Advanced Scientific Research Articles

A Novel Noise-Aware Classical Optimizer for Variational Quantum Algorithms

A key component of variational quantum algorithms (VQAs) is the choice of classical optimizer employed to update the parameterization of an ansatz. It is well recognized that quantum algorithms will, for the foreseeable future, necessarily be run on noisy devices with limited fidelities. Thus, the evaluation of an objective function (e.g., the guiding function in the quantum approximate optimization algorithm (QAOA) or the expectation of the electronic Hamiltonian in variational quantum eigensolver (VQE)) required by a classical optimizer is subject not only to stochastic error from estimating an expected value but also to error resulting from intermittent hardware noise. Model-based derivative-free optimization methods have emerged as popular choices of a classical optimizer in the noisy VQA setting, based on empirical studies. However, these optimization methods were not explicitly designed with the consideration of noise. In this work we adapt recent developments from the “noise-aware numerical optimization” literature to these commonly used derivative-free model-based methods. We introduce the key defining characteristics of these novel noise-aware derivative-free model-based methods that separate them from standard model-based methods. We study an implementation of such noise-aware derivative-free model-based methods and compare its performance on demonstrative VQA simulations to classical solvers packaged in scikit-quant. History: Accepted by Giacomo Nannicini, Area Editor for Quantum Computing and Operations Research. Accepted for Special Issue. Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers and the Office of Advanced Scientific Computing Research, Accelerated Research for Quantum Computing program under contract number DE-AC02-06CH11357. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0177 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0177 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Islamic Civilizations from the Lense of Advanced Scientific Legacies: Insights from Kitab Tabaqat al-Umam

Islamic Civilizations from the Lense of Advanced Scientific Legacies: Insights from Kitab Tabaqat al-Umam

Perspectives on AI Architectures and Codesign for Earth System Predictability

Abstract Recently, the U.S. Department of Energy (DOE), Office of Science, Biological and Environmental Research (BER), and Advanced Scientific Computing Research (ASCR) programs organized and held the Artificial Intelligence for Earth System Predictability (AI4ESP) workshop series. From this workshop, a critical conclusion that the DOE BER and ASCR community came to is the requirement to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence (AI) across the field, laboratory, modeling, and analysis activities, called model experimentation (ModEx). BER’s ModEx is an iterative approach that enables process models to generate hypotheses. The developed hypotheses inform field and laboratory efforts to collect measurement and observation data, which are subsequently used to parameterize, drive, and test model (e.g., process based) predictions. A total of 17 technical sessions were held in this AI4ESP workshop series. This paper discusses the topic of the AI Architectures and Codesign session and associated outcomes. The AI Architectures and Codesign session included two invited talks, two plenary discussion panels, and three breakout rooms that covered specific topics, including 1) DOE high-performance computing (HPC) systems, 2) cloud HPC systems, and 3) edge computing and Internet of Things (IoT). We also provide forward-looking ideas and perspectives on potential research in this codesign area that can be achieved by synergies with the other 16 session topics. These ideas include topics such as 1) reimagining codesign, 2) data acquisition to distribution, 3) heterogeneous HPC solutions for integration of AI/ML and other data analytics like uncertainty quantification with Earth system modeling and simulation, and 4) AI-enabled sensor integration into Earth system measurements and observations. Such perspectives are a distinguishing aspect of this paper. Significance Statement This study aims to provide perspectives on AI architectures and codesign approaches for Earth system predictability. Such visionary perspectives are essential because AI-enabled model-data integration has shown promise in improving predictions associated with climate change, perturbations, and extreme events. Our forward-looking ideas guide what is next in codesign to enhance Earth system models, observations, and theory using state-of-the-art and futuristic computational infrastructure.

Learning Symbolic Expressions: Mixed-Integer Formulations, Cuts, and Heuristics

In this paper, we consider the problem of learning a regression function without assuming its functional form. This problem is referred to as symbolic regression. An expression tree is typically used to represent a solution function, which is determined by assigning operators and operands to the nodes. Cozad and Sahinidis propose a nonconvex mixed-integer nonlinear program (MINLP), in which binary variables are used to assign operators and nonlinear expressions are used to propagate data values through nonlinear operators, such as square, square root, and exponential. We extend this formulation by adding new cuts that improve the solution of this challenging MINLP. We also propose a heuristic that iteratively builds an expression tree by solving a restricted MINLP. We perform computational experiments and compare our approach with a mixed-integer program–based method and a neural network–based method from the literature. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This work was supported by the Applied Mathematics activity within the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research [Grant DE-AC02-06CH11357]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0050 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0050 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

A Fast Temporal Decomposition Procedure for Long-Horizon Nonlinear Dynamic Programming

We propose a fast temporal decomposition procedure for solving long-horizon nonlinear dynamic programs. The core of the procedure is sequential quadratic programming (SQP) that utilizes a differentiable exact augmented Lagrangian as the merit function. Within each SQP iteration, we approximately solve the Newton system using an overlapping temporal decomposition strategy. We show that the approximate search direction is still a descent direction of the augmented Lagrangian provided the overlap size and penalty parameters are suitably chosen, which allows us to establish the global convergence. Moreover, we show that a unit step size is accepted locally for the approximate search direction and further establish a uniform, local linear convergence over stages. This local convergence rate matches the rate of the recent Schwarz scheme (Na et al. 2022). However, the Schwarz scheme has to solve nonlinear subproblems to optimality in each iteration, whereas we only perform a single Newton step instead. Numerical experiments validate our theories and demonstrate the superiority of our method. Funding: This work was supported by the National Science Foundation [Grant CNS-1545046] and the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research [Grant DE-AC02-06CH11347].

PASC23 Scales to New Heights, June 26--28

The Platform for Advanced Scientific Computing (PASC) Conference, co-sponsored by SIGHPC and the Swiss National Supercomputing Centre (CSCS), takes place from June 26 to 28, 2023 at the Congress Center Davos, located in Davos, Switzerland. This year's theme is Computing Across Scales, Domains, and Communities.

Synergies Between Operations Research and Quantum Information Science

This article highlights synergies between quantum information science (QIS) and operations research for QIS-curious operations researchers (and vice versa), and the author challenges the community to discover more. History: This “Challenge” paper was invited by the Editor in Chief and based on the topics raised by the author at his plenary address at the 2022 INFORMS Computing Society Conference in Tampa, Florida. Funding: This material is based on work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Accelerated Research in Quantum Computing, Fundamental Algorithmic Research for Quantum Computing. This article has been authored by an employee of National Technology & Engineering Solutions of Sandia, LLC, under Contract DE-NA0003525 with the U.S. Department of Energy (DOE).

First person – Pavitra Prakash

ABSTRACTFirst Person is a series of interviews with the first authors of a selection of papers published in Disease Models & Mechanisms, helping early-career researchers promote themselves alongside their papers. Pavitra Prakash is first author on ‘ Hsp40 overexpression in pacemaker neurons delays circadian dysfunction in a Drosophila model of Huntington's disease’, published in DMM. Pavitra is a PhD student in the lab of Dr Sheeba Vasu at Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore, India, investigating the bi-directional relationship between circadian health and neurodegenerative diseases.

The Contribution Rate of Advanced Scientific and Technological Progress and the Internal Circulation of China's Economy

This study observes the impact of China's advanced scientific and technological progress contribution rate on China's internal circular economy from 1958 to 2020, and formulates China's appropriate economic policies. Firstly, the method of taking logarithm by difference is used to make the variables studied stable, the VAR model is established, and the stability of the model is tested. Then, this study uses the Granger causality test to evaluate the relationship between the contribution rate of advanced science and technology, consumption, social fixed investment, and macroeconomic climate index. The impulse response function shows that in the long run, the contribution rate of advanced scientific and technological progress will not only promote the improvement of consumption but also improve the macroeconomic climate index and social fixed asset investment. Variance decomposition shows that with time, the interaction between consumption and the contribution rate of advanced scientific and technological progress gradually deepens, and the influence of other variables is relatively stable. Therefore, China needs to increase its investment in scientific research and improve the contribution rate of advanced scientific and technological progress, which will help promote China's implementation of the internal circular economy strategy.

Discussion on Research Mode of Advanced Scientific and Technological Innovation

Discussion on Research Mode of Advanced Scientific and Technological Innovation

Engineering the Quantum Scientific Computing Open User Testbed

The Quantum Scientific Computing Open User Testbed (QSCOUT) at Sandia National Laboratories is a trapped-ion qubit system designed to evaluate the potential of near-term quantum hardware in scientific computing applications for the US Department of Energy (DOE) and its Advanced Scientific Computing Research (ASCR) program. Similar to commercially available platforms, most of which are based on superconducting qubits, it offers quantum hardware that researchers can use to perform quantum algorithms, investigate noise properties unique to quantum systems, and test novel ideas that will be useful for larger and more powerful systems in the future. However, unlike most other quantum computing testbeds, QSCOUT uses trapped $^{171}$Yb$^{+}$ ions as the qubits, provides full connectivity between qubits, and allows both quantum circuit and low-level pulse control access to study new modes of programming and optimization. The purpose of this manuscript is to provide users and the general community with details of the QSCOUT hardware and its interface, enabling them to take maximum advantage of its capabilities.

A review of metabolic syndrome parameters: Key players in aging pathogenesis

Metabolic syndrome represents a cluster of metabolic abnormalities (e.g., central obesity, hypertension, dyslipidemia, atherogenic, and insulin resistance), and is strongly linked to an enhanced risk for developing atherosclerotic, diabetes and nonatherosclerotic cardiovascular disease. To determine to what extent metabolic syndrome parameters are involved in the pathogenesis, and progression of aging. The scientific literature (Scopus, Google scholar, and PubMed) was searched from 1980 and 2020. The relationship among metabolic syndrome profiles and the aging is emerging as a public health question of importance to primary care practitioners. A number of studies have suggested a link between metabolic syndrome parameters with aging. Therefore, further investigations on the parameters of metabolic syndrome may be involved in the pathogenesis of aging. This review emphasizes the studies investigating the roles of metabolic syndrome, oxidative stress, inflammation, dyslipidemia, insulin resistance, overweight, high blood pressure, cardiovascular, type 2 diabetes in aging. © 2020, Advanced Scientific Research. All rights reserved.

Emerging Nanomaterials for Light‐Driven Reactions: Past, Present, and Future

Emerging Nanomaterials for Light‐Driven Reactions: Past, Present, and Future

Advancing Fusion with Machine Learning Research Needs Workshop Report

Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.

Analysis of Covid-19 Cases in the Philippines Using Sigmoidal-type Models

In this paper, a new method of curve-fitting analysis is introduced, and applied to several sigmoidal-type models in order to come up with an estimate of the accumulated number of COVID-19 positive cases in the Philippines The best fit model is obtained using a new criterion and will then be utilized for forward predictions of COVID-19 cases in the Philippines © 2020, Institute of Advanced Scientific Research, Inc All rights reserved

Achieving successful pulpal anesthesia during endodontic treatment of mandibular molars: A systematic review and meta-analysis

Background and aim: A study that show useful and secure intervention to attain pulpal anesthesia patients undergoing nonsurgical endodontic treatment for mandibular molars with symptomatic irreversible pulpitis, it is more than necessary and important, so the aim of this systematic review and meta-analysis was achieving successful pulpal anesthesia during endodontic treatment of mandibular molars. Method: MEDLINE, PubMed, Cochrane Library, Embase, ISI, google scholar were used as electronic databases to perform a systematic literature between 2015 to 2019. A commercially available software program (Endnote X9) was used for electronic title management. Searches were performed with keywords, “Endodontic”, “Endodontic treatment;”,” irreversible pulpitiss”,” pulpitiss”, “local anesthetics” “Pulpal”,“Anesthesia Strategies”, “Anesthesia”, “Mandibular Molar”,” Symptomatic Irreversible Pulpitis”. Result: A total of 151 potentially relevant titles and abstracts were found during the electronic and manual search. Finally, a total of 13 publications fulfilled the inclusion criteria required for this systematic review. Comparing intervention groups with each other using 2 lidocaine with 1:100,000 epinephrine was the least effective intervention to achieve pulpal anesthesia 1.095 (95 CI: 1.046 – 1.147) with Heterogeneity found (I2 = 28.79; P = 0.000). Conclusion: Using 2 lidocaine with 1:100,000 epinephrine was the least effective intervention to achieve pulpal anesthesia. © 2020, Advanced Scientific Research. All rights reserved.

Comparative Study of the Tocolytic Effects of Magnesium Sulfate and Indomethacin on Pregnancy Duration and Neonatal Outcomes in Preterm Labor: A Clinical Trial

Introduction: Preterm labor is still one of the most serious complications of pregnancy. Many drugs have been used to control preterm labor, but their definitive effect is not known. Hence, this study was conducted to compare the tocolytic effects of magnesium sulfate and indomethacin on pregnancy duration and neonatal outcomes in preterm labor. Methodology: The present study was a randomized clinical trial study conducted at Kamali Hospital of Alborz Province. Research samples included 211 mothers with singleton pregnancy, healthy amniotic membrane, gestational age over 25 and less than 32 weeks, regular uterine activity as contraction in 20 minutes or cervical changes of 1 cm/h or effacement equal or more than 80. Sampling was performed using convenience and randomized method through a random number table. Accordingly, both groups received the initial neuroprotection of magnesium sulfate at doses of 6 mg. Then, subjects of one group received 50 mg of indomethacin suppository at 8-hour intervals so that they could finally receive 4 doses. The subjects of another group received magnesium sulfate 2 g/h as intravenous infusion for at least 12 hours after discontinuation of contraction. Then, two groups were compared in terms of difference of gestational age at hospitalization and discharge times, neonatal Apgar score, and rate of neonatal hospitalization in ICU though SPSS software. Results: The results of independent t-test showed that there was no significant differences between two groups in terms of gestational age at discharge and hospitalization times (p <0.8). Also, the rate of neonatal hospitalization in sulfate group was lower than that of the indomethacin group and this difference was statistically significant (p <0.03). The most common cause of hospitalization in ICU was respiratory distress that was higher in the indomethacin group. Conclusion: Magnesium sulfate reduced neonatal hospitalization in ICU compared to indomethacin. The use of tocolytic magnesium sulfate and indomethacin increased the duration of pregnancy, but no difference was seen between these tocolytics. © 2020, Advanced Scientific Research. All rights reserved.

The Effect of Economic Sanctions on Treatment of Thalassemia Major Referring to Selected Medical Centers in Fars Province

Background: In the list of sanctions, as a destructive political tool against some countries, drug trade is not officially prohibited, but according to existing reports, patients in sanctioned countries are having difficulty obtaining the drugs they need in practice. The purpose of this study was to investigate the effect of US and EU economic sanctions against Iran on the treatment of thalassemia major patients in Iran. Methods: To achieve the objectives of the study, 249 major thalassemia patients referred to five treatment centers in Fars province (August, 2019), who were treated with Deferasirox oral medications (Osveral or Exjade) and injectable forms (desferal), were studied and the type of medication used, their access to these drugs, and serum ferritin levels before and one year after the last economic sanction, were recorded in a Demographic information form. Data were analyzed by SPSS software using Kappa coefficient. Results: The results of the study showed that after the latest Iranian sanction, the types of medications used changed from foreign and injectable (Desferal and Exjade) to Iranian medication (Osvral), but serum ferritin levels did not change significantly. Conclusions: During the economic sanctions, the use of Iranian oral iron repellent drugs could reduce the health system’s budget for the annual cost of treating thalassemia major patients. © 2020, Advanced Scientific Research. All rights reserved.

Determinants of Adolescent First Births in Indonesia

Adolescent childbirth remains a central issue for lower-middle-income countries such as Indonesia. The available literature suggests that this is associated with several adverse outcomes, including health and socioeconomic factors. This study aimed to evaluate determinants that correlate with mothers in Indonesia who first gave birth when they were adolescents. This study is a cross-sectional and examined data collected by Indonesia Demographic and Health Survey (IDHS) in 2017. We investigated the variables of educational level, wealth index, residence, and access to media (frequency of reading newspaper or magazine, listening to the radio, watching television, using the internet) and their correlation with a weighted sample of women who had their first live birth when they were younger than 19 years old. We used multivariate logistic regression to adjust for confounding factors and to analyze the determinants of adolescent first birth. Education (AOR=15.39; 95 CI=11.13-21.26), wealth index (AOR=1.22; 95 CI=1.08-1.39), place of residence (AOR=1.23; 95 CI=1.13-1.34), frequency of reading newspaper or magazine (AOR=1.31; 95 CI=1.11-1.55), and frequency of using internet (AOR=1.30; 95 CI=1.02-1.65) were significant determinants of.adolescent first births in Indonesia. This study recommends better access to education for poor and lower educated young women to be prioritized. Policies and programs that support the wellbeing of adolescents to achieve the best outcome of health reproduction are needed. © Advanced Scientific Research. All rights reserved.

Factors Associated with the Use of Contraception among Women Age 15-24 Years in Indonesia

Contraception use among women younger than 24 years old remains deficient in Indonesia. This study analyzes factors associated with the use of contraception in women of 15-24 years old in Indonesia. This study examined data were extracted from the 2017 Indonesian Demographic and Health Survey. A cross-sectional design was used as an approach to analyze data of 3,876 married women in their 15-24 years of age and their use of contraception. Chi-square test and binary logistic regression were employed to evaluate the predictors of contraceptive use. Our investigation found that the use of contraception at the time of the survey was 57.5. The findings in this study indicate that mother's age 15-19 years AOR = 1.58; 95% CI = 1.10-2.26, higher education mother AOR=5.37; 95% CI = 1.84-15.65, number of children more than three AOR = 25.58; 95% CI = 11.19-58.47, and territory in western Indonesia AOR = 2.78; 95% CI = 1.91-4.05 are significantly correlated with contraceptive use among the women. Thus, this suggests that the use of contraception among women aged 15-24 years in Indonesia is related to age, education, number of children in the family, and region of residence. This study recommends that a multisectoral approach is needed to solve this problem by taking into account uneducated women who live in central and eastern Indonesia. The need for more comprehensive education related to the use of contraception for pregnancy planning and the number of children by involving the husband as a decision-maker in the family. © Advanced Scientific Research. All rights reserved.