bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring interoperability of distributed Ledger and Decentralized Technology adoption in virtual enterprises
Authors: Bokolo Anthony Jnr. 
Published: July 2022

AbstractThe breakthrough of Distributed Ledger Technologies (DLT) has enabled the emergence and implementation of a wide range of digital platforms in Virtual Enterprises (VE) which collaborate to provide digital services. DLT has the potential to revolutionize VE by offering transparent, decentralized, trustworthy, data provenance, reliable, and auditable features. Yet, the full deployment of DLT systems and digital platforms is still limited since some systems are operating in isolation. Hence, DLT interoperability is one of the challenges inhibiting widespread adoption of DLT platforms. DLT interoperability represents the ability for one distributed ledger platform to interact and share data with other legacy digital applications. It is inevitable to orchestrate these digital platforms fragments by introducing a cross-DLT platform integration to govern data usage within VE. Presently, already proposed approaches for DLT interoperability such as naive relay, sidechain, oracle solutions notary scheme, or relay chain are mostly not employed as they are either resource-intensive or too expensive to operate. Therefore, this paper presents a layered architecture that aids interoperability of DLT, and digital platforms based on IOTA Tangle. Design science method is adopted, and case demonstration is carried out to show how IOTA Tangle enable VE to provide an innovative virtual asset payment platform for seamless electric mobility as a service to clients. IOTA was employed as the DLT platform due to its data traceability, immutability, and tamper-proof features which allow for verification of integrity of data. IOTA offers flexibility and performance to support a reliable digital solution. Findings from this study presents a layered architecture that aids IOTA Tangle to make requests, inter-communicate, and share data via RESTful application programming interface as gateway with other external digital platforms deployed by VE to achieve an interoperable eco-system.
Enhanced biocontrol services in artificially selected strains of Orius laevigatus
Authors: Mendoza José Enrique , Balanza Virginia , Rodríguez-Gómez Amador , Cifuentes Dina , Bielza Pablo 
Published: July 2022

AbstractAugmentative biological control in protected crops relies mainly on omnivorous predators. Their performance as biological control agents (BCA) depends on several characteristics of the species, which in turn may differ among strains within a species. We have recently reported the achievement of twoOrius laevigatus(Hemiptera: Anthocoridae) strains showing a significant larger body size or better fitness when feeding on pollen, two characteristics having a key impact on field performance. However, selection towards a specific trait might result in trade-offs, such as reduced predation capacity, which may impair control efficiency. Therefore, the predation capacity of these selected populations was tested in laboratory as a first step prior to its field use. Functional response to different densities ofFrankliniella occidentalis(Thysanoptera: Thripidae) (adults and larvae) andMyzus persicae(Hemiptera: Aphididae) (nymphs) were studied in the large-sized and pollen-tolerantO. laevigatusstrains in comparison with commercial and wild populations. A type-II functional response was observed regardless of the population. Body size was significantly related to thrips but not to aphid predation. The large-sized strain showed a superior predation capacity, both on thrips larvae and especially on adult thrips, although not on aphids. Therefore, the larger body size of the selected strain may increase its effectiveness as BCA of thrips. Regarding the pollen-tolerant strain, no trade-offs were observed in predation rates on adults or larvae of thrips, but it showed higher predation capacity on aphid nymphs, suggesting an expanded prey range. Implications of such enhanced biocontrol services on crop protection are also discussed.
(Re)Conceptualizing decision-making tools in a risk governance framework for emerging technologies—the case of nanomaterials
Authors: Mullins Martin , Himly Martin , Llopis Isabel Rodríguez , Furxhi Irini , Hofer Sabine , Hofstätter Norbert , Wick Peter , Romeo Daina , Küehnel Dana , Siivola Kirsi , Catalán Julia , Hund-Rinke Kerstin , Xiarchos Ioannis , Linehan Shona , Schuurbiers Daan , Bilbao Amaia García , Barruetabeña Leire , Drobne Damjana 
Published: July 2022

AbstractThe utility of decision-making tools for the risk governance of nanotechnology is at the core of this paper. Those working in nanotechnology risk management have been prolific in creating such tools, many derived from European FP7 and H2020-funded projects. What is less clear is how such tools might assist the overarching ambition of creating a fair system of risk governance. In this paper, we reflect upon the role that tools might and should play in any system of risk governance. With many tools designed for the risk governance of this emerging technology falling into disuse, this paper provides an overview of extant tools and addresses their potential shortcomings. We also posit the need for a data readiness tool. With the EUs NMP13 family of research consortia about to report to the Commission on ways forward in terms of risk governance of this domain, this is a timely intervention on an important element of any risk governance system.
Optimal droplet transfer mode maintenance for wire + arc additive manufacturing (WAAM) based on deep learning
Authors: Qin Jian , Wang Yipeng , Ding Jialuo , Williams Stewart 
Published: July 2022

AbstractIn the last decade, wire + arc additive manufacturing (WAAM), which is one of the most promising metal additive manufacturing technologies, has been attracting high interest from both academia and industry. WAAM systems are increasingly employed in the industry and academia, but there are still several challenges and barriers to process stability control. The process stability is highly dependent on how the molten feed wire is added into the melt pool, which is known as the droplet transfer mode. To ensure a stable WAAM deposition process, it is essential to maintain the transfer mode in a suitable stable status. Without an effective transfer mode control method, the operators need to determine and control the transfer mode based on their experience using manual adjustment, which is difficult to achieve in a long period of production process. In this paper, a deep learning-based technology was proposed for the control of the droplet transfer mode based on the data collected from the WAAM process. A long short term memory neural network was applied as the core transfer mode classification model. A time-series data, arc voltage, was collected and statistical and frequency features were extracted, which included 11 relevant features, as the inputs of the classification model. Then, the distance between the melted wire and the melt pool was adjusted based on the determined transfer mode to keep a suitable stability of the process. A case study was used to evaluate the proposed approach and to show its merit. The proposed approach was compared to three commonly used machine learning algorithms,k-nearest neighbours, support vector machine, and decision tree. The proposed method obtained the highest accuracy in determining the transfer mode, which was over 91%. The performance of the proposed approach was also evaluated by the single-pass and oscillated wall building. The proposed deep learning based approach improved the process stability in real-time, which resulted in better deposition qualities, in terms of geometry size and processing cleanliness compared to without control. Furthermore, this data-driven method could be applied to other WAAM processes and materials.
Computing the sequence of k-cardinality assignments
Authors: Rosenmann Amnon 
Published: July 2022

AbstractThe k-cardinality assignment (k-assignment, for short) problem asks for finding a minimal (maximal) weight of a matching of cardinality k in a weighted bipartite graph $$K_{n,n}$$

K

n
,
n

, $$k \le n$$

k
≤
n

. Here we are interested in computing the sequence of all k-assignments, $$k=1,\ldots ,n$$

k
=
1
,
…
,
n

. By applying the algorithm of Gassner and Klinz (2010) for the parametric assignment problem one can compute in time $${\mathcal {O}}(n^3)$$

O
(

n
3

)

the set of k-assignments for those integers $$k \le n$$

k
≤
n

which refer to essential terms of the full characteristic maxpolynomial $${\bar{\chi }}_{W}(x)$$

χ

¯

W

(
x
)

of the corresponding max-plus weight matrix W. We show that $${\bar{\chi }}_{W}(x)$$

χ

¯

W

(
x
)

is in full canonical form, which implies that the remaining k-assignments refer to semi-essential terms of $${\bar{\chi }}_{W}(x)$$

χ

¯

W

(
x
)

. This property enables us to efficiently compute in time $${\mathcal {O}}(n^2)$$

O
(

n
2

)

all the remaining k-assignments out of the already computed essential k-assignments. It follows that time complexity for computing the sequence of all k-cardinality assignments is $${\mathcal {O}}(n^3)$$

O
(

n
3

)

, which is the best known time for this problem.
Shock waves in gas flows with nanoparticles
Authors: Avramenko Andriy A. , Shevchuk Igor V. , Dmitrenko Nataliia P. , Skitsko Ivan F. 
Published: July 2022

AbstractThe paper focuses on the analytical analysis of the propagation of a normal shock wave in an adiabatic gas flow with nanoparticles. A modified Rankine–Hugoniot model was used for this purpose. A solution is obtained for the Rankine–Hugoniot conditions in a gas flow with different nanoparticle concentrations, which makes it possible to analyze the dynamics of variation of the parameters of this type of flow under a shock wave. The variation of velocity, pressure and entropy production of the adiabatic gas flow during a direct shock wave depending on the concentration of nanoparticles in the gas was depicted graphically. It was revealed that increasing the nanoparticle concentration to φ ~ 0.1 weakens the effect of the shock wave, and then, after passing the zone of minimum parameters, the intensity of the shock wave increases.
Towards a systematic evaluation of moral bioenhancement
Authors: Kudlek Karolina 
Published: July 2022

AbstractThe ongoing debate about moral bioenhancement (MBE) has been exceptionally stimulating, but it is defined by extreme polarization and lack of consensus about any relevant aspect of MBE. This article reviews the discussion on MBE, showing that a lack of consensus about enhancements’ desirable features and the constant development of the debate calls for a more rigorous ethical analysis. I identify a list of factors that may be of crucial importance for illuminating the matters of moral permissibility in the MBE debate and which could help us move beyond the current lack of consensus. More precisely, I propose three important theoretical and normative standards that MBE should satisfy if we wish to mitigate the concerns about its utter impermissibility. Systematically assessing MBE interventions across the presented categories should provide valuable conclusions about its theoretical soundness and feasibility, its compatibility with fundamental moral norms, and its compatibility with or facilitation of socio-political goals of equality and justice.
Cyclic Gate Recurrent Neural Networks for Time Series Data with Missing Values
Authors: Weerakody Philip B. , Wong Kok Wai , Wang Guanjin 
Published: July 2022

AbstractGated Recurrent Neural Networks (RNNs) such as LSTM and GRU have been highly effective in handling sequential time series data in recent years. Although Gated RNNs have an inherent ability to learn complex temporal dynamics, there is potential for further enhancement by enabling these deep learning networks to directly use time information to recognise time-dependent patterns in data and identify important segments of time. Synonymous with time series data in real-world applications are missing values, which often reduce a model’s ability to perform predictive tasks. Historically, missing values have been handled by simple or complex imputation techniques as well as machine learning models, which manage the missing values in the prediction layers. However, these methods do not attempt to identify the significance of data segments and therefore are susceptible to poor imputation values or model degradation from high missing value rates. This paper develops Cyclic Gate enhanced recurrent neural networks with learnt waveform parameters to automatically identify important data segments within a time series and neglect unimportant segments. By using the proposed networks, the negative impact of missing data on model performance is mitigated through the addition of customised cyclic opening and closing gate operations. Cyclic Gate Recurrent Neural Networks are tested on several sequential time series datasets for classification performance. For long sequence datasets with high rates of missing values, Cyclic Gate enhanced RNN models achieve higher performance metrics than standard gated recurrent neural network models, conventional non-neural network machine learning algorithms and current state of the art RNN cell variants.
Prediction with expert advice applied to the problem of prediction with expert advice
Authors: Herrmann Daniel A. 
Published: July 2022

AbstractWe often need to have beliefs about things on which we are not experts. Luckily, we often have access to expert judgements on such topics. But how should we form our beliefs on the basis of expert opinion when experts conflict in their judgments? This is the core of the novice/2-expert problem in social epistemology. A closely related question is important in the context of policy making: how should a policy maker use expert judgments when making policy in domains in which she is not herself an expert? This question is more complex, given the messy and strategic nature of politics. In this paper we argue that the prediction with expert advice (PWEA) framework from machine learning provides helpful tools for addressing these problems. We outline conditions under which we should expert PWEA to be helpful and those under which we should not expect these methods to perform well.
CuCe-Ferrite/TiO2 Nanocomposite as an Efficient Magnetically Separable Photocatalyst for Dye Pollutants Decolorization
Authors: Helmy Elsayed T. , Soliman Usama A. , Elbasiony A. M. , Nguyen Ba-Son 
Published: July 2022

AbstractIn this work, a magnetically separated photocatalyst with great efficiency CuCe-Ferrite/TiO2composite was prepared and characterized by X-ray diffraction (XRD), UV–Vis spectrophotometry, Fourier transformer infra-red spectroscopy (FTIR), field emission scanning electron microscopy (FE-SEM), high resolution transmission electron microscopy (HR-TEM), energy dispersive X-ray spectroscopy (EDX) and vibrating sample magnetometer (VSM). Single-phase cubic spinel was formed by calcining the prepared sample at a temperature of 550 °C, according to the results. Different concentrations of reactive red 250 (RR250) dye photodegradation was evaluated using different doses of CuCe-ferrite/ TiO2and TiO2NPs. Higher efficiency of RR250 photodegradation up to 100% was obtained using CuCe-ferrite/ TiO2. The photodegradation efficiency was confirmed using chemical oxygen demand (COD) test of both treated and untreated samples. The oxidation process was mostly mediated by photogenerated.O2−according to scavenger test results. The catalyst possess higher photodegradation efficiency even after regeneration for ten times.Graphical Abstract
A framework for supporting systems thinking and computational thinking through constructing models
Authors: Shin Namsoo , Bowers Jonathan , Roderick Steve , McIntyre Cynthia , Stephens A. Lynn , Eidin Emil , Krajcik Joseph , Damelin Daniel 
Published: July 2022

AbstractWe face complex global issues such as climate change that challenge our ability as humans to manage them. Models have been used as a pivotal science and engineering tool to investigate, represent, explain, and predict phenomena or solve problems that involve multi-faceted systems across many fields. To fully explain complex phenomena or solve problems using models requires both systems thinking (ST) and computational thinking (CT). This study proposes a theoretical framework that uses modeling as a way to integrate ST and CT. We developed a framework to guide the complex process of developing curriculum, learning tools, support strategies, and assessments for engaging learners in ST and CT in the context of modeling. The framework includes essential aspects of ST and CT based on selected literature, and illustrates how each modeling practice draws upon aspects of both ST and CT to support explaining phenomena and solving problems. We use computational models to show how these ST and CT aspects are manifested in modeling.
The impact of particle radiotherapy on the functioning of cardiac implantable electronic devices: a systematic review of in vitro and in vivo studies according to PICO criteria
Authors: Barcellini Amelia , Dusi Veronica , Mirandola Alfredo , Ronchi Sara , Riva Giulia , Dal Mas Francesca , Massaro Maurizio , Vitolo Viviana , Ciocca Mario , Rordorf Roberto , Orlandi Ester 
Published: July 2022

AbstractThe number of oncological patients who may benefit from proton beam radiotherapy (PBT) or carbon ion radiotherapy (CIRT), overall referred to as particle radiotherapy (RT), is expected to strongly increase in the next future, as well as the number of cardiological patients requiring cardiac implantable electronic devices (CIEDs). The management of patients with a CIED requiring particle RT deserves peculiar attention compared to those undergoing conventional photon beam RT, mostly due to the potential generation of secondary neutrons by particle beams interactions. Current consensus documents recommend managing these patients as being at intermediate/high risk of RT-induced device malfunctioning regardless of the dose on the CIED and the beam delivery method used, despite the last one significantly affects secondary neutrons generation (very limited neutrons production with active scanning as opposed to the passive scattering technique). The key issues for the current review were expressed in four questions according to the Population, Intervention, Control, Outcome criteria. Three in vitro and five in vivo studies were included. Based on the available data, PBT and CIRT with active scanning have a limited potential to interfere with CIED that has only emerged from in vitro study so far, while a significant potential for neutron-related, not severe, CIED malfunctions (resets) was consistently reported in both clinical and in vitro studies with passive scattering.
The hypertrophic amygdala shape associated with anxiety in patients with primary dysmenorrhea during pain-free phase: insight from surface-based shape analysis
Authors: Yu Siyi , Wei Wei , Liu Liying , Guo Xiaoli , Shen Zhifu , Tian Jin , Zeng Fang , Liang Fanrong , Yang Jie 
Published: July 2022

AbstractBackgroundPrimary dysmenorrhea (PDM) is highly associated with mood symptoms. However, the neuropathology of these comorbidities is unclear. In the present study, we aimed to investigate the structural changes in the amygdala of patients with PDM during the pain-free phase using a surface-based shape analysis.MethodsForty-three PDM patients and forty healthy controls were recruited in the study, and all participants underwent structural magnetic resonance imaging scans during their periovulatory phase. FMRIB’s Integrated Registration and Segmentation Tool (FIRST) was employed to assess the subcortical volumetric and surface alterations in patients with PDM. Moreover, correlation and mediation analyses were used to detect the clinical significance of the subcortical morphometry alteration.ResultsPDM patients showed hypertrophic alteration of the amygdala in the left superficial nuclei and right basolateral and superficial nuclei but not for the whole amygdala volume. The hypertrophic amygdala was associated with disease duration, pain severity and anxiety symptoms during the menstrual period. Furthermore, the hypertrophic left amygdala could mediate the association between disease duration and anxiety severity.ConclusionsThe results of the current study demonstrated that the localized amygdala shape hypertrophy was present in PDM patients even in the pain-free phase. In addition, the mediator role of the hypertrophic amygdala indicates the potential target of amygdala for anxiety treatment in PDM treatment in the pain-free phase.
The Infinite-Dimensional Standard and Strict Bounded Real Lemmas in Continuous Time: The Storage Function Approach
Authors: Ball J. A. , ter Horst S. , Kurula M. 
Published: July 2022

AbstractThe bounded real lemma (BRL) is a classical result in systems theory, which provides a linear matrix inequality criterium for dissipativity, via the Kalman-Yakubovich-Popov (KYP) inequality. The BRL has many applications, among others in $$H^\infty $$

H
∞

control. Extensions to infinite dimensional systems, although already present in the work of Yakubovich, have only been studied systematically in the last few decades. In this context various notions of stability, observability and controllability exist, and depending on the hypothesis one may have to allow the KYP-inequality to have unbounded solutions which forces one to consider the KYP-inequality in a spatial form. In the present paper we consider the BRL for continuous time, infinite dimensional, linear well-posed systems. Via an adaptation of Willems’ storage function approach we present a unified way to address both the standard and strict forms of the BRL. We avoid making use of the Cayley transform and work only in continuous time. While for the standard bounded real lemma, we obtain analogous results as there exist for the discrete time case, when treating the strict case additional conditions are required, at least at this stage. This might be caused by the fact that the Cayley transform does not preserve exponential stability, an important property in the strict case, when transferring a continuous-time system to a discrete-time system.
An imaginary refined count for some real rational curves
Authors: Blomme Thomas 
Published: July 2022

AbstractIn 2015, Mikhalkin introduced a refined count for the real rational curves in a toric surface which pass through a set consisting of real points and pairs of complex conjugated points chosen generically on the toric boundary of the surface. He then proved that the result of this refined count depends only on the number of pairs of complex conjugated points on each toric divisor. Using the tropical geometry approach and the correspondence theorem, we address the computation of the refined count when the pairs of complex conjugated points are chosen purely imaginary and belonging to the same component of the toric boundary. Despite the non-genericity, we relate this refined count for purely imaginary values to the refined invariant of Mikhalkin for generic values. That allows us to extend the relation between these classical refined invariants and the tropical refined invariants from Block–Göttsche.