bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of multiple modes of UltraPulse fractional CO2 laser treatment on extensive scarring: a retrospective study
Authors: Ge Xiaojing , Sun Yute , Lin Jing , Zhou Fang , Yao Gang , Su Xin 
Published: August 2021

AbstractThe main therapeutic options for extensive scarring (e.g., > 20% of the total body surface area, or TBSA) after burns and trauma have focused on conservative treatments, such as compression, moisturization, and topical agent application. However, these treatments may not achieve optimal effects due to the large size and complexity of the scars. UltraPulse fractional CO2 laser treatment is a novel approach that is currently a subject of intense interest; this treatment is most widely used to improve texture, pliability, and pigmentation in all types of scars. However, no studies on the independent use of UltraPulse fractional CO2 laser treatment for extensive scars have been reported. This retrospective study evaluated a total of 21 patients, whose scars covered 20 to 65% TBSA. Scar thickness was measured by ultrasonography before treatment. Personalized treatment modalities and parameters were set according to the scar type and thickness. Scar formation and treatment effects were evaluated by photography, the Patient and Observer Scar Assessment Scale (POSAS), and patients’ judgment of effectiveness. Where the scars covered joints, joint function was assessed by measuring the maximum range of motion (ROM). With laser therapy, scars became flatter and lighter; furthermore, pruritus, pain, and discomfort decreased significantly. POSAS scores significantly decreased after laser therapy, including the item scores for pain and pruritus. There were no instances of joint contracture, ROM reduction, apparent functional impairment, serious adverse events, or comorbidities. This study demonstrates the safety and efficiency of UltraPulse fractional CO2 laser treatment for extensive scarring.
Investigating the role of endogenous estrogens, hormone replacement therapy, and blockade of estrogen receptor-α activity on breast metabolic signaling
Authors: Arnone Alana A. , Cline J. Mark , Soto-Pantoja David R. , Cook Katherine L. 
Published: August 2021

AbstractPurposeMenopause is associated with an increased risk of estrogen receptor-positive (ER +) breast cancer. To characterize the metabolic shifts associated with reduced estrogen bioavailability on breast tissue, metabolomics was performed from ovary-intact and ovariectomized (OVX) female non-human primates (NHP). The effects of exogenous estrogen administration or estrogen receptor blockade (tamoxifen treatment) on menopause-induced metabolic changes were also investigated.MethodsBilateral ovariectomies were performed on female cynomolgus macaques (Macaca fascicularis) to model menopause. OVX NHP were then divided into untreated (n = 13), conjugated equine estrogen (CEE)-treated (n= 13), or tamoxifen-treated (n = 13) subgroups and followed for 3 years. Aged-matched ovary-intact female NHP (n = 12) were used as a premenopausal comparison group. Metabolomics was performed on snap-frozen breast tissue.ResultsChanges in several different metabolic biochemicals were noted, particularly in glucose and fatty acid metabolism. Specifically, glycolytic, Krebs cycle, acylcarnitines, and phospholipid metabolites were elevated in breast tissue from ovary-intact NHP and OVX + CEE in relation to the OVX and OVX + tamoxifen group. In contrast, treatment with CEE and tamoxifen decreased several cholesterol metabolites, compared to the ovary-intact and OVX NHP. These changes were accompanied by elevated bile acid metabolites in the ovary-intact group.ConclusionAlterations in estrogen bioavailability are associated with changes in the mammary tissue metabolome, particularly in glucose and fatty acid metabolism. Changes in these pathways may represent a bioenergetic shift in gland metabolism at menopause that may affect breast cancer risk.
Technology supported learning and pedagogy in times of crisis: the case of COVID-19 pandemic
Authors: Ahmed Vian , Opoku Alex 
Published: August 2021

AbstractOnline teaching within disciplines such as Engineering require experiential learning that equip future graduates with highly intellectual and professional skills to meet the demands of employers and the industry. The outbreak of COVID-19 however, has shifted the academic community into new landscapes that require educators and students to adapt and manage their expectations. Although literature reports on research attempts to study the implications of Covid-19 on the Higher Education curricular, little has been reported on its impact on Engineering Education. This paper therefore uses the theory of Emergency Management Life Cycle (mitigation, preparedness, response, and recover) as a lens to examine the challenges faced by students and academics and coping mechanism during the COVID period. This study adopts a mixed method approach using a case study from the College of Engineering at a Higher Education Institution in the UAE due to the sudden migration to online teaching amid COVID-19. Data is collected through interviews and surveys with both students and instructors on challenges, strategies and online delivery good practices that enhanced students’ learning experience. The results show that, Technology Supported Learning tools are capable of enhancing students’ experiential learning and associated competencies, however there were a number of pedagogical, technological and psychological challenges that faced students and instructors as a result of the sudden migration online, which are likely to play a role in the impediment of the students’ learning cycle, due to the lack of preparedness in response to the state of emergency created by Covid-19. Despite these challenges, the study found that instructors with effective communication skills and teaching style, competent use of technology, flexible, friendly and supportive attitude towards teaching, played a positive role in mitigating for the lack of preparedness in response to sudden migration online. The study also reveals that by overcoming some of the technical challenges such as slow internet connection and interruptions, lessons learnt from the sudden migration to online delivery amid COVID-19, will help create new opportunities for the use of blended learning approaches to meet the needs of the on-going COVID and future online deliveries.
The Income Gradient in Mortality during the Covid-19 Crisis: Evidence from Belgium
Authors: Decoster André , Minten Thomas , Spinnewijn Johannes 
Published: August 2021

AbstractWe use population-wide data from linked administrative registers to study the distributional pattern of mortality before and during the first wave of the Covid-19 pandemic in Belgium. Over the March-May 2020 study period, excess mortality is only found among those aged 65 and over. For this group, we find a significant negative income gradient in excess mortality, with excess deaths in the bottom income decile more than twice as high as in the top income decile for both men and women. However, given the high inequality in mortality in normal times, the income gradient in all-cause mortality is only marginally steeper during the peak of the health crisis when expressed in relative terms. Leveraging our individual-level data, we gauge the robustness of our results for other socioeconomic factors and decompose the role of individual vs. local effects. We provide direct evidence that geographic location effects on individual mortality are particularly strong during the first wave of the Covid-19 pandemic, channeling through the local number of Covid infections. This makes inference about the income gradient in excess mortality based on geographic variation misguided.
Post-Dropping Behavior of Potato Aphids (Macrosiphum euphorbiae)
Authors: Humphreys Rosalind K. , Ruxton Graeme D. , Karley Alison J. 
Published: August 2021

AbstractDropping behavior is an effective antipredator defense utilized by many insects including aphids, which drop from plants to lower plant parts or underlying substrates to avoid attack from predatory invertebrates. While research commonly focusses on triggers of dropping, less attention is given to what happens to prey individuals following escape drops. In this study, the duration of tonic immobility, recovery rates, and cases of “instant recovery” (re-clinging to lower plant parts) exhibited by potato aphids (Macrosiphum euphorbiae) that dropped from potted seedlings in response to introduced ladybird (Adalia bipunctata) adults, lacewing (Chrysoperla carnea) larvae, and a standardized tactile stimulus were investigated in relation to a range of environmental factors. Air temperature had a negative correlation with the duration of post-dropping tonic immobility; as temperature increased, time spent motionless decreased. Aphids also showed a pattern of increased recovery rate at higher temperatures. Aphids may be selected to move off the substrate quicker to avoid risks of overheating/desiccation at higher temperatures; and/or higher body temperature facilitates locomotion. Stimulus type also influenced recovery rate back to the original seedling, with aphids generally recovering after the standardized stimulus quicker than after dropping triggered by a real predator. Considering cases of instant recovery onto lower-reaches of the host seedling, seedling height influenced the likelihood of re-clinging, with aphids that managed to instantly recover dropping from, on average, taller seedlings than aphids that dropped to the substrate. Plant architecture could mitigate the costs of dropping for aphids, but further studies quantifying understory foliage cover are needed.
AI-Based Self-Learning System in Distributed Structural Health Monitoring and Control
Authors: Yan Kai , Lin Xin , Ma Wenfeng , Zhang Yuxiao 
Published: August 2021

AbstractArtificial intelligence is predicted to play a big part in self-learning, industrial automation that will negotiate the bandwidth of structural health and control systems. The industrial structural health and control system based on discrete sensors possesses insufficient spatial coverage of sensing information, while the distributed condition monitoring has been mainly studied at the sensor level, relatively few studies have been conducted at the artificial intelligence level. This paper presents an innovative method for distributed structural health and control systems based on artificial intelligence. The structural condition was divided into regional and local features, the feature extraction and characterization are performed separately. Structural abnormality recognition and risk factor calculation method were proposed by considering the response values and the distribution patterns of both the regional and the local structural behaviours. The test results show that the method can effectively identify the full-scale and local damage of the structure, respectively. Subsequently, structural safety assessment method for long-span structures at kilometres level in view of fully length strain distributions measured by distributed fiber optic sensors were developed. A series of load tests on the long-span structure were carried out. Finite element (FE) model was developed using finite element code, ABAQUS, and an extensive parametric study was conduct to explore the effect of load cases on the structural responses. The differences in the structural response results among load test, structural safety assessment and FE simulation were investigated. It is shown that AI-based self-learning system could offer suitable speed in deployment, reliability in solution and flexibility to adjust in distributed structural health monitoring and control.
Knowledge is a mental state (at least sometimes)
Authors: Bricker Adam Michael 
Published: August 2021

AbstractIt is widely held in philosophy that knowing is not a state of mind. On this view, rather than knowledge itself constituting a mental state, when we know, we occupy a belief state that exhibits some additional non-mental characteristics. Fascinatingly, however, new empirical findings from cognitive neuroscience and experimental philosophy now offer direct, converging evidence that the brain can—and often does—treat knowledge as if it is a mental state in its own right. While some might be tempted to keep the metaphysics of epistemic states separate from the neurocognitive mechanics of our judgements about them, here I will argue that these empirical findings give us sufficient reason to conclude that knowledge is at least sometimes a mental state. The basis of this argument is the epistemological principle of neurocognitive parity—roughly, if the contents of a given judgement reflect the structure of knowledge, so do the neurocognitive mechanics that produced them. This principle, which I defend here, straightforwardly supports the inference from the empirical observation that the brain sometimes treats knowledge like a mental state to the epistemological conclusion that knowledge is at least sometimes a mental state. All told, the composite, belief-centric metaphysics of knowledge widely assumed in epistemology is almost certainly mistaken.
Sharing quantum steering among multiple Alices and Bobs via a two-qubit Werner state
Authors: Han Xinhong , Xiao Ya , Qu Huichao , He Runhong , Fan Xuan , Qian Tian , Gu Yongjian 
Published: August 2021

AbstractQuantum steering, a type of quantum correlation with unique asymmetry, has important applications in asymmetric quantum information tasks. We consider a new quantum steering scenario in which one half of a two-qubit Werner state is sequentially measured by multiple Alices and the other half by multiple Bobs. We find that the maximum number of Alices who can share steering with a single Bob increases from 2 to 5 when the number of measurement settings N increases from 2 to 16. Furthermore, we find a counterintuitive phenomenon that for a fixed N, at most 2 Alices can share steering with 2 Bobs, while 4 or more Alices are allowed to share steering with a single Bob. We further analyze the robustness of the steering sharing by calculating the required purity of the initial Werner state, the lower bound of which varies from 0.503(1) to 0.979(5). Finally, we show that our both-sides sequential steering sharing scheme can be applied to control the steering ability, even the steering direction, if an initial asymmetric state or asymmetric measurement is adopted. Our work gives insights into the diversity of steering sharing and can be extended to study the problems such as genuine multipartite quantum steering when the sequential unsharp measurement is applied.
Uncovering the Activity of Alkaline Earth Metal Hydrogenation Catalysis Through Molecular Volcano Plots
Authors: Das Shubhajit , Tobel Bart De , Alonso Mercedes , Corminboeuf Clémence 
Published: August 2021

AbstractRecent advances in alkaline earth (Ae) metal hydrogenation catalysis have broadened the spectrum of potential catalysts to include candidates from the main group, providing a sustainable alternative to the commonly used transition metals. Although Ae-amides have already been demonstrated to catalyze hydrogenation of imines and alkenes, a lucid understanding of how different metal/ligand combinations influence the catalytic activity is yet to be established. In this article, we use linear scaling relationships and molecular volcano plots to assess the potential of the Ae metal-based catalysts for the hydrogenation of alkenes. By analyzing combinations of eight metals (mono-, bi-, tri-, and tetravalent) and seven ligands, we delineate the impact of metal-ligand interplay on the hydrogenation activity. Our findings highlight that the catalytic activity is majorly determined by the charge and the size of the metal ions. While bivalent Ae metal cations delicately regulate the binding and the release of the reactants and the products, respectively, providing the right balance for this reaction, ligands play only a minor role in determining their catalytic activity. We show how volcano plots can be utilized for the rapid screening of prospective Ae catalysts to establish a guideline to achieve maximum activity in facilitating the hydrogenation process.
Towards a Polymer-Brush-Based Friction Modifier for Oil
Authors: Gmür Tobias A. , Mandal Joydeb , Cayer-Barrioz Juliette , Spencer Nicholas D. 
Published: August 2021

AbstractTo meet the need for oil-compatible friction modifier additives that can significantly reduce energy consumption in the boundary-lubrication regime, a macromolecular design approach has been taken. The aim was to produce a lubricious polymer film on the sliding surfaces. A series of readily functionalizable block copolymers carrying an oleophilic poly(dodecyl methacrylate) block and a functionalizable poly(pentafluorophenyl methacrylate) block of various lengths was synthesized by means of reversible addition-fragmentation chain-transfer (RAFT) polymerization. The poly(pentafluorophenyl methacrylate) block was used to attach surface-active nitrocatechol anchoring groups to the polymer. The friction-reduction properties of these polymers were assessed with 0.5 wt% solutions in hexadecane by means of rolling-sliding macroscopic tribological tests. Block copolymers with roughly equal block lengths and moderate molecular weights were significantly more effective at friction reduction than all other architectures investigated. They also displayed lower friction coefficients than glycerol monooleate—a commercially used additive. The film-formation ability of these polymers was examined using a quartz-crystal microbalance with dissipation (QCM-D), by monitoring their adsorption onto an iron oxide-coated QCM crystal. The polymer with highest lubrication efficiency formed a thin film of ~ 17 nm thickness on the crystal, indicating the formation of a polymer brush. Interferometric rolling-sliding experiments with the same polymer showed a separating film thickness of ~ 20 nm, which is consistent with the QCM-D value, bearing in mind the compression of the adsorbed layers on the two sliding surfaces during tribological testing.
Graphical Abstract
Integrated 3D geomechanical characterization of a reservoir: case study of "Fuja" field, offshore Niger Delta, Southern Nigeria
Authors: Agoha C. C. , Opara A. I. , Okeke O. C. , Okereke C. N. , Onwubuariri C. N. , Akiang F. B. , Osaki L. J. , Omenikolo I. A. 
Published: August 2021

Abstract3D geomechanical characterization of "Fuja" field reservoirs, Niger Delta, was carried out to evaluate the mechanical properties of the reservoir rock which will assist in reducing drilling and exploitation challenges faced by operators. Bulk density, sonic, and gamma-ray logs from four wells were integrated with 3D seismic data and core data from the area to estimate the elastic and inelastic rock properties, pore pressure, total vertical stress, as well as maximum and minimum horizontal stresses within the reservoirs from empirical equations, using Petrel and Microsoft Excel software. 3D geomechanical models of these rock properties and cross-plots showing the relationship between the elastic and inelastic properties were also generated. From the results, Young's modulus, bulk modulus, bulk compressibility, shear modulus, Poisson's ratio, and unconfined compressive strength recorded average values of 5.11 GPa, 5.10 GPa, 0.023 GPa−1$$,$$
,
2.39 GPa, 0.39, and 39.0 GPa, respectively, in the sand, and 6.08 GPa, 6.09 Gpa, 0.016 GPa−1 2.84 GPa, 0.42, and 42.3 GPa, respectively, in shale, implying that the sand is less elastic and ductile and will deform before the shale under similar stress conditions. Results also revealed mean pore pressures of 13,248 psi and 15,220 psi in sand and shale units, respectively, mean total vertical stress of 28,193 psi, mean maximum horizontal stress of 26,237 psi, and mean minimum horizontal stress of 21,532 psi. From the geomechanical models, the rock elastic and inelastic parameters revealed higher values around the northeastern and parts of the eastern and western portions of the reservoir implying that mechanical rock deformation will be minimal in these sections of the field compared to other sections during drilling and post-drilling activities. The generated cross-plots indicate that a relationship exists between the elastic rock properties and unconfined compressive strength. Stress estimations within the reservoirs in relation to the obtained elastic and rock strength parameters show that the reservoirs are stable. These results will be invaluable in mitigating exploration and exploitation challenges.
Simulating language knowledge across the EU: language regimes, language learning and consequences for linguistic disenfranchisement
Authors: Civico Marco 
Published: August 2021

AbstractThe objective of this paper is to develop an simulation model able to test different language education orientations and their consequences for the EU population in terms of linguistic disenfranchisement, that is, the inability of citizens to understand EU documents and parliamentary discussions should their native language(s) no longer be official. I will focus on the impact of linguistic distance and language learning. Ideally, this model would be a tool to help EU policy makers make informed decisions about language practices and education policies, taking into account their consequences in terms of diversity and linguistic disenfranchisement. The model can be used to force agents to make certain choices in terms of language skills acquisition. The user can then go on to compare different scenarios in which language skills are acquired according to different rationales. The idea is that, by forcing agents to adopt certain language learning strategies, the model user can simulate policies promoting the acquisition of language skills and get an idea of their impact. In this way, the model allows not only to sketch various scenarios of the evolution of language skills among EU citizens, but also to estimate the level of disenfranchisement in each of these scenarios.
Robust Attitude Controller for NASA Astrobee Robots Operating in the ISS
Authors: Ruggiero Dario 
Published: August 2021

AbstractFree-flying robots have been recently developed to operate on-board the International Space Station (ISS) as semi-autonomous robotic assistants. NASA Astrobee robots are the new free-flyers proposed by NASA, and equipped with a 3 degree of freedom manipulator. This research aims to realize an accurate mathematical model of the robot attitude dynamics, and an attitude controller for the NASA Astrobee system. The robot has been modeled coupling the attitude dynamics with the manipulator motion, and robust attitude controllers are proposed to stabilize disturbances and configuration changes induced by the manipulator usage. First, a second-order twisting sliding mode controller is designed to achieve a trade-off among control law flexibility, robustness and accuracy. Among robust control strategies, sliding mode controllers are characterized as low complexity, and low computational cost control methods, making the system able to achieve reactivity and stability also when parameters are changed. The performance of the proposed control method are compared with a backstepping controller that include an iterative algorithm to compute an adaptive control, as function of disturbances and orientation error. The performance are evaluated and analyzed considering several scenarios in Matlab Simulink simulations, and combining Simulink and ROS Gazebo to run co-simulations with the NASA Astrobee simulator. Simulations with variable body mass, links masses, and different gripped objects have been run to test the controllers performance in off-design conditions.