Showing papers by "University of North Carolina at Charlotte published in 2022"
TL;DR: In this paper, a convolutional neural network-based arc detection model named ArcNet was proposed, which achieved an average runtime of 31 ms/sample of 1 cycle at 10 kHz sampling rate, which proves the feasibility of practical hardware deployment for realtime processing.
Abstract: AC series arc is dangerous and can cause serious electric fire hazards and property damage. This article proposed a convolutional neural network -based arc detection model named ArcNet. The database of this research is collected from eight different types of loads according to IEC62606 standard. The two most common types of arcs, including arcs from a loose connection of cables and those caused by the failure of the insulation, are generated in testing and included in the database. Using the database of raw current, experimental results indicate ArcNet can achieve a maximum of 99.47% arc detection accuracy at 10 kHz sampling rate. The model is also implemented in Raspberry Pi 3B for classification accuracy. A tradeoff study between the arc detection accuracy and model runtime has been conducted. The proposed ArcNet obtained an average runtime of 31 ms/sample of 1 cycle at 10 kHz sampling rate, which proves the feasibility of practical hardware deployment for real-time processing.
48 citations
TL;DR: In this paper , the authors review the unique properties of nucleic acid nanoparticles in the context of therapeutic applications and discuss their associated translational challenges, including their ability to incorporate conditional activations of therapeutic targeting and release functions that enable diagnosis and therapy of cancer, regulation of blood coagulation disorders, and the development of novel vaccines, immunotherapies, and gene therapies.
19 citations
TL;DR: In this article, a remote estimation method based on neural network filter (NNF) and generalized damping recursive least square (GDRLS) is proposed to solve large-scale smart meter verification and periodic replacement problems, which can effectively address the problem that large loss estimation errors merge small smart meter errors.
Abstract: To solve large-scale smart meter verification and periodic replacement problems, a remote estimation method based on neural network filter (NNF) and generalized damping recursive least square (GDRLS) is proposed. In this article, a smart meter error estimation model with a loss noise filter is built. A typical loss noise filter is designed with a neural network, so that the filtered loss noise meets the Gauss–Markov condition, which paves the way for the best linear unbiased estimation (BLUE). GDRLS algorithm is applied to solve the novel estimation model, which can effectively address the problem that large loss estimation errors merge small smart meter errors. Then, a complete process of the proposed method is constructed, which can estimate both the user smart meter errors, and the loss noises accurately. Finally, the effectiveness, superiority, and applicability of the proposed method are verified through simulation analysis and practical distribution network application.
17 citations
TL;DR: In this paper , the structure of the spike protein's receptor binding domain was predicted to change, which may reduce antibody interaction without completely evading existing neutralizing antibodies and therefore current vaccines.
Abstract: The genome of the SARS-CoV-2 Omicron variant (B.1.1.529) was released on November 22, 2021, which has caused a flurry of media attention due the large number of mutations it contains. These raw data have spurred questions around vaccine efficacy. Given that neither the structural information nor the experimentally-derived antibody interaction of this variant are available, we have turned to predictive computational methods to model the mutated structure of the spike protein's receptor binding domain and posit potential changes to vaccine efficacy. In this study, we predict some structural changes in the receptor-binding domain that may reduce antibody interaction without completely evading existing neutralizing antibodies (and therefore current vaccines).
16 citations
07 Jan 2022-Materials Science and Engineering A-structural Materials Properties Microstructure and Processing
TL;DR: In this article, the authors investigated the failure behavior and its mechanisms of the material under uniaxial impact loading and found that increasing oxygen content led to increased propensity for adiabatic shear failure in CP-Ti.
Abstract: Given that commercial purity titanium (CP-Ti) doped with a high oxygen concentration has recently been reported with high mechanical performance, the present work investigated the failure behavior and its mechanisms of the material under uniaxial impact loading. Besides the strong hardening effect of oxygen, it was found that increasing oxygen content led to increased propensity for adiabatic shear failure in CP-Ti. The texture or grain orientation was also found to have profound influence on the formation of adiabatic shear band (ASB). Microstructural examinations on the postmortem high oxygen CP-Ti suggested that uniform and equi-axed nano-grains were produced within the ASB. The orientations of these nano-grains were analyzed using the precession electron diffraction (PED) technique. It provides direct evidence of phase transformation occurring in ASB. Then we demonstrate that the nano-grains in ASB formed from parent grains by phase transformation, indicating that the α→β→α phase transformation process takes part in ASB evolution and is an underlying mechanism of grain refinement. As such, this result is a supplement for traditional well-accepted dynamic recrystallization mechanism of ASB evolution.
15 citations
TL;DR: In this paper , a new approach to understand people's varied responses to the COVID-19 pandemic was used to analyze the general public's reactions during which mortality is salient.
13 citations
TL;DR: In this article, the authors focus on the use of multiple reference data sources to reduce geocoding errors, the validity of online geocoders and how confidentiality may be breached, the impact of geoimputation techniques on travel estimates, residential mobility and how it affects accessibility metrics and clustering, and modeling errors in the American Community Survey.
13 citations
TL;DR: In this article, a machine learning model was proposed to predict the brittle fracture of polycrystalline graphene under tensile loading, which employed a convolutional neural network, bidirectional recurrent neural network and fully connected layer to process the spatial and sequential features.
12 citations
TL;DR: Jadeite as mentioned in this paper extracts the Interprocedural Control Flow Graph (ICFG) from a given Java bytecode file and then prunes the ICFG and converts it into an adjacency matrix.
11 citations
TL;DR: In this paper , a group of Black academics called on the graduate medical education community to reset its trajectory toward solutions for achieving diversity, improving inclusion, and combating racism using education as the new vector.
Abstract: The COVID-19 pandemic highlighted the great achievements that the biomedical community can accomplish, but raised the question: Can the same medical community that developed a complex vaccine in less than a year during a pandemic help to defeat social injustice and ameliorate the epidemic of health inequity? In this article, the authors, a group of Black academics, call on the graduate medical education (GME) community to reset its trajectory toward solutions for achieving diversity, improving inclusion, and combating racism using education as the new vector. Sponsoring institutions, which include universities, academic medical centers, teaching hospitals, and teaching health centers, are the center of the creation and dissemination of scholarship. They are often the main sources of care for many historically marginalized communities. The GME learning environment must provide the next generation of medical professionals with an understanding of how racism continues to have a destructive influence on health care professionals and their patients. Residents have the practical experience of longitudinal patient care, and a significant portion of an individual's professional identity is formed during GME; therefore, this is a key time to address explicit stereotyping and to identify implicit bias at the individual level. The authors propose 3 main reset strategies for GME-incorporating inclusive pedagogy and structural competency into education, building a diverse and inclusive learning environment, and activating community engagement-as well as tactics that sponsoring institutions can adapt to address racism at the individual learner, medical education program, and institutional levels. Sustained, comprehensive, and systematic implementation of multiple tactics could make a significant impact. It is an academic and moral imperative for the medical community to contribute to the design and implementation of solutions that directly address racism, shifting how resident physicians are educated and modeling just and inclusive behaviors for the next generation of medical leaders.
10 citations
01 Jan 2022-Precision Engineering-journal of The International Societies for Precision Engineering and Nanotechnology
TL;DR: In this article, the dimensional accuracy of industrial X-ray computed tomography (CT) measurements as a function of the geometric magnification (Mg) used for CT scanning is evaluated.
Abstract: This paper evaluates the dimensional accuracy of industrial X-ray computed tomography (CT) measurements as a function of the geometric magnification (Mg) used for CT scanning. Two plates of square-shaped geometry with 28 drilled holes, one made of aluminum (48 mm × 48 mm × 8 mm) and a second one made of steel (6 mm × 6 mm × 1 mm), are used as measuring workpieces to test fourteen different magnification axis positions in a metrology-grade X-ray CT system. In addition to measuring the diameter and roundness of each hole, distance measurements between the holes are evaluated—both as uni-directional (center-to-center) and bi-directional (point-to-point or edge-to-edge) lengths. The variance of CT dimensional measurements, with respect to reference data obtained from tactile coordinate measurement machines (CMMs), is studied. It is found that the measurement deviations for uni-directional distances are approximately independent of Mg and mostly within a ±4 μm range. In contrast, for measurements of diameter, roundness, and bi-directional lengths, the deviations between CT and CMM data extend outside the ±4 μm limits and strongly depend on Mg (which is a variable inversely proportional to the voxel size of CT scan, VX). More specifically, decreasing Mg or, equivalently, increasing VX, generally leads to larger deviations in the dimensional data. To include the magnification dependence on the estimations of CT measurement deviations, based on the experimental data presented throughout this paper, the authors propose that the maximum error of CT length measurements can be expressed as E m a x = ( A + X / B + L / K ) μm, with A , B , and K being constant factors (determined for a particular CT measuring setup), L the dimension of the measuring length (in mm), and X the magnification axis position of the CT scanner (also in mm). Whereas the term “ L / K ” considers error influences from the size of the sample, the term “ X / B ” accounts for influences from magnification (and voxel size) selections during CT measurement.
TL;DR: In this paper , the authors performed a systematic assessment of the specified dynamics scheme in the Community Earth System Model version 2, Whole Atmosphere Community Climate Model version 6 (CESM2 (WACCM6)), which proactively nudges the circulation toward the reference meteorology.
Abstract: Abstract. Specified dynamics schemes are ubiquitous modeling tools for isolating the roles of dynamics and transport on chemical weather and climate. They typically constrain the circulation of a chemistry–climate model to the circulation in a reanalysis product through linear relaxation. However, recent studies suggest that these schemes create a divergence in chemical climate and the meridional circulation between models and do not accurately reproduce trends in the circulation. In this study we perform a systematic assessment of the specified dynamics scheme in the Community Earth System Model version 2, Whole Atmosphere Community Climate Model version 6 (CESM2 (WACCM6)), which proactively nudges the circulation toward the reference meteorology. Specified dynamics experiments are performed over a wide range of nudging timescales and reference meteorology frequencies, with the model's circulation nudged to its own free-running output – a clean test of the specified dynamics scheme. Errors in the circulation scale robustly and inversely with meteorology frequency and have little dependence on the nudging timescale. However, the circulation strength and errors in tracers, tracer transport, and convective mass flux scale robustly and inversely with the nudging timescale. A 12 to 24 h nudging timescale at the highest possible reference meteorology frequency minimizes errors in tracers, clouds, and the circulation, even up to the practical limit of one reference meteorology update every time step. The residual circulation and eddy mixing integrate tracer errors and accumulate them at the end of their characteristic transport pathways, leading to elevated error in the upper troposphere and lower stratosphere and in the polar stratosphere. Even in the most ideal case, there are non-negligible errors in tracers introduced by the nudging scheme. Future development of more sophisticated nudging schemes may be necessary for further progress.
TL;DR: In this article, the effect of the processing methods and parameters on the efficacy of grain refinement and texture evolution through dynamic recrystallization, and eventually on the mechanical properties of the as-processed materials were investigated.
TL;DR: In this article , a matrix description of orbital angular momentum (OAM) transformations in scalar optical beams by non-local, linear systems is introduced and the amplitude transfer function (ATF) is generalized to include information about mode-to-mode OAM coupling.
Abstract: A matrix description of orbital angular momentum (OAM) transformations in scalar optical beams by non-local, linear systems is introduced and the amplitude transfer function (ATF) is generalized to include information about mode-to-mode OAM coupling. In particular, the analysis of radially independent systems suggests the existence of unexplored types of OAM transforming systems. The results are extended to random beams and random non-local systems and the optical transfer function (OTF) is generalized to the OAM space.
TL;DR: In this paper, the authors examined associations between key risk factors for occupational stress among the intimate partner violence (IPV) and sexual assault (SA) workforce and found that gaps in knowledge related to occupational stress remain.
Abstract: Gaps in knowledge related to occupational stress among the intimate partner violence (IPV) and sexual assault (SA) workforce remain. This study examined associations between key risk factors for oc...
TL;DR: In this article, the effects of a high-fat diet on circulating levels of insulin, glucose, and select inflammatory/metabolic markers were investigated in female C57BL/6J mice, and it was shown that the improved metabolic profile of obese mice consuming EPA was associated with a reduction of key gut Gram-negative bacteria that contribute toward impaired glucose metabolism.
TL;DR: In this paper , uncertainty quantification, Bayesian statistics, and density functional theory are synthesized to identify the active sites for the non-oxidative propane dehydrogenation on platinum catalysts.
Abstract: Uncertainty quantification, Bayesian statistics, the reported experimental literature, and density functional theory are synthesized to identify the active sites for the non-oxidative propane dehydrogenation on platinum catalysts. This study tests three different platinum surface models as active sites, Pt(100), Pt(111), and Pt(211), and two different methodologies for generating uncertainty, using data from four density functional theory functionals and data from the BEEF–vdW ensembles. By comparing these three surface facets using two uncertainty sources, a total of six different computational models were evaluated. Three experimental data sets, with varying numbers of reported observables, such as turnover frequencies, selectivity to propylene, apparent activation energy, and reaction orders, are calibrated and validated for these six models. This study finds no evidence for Pt(100) as the dominant active facet and finds that Pt(211) has some evidence for being the most relevant active site on the catalyst. In addition, all four functional models were excluded from final data analysis due to poor “goodness-of-fit”. In contrast, the BEEF–vdW model with ensembles (BMwEs) was found to pass “goodness-of-fit” for most of the models tested. Finally, for both Pt(111) and Pt(211), this study finds that the majority of simulations found the kinetically rate-controlling step the first dehydrogenation step from propane to C3H7*.
01 Jan 2022
TL;DR: In this paper, the effects of traffic-induced delay and dropout on the finite-horizon quality of control of an individual stochastic linear time-invariant system, where quality-of-control is measured by an expected quadratic cost function, are analyzed.
Abstract: Transmission delay and packet dropout are inevitable network-induced phenomena that severely compromise the control performance of network control systems. The real-time network traffic is a major dynamic parameter that directly influences delay and reliability of transmission channels, and thus, acts as an unavoidable source of induced coupling among all network sharing systems. In this letter, we analyze the effects of traffic-induced delay and dropout on the finite-horizon quality-of-control of an individual stochastic linear time-invariant system, where quality-of-control is measured by an expected quadratic cost function. We model delay and dropout of the channel as generic stochastic processes that are correlated with the real-time network traffic induced by the rest of network users. This approach provides a pathway to determine the required networking capabilities to achieve a guaranteed quality-of-control for systems operating over a shared-traffic network. Numerical evaluations are performed using realistic stochastic models for delay and dropout. As a special case, we consider exponential distribution for delay with its rate parameter being traffic-correlated, and traffic-correlated Markov-based packet drop model.
TL;DR: In this paper, a bottom-up city building heat emission model (CityBHEM) was developed to investigate temporal variations of building AH from three components (i.e., envelope convection, zone infiltration and exfiltration, and HVAC system) for all buildings in Boston, United States.
Abstract: Anthropogenic heat (AH) emission from buildings is a key contributor to the urban heat island (UHI) effect. Although an improved understanding of spatiotemporal patterns of building AH is highly needed for mitigating UHI effect, such information is still limited in high spatiotemporal resolutions at the city level. In this study, a bottom-up city building heat emission model (CityBHEM) was developed to investigate temporal variations of building AH from three components (i.e., envelope convection, zone infiltration and exfiltration, and HVAC system) for all buildings in Boston, United States. First, buildings in Boston were grouped into eleven commercial and five residential building prototypes based on building type, construction year, and foundation type. Second, an end-use-based calibration was developed to calibrate CityBHEM using U.S. Energy Information Administration's survey data. Finally, AH from all buildings in the city under actual weather conditions was calculated using the calibrated CityBHEM model together with building types and sizes. Results indicate that total building AH density of Back Bay neighborhood reaches the maximum value of 526 kWh/m2 in summer (56% of HVAC system and 44% of envelope convection) and the minimum value of 369 kWh/m2 in winter (54% of HVAC system, 24% of envelope convection and 22% of zone infiltration and exfiltration). In contrast, total building AH density of suburban neighborhoods is lower than 30 kWh/m2 in summer and 20 kWh/m2 in winter. Given that key inputs are publicly available, CityBHEM is transferable to other U.S. cities, enabling us to explore practical building energy-saving strategies for mitigating AH.
TL;DR: In this article , the authors presented a deep learning architecture that transferred features from a 152 residual layer network (ResNet) for predicting the level of healthiness of food images that were built using images from the Google images search engine gathered in 2020.
Abstract: Obesity is a modern public health problem. Social media images can capture eating behavior and the potential implications to health, but research for identifying the healthiness level of the food image is relatively under-explored. This study presents a deep learning architecture that transfers features from a 152 residual layer network (ResNet) for predicting the level of healthiness of food images that were built using images from the Google images search engine gathered in 2020. Features learned from the ResNet 152 were transferred to a second network to train on the dataset. The trained SoftMax layer was stacked on top of the layers transferred from ResNet 152 to build our deep learning model. We then evaluate the performance of the model using Twitter images in order to better understand the generalizability of the methods. The results show that the model is able to predict the images into their respective classes, including Definitively Healthy, Healthy, Unhealthy and Definitively Unhealthy at an F1-score of 78.8%. This finding shows promising results for classifying social media images by healthiness, which could contribute to maintaining a balanced diet at the individual level and also understanding general food consumption trends of the public.
TL;DR: In this article , the authors developed machine learning models to analyze interactive pathways of factors associated with lung cancer screening using low-dose computed tomography (LDCT) for lung cancer prevention in the US.
Abstract: Lung cancer is the second common cancer and a leading cause of cancer-related death in the US. Unfavorably, the prevalence of using low-dose computed tomography (LDCT) for lung cancer prevention in the US has remained below 4% over time. The purpose of this study is to develop machine learning models to analyze interactive pathways of factors associated with lung cancer screening use with the LDCT. The study was based on the data retrieved from the 2018 Behavioral Risk Factor Surveillance System. After dealing with missing values, 86 variables and 710 samples were included in the decision tree model and the random forest model. The data were randomly split into training (569/710, 80%) and testing (141/710, 20%) sets. Gini impurity is used to select and determine the optimal split of the nodes in the model. Machine learning performance was evaluated by model accuracy, sensitivity, specificity, F1 score, etc. The average performance metrics of the decision tree model were obtained: average accuracy is 67.78%, F1 score is 65.76%, sensitivity is 62.52%, and specificity is 73.57% based on 100 runs. In the decision model, nine interactive pathways were identified among the following factors: average drinks per month, BMI, diabetes, first smoke age, years of smoking, year(s) quit smoking, sex, last sigmoidoscopy or colonoscopy, last dental visit, general health, insurance, education, and last Pap test. Lung cancer screening utilization is the result of the interplay of multifactors. Lung cancer screening programs in clinical settings should not only focus on patients' smoking behaviors but also consider other socioeconomic factors.
TL;DR: In this paper , the authors developed a computational model based on the transformer architecture able to predict the immune activities of nucleic acid nanoparticles (NANPs) in order to guide the design of NANPs to the desired immunological outcome, a step crucial for the success of personalized therapies.
Abstract: Nucleic acid nanoparticles, or NANPs, rationally designed to communicate with the human immune system, can offer innovative therapeutic strategies to overcome the limitations of traditional nucleic acid therapies. Each set of NANPs is unique in their architectural parameters and physicochemical properties, which together with the type of delivery vehicles determine the kind and the magnitude of their immune response. Currently, there are no predictive tools that would reliably guide the design of NANPs to the desired immunological outcome, a step crucial for the success of personalized therapies. Through a systematic approach investigating physicochemical and immunological profiles of a comprehensive panel of various NANPs, the research team developes and experimentally validates a computational model based on the transformer architecture able to predict the immune activities of NANPs. It is anticipated that the freely accessible computational tool that is called an "artificial immune cell," or AI-cell, will aid in addressing the current critical public health challenges related to safety criteria of nucleic acid therapies in a timely manner and promote the development of novel biomedical tools.
TL;DR: A phase one computational design analysis study of a hydrokinetic horizontal parallel stream direct-drive (no gear box) counter-rotating Darrieus turbine system is presented in this paper .
Abstract: This paper introduces a phase one computational design analysis study of a hydrokinetic horizontal parallel stream direct-drive (no gear box) counter-rotating Darrieus turbine system. This system consists of two Darrieus rotors that are arranged in parallel and horizontal to the water stream and operate in counter-rotation due to the incoming flow. One of the rotors directly drives an armature coil rotor and the other one a permanent magnet generator. A two-dimensional (2-D) and three-dimensional (3-D) computational fluid dynamic (CFD) simulation study was conducted to assess the hydrokinetic performance of the design. From a high computational cost and time perspective, the simulation setup was reduced from a 3-D to a 2-D analysis. Although useful information was obtained from the 3-D simulations, the output performance could be assessed with the 2-D simulations without compromising the integrity of the turbine output results. A scaled experimental design prototype was developed for static (non-movement of the rotors with dynamic fluid flow) particle image velocimetry (PIV) studies. The PIV studies were used as a benchmark for validating and verifying the CFD simulations. This paper outlines the prototype development, PIV experimental setup and results, computational simulation setup and results, as well as recommendations for future work that could potentially improve overall performance of the proposed design.
TL;DR: In this paper, a comprehensive approach to facilitate data sharing was designed with a focus on engaging stakeholders and generating solutions to commonly reported barriers, such as trust, legal, and governance barriers.
Abstract: Context Community violence is a public health problem that erodes social infrastructure. Structural racism contributes to the disparate concentration of violence in communities of color. In Mecklenburg County, North Carolina, increasing trends in community violence show racial and geographic disparities that emphasize the need for cross-sector, data-driven approaches to program and policy change. Cross-sector collaborations are challenged by data sharing barriers that hinder implementation. Program In response to community advocacy, Mecklenburg County Public Health (MCPH) launched a Community Violence Prevention Plan with evidence-based programming. The Cure Violence (CV) model, a public health approach to disrupting violence through equitable resource provision, network building, and changing norms, was implemented at the community level. The Health Alliance for Violence Intervention (HAVI) model, a hospital-based screening and case management intervention for victims of violence, was implemented at Carolinas Medical Center in Charlotte, the region's only level I trauma center. Methods A data collaborative was created to optimize evaluation of CV and HAVI programs including MCPH, the city of Charlotte, Atrium Health, Charlotte-Mecklenburg Schools, Johnson C. Smith University, and the University of North Carolina Charlotte. A comprehensive approach to facilitate data sharing was designed with a focus on engaging stakeholders and generating solutions to commonly reported barriers. Structured interviews were used to inform a solution-focused strategy. Results Stakeholders reported perceptions of their organization's barriers and facilitators to cross-sector data sharing. Common technology, legal, and governance barriers were addressed through partnership with a local integrated data system. Solutions for trust and motivational challenges were built into ongoing collaborative processes. Discussion Data silos inhibit the understanding of complex public health issues such as community violence, along with the design and evaluation of collective impact efforts. This approach can be replicated and scaled to support cross-sector collaborations seeking to influence social and health inequities stemming from structural racism.
TL;DR: In this article, the authors compared gene expression of early development, post-natal pig mammary glands to the literature reported genes implicated in different subclasses of human breast cancer, including NUCB2, ANGPTL4 and ACE.
TL;DR: In this paper , the authors investigated how acquired chemoresistance impacts the efficacy of VSV-based OV therapy against pancreatic ductal adenocarcinoma (PDAC).
TL;DR: In this paper , a new class of structured beams, called vector specific non-uniformly correlated (RPHNUC) beams, have been proposed, which combine nonuniform polarization and non-uneiform correlation, and they exhibit propagation features not seen in conventional vector PCBs.
Abstract: With the development of the unified theory of coherence and polarization, the novel physical properties generated by different correlation structures of vector partially coherent beams (PCBs) have attracted much attention. Recently, a new class of structured beams have been proposed [Opt. Lett.45, 3824 (2020)10.1364/OL.397316], called vector specific non-uniformly correlated beams. These beams combine non-uniform polarization and non-uniform correlation, and they exhibit propagation features not seen in conventional vector PCBs. In this Letter, we continue the analysis of the previous work, taking radially polarized Hermite non-uniformly correlated (RPHNUC) beams as an example, and focus on the physical interpretation of the peculiar propagation features of such beams. We verify the predicted behavior of RPHNUC beams through experiment.
TL;DR: The splitting number of the reals as discussed by the authors is a cardinal characteristic of the Hausdorff space which is connected to Efimov's problem, which asks whether every infinite compact space must contain either a non-trivial convergent sequence, or a copy of βN.
TL;DR: In this paper , the effect of substrates on the femtosecond ablation of 2D materials is studied using MoS2 as an example, and an intrinsic ablation threshold is proposed as a true threshold parameter.
Abstract: Laser direct writing is an attractive method for patterning 2D materials without contamination. Literature shows that the ultrafast ablation threshold of graphene across substrates varies by an order of magnitude. Some attribute it to the thermal coupling to the substrates, but it remains by and large an open question. For the first time the effect of substrates on the femtosecond ablation of 2D materials is studied using MoS2 as an example. We show unambiguously that femtosecond ablation of MoS2 is an adiabatic process with negligible heat transfer to the substrates. The observed threshold variation is due to the etalon effect which was not identified before for the laser ablation of 2D materials. Subsequently, an intrinsic ablation threshold is proposed as a true threshold parameter for 2D materials. Additionally, we demonstrate for the first time femtosecond laser patterning of monolayer MoS2 with sub-micron resolution and mm/s speed. Moreover, engineered substrates are shown to enhance the ablation efficiency, enabling patterning with low-power ultrafast oscillators. Finally, a zero-thickness approximation is introduced to predict the field enhancement with simple analytical expressions. Our work clarifies the role of substrates on ablation and firmly establishes ultrafast laser ablation as a viable route to pattern 2D materials.
01 May 2022
TL;DR: This article found that stronger prior lending relationships between acquisition loan lenders and acquisition targets are associated with lower spreads and fewer covenant restrictions on acquisition loans, and that the results are unlikely to be driven by unobservable acquirer, target, or lender characteristics.
Abstract: We study whether and how banks reuse information across different but related borrowers in financing mergers and acquisitions. We find that stronger prior lending relationships between acquisition loan lenders and acquisition targets are associated with lower spreads and fewer covenant restrictions on acquisition loans. We show that the results are unlikely to be driven by unobservable acquirer, target, or lender characteristics. Consistent with the information asymmetry hypothesis, the effect is stronger when information asymmetry about the target firm is higher. We also find that the result is not driven by the coinsurance effect.