Showing papers by "University of South Carolina published in 2019"

Highly active atomically dispersed CoN 4 fuel cell cathode catalysts derived from surfactant-assisted MOFs: carbon-shell confinement strategy

Abstract: Development of platinum group metal (PGM)-free catalysts for oxygen reduction reaction (ORR) is essential for affordable proton exchange membrane fuel cells. Herein, a new type of atomically dispersed Co doped carbon catalyst with a core–shell structure has been developed via a surfactant-assisted metal–organic framework approach. The cohesive interactions between the selected surfactant and the Co-doped zeolitic imidazolate framework (ZIF-8) nanocrystals lead to a unique confinement effect. During the thermal activation, this confinement effect suppressed the agglomeration of Co atomic sites and mitigated the collapse of internal microporous structures of ZIF-8. Among the studied surfactants, Pluronic F127 block copolymer led to the greatest performance gains with a doubling of the active site density relative to that of the surfactant-free catalyst. According to density functional theory calculations, unlike other Co catalysts, this new atomically dispersed Co–N–C@F127 catalyst is believed to contain substantial CoN2+2 sites, which are active and thermodynamically favorable for the four-electron ORR pathway. The Co–N–C@F127 catalyst exhibits an unprecedented ORR activity with a half-wave potential (E1/2) of 0.84 V (vs. RHE) as well as enhanced stability in the corrosive acidic media. It also demonstrated high initial performance with a power density of 0.87 W cm−2 along with encouraging durability in H2–O2 fuel cells. The atomically dispersed Co site catalyst approaches that of the Fe–N–C catalyst and represents the highest reported PGM-free and Fe-free catalyst performance.

A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

Abstract: In recent years, there has been a dramatic increase in the use of unmanned aerial vehicles (UAVs), particularly for small UAVs, due to their affordable prices, wide availability, and relative ease of operability. Existing and future applications of UAVs include remote surveillance and monitoring, relief operations, package delivery, and communication backhaul infrastructure. Additionally, UAVs are envisioned as an important component of 5G wireless technology and beyond. The unique application scenarios for UAVs necessitate accurate air-to-ground (AG) propagation channel models for designing and evaluating UAV communication links for control/non-payload as well as payload data transmissions. These AG propagation models have not been investigated in detail, relative to terrestrial propagation models. In this paper, a comprehensive survey is provided on available AG channel measurement campaigns, large and small scale fading channel models, their limitations, and future research directions for UAV communication scenarios.

Demystifying IoT Security: An Exhaustive Survey on IoT Vulnerabilities and a First Empirical Look on Internet-Scale IoT Exploitations

Abstract: The security issue impacting the Internet-of-Things (IoT) paradigm has recently attracted significant attention from the research community. To this end, several surveys were put forward addressing various IoT-centric topics, including intrusion detection systems, threat modeling, and emerging technologies. In contrast, in this paper, we exclusively focus on the ever-evolving IoT vulnerabilities. In this context, we initially provide a comprehensive classification of state-of-the-art surveys, which address various dimensions of the IoT paradigm. This aims at facilitating IoT research endeavors by amalgamating, comparing, and contrasting dispersed research contributions. Subsequently, we provide a unique taxonomy, which sheds the light on IoT vulnerabilities, their attack vectors, impacts on numerous security objectives, attacks which exploit such vulnerabilities, corresponding remediation methodologies and currently offered operational cyber security capabilities to infer and monitor such weaknesses. This aims at providing the reader with a multidimensional research perspective related to IoT vulnerabilities, including their technical details and consequences, which is postulated to be leveraged for remediation objectives. Additionally, motivated by the lack of empirical (and malicious) data related to the IoT paradigm, this paper also presents a first look on Internet-scale IoT exploitations by drawing upon more than 1.2 GB of macroscopic, passive measurements’ data. This aims at practically highlighting the severity of the IoT problem, while providing operational situational awareness capabilities, which undoubtedly would aid in the mitigation task, at large. Insightful findings, inferences and outcomes in addition to open challenges and research problems are also disclosed in this paper, which we hope would pave the way for future research endeavors addressing theoretical and empirical aspects related to the imperative topic of IoT security.

DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection

Abstract: Cracks are typical line structures that are of interest in many computer-vision applications. In practice, many cracks, e.g., pavement cracks, show poor continuity and low contrast, which bring great challenges to image-based crack detection by using low-level features. In this paper, we propose DeepCrack-an end-to-end trainable deep convolutional neural network for automatic crack detection by learning high-level features for crack representation. In this method, multi-scale deep convolutional features learned at hierarchical convolutional stages are fused together to capture the line structures. More detailed representations are made in larger scale feature maps and more holistic representations are made in smaller scale feature maps. We build DeepCrack net on the encoder–decoder architecture of SegNet and pairwisely fuse the convolutional features generated in the encoder network and in the decoder network at the same scale. We train DeepCrack net on one crack dataset and evaluate it on three others. The experimental results demonstrate that DeepCrack achieves $F$ -measure over 0.87 on the three challenging datasets in average and outperforms the current state-of-the-art methods.

Towards a more reliable historical reanalysis: improvements for version 3 of the Twentieth Century Reanalysis system

Abstract: Historical reanalyses that span more than a century are needed for a wide range of studies, from understanding large‐scale climate trends to diagnosing the impacts of individual historical extreme weather events. The Twentieth Century Reanalysis (20CR) Project is an effort to fill this need. It is supported by the National Oceanic and Atmospheric Administration (NOAA), the Cooperative Institute for Research in Environmental Sciences (CIRES), and the U.S. Department of Energy (DOE), and is facilitated by collaboration with the international Atmospheric Circulation Reconstructions over the Earth initiative. 20CR is the first ensemble of sub‐daily global atmospheric conditions spanning over 100 years. This provides a best estimate of the weather at any given place and time as well as an estimate of its confidence and uncertainty. While extremely useful, version 2c of this dataset (20CRv2c) has several significant issues, including inaccurate estimates of confidence and a global sea level pressure bias in the mid‐19th century. These and other issues can reduce its effectiveness for studies at many spatial and temporal scales. Therefore, the 20CR system underwent a series of developments to generate a significant new version of the reanalysis. The version 3 system (NOAA‐CIRES‐DOE 20CRv3) uses upgraded data assimilation methods including an adaptive inflation algorithm; has a newer, higher‐resolution forecast model that specifies dry air mass; and assimilates a larger set of pressure observations. These changes have improved the ensemble‐based estimates of confidence, removed spin‐up effects in the precipitation fields, and diminished the sea‐level pressure bias. Other improvements include more accurate representations of storm intensity, smaller errors, and large‐scale reductions in model bias. The 20CRv3 system is comprehensively reviewed, focusing on the aspects that have ameliorated issues in 20CRv2c. Despite the many improvements, some challenges remain, including a systematic bias in tropical precipitation and time‐varying biases in southern high‐latitude pressure fields.

In situ Raman spectroscopic evidence for oxygen reduction reaction intermediates at platinum single-crystal surfaces

Abstract: Developing an understanding of structure–activity relationships and reaction mechanisms of catalytic processes is critical to the successful design of highly efficient catalysts. As a fundamental reaction in fuel cells, elucidation of the oxygen reduction reaction (ORR) mechanism at Pt(hkl) surfaces has remained a significant challenge for researchers. Here, we employ in situ electrochemical surface-enhanced Raman spectroscopy (SERS) and density functional theory (DFT) calculation techniques to examine the ORR process at Pt(hkl) surfaces. Direct spectroscopic evidence for ORR intermediates indicates that, under acidic conditions, the pathway of ORR at Pt(111) occurs through the formation of HO2*, whereas at Pt(110) and Pt(100) it occurs via the generation of OH*. However, we propose that the pathway of the ORR under alkaline conditions at Pt(hkl) surfaces mainly occurs through the formation of O2−. Notably, these results demonstrate that the SERS technique offers an effective and reliable way for real-time investigation of catalytic processes at atomically flat surfaces not normally amenable to study with Raman spectroscopy. The oxygen reduction reaction, catalysed by platinum, is a crucial process in the operation of fuel cells, but the mechanistic pathways through which it occurs remain a matter for debate. Here, the authors use in situ Raman spectroscopy to identify key intermediates for this reaction at different atomically flat platinum surfaces, shedding light on the mechanism.

Exercise is medicine in oncology: Engaging clinicians to help patients move through cancer.

Abstract: Multiple organizations around the world have issued evidence-based exercise guidance for patients with cancer and cancer survivors. Recently, the American College of Sports Medicine has updated its exercise guidance for cancer prevention as well as for the prevention and treatment of a variety of cancer health-related outcomes (eg, fatigue, anxiety, depression, function, and quality of life). Despite these guidelines, the majority of people living with and beyond cancer are not regularly physically active. Among the reasons for this is a lack of clarity on the part of those who work in oncology clinical settings of their role in assessing, advising, and referring patients to exercise. The authors propose using the American College of Sports Medicine's Exercise Is Medicine initiative to address this practice gap. The simple proposal is for clinicians to assess, advise, and refer patients to either home-based or community-based exercise or for further evaluation and intervention in outpatient rehabilitation. To do this will require care coordination with appropriate professionals as well as change in the behaviors of clinicians, patients, and those who deliver the rehabilitation and exercise programming. Behavior change is one of many challenges to enacting the proposed practice changes. Other implementation challenges include capacity for triage and referral, the need for a program registry, costs and compensation, and workforce development. In conclusion, there is a call to action for key stakeholders to create the infrastructure and cultural adaptations needed so that all people living with and beyond cancer can be as active as is possible for them.

Adherence in Hypertension

Abstract: The global epidemic of hypertension is largely uncontrolled and hypertension remains the leading cause of noncommunicable disease deaths worldwide. Suboptimal adherence, which includes failure to initiate pharmacotherapy, to take medications as often as prescribed, and to persist on therapy long-term, is a well-recognized factor contributing to the poor control of blood pressure in hypertension. Several categories of factors including demographic, socioeconomic, concomitant medical-behavioral conditions, therapy-related, healthcare team and system-related factors, and patient factors are associated with nonadherence. Understanding the categories of factors contributing to nonadherence is useful in managing nonadherence. In patients at high risk for major adverse cardiovascular outcomes, electronic and biochemical monitoring are useful for detecting nonadherence and for improving adherence. Increasing the availability and affordability of these more precise measures of adherence represent a future opportunity to realize more of the proven benefits of evidence-based medications. In the absence of new antihypertensive drugs, it is important that healthcare providers focus their attention on how to do better with the drugs they have. This is the reason why recent guidelines have emphasize the important need to address drug adherence as a major issue in hypertension management.

Review of MXenes as new nanomaterials for energy storage/delivery and selected environmental applications

Abstract: Energy and environmental issues presently attract a great deal of scientific attention. Recently, two-dimensional MXenes and MXene-based nanomaterials have attracted increasing interest because of their unique properties (e.g., remarkable safety, a very large interlayer spacing, environmental flexibility, a large surface area, and thermal conductivity). In 2011, multilayered MXenes (Ti3C2Tx, a new family of two-dimensional (2D) materials) produced by etching an A layer from a MAX phase of Ti3AlC2, were first described by researchers at Drexel University. The term “MXene” was coined to distinguish this new family of 2D materials from graphene, and applies to both the original MAX phases and MXenes fabricated from them. We present a comprehensive review of recent studies on energy and environmental applications of MXene and MXene-based nanomaterials, including energy conversion and storage, adsorption, membrane, photocatalysis, and antimicrobial. Future research needs are discussed briefly with current challenges that must be overcome before we completely understand the extraordinary properties of MXene and MXene-based nanomaterials.

Understanding the Model Size Effect on SEM Fit Indices

Abstract: This study investigated the effect the number of observed variables (p) has on three structural equation modeling indices: the comparative fit index (CFI), the Tucker-Lewis index (TLI), and the root mean square error of approximation (RMSEA). The behaviors of the population fit indices and their sample estimates were compared under various conditions created by manipulating the number of observed variables, the types of model misspecification, the sample size, and the magnitude of factor loadings. The results showed that the effect of p on the population CFI and TLI depended on the type of specification error, whereas a higher p was associated with lower values of the population RMSEA regardless of the type of model misspecification. In finite samples, all three fit indices tended to yield estimates that suggested a worse fit than their population counterparts, which was more pronounced with a smaller sample size, higher p, and lower factor loading.

Thermally Driven Structure and Performance Evolution of Atomically Dispersed FeN4 Sites for Oxygen Reduction

Abstract: FeN4 moieties embedded in partially graphitized carbon are the most efficient platinum group metal free active sites for the oxygen reduction reaction in acidic proton-exchange membrane fuel cells. However, their formation mechanisms have remained elusive for decades because the Fe-N bond formation process always convolutes with uncontrolled carbonization and nitrogen doping during high-temperature treatment. Here, we elucidate the FeN4 site formation mechanisms through hosting Fe ions into a nitrogen-doped carbon followed by a controlled thermal activation. Among the studied hosts, the ZIF-8-derived nitrogen-doped carbon is an ideal model with well-defined nitrogen doping and porosity. This approach is able to deconvolute Fe-N bond formation from complex carbonization and nitrogen doping, which correlates Fe-N bond properties with the activity and stability of FeN4 sites as a function of the thermal activation temperature.

Structural insight into substrate and inhibitor discrimination by human P-glycoprotein

Abstract: ABCB1, also known as P-glycoprotein, actively extrudes xenobiotic compounds across the plasma membrane of diverse cells, which contributes to cellular drug resistance and interferes with therapeutic drug delivery. We determined the 3.5-angstrom cryo-electron microscopy structure of substrate-bound human ABCB1 reconstituted in lipidic nanodiscs, revealing a single molecule of the chemotherapeutic compound paclitaxel (Taxol) bound in a central, occluded pocket. A second structure of inhibited, human-mouse chimeric ABCB1 revealed two molecules of zosuquidar occupying the same drug-binding pocket. Minor structural differences between substrate- and inhibitor-bound ABCB1 sites are amplified toward the nucleotide-binding domains (NBDs), revealing how the plasticity of the drug-binding site controls the dynamics of the adenosine triphosphate-hydrolyzing NBDs. Ordered cholesterol and phospholipid molecules suggest how the membrane modulates the conformational changes associated with drug binding and transport.

The Scientific foundation for the Physical Activity Guidelines for Americans, 2nd Edition.

Abstract: Background: The 2018 Physical Activity Guidelines Advisory Committee Scientific Report provides the evidence base for the Physical Activity Guidelines for Americans, 2nd Edition. Methods: The 2018 ...

The Belle II Physics Book

Abstract: The Belle II detector will provide a major step forward in precision heavy flavor physics, quarkonium and exotic states, searches for dark sectors, and many other areas. The sensitivity to a large number of key observables can be improved by about an order of magnitude compared to the current measurements, and up to two orders in very clean search measurements. This increase in statistical precision arises not only due to the increased luminosity, but also from improved detector efficiency and precision for many channels. Many of the most interesting observables tend to have very small theoretical uncertainties that will therefore not limit the physics reach. This book has presented many new ideas for measurements, both to elucidate the nature of current anomalies seen in flavor, and to search for new phenomena in a plethora of observables that will become accessible with the Belle II dataset. The simulation used for the studiesinthis book was state ofthe artat the time, though weare learning a lot more about the experiment during the commissioning period. The detector is in operation, and working spectacularly well.

Consumer adoption of the Uber mobile application: Insights from diffusion of innovation theory and technology acceptance model

Abstract: The sharing economy literature has largely concentrated on the examination of peer-to-peer accommodation platforms such as Airbnb, with little attention paid on other innovations in collaborative consumption. This study investigates consumer adoption of the Uber mobile application through lenses of two theoretical models – Diffusion of Innovation Theory and Technology Acceptance Model. The results suggest that relative advantage, compatibility, complexity, observability, and social influence have a significant influence on both perceived usefulness and perceived ease of use, which in turn lead to subsequent consumer attitudes and adoption intentions. This study demonstrates the integration of the two classic adoption theories.

Physical activity, exercise, and chronic diseases: A brief review

Abstract: Chronic diseases are the leading cause of death worldwide with increasing prevalence in all age groups, genders, and ethnicities. Most chronic disease deaths occur in middle- to low-income countries but are also a significant health problem in developed nations. Multiple chronic diseases now affect children and adolescents as well as adults. Being physically inactive is associated with increased chronic disease risk. Global societies are being negatively impacted by the increasing prevalence of chronic disease which is directly related to rising healthcare expenditures, workforce complications regarding attendance and productivity, military personnel recruitment, and academic success. However, increased physical activity (PA) and exercise are associated with reduced chronic disease risk. Most physiologic systems in the body benefit positively from PA and exercise by primary disease prevention and secondary disease prevention/treatment. The purpose of this brief review is to describe the significant global problem of chronic diseases for adults and children, and how PA and exercise can provide a non-invasive means for added prevention and treatment.

Plasma protein patterns as comprehensive indicators of health.

Abstract: Proteins are effector molecules that mediate the functions of genes1,2 and modulate comorbidities3–10, behaviors and drug treatments11. They represent an enormous potential resource for personalized, systemic and data-driven diagnosis, prevention, monitoring and treatment. However, the concept of using plasma proteins for individualized health assessment across many health conditions simultaneously has not been tested. Here, we show that plasma protein expression patterns strongly encode for multiple different health states, future disease risks and lifestyle behaviors. We developed and validated protein-phenotype models for 11 different health indicators: liver fat, kidney filtration, percentage body fat, visceral fat mass, lean body mass, cardiopulmonary fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk and primary cardiovascular event risk. The analyses were prospectively planned, documented and executed at scale on archived samples and clinical data, with a total of ~85 million protein measurements in 16,894 participants. Our proof-of-concept study demonstrates that protein expression patterns reliably encode for many different health issues, and that large-scale protein scanning12–16 coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health. We anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check. Large-scale aptamer-based scanning of plasma proteins coupled with machine learning demonstrates proof-of-concept and feasibility of an individualized health check using a single blood sample.

Upcycling Single-Use Polyethylene into High-Quality Liquid Products

Abstract: Our civilization relies on synthetic polymers for all aspects of modern life; yet, inefficient recycling and extremely slow environmental degradation of plastics are causing increasing concern abou...

Prevalence of vaping and smoking among adolescents in Canada, England, and the United States: repeat national cross sectional surveys.

Abstract: Objective To examine differences in vaping and smoking prevalence among adolescents in Canada, England, and the United States. Design Repeat cross sectional surveys. Setting Online surveys in Canada, England, and the US. Participants National samples of 16 to 19 year olds in 2017 and 2018, recruited from commercial panels in Canada (n=7891), England (n=7897), and the US (n=8140). Main outcome measures Prevalence of vaping and smoking was assessed for use ever, in the past 30 days, in the past week, and on 15 days or more in the past month. Use of JUUL (a nicotine salt based electronic cigarette with high nicotine concentration) and usual vaping brands were also assessed. Logistic regression models examined differences in vaping and smoking between countries and over time. Results The prevalence of vaping in the past 30 days, in the past week, and on 15 days or more in the past month increased in Canada and the US between 2017 and 2018 (P Conclusions Between 2017 and 2018, among 16 to 19 year olds the prevalence of vaping increased in Canada and the US, as did smoking in Canada, with little change in England. The rapidly evolving vaping market and emergence of nicotine salt based products warrant close monitoring.

Reactivation of PTEN tumor suppressor for cancer treatment through inhibition of a MYC-WWP1 inhibitory pathway

Abstract: Activation of tumor suppressors for the treatment of human cancer has been a long sought, yet elusive, strategy. PTEN is a critical tumor suppressive phosphatase that is active in its dimer configuration at the plasma membrane. Polyubiquitination by the ubiquitin E3 ligase WWP1 (WW domain-containing ubiquitin E3 ligase 1) suppressed the dimerization, membrane recruitment, and function of PTEN. Either genetic ablation or pharmacological inhibition of WWP1 triggered PTEN reactivation and unleashed tumor suppressive activity. WWP1 appears to be a direct MYC (MYC proto-oncogene) target gene and was critical for MYC-driven tumorigenesis. We identified indole-3-carbinol, a compound found in cruciferous vegetables, as a natural and potent WWP1 inhibitor. Thus, our findings unravel a potential therapeutic strategy for cancer prevention and treatment through PTEN reactivation.

Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning

Abstract: This paper focuses on bearing fault diagnosis with limited training data. A major challenge in fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type under all working conditions. Recently deep learning based fault diagnosis methods have achieved promising results. However, most of these methods require large amount of training data. In this study, we propose a deep neural network based few-shot learning approach for rolling bearing fault diagnosis with limited data. Our model is based on the siamese neural network, which learns by exploiting sample pairs of the same or different categories. Experimental results over the standard Case Western Reserve University (CWRU) bearing fault diagnosis benchmark dataset showed that our few-shot learning approach is more effective in fault diagnosis with limited data availability. When tested over different noise environments with minimal amount of training data, the performance of our few-shot learning model surpasses the one of the baseline with reasonable noise level. When evaluated over test sets with new fault types or new working conditions, few-shot models work better than the baseline trained with all fault types. All our models and datasets in this study are open sourced and can be downloaded from https://mekhub.cn/as/fault_diagnosis_with_few-shot_learning/ .

Platforms in the peer-to-peer sharing economy

Abstract: The purpose of this paper is to examine peer-to-peer sharing platform business models, their sources of competitive advantage, and the roles, motivations and behaviors of key actors in their ecosystems.,This paper uses a conceptual approach that is rooted in the service, tourism and hospitality, and strategy literature.,First, this paper defines key types of platform business models in the sharing economy anddescribes their characteristics. In particular, the authors propose the differentiation between sharing platforms of capacity-constrained vs capacity-unconstrained assets and advance five core properties of the former. Second, the authors contrast platform business models with their pipeline business model counterparts to understand the fundamental differences between them. One important conclusion is that platforms cater to vastly more heterogeneous assets and consumer needs and, therefore, require liquidity and analytics for high-quality matching. Third, the authors examine the competitive position of platforms and conclude that their widely taken “winner takes it all” assumption is not valid. Primary network effects are less important once a critical level of liquidity has been reached and may even turn negative if increased listings raise friction in the form of search costs. Once a critical level of liquidity has been reached, a platform’s competitive position depends on stakeholder trust and service provider and user loyalty. Fourth, the authors integrate and synthesize the literature on key platform stakeholders of platform businesses (i.e. users, service providers, and regulators) and their roles and motivations. Finally, directions for further research are advanced.,This paper helps platform owners, service providers and users understand better the implications of sharing platform business models and how to position themselves in such ecosystems.,This paper integrates the extant literature on sharing platforms, takes a novel approach in delineating their key properties and dimensions, and provides insights into the evolving and dynamic forms of sharing platforms including converging business models.

First measurement of neutrino oscillation parameters using neutrinos and antineutrinos by NOvA.

Abstract: The NOvA experiment has seen a 4.4σ signal of ν[over ¯]_{e} appearance in a 2 GeV ν[over ¯]_{μ} beam at a distance of 810 km. Using 12.33×10^{20} protons on target delivered to the Fermilab NuMI neutrino beamline, the experiment recorded 27 ν[over ¯]_{μ}→ν[over ¯]_{e} candidates with a background of 10.3 and 102 ν[over ¯]_{μ}→ν[over ¯]_{μ} candidates. This new antineutrino data are combined with neutrino data to measure the parameters |Δm_{32}^{2}|=2.48_{-0.06}^{+0.11}×10^{-3} eV^{2}/c^{4} and sin^{2}θ_{23} in the ranges from (0.53-0.60) and (0.45-0.48) in the normal neutrino mass hierarchy. The data exclude most values near δ_{CP}=π/2 for the inverted mass hierarchy by more than 3σ and favor the normal neutrino mass hierarchy by 1.9σ and θ_{23} values in the upper octant by 1.6σ.

Radiation-grafted anion-exchange membranes: the switch from low- to high-density polyethylene leads to remarkably enhanced fuel cell performance

Abstract: Herein we detail the development of a new high-density polyethylene-(HDPE)-based radiation-grafted anion-exchange membrane (RG-AEM) that achieves a surprisingly high peak power density and a low in situ degradation rate (with configurations tailored to each). We also show that this new AEM can be successfully paired with an exemplar non-Pt-group cathode.

Walking the walk or talking the talk? Corporate social responsibility decoupling in emerging market multinationals

Abstract: Research shows that emerging market multinational enterprises (EM-MNEs) increasingly use corporate social responsibility (CSR) reporting as a global legitimation strategy. Less is known about when their CSR reporting is decoupled from their CSR performance. Drawing on neo-institutional theory, we argue that EM-MNEs’ CSR decoupling is shaped by their dual embeddedness in their home countries and the global institutional environment. We then examine how EM-MNEs’ home country institutional voids and degree of internationalization affect their tendency to engage in such decoupling. Our model receives partial support in a study of 93 MNEs from 15 emerging markets between 2005 and 2012.