bluuebunny/crossref_metadata_2025_split huggingface dataset:
Radial diffusion model for fragrance materials: Prediction and validation
Authors: Almeida Rafael N. , Rodrigues Alírio E. , Vargas Rubem M. F. , Cassel Eduardo 
Published: June 2021

AbstractA predictive model based on Fick's second law for radial diffusion is proposed and validated for modeling the diffusion of fragrance materials. A pure component, two binary systems, and a ternary system were used for validation. The model combines the prediction model to represent the liquid phase nonidealities, using the UNIFAC group contribution method, with the Fickian radial diffusion approach. The experimental headspace concentrations were measured in a diffusion chamber using the solid‐phase microextraction technique and quantified using gas chromatography with a flame ionization detector. The numerical solutions were obtained along with an analytical model considering constant surface concentration. The odor intensities of the studied systems were calculated using Stevens' power law and the strongest component model, respectively. The numerical simulation presented good adherence to the experimental gas concentration data. The proposed methodology is an efficient and validated tool to assess the radial diffusion of fragrance and volatile systems.
Taking new “poke” at femoral access: Applying optimal technique to minimize complications
Authors: Vetrovec George W. 
Published: June 2021

Key Points
Percutaneous coronary intervention femoral access‐related complications are reduced using a 21‐Gauge micropuncture needle compared to a standard 18‐Gauge access.
An absolute >1% reduction in groin vascular complications.
Future studies to assess the optimal benefits possible with multiple best practices.
Layered culprit lesions: A new insight
Authors: Musumeci Giuseppe , Annibali Gianmarco 
Published: June 2021

Key Points
Previous studies reported that larger plaque burden and smaller minimal luminal diameter at culprit lesions before treatment were predictors for restenosis.
Layered plaques are characterized by the presence of harder collagen than soft lipid plaques and require more aggressive post‐dilatation.
Large scale prospective studies with longer follow up are warranted to further characterize these features and their potential clinical implications.
Optimising Adsorptive Stripping Voltammetry: Strategies and Limitations
Authors: Wang Yuanzhe , Kumar Archana Kaliyaraj Selva , Compton Richard G. 
Published: June 2021

AbstractThe widely employed electroanalytical technique of adsorptive stripping voltammetry (AdsSV) is critically assessed and evaluated at a wide and diverse range of unmodified and nano‐particle modified carbon electrodes using the analyte fipronil as a paradigmatic case. The generic electroanalytical performances of the nano‐particle modified electrodes are investigated and compared with the unmodified electrodes revealing similar LOD values and pointing to intrinsic limitations of AdsSV arising from the non‐independence of the Faradaic and capacitive signals during the stripping step. Methods for facilitating the adsorption or using different waveforms that may offer a more favourable limit of detection (LOD) at the nano‐particle modified electrodes are suggested and assessed, specifically the use of adsorption onto particles prior to their use for modifying electrodes and also the recently introduced method of semi‐circular voltammetry.
TRR 277: Additive manufacturing in construction
Authors: Kloft Harald , Gehlen Christoph , Dörfler Kathrin , Hack Norman , Henke Klaudius , Lowke Dirk , Mainka Jeldrik , Raatz Annika 
Published: June 2021

AbstractThe building industry is one of the least digitalized sectors of the global economy to date. Unlike other industrial sectors, the manufacturing of buildings is characterized by traditional handicraft techniques and individualized construction processes. Digital production technologies from other industrial sectors have not become established in the construction industry because they do not allow the necessary individualization or are uneconomical to transfer to construction. The advantages of additive manufacturing (AM) technologies are that automation and individualization are not contradictory. Furthermore, a new design strategy is embedded in 3D printing, namely to build up material only where it is structurally or functionally needed. AM is, therefore, both economical and resource‐efficient. The DFG Collaborative Research Centre/Transregio TRR 277 Additive Manufacturing in Construction (AMC) of the two universities TU Braunschweig and TU Munich wants to research the technology of AM fundamentally and also contributes to the digitization of the construction industry. This article gives an overview of the goals, work programme, and methods of the TRR 277, as well as its three focus areas and 18 scientific research projects.
A methodological overview to defining and measuring “digital” financial literacy
Authors: Lyons Angela C. , Kass‐Hanna Josephine 
Published: June 2021

AbstractResearchers and financial practitioners alike recognize the importance of defining and measuring financial literacy (FL) to better understand its relationship to financial behavior and decision‐making. Despite many efforts, there is still no widely accepted definition or methodological approach for measuring FL. The rapid expansion of digital financial services (DFS), which promises to enhance financial inclusion and improve personal financial management, has brought to light a new challenge: linking FL to digital literacy (DL) and assessing their dual effect on financial outcomes. Recent research has even proposed a framework to operationalize the emerging concept of digital financial literacy (DFL), as traditional FL definitions and metrics have become insufficient to capture the specificities of financial services within a digital context. This survey article discusses empirical research techniques being used to assess FL, DL, and most recently DFL. It highlights the characteristics and limitations of these approaches and suggests ways to address some of the challenges related to the construction, testing, weighting, and standardization of multidimensional measures, as well as methodological issues related to modeling and estimation. The article is a helpful guide to researchers and practitioners interested in FL in general and in the emerging concept of “digital” financial literacy.
Adsorption of Biogenic Amines from Synthetic and Real Wastewater using a Zwitterion‐Functionalized Blast Furnace Slag
Authors: Dauda Mohammed , Al‐Malack Muhammad H. , Basheer Chanbasha 
Published: June 2021

AbstractBlast furnace slag functionalized with sulfamic acid (zwitterion) shows an extraordinary potential for the removal of biogenic amines from spiked water and real wastewater samples. The functionalized slag is characterized using scanning electron microscopy‐energy dispersive spectroscopy (SEM/EDS), Fourier transform infrared spectroscopy (FTIR), and acid site density test. Its performance toward the removal of selected biogenic amines (putrescine, tyramine, and 2‐phenylethylamine) is evaluated at different experimental conditions that include pH, contact time, adsorbent dosage, and initial concentration. The central composite experimental design is employed in investigating the effect of these individual factors and their interactions. The adsorption experiment results fit both the Langmuir isotherm model and pseudo‐second‐order kinetic model based on the computed high coefficient of determination (R2) values. Maximum adsorption capacity values of 80.64 mg g−1 for 2‐phenylethylamine, 12.5 mg g−1 for putrescine, and 64.52 mg g−1 for tyramine are estimated from the Langmuir isotherm model. Hydrogen bonding interaction between the S═O groups of the functionalized slag and protonated amino groups of biogenic amines is speculated as the possible mechanism of adsorption. The prepared functionalized slag also exhibits good adsorptive removal of biogenic amines in a date palm fruit processing industrial wastewater sample.
Photo/electrochemical Carbon Dioxide Conversion into C3+ Hydrocarbons: Reactivity and Selectivity
Authors: Abdelnaby Mahmoud M. , Liu Kaili , Hassanein Khaled , Yin Zongyou 
Published: June 2021

AbstractProducing high‐value fuels and chemicals via photo/electrochemical CO2 reduction reaction (CO2RR) remains an attractive goal to mitigate the negative impact of CO2 emissions and provide a sustainable energy source. The large industrial scale is currently discriminated by the relatively low product selectivity (a high cost is expected for separating the products) and the activity. The selective CO2 reduction into higher‐order multi‐carbon products is desirable from the economic point of view. Yet, most of the reported electrocatalysts have produced C1 and C2 products; however, the production of C3 products is less common. Cu‐based catalysts are the most documented systems to produce C3 products because of the exclusive C−C coupling ability of the Cu system. However, creating multi‐carbon products on non‐Cu catalysts is unfairly discussed. Growing the research activity on non‐Cu catalysts will enrich the categories of alternative catalysts and propose more understanding of the CO2RR mechanism. This should guide the development of more creative catalysts with the optimum configuration for high activity and selectivity for high‐value C3 products. The catalysts′ development progress, including metallic Cu, biphase, or bimetallic Cu, non‐Cu‐based catalysts, has been discussed in light of the catalyst activity and selectivity. Some insights on the reaction mechanism for the desired C3 product (most commonly, n‐propanol) and other C3+ products are also discussed.
Contextual defeasible reasoning framework for heterogeneous knowledge sources
Authors: Mahfooz ul Haque Hafiz , Muhammad Akhtar Salwa , Uddin Ijaz 
Published: June 2021

AbstractRecent years have witnessed the rapid advances of smart computing paradigms in a ubiquitous environment. These paradigms make human life much easier, comfortable, secure and hassle free. In a smart computing environment, it is a fact that human users interact with the systems dynamically with or without human intervention using different modalities. The core emphasize is given on the intelligent systems that run in a highly decentralized environment with different communication mechanism. Literature highlighted numerous formalisms to bridge the communication modalities for different knowledge sources. Among others, Multi‐context System (MCS) has been advocated as one of the most suitable formalism to interlink different contexts (domains) dynamically in the distributed environment. However, interaction of these knowledge sources sometime may produce inconsistent and conflicting results. In this work, we presents a contextual defeasible reasoning based multi‐agent formalism to handle the inconsistency issues. This framework relies on the semantic knowledge sources which allow us to model context‐aware non‐monotonic reasoning agents to infer the desired goals using the extracted rules from the ontologies and handles inconsistencies using conflicting contextual information. We illustrate the validity and correctness of the proposed formalism using a simple case study of a smart healthcare system with the prototypal implementation of the system.
Macrocyclic Aromatic Amide Foldamer: Synthesis, Twisted‐to‐Boxlike Conformational Switching, and Molecular Recognition
Authors: Li Dongyao , Qin Tian , Liu Xinlu , Zhang Boyuan , Zhong Fangrui , Ma Chunmiao , Gan Quan 
Published: June 2021

AbstractIn this study, a twisted macrocycle was synthesized via ring closure of a double‐helical aromatic oligoamide foldamer with two disulfide bridges. Single‐crystal X‐ray structure and NMR spectroscopy demonstrate the twisted conformation of macrocycle both in the solid state and in solution. As a result of the rearrangement of hydrogen bonding preference, the twisted conformation could be transformed to boxlike through protonation of the pyridine segments of macrocycle. In addition, the NMR titration experiments revealed that flat aromatic guests (e. g., coronene and perylene) could bind to the boxlike macrocycle with a 1 : 1 binding stoichiometry. The addition of base to the host‐guest complexes resulted in conformational reversal of the macrocycle from boxlike to twisted as well as the release of guest.
Generating Enteric Nervous System Progenitors from Human Pluripotent Stem Cells
Authors: Gogolou Antigoni , Frith Thomas J.R. , Tsakiridis Anestis 
Published: June 2021

AbstractThe intrinsic innervation of the gastrointestinal (GI) tract is comprised of enteric neurons and glia, which are buried within the wall of the bowel and organized into two concentric plexuses that run along the length of the gut forming the enteric nervous system (ENS). The ENS regulates vital GI functions including gut motility, blood flow, fluid secretion, and absorption and thus maintains gut homeostasis. During vertebrate development it originates predominantly from the vagal neural crest (NC), a multipotent cell population that emerges from the caudal hindbrain region, migrates to and within the gut to ultimately generate neurons and glia in response to gut‐derived signals. Loss of GI innervation due to congenital or acquired defects in ENS development causes enteric neuropathies which lack curative treatment. Human pluripotent stem cells (hPSCs) offer a promising in vitro source of enteric neurons for modeling human ENS development and pathology and potential use in cell therapy applications. Here we describe in detail a differentiation strategy for the derivation of enteric neural progenitors and neurons from hPSCs through a vagal NC intermediate. Using a combination of instructive signals and retinoic acid in a dose/time dependent manner, vagal NC cells commit into the ENS lineage and develop into enteric neurons and glia upon culture in neurotrophic media. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC.Basic Protocol 1: Generation of vagal neural crest/early ENS progenitors from hPSCsBasic Protocol 2: Differentiation of hPSC‐derived vagal NC/early ENS progenitors to enteric neurons and glia
Planar circuit analysis of ultra‐wideband millimeter‐wave inductor using transmission line sections
Authors: Ashrafian Ahmadali , Mohammad‐Taheri Mahmoud , Naser‐Moghaddasi Mohammad , Khatir Mehdi , Ghalamkari Behbod 
Published: June 2021

AbstractRegarding the capacitive nature of input/output impedances of millimeter‐wave amplifiers, it is necessary to utilize wideband inductive elements for the design of input/output impedance matching circuits. In this paper, the design and optimization of an ultra‐wideband transmission line inductor with an inductance of less than 100 pH are introduced for using at frequencies above 50 GHz. The reason of using this inductor is its superior performance compared to that of a spiral inductor and single‐coil inductor. The inductor consists of three rectangular microstrip line segments with nonequal and variable characteristic impedances. The design is done by calculating the impedance matrix using planar circuit analysis (PCA) based on a planar waveguide model, segmentation/desegmentation methods, and an intelligent algorithm in MATLAB. With this design method, the effect of discontinuities, fringing fields at the edges of the microstrip, and the conductor as well as dielectric losses can be considered while minimizing the dispersion effect. In this study, to reduce the calculation time of the impedance matrix, the doubly infinite series are reduced to a singly infinite series by summing the inner sum for a rectangular segment whose one side is shorted and three other sides are open.
Inverse Bremsstrahlung absorption coefficient in inhomogeneous plasma with nonextensive electron velocity distribution and Tsallis exponential electron density profile
Authors: Babapoor Abolfazl , Sobhanian Samad , Kouhi Mohammad , Talebzadeh Robabeh 
Published: June 2021

AbstractInverse bremsstrahlung (collisional) absorption of the laser beam is studied in plasma with a generalized (q‐nonextensive) electron velocity distribution and some kind of generalized electron density profile. It is shown that for some values of parameters designating the q‐nonextensive electron velocity distribution function and its generalized density profile, the calculated absorption coefficient reduces to the already known cases with Maxwellian velocity distribution with linear and exponential density profiles.
Crab‐driven processing does not explain leaf litter‐deposition in mangrove crab burrows
Authors: Forgeron Sophie J. , Quadros Aline F. , Zimmer Martin 
Published: June 2021

AbstractMany mangrove crab species drag freshly fallen leaf litter into their burrows and store it there for some time prior to consumption. Potential explanations for this behavior include (a) avoidance of competition for a scarce resource, (b) prevention of removal of a scarce resource upon tidal outwelling, or (c) processing of an initially unpalatable food source, be it physically through leaching, through the activity of the sediment microbiota, or driven by the activity of the crab itself. To test the latter, we study the effect of burying mangrove leaf litter on two relevant physicochemical litter characteristics and on its digestibility to crabs in a laboratory experiment, using artificial substratum with low microbial activity. Freshly fallen leaves of two common mangrove species, Bruguiera gymnorhiza and Ceriops tagal, were left for two weeks either inside burrows used by Neosamartium asiaticum (Sesarmidae), or on top of the substratum, before they were offered to the crabs as food. Leaf toughness and total phenolic content differed significantly between mangrove species. Upon two weeks of decay, phenolic content of both leaf litter species and toughness of B. gymnorhiza​ changed significantly. However, neither litter characteristcs nor assimilation efficiency differed between treatments. We conclude that storing mangrove leaf litter in crab burrows in a microbe‐poor environment does not affect litter digestibility. Hence, crabs themselves do not contribute to litter processing during storage inside the burrow. If it is litter processing, rather than the avoidance of competition or litter removal by tides, that renders the storage of litter inside the burrow advantageous, it will be microbial activity that drives this process. Further studies should explicitly focus on processing through the activity of the sediment microbiota.
Disentangling the role of oomycete soil pathogens as drivers of plant–soil feedbacks
Authors: Domínguez‐Begines Jara , Ávila José M. , García Luis V. , Gómez‐Aparicio Lorena 
Published: June 2021

AbstractInteractions among plant species and their soil biota drive plant–soil feedbacks (PSFs) that play a major role in the dynamics and diversity of plant communities. Among the different components of the soil community, pathogens are considered to be the main drivers of negative PSFs. Despite this, the number of studies that have experimentally quantified the contribution of soil pathogens to PSFs remains considerably low. Here we conducted a greenhouse experiment with oomycete‐specific fungicide to quantify the contribution of soil pathogens, and particularly oomycete pathogens, to individual and pairwise PSFs in forest communities. We used as a case study Mediterranean mixed forests dominated by Quercus suber and invaded by the oomycete pathogen Phytophthora cinnamomi. The fungicide treatment was crossed with a competition treatment to explore how conspecific neighbors might modify pathogen effects. To place the results of the experiment in a wider context, we also conducted a systematic review of published papers that explicitly used fungicide to explore the role of pathogens in PSF experiments. Our experimental results showed that oomycete pathogens were the main drivers of individual PSFs in the study forests. Oomycete effects varied among tree species according to their susceptibility to P. cinnamomi, driving negative PSFs in the highly susceptible Q. suber but not in the coexistent Olea europaea. Oomycete‐driven PSFs were not modified by intraspecific competition. Oomycete pathogens were also major contributors to negative pairwise PSFs assumed to promote species coexistence. Results from the systematic review supported the novelty of our experimental results, since only three studies had previously used oomycete‐specific fungicide in a PSF context and none in systems invaded by exotic oomycetes. Overall, our results provide novel evidence of oomycete pathogens (including the exotic P. cinnamomi) as fundamental drivers of negative individual and pairwise PSFs with implications for species coexistence in invaded communities. Although in the short‐term invasive pathogens might contribute to species coexistence by causing self‐limitation in dominant species, strong inter‐specific variation in self‐limitation might undermine coexistence in the long‐term. Because of the increasing number of exotic oomycetes worldwide, further attention should be given to oomycetes as drivers of PSFs in plant communities.
UcnPowerNet: Residual learning based clustering and interference coordination in 5G user‐centric networks
Authors: Liu Jianghui , Zhang Hongtao 
Published: June 2021

AbstractIn 5th Generation and beyond (5G&B) wireless networks, artificial intelligence can be explored to address problems with uncertain, time‐variant, and complex features. User‐centric network (UCN) can eliminate cell boundaries and reduce interference. And as for the intelligent resource management and interference coordination, the scalability of existing deep learning‐based works degrades greatly as the size of the network increases, which requires more layers to be stacked and causes gradient vanishing problem. In this article, a new deep learning‐based resource management model, UcnPowerNet, is proposed to approximate iterative algorithms for dynamic clustering and interference coordination in complicated UCN, where multiple residual blocks are concatenated with shortcut connections to asymptotically learn the mapping between input and output. Specifically, a cooperative weighted minimum mean square error (WMMSE) algorithm for UCN is carried out in the real domain to generate large near‐optimal training datasets; then we train UcnPowerNet to approximate the output of WMMSE algorithm by minimizing the loss function of mean square error. Moreover, convolutional layer and normalization layer are leveraged to decrease the number of weights and avoid gradients vanishing, respectively. Extensive experiments demonstrate the high approximation accuracy of UcnPowerNet with 94.90% sum‐rate relative to the conventional iterative algorithm while achieving more than 100 speed up.