scispace - formally typeset
Search or ask a question

Showing papers by "Lehigh University published in 2022"


Journal ArticleDOI
TL;DR: In this paper, a probabilistic assessment of the sea-level rise hazard is performed using the available data and climate models considering several climate change emission scenarios, considering the uncertainties associated with fault movement (i.e., rake angles and average stress drops).

20 citations


Journal ArticleDOI
TL;DR: The art of formulating and solving a class of stochastic resource-constrained scheduling problems for elective surgery scheduling and downstream capacity planning is described and areas of opportunity for developing tractable, implementable, and data-driven approaches that might be applicable within and outside healthcare operations.

18 citations


Journal ArticleDOI
Wenting Cheng1
TL;DR: In this article , the authors conducted a systematic review using the following key concepts "children under five years of age" and "pneumonia" and hypoxaemia" by searching in 11 bibliographic databases and citation indices and included all articles published between Nov 1, 2008, and Oct 8, 2021, based on observational studies and control arms of randomised and non-randomised controlled trials.

17 citations


Journal ArticleDOI
TL;DR: In this article, the parameters of cumulative prospect theory (CPT) were calibrated through a survey among students and practicing engineers in the field of structural engineering, and the fitted model was applied to life-cycle maintenance of a steel girder bridge.

16 citations


Journal ArticleDOI
TL;DR: In this article , a comparative all-atom molecular dynamics simulation study of 18 biomembrane systems with lipid compositions corresponding to eukaryotic, bacterial, and archaebacterial membranes together with three single-component lipid bilayers is presented.
Abstract: We present a comparative all-atom molecular dynamics simulation study of 18 biomembrane systems with lipid compositions corresponding to eukaryotic, bacterial, and archaebacterial membranes together with three single-component lipid bilayers. A total of 105 lipid types used in this study include diverse sterols and glycerol-based lipids with acyl chains of various lengths, unsaturation degrees, and branched or cyclic moieties. Our comparative analysis provides deeper insight into the influences of sterols and lipid unsaturation on the structural and mechanical properties of these biomembranes, including water permeation into the membrane hydrocarbon core. For sterol-containing membranes, sterol fraction is correlated with the membrane thickness, the area compressibility modulus, and lipid order but anticorrelated with the area per lipid and sterol tilt angles. Similarly, for all 18 biomembranes, lipid order is correlated with the membrane thickness and area compressibility modulus. Sterols and lipid unsaturation produce opposite effects on membrane thickness, but only sterols influence water permeation into the membrane. All membrane systems are accessible for public use in CHARMM-GUI Archive. They can be used as templates to expedite future modeling of realistic cell membranes with transmembrane and peripheral membrane proteins to study their structure, dynamics, molecular interactions, and function in a nativelike membrane environment.

16 citations


Journal ArticleDOI
Toko1
TL;DR: In this article , the authors focus on stochastic optimization approaches for elective surgery scheduling and downstream capacity planning, and provide an analysis of existing SO approaches and their challenges, and highlight areas of opportunity for developing tractable, implementable and data-driven approaches that might be applicable within and outside healthcare operations, particularly where multiple entities/jobs share the same downstream limited resources.

13 citations


Journal ArticleDOI
TL;DR: In this article, the authors presented a technique for the probabilistic simulation of power transmission systems under hurricane events and provided fundamental insights on the modeling and quantification of power system performance and resilience.

13 citations


Journal ArticleDOI
TL;DR: In this article , the parameters of cumulative prospect theory (CPT) were calibrated through a survey among students and practicing engineers in the field of structural engineering, and the fitted model was applied to life-cycle maintenance of a steel girder bridge.

12 citations


Journal ArticleDOI
TL;DR: In this paper, a multivariate variational mode decomposition-informed canonical correlation analysis (MVMD-CCA) was proposed to improve the decoding performance of SSVEP patterns.

11 citations


Journal ArticleDOI
TL;DR: A human judgment approach to epidemiological forecasting of COVID-19 outbreak was proposed in this paper . But the human judgment was not used to predict the number of cases of human monkeypox in the United States.

11 citations


Journal ArticleDOI
TL;DR: In this article , a quantitative comparison of two types of feed-forward control for two important examples, a distillation column and a chemical reactor, is presented, where the output signals from the feedback controller and the feedforward controller are added to produce the manipulated variable in the process.

Journal ArticleDOI
TL;DR: A design procedure coupling the influence matrix method and genetic algorithms to optimize stay cables in cable-stayed bridges is presented and is utilized in the preliminary design of a twin towers double-cable planes cable-Stayed bridge to be located in Ferrara, Italy.
Abstract: Structural optimization is an important tool for structural designers that helps them to find innovative design solutions and structural forms with a better exploitation of materials as well as dec...

Journal ArticleDOI
TL;DR: In this article, a neural regression model is proposed for input estimation of nonlinear dynamic systems. But the model is not feasible solutions for problems in which the underlying behavior is not sufficiently known.

Journal ArticleDOI
TL;DR: In this paper , the authors investigate how earthquake rupture heterogeneity and directivity can affect back-projection results (imaged location and beam power) using synthetic earthquake models, and derive an equation based on Doppler theory to relate the wavelength of heterogeneity with seismic frequency.
Abstract: The back projection method is a tremendously powerful technique for investigating the time dependent earthquake source, but its physical interpretation is elusive. We investigate how earthquake rupture heterogeneity and directivity can affect back-projection results (imaged location and beam power) using synthetic earthquake models. Rather than attempting to model the dynamics of any specific real earthquake, we use idealized kinematic rupture models, with constant or varying rupture velocity, peak slip rate, and fault-local strike orientation along unilateral or bilateral rupturing faults, and perform back-projection with the resultant synthetic seismograms. Our experiments show back-projection can track only heterogeneous rupture processes; homogeneous rupture is not resolved in our synthetic experiments. The amplitude of beam power does not necessarily correlate with the amplitude of any specific rupture parameter (e.g., slip rate or rupture velocity) at the back-projected location. Rather, it depends on the spatial heterogeneity around the back-projected rupture front, and is affected by the rupture directivity. A shorter characteristic wavelength of the source heterogeneity or rupture directivity towards the array results in strong beam power in higher frequency. We derive an equation based on Doppler theory to relate the wavelength of heterogeneity with synthetic seismogram frequency. This theoretical relation can explain the frequency- and array-dependent back-projection results not only in our synthetic experiments but also to analyze the 2019 M7.6 bilaterally rupturing New Ireland earthquake. Our study provides a novel perspective to physically interpret back-projection results and to retrieve information about earthquake rupture characteristics.

Journal ArticleDOI
01 Jan 2022
TL;DR: In this article , a conceptual study of transfer learning for molecular property prediction is presented, showing that a large overlap of the underlying features of the two tasks (specifically greater than 50%) is required for transfer learning to improve the model for the target task.
Abstract: Transfer learning is a concept whereby data-driven models can be developed for tasks (e.g. molecular properties) with limited data availability (target task) by sharing information from a related task. In the context of chemical engineering, the two tasks can either pertain to related properties or to the same property calculated or measured in two different ways (with differing accuracies or resolution). Using an ensemble of linear and interpretable models, in this work, we present a conceptual study to explicate when transfer learning can be beneficial. We show that a large overlap of the underlying features of the two tasks (specifically greater than 50%) is required for transfer learning to improve the model for the target task. On the other hand, transferring information (in particular, information regarding salient features) from an uncorrelated task can be detrimental to train a model for the target task. Subsequently, we present three illustrative examples of transfer learning for molecular property prediction and rationalize the usefulness of transferred information based on the inferences from our conceptual studies. This work, thus, provides a simplified analysis of the concept of transfer learning for building molecular property models.

Journal ArticleDOI
TL;DR: In the wake of a disaster, such interdependencies may be disrupted as discussed by the authors, such as hurricanes and floods, and critical infrastructure systems are interdependent to ensure normal operations for supporting a national economy and social well-being.
Abstract: Critical infrastructure systems are interdependent to ensure normal operations for supporting a national economy and social well-being. In the wake of a disaster, such interdependencies may...

Journal ArticleDOI
Sarathi Kalra1
TL;DR: In this paper , the authors examine a representative nanocrystalline BCC refractory MPEA and identify a crossover from a Hall-Petch to inverse-HallPetch relation, which is correlated to dislocation stacking at the grain boundary when dislocation density reaches a maximum.

Journal ArticleDOI
Jue Ma N.A.1
01 Jan 2022
TL;DR: In this article , a facile thermal decomposition method was utilized to synthesize mesoporous Cu-doped MgO nanoparticles, which were shown to be efficient photo-Fenton-like catalysts for degrading emerging pharmaceutical contaminants in wastewater and completely oxidized salicylic acid within 1 h under optimized conditions.
Abstract: A facile thermal decomposition method was utilized to synthesize mesoporous Cu-doped MgO nanoparticles. These Cu-MgO nanoparticles were shown to be efficient photo-Fenton-like catalysts for degrading emerging pharmaceutical contaminants in wastewater and completely oxidized salicylic acid within 1 h under optimized conditions. Tetracycline was shown to be converted to other intermediates with a large portion of it undergoing full mineralization. Batch experiments were conducted to demonstrate the effects of Cu loading on MgO, overall catalyst loading, and H2O2 concentration on the salicylic acid and tetracycline conversion and rate constants. Quenching experiments revealed that both •OH radicals or HO2•/•O2- radicals were involved in the reaction, with the latter showing a higher contribution. The surface dissolution of MgO was shown to increase solution pH which completely prevented Cu from leaching out while retaining high activity. The catalyst reusability was shown to be satisfactory with high activity and conversion being preserved over five cycles.

Journal ArticleDOI
TL;DR: In this paper, the authors studied the viscoelastic sliding of a rigid cylinder on a visco-elastic half space with a single characteristic retardation time and showed that the total friction force can be decomposed into visco and hydrodynamic components.
Abstract: We study the lubricated sliding of a rigid cylinder on a viscoelastic half space with a single characteristic retardation time. Besides the generalized inverse Hersey number $$\beta$$ , which is the sole parameter governing elastic lubrication, the viscoelastic lubrication solution depends on two additional dimensionless parameters: $$\alpha$$ and $$f$$ . $$\alpha$$ is the characteristic retardation time divided by the time for the rigid cylinder to move one contact width and f determines the strength of viscoelasticity. We have developed a numerical scheme to solve this viscoelastic lubrication problem. Our numerical results show that the total friction force can be decomposed into viscoelastic and hydrodynamic components. The viscoelastic component of the friction is well approximated by the dry limit in which the liquid layer is all squeezed out and the resistance to sliding is due entirely to viscoelastic dissipation. The hydrodynamic limit is well approximated by a modification of the elastic limit in which friction is due entirely to hydrodynamics. We also study the dependence of the hydrodynamic pressure, film thickness and the friction coefficient on these parameters.

Journal ArticleDOI
Lawrie Balfour1
TL;DR: In this article , a tip-enhanced light nearfield in atomic force microscopy (AFM) operated in tapping and peak force tapping modes is introduced to bypass the optical diffraction limit and improve the sensitivity for mid-IR methods.

Journal ArticleDOI
TL;DR: In this paper , a SiC/graphite FGM specimen has been fabricated using SPS and the interface between the adjacent layers of the sintered specimen exhibits no apparent defects such as gaps or delaminations.

Journal ArticleDOI
danedeniyal1
TL;DR: The authors examined the cognitive and behavioral outcomes of certainty about the future during periods of societal uncertainty and found that future certainty is linked to intellectual blindness and antisocial behaviors during important periods of society uncertainty.

Journal ArticleDOI
TL;DR: In this article , a framework for reformulating and solving optimization problems that generalizes the well-known framework originally introduced by Benders is described, and details of the application of the procedures to several classes of optimization problems under the umbrella of multilevel/multistage mixed integer linear optimization problems are discussed.
Abstract: We describe a framework for reformulating and solving optimization problems that generalizes the well-known framework originally introduced by Benders. We discuss details of the application of the procedures to several classes of optimization problems that fall under the umbrella of multilevel/multistage mixed integer linear optimization problems. The application of this abstract framework to this broad class of problems provides new insights and a broader interpretation of the core ideas, especially as they relate to duality and the value functions of optimization problems that arise in this context.

Journal ArticleDOI
Sera Cremonini1
TL;DR: In this paper , the authors show that higher-derivative corrected black holes may be self-attractive or self-repulsive, depending on the value of the Wilson coefficients and the VEVs of scalar moduli.
Abstract: A bstract In two-derivative theories of gravity coupled to matter, charged black holes are self-attractive at large distances, with the force vanishing at zero temperature. However, in the presence of massless scalar fields and four-derivative corrections, zero-temperature black holes no longer need to obey the no-force condition. In this paper, we show how to calculate the long-range force between such black holes. We develop an efficient method for computing the higher-derivative corrections to the scalar charges when the theory has a shift symmetry, and compute the resulting force in a variety of examples. We find that higher-derivative corrected black holes may be self-attractive or self-repulsive, depending on the value of the Wilson coefficients and the VEVs of scalar moduli. Indeed, we find black hole solutions which are both superextremal and self-attractive. Furthermore, we present examples where no choice of higher-derivative coefficients allows for self-repulsive black hole states in all directions in charge space. This suggests that, unlike the Weak Gravity Conjecture, which may be satisfied by the black hole spectrum alone, the Repulsive Force Conjecture requires additional constraints on the spectrum of charged particles.

Journal ArticleDOI
TL;DR: A vision of including information about chemical transformations in molecule design procedures is presented, highlighting rigorous optimization and machine learning approaches such as generative modeling and reinforcement learning.
Abstract: Computational design of molecules for optimal performance is of interest in many fields, including chemical engineering. Often, however, these methods, in particular those based on rigorous mathematical optimization, do not explicitly take into consideration chemistry information, such as (but not limited to) synthesis feasibility. This opinion article discusses traditional and current approaches through examples from the literature where properties that depend on chemical transformations of the molecule are incorporated in the design process. Through these examples, the article highlights the importance of cheminformatics, graph theory, and machine learning in: (1) representation of the molecules, (2) reaction prediction and generation, and (3) property estimation. The article finally presents a vision of including information about chemical transformations in molecule design procedures, highlighting rigorous optimization and machine learning approaches such as generative modeling and reinforcement learning.

Journal ArticleDOI
TL;DR: In this paper , a neural network kinetic model is developed for oxidative coupling of methane (OCM), which is designed in cognizance of the underlying chemistry and associated reactor balance equations and trained on publicly available high throughput experimental data spanning a large material space of supported mixed metal oxide catalysts.
Abstract: A neural network kinetic model is developed for oxidative coupling of methane (OCM). The model is designed in cognizance of the underlying chemistry and associated reactor balance equations and trained on publicly available high throughput experimental data spanning a large material space of supported mixed metal oxide catalysts. The resultant model is then used to evaluate one of the most popular catalysts for OCM, viz. MnNa2WO4/SiO2, to understand the reaction kinetics and sensitivity of the catalyst to changing different components of the catalyst. The predicted activation barrier for methane conversion is 251 kJ mol−1, and the rate r ∼ CH 4 0.7 O 2 0.6 . Furthermore, the reference catalyst is local optimal as small changes to its composition, for example, by changing the individual metals or the support, did not improve (or often substantially reduced) methane consumption or the C2 formation rate.

Journal ArticleDOI
TL;DR: In this paper, the effects of H2S on the surface chemical composition and structure of the resulting catalysts were systematically studied, and the results showed isolated and well-dispersed Sn oxide sites of the deposited catalyst with nanoparticles observed only at relatively high coverages of 10% Sn at a coverage ranging from 0.48 to 2.8 Sn atoms.

Journal ArticleDOI
TL;DR: In this article , the authors present the ElasTool toolkit for predicting the mechanical properties of 2D materials and heterostructures including their temperature-dependent mechanical properties, and develop a machine learning algorithm for exploring predicted properties.
Abstract: An efficient automated toolkit for predicting the mechanical properties of materials can accelerate new materials design and discovery; this process often involves screening large configurational space in high-throughput calculations. Herein, we present the ElasTool toolkit for these applications. In particular, we use the ElasTool to study diversity of 2D materials and heterostructures including their temperature-dependent mechanical properties, and developed a machine learning algorithm for exploring predicted properties.

Journal ArticleDOI
Muzhe Yang1
TL;DR: In this article , the authors exploit an exogenous change in water chemistry that resulted in lead leaching into the tap water of one plant's service area, but not the other's, to identify a causal effect of prenatal lead exposure on fetal health.