus using a single time discretization. Our methods are illustrated on simulated and real data. Copyright © 2022, The Authors. All rights reserved.

关键词： Mean square error

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Profiles of readiness: Using a blended framework to explore what it takes for faculty to be ready to change instructional practice

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Journal of Geoscience Education 2021年第3期69卷 281-299页

作者： Hauk, Shandy St John, Kristen Jones, Megan Mathematics Department San Francisco State University San Francisco CA United States Science Technology Engineering & Mathematics Program WestEd San Francisco CA United States Department of Geology and Environmental Science James Madison University Harrisonburg VA United States Earth and Environmental Science North Hennepin Community College Brooklyn Park MN United States

In answer to calls for research about professional change, this study addressed the question: What is involved in college science faculty readiness for change in instructional practice? The setting was a professional development experience in oceanography/marine science and paleoclimatology among 32 faculty from 2- and 4-year colleges. Ten of the 32 participated in interviews, and all provided survey responses and documents used in the study. Cycles of inductive analysis generated three example case stories to illustrate a new model for exploring faculty readiness for change in teaching. The model blends results from the health sciences on readiness for behavioral change with research on the personal, external, professional, and consequence domains of a professional change environment. The blended model attends to how an instructor draws on the domains to (a) see an instructional challenge as requiring intentional action to be resolved;(b) notice new significance (for the instructor) in some aspect of instructional practice;(c) feel able to manage instructional stressors/challenges;(d) have commitment to initiate/sustain change;and (e) perceive adequate support in undertaking change. Profiles of instructional readiness for change are represented by composite cases named Lee, Pat, and Chris. In the case of Lee, factor (c) drove change efforts;for Pat, factors (a) and (b) were in the forefront;and for Chris it was factors (d) and (e). The three cases are valuable both as sketches of the blended model in use and as touchstones for future research and development related to postsecondary faculty professional learning. © 2021 The Author(s). Published with license by Taylor and Francis Group, LLC.

关键词： commitment Faculty professional development readiness support

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Air Particulate Matters Auto-Rule-Based Labeling to Support Long-Distance Run Environment Data Classification

Air Particulate Matters Auto-Rule-Based Labeling to Support ...

引用

International Conference on Computer Applications and Industrial Electronics (ICCAIE)

作者： Wandy Wandy Kusworo Adi Media Anugerah Ayu Doctoral Program of Information Systems School of Postgraduate Studies Diponegoro University Semarang Indonesia Department of Physics Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Science Faculty of Engineering and Technology Sampoerna University South Jakarta Indonesia

Sports science is an interdisciplinary and multidisciplinary science that strives to increase athletic performance and endurance. Sport science recognizes and prevents injuries. Sensors and statistics formalize Sports science. Runners need coaches and teams to support them before, during, and after the run race. Coaches generate running training plans to boost performance. Running race performances may be impacted by air pollution exposure while training, so coaches should consider limiting air pollution exposure when training. One of the external factors is Particulate Matter (PM 2.5 and PM 10 ). Sensors connecting to the Internet can record external factors and produce csv data. The foundation of supervised machine learning is the labeling process. Labeling a set of data is one of the laborious and time-consuming phases in every machine-learning application because it requires verifying the accuracy of the labels and making any necessary revisions. This research aimed to find a solution to automatically label numerous air particulate matter raw data using a rule based on parameters to reduce manual work, human errors and faster processes. This labeled data will later be used for supervised machine learning classification to support the coach in generating training programs for the runners in a Sports Information System. Based on Indonesia Air Quality index rule-based approach, labeled text data in csv has been generated and tested with PM 2.5 and PM 10 parameters in three scenarios with a 100% success rate. It was possible to automate the labeling process, and it explained how automation results in fast and accurate results.

关键词：

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Atmospheric dynamics and early warning system low level windshear for airport runway hazard mitigations 10

Atmospheric dynamics and early warning system low level wind...

引用

10th International Seminar on New Paradigm and Innovation of Natural sciences and itsApplication, ISNPINSA 2020

作者： Gernowo, R. Subagio, A. Adi, K. Widodo, A.P. Widodo, C.E. Putranto, A.B. Department of Physics Faculty of Science and Mathematics Diponegoro University Indonesia Department of Informatics Faculty of Science and Mathematics Diponegoro University Indonesia Study Program of Automation Engineering Technology Vocational School Diponegoro University Jl. Prof. Soedarto SH. Tembalang Semarang50275 Indonesia

Natural disaster nowadays often occur in Indonesia due to climate change, especially those related to atmospheric dynamics anomalies. The statutory event of atmospheric turbulence statistically is quite significant in its improvement. This is an important issue to conduct research both in the analysis of atmospheric dynamics and early detection solutions for these events. The Windshear phenomenon is an atmospheric turbulence disaster that occurs below an altitude of 3000 feet, often known as low level windshear (LLWS), which is very dangerous for planes taking take-off or landing. Its effect on the aircraft will result in increased airflow on the wings, so this increased airflow will result in a sudden increase in aircraft speed. If the pilot is not aware of the indication of the windshear, he will instinctively throttle back to reduce/compensate for the aircraft's speed. However, once the plane passes through the wind shear zone, the wind suddenly turns into a downdraft. The design of windshear early detection devices have been developed to complement research on the dynamics of the atmosphere. The results obtained are anomalous changes in temperature and atmospheric pressure at this time, and the windshear sensor can detect it well. © Published under licence by IOP Publishing Ltd.

关键词： Aircraft

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Antithetic Multilevel Methods for Elliptic and Hypo-Elliptic Diffusions with Applications

arXiv

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arXiv 2024年

作者： Iguchi, Yuga Jasra, Ajay Maama, Mohamed Beskos, Alexandros Department of Statistical Science University College London LondonWC1E 6BT United Kingdom School of Data Science The Chinese University of Hong Kong Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this paper we present a new antithetic multilevel Monte Carlo (MLMC) method for the estimation of expectations with respect to laws of diffusion processes that can be elliptic or hypo-elliptic. In particular, we consider the case where one has to resort to time discretization of the diffusion and numerical simulation of such schemes. Motivated by recent developments, we introduce a new MLMC estimator of expectations, which does not require simulation of intractable Lévy areas but has a weak error of order 2 and achieves the optimal computational complexity. We then show how this approach can be used in the context of the filtering problem associated to partially observed diffusions with discrete time observations. We illustrate with numerical simulations that our new approaches provide efficiency gains for several problems relative to some existing methods. © 2024, CC BY.

关键词： Diffusion

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Dynamic programming Algorithm using Furniture Industry 0/1 Knapsack Problem 5

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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5th International Conference on Cybernetics and Intelligent Systems, ICORIS 2023

作者： Anditta, Mietha Amartya, Natashya Warnars, Laurens Spits Warnars, Harco Leslie Hendric Spits Ramadhan, Arief Siswanto, Teddy Mantoro, Teddy Noordin, Nurulhuda Bina Nusantara University School of Computer Science Computer Science Departement Jakarta11530 Indonesia Bina Nusantara University School of Information Systems Information Systems Departement Jakarta11530 Indonesia Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Departement Jakarta11530 Indonesia Telkom University Pjj Informatika Bandung40257 Indonesia Trisakti University Information Systems Department Jakarta11440 Indonesia Sampoerna University Faculty of Engineering and Technology Jakarta12760 Indonesia Universiti Teknologi Mara School of Computing Sciences College of Computing Informatics and Mathematics Selangor Malaysia

ISBN: (纸本)9798350369489

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset. © 2023 IEEE.

关键词： Dynamic programming

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