bluuebunny/crossref_metadata_2025_split huggingface dataset:
Thickness Effects of Surface Direct Fluorination and Plasma Modification on Ultra‐High Molecular Weight Polyethylene Ultrathin Membranes
Authors: Yang Kailin , Xu Zhao , Li Runlai , Liu Yang , Sun Weilong , Tang Yong , Liu Xiangyang , Fu Qiang 
Published: October 2022

AbstractFor the first time, an ultrathin type of ultra‐high molecular weight polyethylene (UHMWPE) membrane of only 350 nm thickness is surface modified by direct fluorination and plasma treatments. Then a notable suppression in crystallinity is observed. A total of four kinds of thickness, from 350 nm to 6 µm, are investigated to correlate the modification effect on the thickness. Microscopically, the continuous shish‐kebab structure transforms into scattered lamellae clusters. And the damage to the surface morphologies increases as the thickness decreases, yet the resultant contact angles remain almost constant. Especially for the 350 nm thin UHMWPE, the mildest fluorination treatment slightly changes the contact angle, while the multiscale structure changes significantly from crystalline to mesoscopic level. As thickness decreases from 6 µm to 350 nm, the modified volume ratio of membranes increases, and the volumetric modification is almost realized for the 350 nm membrane.
Nonlinear dynamics of modulated waves on graphene like quantum graphs
Authors: Gilg Steffen , Schneider Guido , Uecker Hannes 
Published: October 2022

AbstractWe consider cubic Klein–Gordon equations on infinite two‐dimensional periodic metric graphs having for instance the form of graphene. At non‐Dirac points of the spectrum, with a multiple scaling expansion Nonlinear Schrödinger (NLS) equations can be derived in order to describe slow modulations in time and space of traveling wave packets. Here we justify this reduction by proving error estimates between solutions of the cubic Klein–Gordon equations and the associated NLS approximations. Moreover, we discuss the validity of the modulation equations appearing by the same procedure at the Dirac points of the spectrum.
Social isolation and social anxiety as drivers of generation Z's willingness to share personal information on social media
Authors: Lyngdoh Teidorlang , El‐Manstrly Dahlia , Jeesha Krishnan 
Published: October 2022

AbstractGeneration Z's (gen z) sharing of personal information on social media is a growing phenomenon with significant ramifications. Existing research, however, focuses on examining the role of social and/or psychological factors and fails to consider how and when social, psychological, and organizational factors affect gen z's willingness to share personal information on social media. To fill this gap, we propose a conceptual model based on the tenets of sociometer theory, to understand the dynamics of gen z's willingness to share personal information while considering its process and boundary conditions. Using a sequential multi‐study design, we conducted an experiment followed by a survey to test our hypotheses using data collected from gen z in India. Our findings show that when gen z feels socially isolated/anxious, they are more likely to share personal information on social media. The effect of social isolation on sharing of personal information increases when gen z fear that they are missing out on the rewarding experiences others are having, are engaged in repetitive negative thoughts and perceive their firm's privacy policy as transparent and ethical. Our findings provide a better understanding of why, how, and when gen z's are willing to share personal information on social media. We extend existing limited research on the psychological aspects of digital natives' interaction with modern technologies. Our results equip social media marketing and brand managers with the knowledge they need to increase gen z's willingness to share personal information.
Chemical Behavior of Water Absorption in a Carbon/Epoxy 3D Woven Composite
Authors: Gillet Camille , Nassiet Valérie , Poncin‐Epaillard Fabienne , Hassoune‐Rhabbour Bouchra , Tchalla Tatiana 
Published: October 2022

AbstractIn this paper, moisture absorption of carbon/epoxy‐amine 3D woven composite materials is studied by immersion in distilled water at 70°C. If the moisture uptake measured by gravimetry is slow and weak over time, various antagonistic phenomena appear. Desorption highlights mass losses due to chemical changes in the material that mask the true water content of the aged samples. The X‐ray photoelectron spectrometry (XPS) reveals a decrease in the carbon and nitrogen proportions and an increase in the oxygen proportions after ageing, allowing to observe surface erosion and oxidation of the matrix. Finally, the rise of silicon is a sign of extraction of the silica‐based particles exuded to the surface. This presence is confirmed by Fourier transform infrared spectroscopy (FTIR).
Novel Biobased Polyester Resins for Wood Applications: Morphological, Thermal, and Chemical Characterization
Authors: Malletzidou Lamprini , Papadopoulou Electra , Papadopoulos Lazaros , Markessini Charles , Bikiaris Dimitrios N. , Chrissafis Konstantinos , Vourlias George 
Published: October 2022

AbstractThe present study focuses on the interaction of recently synthesized unsaturated polyester resins (UPRs) with wood. The UPRs are prepared from succinic acid, maleic anhydrite, and ethylene glycol or poly(ethylene glycol) as biobased monomers and crosslinked with acrylic acid. In this framework, small 3‐ply composite specimens (0.1 × 2.5 × 10 cm) of wood veneers are prepared with such binders to investigate their interaction with wood. The produced experimental specimens are studied with Fourier transform infrared spectroscopy, thermogravimetry, and scanning electron microscopy to explore their morphological and thermal degradation characteristics, as well any chemical changes from the interaction of the UPRs with wood. The experimental results indicate no chemical changes from their interaction with wood.
Thermo‐Electric Properties of Poly(lactic) Acid Filled with Carbon‐Based Particles: Experimental and Simulation Study
Authors: Spinelli Giovanni , Guarini Rosella , Kotsilkova Rumiana , Ivanov Evgeni , Menseidov Dzhihan , Romano Vittorio 
Published: October 2022

AbstractPolymer composites filled with high thermal and electrical conductivity nanofillers show enhanced thermo‐electric properties which encourage their use in heat transfer applications. In the present study, nanocomposites based on polylactic acid (PLA) containing up to 9 wt% of multi‐walled carbon nanotubes (trade name: N7000) and graphene nanoplates (trade name: TNIGNP) are produced via melt compounding and then morphologically, electrically, and thermally investigated. The results are correlated to the different characteristic of the fillers and their interaction with the PLA. At the highest investigated filler concentration, an electrical conductivity of about 2 S m–1and a thermal conductivity of 0.725 W m–1 K–1are measured respectively for nanocomposites based on N7000 and TNIGNP, which are decisively higher than the values measured for unfilled PLA (5.9 ×∙10–2 S m–1and 0.205 W m–1 K–1). Moreover, multiphysics simulations are performed on the best performing nanocomposites for evaluating their thermo‐electric properties when an electrical heating or a direct heat flux are applied.
From Hidden Shear‐Elasticity to Opto‐ and Thermo‐Elastic Properties in Confined Polymer Melts and Viscous Liquids
Authors: Noirez Laurence , Kume Eni , Baroni Patrick 
Published: October 2022

AbstractThe newly identified low frequency elasticity of mesoscopic liquids opens the route to the identification of novel dynamic liquid properties. Two experimental examples illustrating the impact of the liquid elasticity in optics and thermodynamics are presented. It is revealed that at mesoscopic scale, liquids are able to convert the mechanical energy in optical and thermal effects. The fluids are submitted to a low frequency (∼Hz) mechanical shear strain (external stress), thus excluding a coupling with molecular time scales. The dynamic response is probed using two in situ methods accessing in real time optical response of the isotropic phase of liquid crystals and probing the thermal response upon shear deformation. In both cases, a dynamic response is identified: the isotropic phase of liquid crystals exhibits a low frequency dynamic birefringence while ordinary liquids exhibit cold and hot thermal waves synchronous to the low frequency excitation.
A transformer neural network for predicting near‐surface temperature
Authors: Alerskans Emy , Nyborg Joachim , Birk Morten , Kaas Eigil 
Published: October 2022

AbstractA new method based on the Transformer model is proposed for post‐processing of numerical weather prediction (NWP) forecasts of 2 m air temperature. The Transformer is a machine learning (ML) model based on self‐attention, which extracts information about which inputs are most important for the prediction. It is trained using time series input from NWP variables and crowd‐sourced 2 m air temperature observations from more than 1000 private weather stations (PWSs). The performance of the new post‐processing model is evaluated using both observational data from PWSs and completely independent observations from the Danish Meteorological Institute (DMI) network of surface synoptic observations (SYNOP) stations. The performance of the Transformer model is compared against the raw NWP forecast, as well as against two benchmark post‐processing models; a linear regression (LR) model and a neural network (NN). The results evaluated using PWS observations show an improvement in the 2 m temperature forecasts with respect to both bias and standard deviation (STD) for all three post‐processing models, with the Transformer model showing the largest improvement. The raw NWP forecast, LR, NN and Transformer model have a bias and STD of 0.34 and 1.96°C, 0.03 and 1.63°C, 0.10 and 1.53°C and 0.02 and 1.13°C, respectively. The corresponding results using DMI SYNOP stations also show improved forecasts, where the Transformer model performs better than both the raw NWP forecast and the two benchmark models. However, a dependence on distance to the coast and cold temperatures is observed.
Speed vs Accuracy: Effect on Ligand Pose Accuracy of Varying Box Size and Exhaustiveness in AutoDock Vina
Authors: Agarwal Rupesh , Smith Jeremy C. 
Published: October 2022

AbstractStructure‐based virtual high‐throughput screening involves docking chemical libraries to targets of interest. A parameter pertinent to the accuracy of the resulting pose is the root mean square deviation (RMSD) from a known crystallographic structure, i. e., the ‘docking power’. Here, using a popular algorithm, Autodock Vina, as a model program, we evaluate the effects of varying two common docking parameters: the box size (the size of docking search space) and the exhaustiveness of the global search (the number of independent runs starting from random ligand conformations) on the RMSD from the PDBbind v2017 refined dataset of experimental protein‐ligand complexes. Although it is clear that exhaustiveness is an important parameter, there is wide variation in the values used, with variation between 1 and >100. We, therefore, evaluated a combination of cubic boxes of different sizes and five exhaustiveness values (1, 8, 25, 50, 75, 100) within the range of those commonly adopted. The results show that the default exhaustiveness value of 8 performs well overall for most box sizes. In contrast, for all box sizes, but particularly for large boxes, an exhaustiveness value of 1 led to significantly higher median RMSD (mRMSD) values. The docking power was slightly improved with an exhaustiveness of 25, but the mRMSD changes little with values higher than 25. Therefore, although low exhaustiveness is computationally faster, the results are more likely to be far from reality, and, conversely, values >25 led to little improvement at the expense of computational resources. Overall, we recommend users to use at least the default exhaustiveness value of 8 for virtual screening calculations.
Hydrocortisone‐Containing Animal‐Derived Food Intake Affects Lipid Nutrients Utilization
Authors: Jia Wei , Wu Xixuan , Shi Lin 
Published: October 2022

ScopeAs the tremendous increases in consumption of animal‐derived food, endogenous hydrocortisone migrating along the food chain to organism arouses extensive attention. This study aims to investigate the cumulative impacts of dietary hydrocortisone intake and mechanistic understanding on metabolism of lipid nutrients.Methods and resultsA total of 120 porcine muscles samples with different concentrations of hydrocortisone are collected at three time points. An operational food chain simulation framework is constructed and 175 lipid molecules are identified by UHPLC‐Q‐Orbitrap HRMS. Compared to the control group, 66 lipid molecules are significantly different, including 17 triglycerides and 31 glycerophospholipids. Integrated analyses of lipidomics and proteomics indicate that hydrocortisone promotes adipose triglyceride lipase and hormone sensitive lipase activity to precondition for triglycerides hydrolysis. Quantitative lipidomics analysis shows the presence of hydrocortisone decreases the concentration of docosahexaenoic acid (3.66 ± 0.15–3.09 ± 0.12 mg kg−1) and eicosapentanoic acid (0.54 ± 0.09–0.48 ± 0.06 mg kg−1). A noteworthy increase of most saturated triglycerides concentration with the prolonging of time is observed.ConclusionsHydrocortisone originating from animal‐derived food induces glycerophospholipids degradation and triglycerides hydrolysis through promoting adipose triglyceride lipase, hormone sensitive lipase, and phosphoglycerate kinase activity and further intervenes lipid nutrients utilization.
Design of an all hollow fiber‐coupled middle infrared laser heterodyne radiometer (LHR)
Authors: Shen Fengjiao , Hu Xueyou , Lu Jun , Xue Zhengyue , Tan Tu , Gao Xiaoming , Chen Weidong 
Published: October 2022

AbstractAn integrated optics method, using hollow fibers which easily ensures optimal beam alignment of the local oscillator (LO) and incoherent solar radiation and promotes high photomixing efficiency, is proposed for a compact, rugged, and high‐efficiency all hollow fiber‐coupled photomixing assembly in the mid‐infrared (IR). The method is adopted for the design of an all‐hollow fiber‐coupled mid‐IR laser heterodyne radiometer (LHR) using an 8 µm external‐cavity quantum cascade laser as LO. Coupling efficiency, attenuation coefficients, total‐loss transmission, and output beam divergence of the HE11 mode in hollow fibers for the all hollow fiber‐coupled mid‐IR LHR are discussed in detail and are shown to highly depend on the launch conditions. Meanwhile, the selection of input and output coupling lenses that are associated with the hollow fibers is also studied for achieving the theoretical loss of the HE1m mode in hollow fibers and high heterodyne performance. It shows that using a hollow fiber for photomixing supersedes conventional spatially separated optics.
Heterogeneous Overhauser‐DNP on 1H dominated by scalar coupling in aqueous solution
Authors: Milani Jonas , Saenz Felipe , Roussel Christophe , Ansermet Jean‐Philippe 
Published: October 2022

AbstractThe Overhauser Dynamic Nuclear Polarization (O‐DNP) of 1H nuclei usually involves a dipolar coupling with the polarizing agent, whereas scalar coupling via hyperfine interactions are more common with 13C nuclei. Here, we show a scalar‐coupling dominated 1H O‐DNP, using polyaniline as a heterogeneous polarizing agent in an aqueous solution.
Magnetic resonance imaging reconstruction using a deep energy‐based model
Authors: Guan Yu , Tu Zongjiang , Wang Shanshan , Wang Yuhao , Liu Qiegen , Liang Dong 
Published: October 2022

AbstractAlthough recent deep energy‐based generative models (EBMs) have shown encouraging results in many image‐generation tasks, how to take advantage of self‐adversarial cogitation in deep EBMs to boost the performance of magnetic resonance imaging (MRI) reconstruction is still desired. With the successful application of deep learning in a wide range of MRI reconstructions, a line of emerging research involves formulating an optimization‐based reconstruction method in the space of a generative model. Leveraging this, a novel regularization strategy is introduced in this article that takes advantage of self‐adversarial cogitation of the deep energy‐based model. More precisely, we advocate alternating learning by a more powerful energy‐based model with maximum likelihood estimation to obtain the deep energy‐based information, represented as a prior image. Simultaneously, implicit inference with Langevin dynamics is a unique property of reconstruction. In contrast to other generative models for reconstruction, the proposed method utilizes deep energy‐based information as the image prior in reconstruction to improve the quality of image. Experimental results imply the proposed technique can obtain remarkable performance in terms of high reconstruction accuracy that is competitive with state‐of‐the‐art methods, and which does not suffer from mode collapse. Algorithmically, an iterative approach is presented to strengthen EBM training with the gradient of energy network. The robustness and reproducibility of the algorithm were also experimentally validated. More importantly, the proposed reconstruction framework can be generalized for most MRI reconstruction scenarios.
Revisiting the superaromatic stabilization energy as a local aromaticity index for excited states
Authors: Jorner Kjell 
Published: October 2022

AbstractThe graph theory of aromaticity is a useful framework for analyzing the aromatic properties of polycyclic aromatic hydrocarbons. We present here the magnetically based superaromatic stabilization energy (m‐SSE) as a local aromaticity index and validate it against its topologically based counterpart t‐SSE. By comparing to DFT‐computed aromaticity indices of triplet state excited polybenzenoid hydrocarbons, we find that semi‐quantitative agreement can be reached by using the variable
method to account for bond‐length alternation. The m‐SSE can further be separated into orbital and circuit contributions to gain insight into the basis of the aromatic properties of individual rings. Fully automated algorithms for calculating both m‐SSE and t‐SSE are now available in the COULSON package. We envision that these graph theoretical aromaticity indices will be of great use to the community to analyze the local aromaticity of excited states.
Evaluation of control for the management of four pest species (Rhagoletis cerasi L., Ceratitis capitata Wied. (Diptera: Tephritidae), Drosophila suzukii (Matsumura) and Zaprionus indianus (Gupta) (Diptera: Drosophilidae)) in organic cherry‐growing area
Authors: Özbek Çatal Burcu , Çalişkan Keçe Asime Filiz , Amangeldİ Zhanerke , Ulusoy Mehmet Rifat 
Published: October 2022

AbstractBACKGROUNDIn recent years, the fruit flies Rhagoletis cerasi L., Ceratitis capitata Wied. (Diptera: Tephritidae), Drosophila suzukii (Matsumura) and Zaprionus indianus (Gupta) (Diptera: Drosophilidae) (Tephritidae and Drosophilidae) have become an increasingly serious problem for cherry production in Turkey, which ranks first among cherry‐producing countries. Intensive chemical control of these pests is used and little information about alternative methods of control is available for these pests, especially in organic farming. The purpose of our study was to determine a new, cost‐efficient and more ecofriendly method of pest control. The study was carried out during 2019–2020 at two different locations in Turkey: Adana‐Pozantı (Alpu, 1070 m, and Belemedik, 700 m).RESULTSThis study investigated the effectiveness against fruit flies of netting covering trees, a mass capture technique and an insecticide suitable for use in organic agriculture (spinosad). A comparative insecticide (thiacloprid) was also applied. The methods performed quite differently from the control and were shown to be particularly effective against R. cerasi and D. suzukii. The most effective control method against the four pest species was the application of netting (100% effect). The other methods of control included in the experiment may also be useful in organic cherry cultivation.CONCLUSIONThe methods presented here meet the requirements for conventional and organic production. Application of netting in particular can form the basis of organic production requirements for the management of fruit flies. It has been observed that fruits ripen a little later, and fruit monilia and aphid damage are less in trees protected by netting. This is a commercial advantage, especially for late maturing varieties, and additional studies are needed for these varieties. © 2022 Society of Chemical Industry.
The effect of grape products on liver enzymes: A systematic review and meta‐analysis of randomized controlled trials
Authors: Zamani Mohammad , Ashtary‐Larky Damoon , Hafizi Nadia , Naeini Fatemeh , Rezaei Kelishadi Mahnaz , Clark Cain C. T. , Davoodi Sayed Hosein , Asbaghi Omid 
Published: October 2022

AbstractThe favorable influence of grape consumption on metabolic diseases has previously been shown in studies. We sought to assess the effects of grape intake on liver enzymes, including alanine aminotransferase (ALT), aspartate aminotransferase (AST), and alkaline phosphatase (ALP), in adults. We performed literature search in online databases, to find eligible randomized controlled trials (RCTs). we considered RCTs that met the following criteria: RCTs consisted of use of grape products on ALT, AST, and ALP in adults (≥18 years) with at least 2 weeks intervention duration. Pooling data from 11 trials showed that grape products intake significantly reduced ALP (p = .010), without any significant changes in ALT (p = .234) and AST (p = .300). In subgroup analysis, we found a significant reduction in ALP, ALT, and AST when the duration of intervention was ≥12 weeks, and when grape seed extract (GSE) was administered. The variable duration and dosage of intervention was one of the sources of bias in our meta‐analysis. Additionally, participants involved in included studies had different physiological status and various age groups. Grape products administration may significantly improve ALT, AST, and ALP in adults in long‐term interventions and/or when GSE is administered. It should be noted that the favorable effects of grape consumption were small and may not reach clinical importance.
Simultaneous hysteresis‐dynamics compensation in high‐speed, large‐range trajectory tracking: A data‐driven iterative control
Authors: Wang Zhihua , Zou Qingze 
Published: October 2022

AbstractIn this article, a data‐driven difference‐inversion‐based iterative control (DDD‐IIC) approach is proposed to compensate for both nonlinear hysteresis and dynamics of Hammerstein systems. Simultaneous hysteresis‐dynamics compensation is needed in control of Hammerstein systems such as smart actuators, where effects of hysteresis and dynamics coexist and become pronounced in high‐speed, large‐range output tracking. Challenges, however, arises as hysteresis modeling, as needed in many existing control methods, can be complicated and prone to uncertainties, and the hysteresis and the dynamics are coupled and tend to change due to the variations of the system conditions (e.g., the aging of smart actuators). The proposed DDD‐IIC technique aims to achieve simultaneous hysteresis‐dynamics compensation with no need for modeling hysteresis and/or dynamics, and with both precision tracking and good robustness against hysteresis/dynamics variations. The convergence of the DDD‐IIC algorithm in the presence of random output disturbance/noise is analyzed. It is shown that when the noise is negligible, exact tracking is achieved and the size of hysteresis accounted is given by the Golden ratio. The proposed DDD‐IIC method is demonstrated via experiments of high‐speed large‐range output tracking on two different types of smart actuators with symmetric and asymmetric hysteresis behavior, respectively.