Article
Multidisciplinary Sciences
Zixi Hu, Jeffrey J. Donatelli, James A. Sethian
Summary: The coefficients for translational and rotational diffusion characterize Brownian motion of particles. The paper proposes a new approach for predicting rotational diffusion coefficient using angular-temporal cross-correlation analysis of XPCS data, which has been verified across a range of simulated data. This method is applicable beyond the specific wavelengths used in the experiments.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2021)
Article
Instruments & Instrumentation
Tao Lei, Yonggen Tan, Zheng Zhang, Siyuan Chen, Jun Feng
Summary: This study describes an optical design for a beamline proposed for the Shenzhen Light Source. The beamline covers an energy range of 2.0 to 20 keV and includes two experimental stations for X-ray absorption spectroscopy (XAS) and X-ray photon correlation spectroscopy (XPCS). The beamline utilizes a 4 m planar undulator as the radiation source, with horizontally and vertically focusing mirrors to enhance flexibility. The design achieves excellent coherence performance for XPCS experiments, with energy resolution better than 1.47 x 10-4 and total flux larger than 1 x 1014 photons/s.
NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT
(2023)
Article
Orthopedics
B. D. Partain, Q. Zhang, M. Unni, J. Aldrich, C. M. Rinaldi-Ramos, S. Narayanan, K. D. Allen
Summary: This study uses X-ray photon correlation spectroscopy (XPCS) to evaluate dynamic interactions between intact cartilage and biofluids at the nanometer scale. Results show that cartilage ECM mobility varies with length scale and fluid environment, demonstrating the importance of biosolid-biofluid interactions in dictating ECM dynamics. XPCS can provide unique insights into spatially resolved cartilage ECM mobility.
OSTEOARTHRITIS AND CARTILAGE
(2021)
Article
Chemistry, Multidisciplinary
Sonja Timmermann, Vladimir Starostin, Anita Girelli, Anastasia Ragulskaya, Hendrik Rahmann, Mario Reiser, Nafisa Begam, Lisa Randolph, Michael Sprung, Fabian Westermeier, Fajun Zhang, Frank Schreiber, Christian Gutt
Summary: Machine learning methods are used to automatically classify X-ray photon correlation maps from protein solution experiments. The correlation maps are matched with simulated maps of liquid-liquid phase separations and interpreted in the simulation framework. This method facilitates the handling of large amounts of dynamic data.
JOURNAL OF APPLIED CRYSTALLOGRAPHY
(2022)
Article
Materials Science, Multidisciplinary
Shaswat Mohanty, Christopher B. Cooper, Hui Wang, Mengning Liang, Wei Cai
Summary: X-ray photon correlation spectroscopy (XPCS) and X-ray speckle visibility spectroscopy (XSVS) are two methods used to study dynamic processes in materials. Interpretation of the data is important to establish the connection between macroscopic responses and microstructural dynamics, and a computational framework is presented to model these experiments. The efficiency and accuracy of two computational methods are compared, and the computed X-ray speckle patterns capture density fluctuations and show agreement with experimental results.
MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING
(2022)
Article
Oncology
Mahbubur Rahman, M. Ramish Ashraf, David J. Gladstone, Petr Bruza, Lesley A. Jarvis, Philip E. Schaner, Xu Cao, Brian W. Pogue, P. Jack Hoopes, Rongxiao Zhang
Summary: This study presents a Monte Carlo beam model and its implementation in a clinical treatment planning system for a modified ultrahigh dose-rate electron FLASH radiation therapy linear accelerator. The accuracy of the beam model was validated, and dose delivery was demonstrated through forward calculation. The results showed that the modified radiation therapy plan had comparable plan quality to conventional therapy.
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
(2022)
Article
Chemistry, Multidisciplinary
Theyencheri Narayanan, Michael Sztucki, Thomas Zinn, Jerome Kieffer, Alejandro Homs-Puron, Jacques Gorini, Pierre Van Vaerenbergh, Peter Boesecke
Summary: This article describes the new technical features and enhanced performance of the ID02 beamline with the Extremely Brilliant Source (EBS) at the ESRF. The beamline enables static and kinetic investigations of a broad range of systems in different size scales and time ranges. Additionally, it allows for multispeckle X-ray photon correlation spectroscopy measurements and relaxation of collimation conditions, resulting in higher flux throughput and lower background. The article highlights the importance of these developments in enabling structural, dynamic, and kinetic investigations of out-of-equilibrium soft matter and biophysical systems.
JOURNAL OF APPLIED CRYSTALLOGRAPHY
(2022)
Article
Optics
Xin Zhou, Jianfeng Sun, Zhigang Fan, Sining Li, Wei Lu, Hailong Zhang
Summary: This paper proposes a scheme combining speckle suppression, adaptive adjustment of aperture diameter, and spatial correlation method to improve the detection performance of flash array imaging LiDAR under daylight conditions. The proposed scheme is verified to effectively improve detection performance in low SNR conditions and experimental results show a significant reduction in speckle contrast and increase in target recovery ratio or signal detection probability. This work is valuable for the effective detection of laser MMF coupling transmitted single-photon LiDAR under daylight conditions.
OPTICS AND LASERS IN ENGINEERING
(2023)
Article
Instruments & Instrumentation
Qingteng Zhang, Eric M. Dufresne, Yasukazu Nakaye, Pete R. Jemian, Takuto Sakumura, Yasutaka Sakuma, Joseph D. Ferrara, Piotr Maj, Asra Hassan, Divya Bahadur, Subramanian Ramakrishnan, Faisal Khan, Sinisa Veseli, Alec R. Sandy, Nicholas Schwarz, Suresh Narayanan
Summary: The performance of the new 52 kHz frame rate Rigaku XSPA-500k detector for X-ray photon correlation spectroscopy (XPCS) applications was characterized at the Argonne Advanced Photon Source. A workflow system was deployed to handle the large data flow produced by the detector, allowing for rapid data reduction and providing human-in-the-loop feedback to experimenters. The XSPA-500k detector, software, and DM workflow system enable efficient acquisition and reduction of up to approximately 10^9 area-detector data frames per day, facilitating XPCS measurements on samples with weak scattering and fast dynamics.
JOURNAL OF SYNCHROTRON RADIATION
(2021)
Article
Optics
Meng-Ru Yun, Fu-Qiang Guo, L-L Yan, Erjun Liang, Y. Zhang, S-L Su, C. X. Shan, Yu Jia
Summary: In this study, we propose a scheme for nonadiabatic noncyclic geometric quantum computation in a decoherence-free subspace (DFS) using a cavity quantum electrodynamics system consisting of nitrogen-vacancy (NV) centers in diamond and a whispering-gallery-mode (WGM) microresonator. This scheme combines the features of DFS insensitivity to decoherence and nonadiabatic noncyclic geometric quantum computation, leading to enhanced robustness and computation efficiency.
Article
Chemistry, Analytical
Suganandha Bharathi Jayabarathi, Mani Maran Ratnam
Summary: This study compares the correlation between 3D surface roughness and laser speckle pattern using two different experimental setups. The results show that the setup using a He-Ne laser provides better results.
Article
Multidisciplinary Sciences
Run-Ze Liu, Yu-Kun Qiao, Han-Sen Zhong, Zhen-Xuan Ge, Hui Wang, Tung-Hsun Chung, Chao-Yang Lu, Yong-Heng Huo, Jian-Wei Pan
Summary: Semiconductor quantum dots have demonstrated deterministic photon pair generation with high polarization entanglement fidelity for quantum information applications. However, the limited photon indistinguishability due to temporal correlation hinders their scalability to multi-photon experiments. In this study, by utilizing quantum interferences to decouple polarization entanglement from temporal correlation, the entanglement fidelity of four-photon Greenberger-Horne-Zeilinger (GHZ) state is improved. This work paves the way for realizing scalable and high-quality multi-photon states from quantum dots.
Article
Mathematics
Alexey Penenko, Evgeny Rusin
Summary: This paper focuses on large-scale inverse problems that require high-performance computing, particularly in the field of regional air quality studies. It addresses the identification of emission sources in a 2D advection-diffusion-reaction model using parallel computing techniques. By transforming the source identification problem into a quasi-linear operator equation with a sensitivity operator, the authors were able to work with heterogeneous measurement data and achieve natural parallelization of numeric algorithms. The parallel algorithm implemented with MPI demonstrated a significant speedup in an inverse modeling scenario for the Lake Baikal region.
Article
Cardiac & Cardiovascular Systems
Federico Landra, Giulia Elena Mandoli, Benedetta Chiantini, Maria Barilli, Giacomo Merello, Giuseppe De Carli, Carlotta Sciaccaluga, Matteo Lisi, Filippo Flamigni, Flavio D'Ascenzi, Marta Focardi, Massimo Fineschi, Alessandro Iadanza, Sonia Bernazzali, Massimo Maccherini, Serafina Valente, Matteo Cameli
Summary: This study aimed to investigate the correlation between left ventricular myocardial work indices and invasively-derived left ventricular stroke work index in patients with advanced heart failure. The results showed that left ventricular global constructive work correlated well with invasively-measured stroke work index, potentially serving as a valuable tool for evaluating myocardial function.
FRONTIERS IN CARDIOVASCULAR MEDICINE
(2022)
Article
Chemistry, Multidisciplinary
Seongjae Lee, Taehyoun Kim
Summary: The research proposes a performance acceleration framework for earthquake source parameter estimation using the Monte Carlo method and CUDA GPU, achieving significant speedups. Additionally, four different line search algorithms were evaluated for performance and correctness in the real earthquake dataset, with one algorithm identified as the most efficient among them.
APPLIED SCIENCES-BASEL
(2021)
Article
Engineering, Marine
Mohammad Yazdi, Faisal Khan, Rouzbeh Abbassi
Summary: Microbiologically Influenced Corrosion (MIC) poses a significant challenge to offshore oil and gas facilities, leading to pinholes and leaks. Preventive (coatings, cathodic protection) and mitigative (inhibitor and biocide treatment) actions are crucial for pipeline integrity management. A multi-objective functional methodology involving dynamic continuous Bayesian network modeling and a genetic algorithm is proposed to minimize operational risks associated with MIC.
Article
Engineering, Environmental
Mohammad Zaid Kamil, Faisal Khan, S. Zohra Halim, Paul Amyotte, Salim Ahmed
Summary: This study aims to develop a framework and tools to extract data from an accident database and establish a generalized accident causation model. By integrating Natural Language Processing, Interpretive Structural Model, and probabilistic methods, the model provides insights into accident factors, interactions, and pathways, and can be used for accident prevention strategies.
PROCESS SAFETY AND ENVIRONMENTAL PROTECTION
(2023)
Article
Engineering, Chemical
James Daley, Faisal Khan, Md. Tanjin Amin
Summary: This study proposes a method to assess the probability of process system failure based on operational data and system knowledge using principal component analysis and Bayesian network. The method can determine the probability of system failure once a fault is detected and provides valuable information for process safety.
PROCESS SAFETY PROGRESS
(2023)
Article
Engineering, Industrial
Vindex Domeh, Francis Obeng, Faisal Khan, Neil Bose, Elizabeth Sanli
Summary: Probabilistic safety assessment using the Bayesian network (BN) is a popular method for developing risk analysis tools. However, the subjectivity introduced by using subject-matter experts in eliciting probabilities for conditional probability tables (CPTs) decreases the reliability of the resulting tool. To address this issue, a probability-scoring scale was proposed to assign probabilities to CPTs, ensuring consistent results among different experts. The scale was applied to a BN-based risk -awareness (RAw) tool for monitoring safety aboard small fishing vessels (SFV), benefiting SFV owners and operators, the commercial fishing industry, and maritime administrations.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Public, Environmental & Occupational Health
Mohammad Zaid Kamil, Mohammed Taleb-Berrouane, Faisal Khan, Paul Amyotte, Salim Ahmed
Summary: Underlying information about failure in free text can be used to determine causation in risk assessment. Advanced methodology is needed to identify the features in natural language expression. This study addresses the knowledge gap by extracting relevant features from textual data to develop cause-effect scenarios. The proposed methodology applies natural language processing and text-mining techniques to extract features and utilizes them in Bayesian networks for risk assessment.
Article
Computer Science, Interdisciplinary Applications
He Wen, Faisal Khan, Salim Ahmed, Syed Imtiaz, Stratos Pistikopoulos
Summary: Human-automation conflict is a frontier subject that needs to be vigilant against, especially under cyberattacks. This study transforms common attacks into understandable representations and explores the conflict under five generalized attacks using game theory. The results show that cyberattacks can significantly cause conflicts, and the control actions can buffer the impact of attacks within a limited range. The conflict risk can be used to distinguish faults and attacks, and appropriate measures can be taken accordingly.
COMPUTERS & CHEMICAL ENGINEERING
(2023)
Article
Engineering, Chemical
He Wen, Md. Tanjin Amin, Faisal Khan, Salim Ahmed, Syed Imtiaz, Efstratios Pistikopoulos
Summary: The conflict between human and artificial intelligence is a critical issue in Process System Engineering. This study proposes a novel methodology to quantify interpretation conflict probability and risk. The results show that interpretation conflict is often hidden or mixed with traditional faults and noises, which can easily be triggered by sensor faults, logic errors, cyberattacks, human mistakes, and misunderstandings.
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
(2023)
Article
Engineering, Marine
Adhitya Ramadhani, Faisal Khan, Bruce Colbourne, Salim Ahmed, Mohammed Taleb-Berrouane
Summary: Offshore structures such as oil platforms are affected by significant environmental loads. The complexity of the offshore environment requires robust models to capture the dependencies among environmental variables. Vine copula models, using both symmetric and asymmetric copula functions, are shown to provide a better estimation of the total environmental load on offshore structures compared to traditional methods. The results have implications for probabilistic structural analysis and design of offshore structures.
Article
Chemistry, Multidisciplinary
Mohammad Asif, Faisal Khan, Kelly Hawboldt, Shams Anwar
Summary: This work uses density functional theory to investigate the mechanism of H2S adsorption/dissociation on the Cr-doped Fe(100) surface. It is found that H2S is weakly adsorbed on Cr-doped Fe, but the dissociated products are strongly chemisorbed. The dissociation of HS is more favorable on Fe compared to Cr-doped Fe. This study also reveals that H2S dissociation is a kinetically facile process and hydrogen diffusion follows a tortuous path. It contributes to a better understanding of sulfide corrosion mechanism and its impact, thus aiding the design of efficient corrosion prevention coatings.
Article
Engineering, Chemical
He Wen, Faisal Khan
Summary: In recent years, the increase in cyber-connected industrial control systems (ICS) has raised cyber and process risks, highlighting the need for an integrated study on cybersecurity and process safety. This study analyzes cyber incidents related to ICS since 1990 and connects them with process accidents using the Bowtie method and the ATT&CK framework. It develops a Bayesian network to account for insignificant probabilities and confirms the vulnerability of the process industry to cyberattacks, with field controllers being the main targets. The study emphasizes the criticality of the safety instrument system (SIS) and the importance of dynamic threat assessment and neutralizing strategies.
CANADIAN JOURNAL OF CHEMICAL ENGINEERING
(2023)
Article
Engineering, Chemical
Stewart W. Behie, Hans J. Pasman, Syeda Zohra Halim, Kathy Shell, Ahmed Hamdy El-Kady, Faisal Khan
Summary: This article discusses the importance of safety systems in high-risk businesses and the necessity of long-term impact for cross-functional programs. The article also emphasizes the core principles and beliefs needed to make necessary adjustments and ensure continued success in all circumstances.
JOURNAL OF LOSS PREVENTION IN THE PROCESS INDUSTRIES
(2023)
Article
Engineering, Marine
Vindex Domeh, Francis Obeng, Faisal Khan, Neil Bose, Elizabeth Sanli
Summary: A quantitative risk analysis approach is proposed to develop a tool for studying loss of stability aboard small fishing vessels. The tool, which incorporates Bayesian network and De Morgan gates, provides a probabilistic assessment of the risk factors responsible for loss of stability occurrence. It is an innovative tool that can proactively ensure the stability of small fishing vessels.
Article
Engineering, Industrial
Uyen Dao, Zaman Sajid, Faisal Khan, Yahui Zhang
Summary: This paper develops a dynamic model to study the impact of corrosion on pipeline equipment failure. It uses a Bayesian network model to understand different risk factors and their interdependencies, transforming the corrosion mechanism into a probabilistic framework. The results are consistent with industrial practices and are crucial for the inspection and maintenance schedule of corroded pipelines.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Transportation Science & Technology
A. Sardar, M. Anantharaman, V. Garaniya, F. Khan
Summary: The maritime industry is essential to the global economy, handling about 90% of worldwide trade and employing over a million seafarers. However, the industry faces a high number of casualties due to human errors in decision-making. This study proposes an AI-based approach, using an Ant Colony Optimization algorithm, to design and validate standardized instructions for daily operational tasks. This solution can optimize task paths, provide clear instructions, and reduce human errors, contributing to improved efficiency in the maritime industry.
TRANSNAV-INTERNATIONAL JOURNAL ON MARINE NAVIGATION AND SAFETY OF SEA TRANSPORTATION
(2023)
Article
Engineering, Industrial
Md. Tanjin Amin, Giordano Emrys Scarponi, Valerio Cozzani, Faisal Khan
Summary: This article examines the effectiveness and accuracy of threshold-based and probit-based methods in assessing domino effects. The results indicate that threshold-based methods are not suitable for quantitative assessment, and there are limitations in probit-based methods for time-dependent domino effect assessment. By utilizing site-specific structural response data and data analytics, a new improved time to failure prediction model is proposed, which demonstrates better performance compared to existing models.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
