bluuebunny/crossref_metadata_2025_split huggingface dataset:
Optimizing the transfer‐learning with pretrained deep convolutional neural networks for first stage breast tumor diagnosis using breast ultrasound visual images
Authors: Saba Tanzila , Abunadi Ibrahim , Sadad Tariq , Khan Amjad Rehman , Bahaj Saeed Ali 
Published: December 2021

AbstractFemale accounts for approximately 50% of the total population worldwide and many of them had breast cancer. Computer‐aided diagnosis frameworks could reduce the number of needless biopsies and the workload of radiologists. This research aims to detect benign and malignant tumors automatically using breast ultrasound (BUS) images. Accordingly, two pretrained deep convolutional neural network (CNN) models were employed for transfer learning using BUS images like AlexNet and DenseNet201. A total of 697 BUS images containing benign and malignant tumors are preprocessed and performed classification tasks using the transfer learning‐based CNN models. The classification accuracy of the benign and malignant tasks is completed and achieved 92.8% accuracy using the DensNet201 model. The results thus achieved compared in state of the art using benchmark data set and concluded proposed model outperforms in accuracy from first stage breast tumor diagnosis. Finally, the proposed model could help radiologists diagnose benign and malignant tumors swiftly by screening suspected patients.
Accounting for cross‐location technological heterogeneity in the measurement of operations efficiency and productivity
Authors: Malikov Emir , Zhang Jingfang , Zhao Shunan , Kumbhakar Subal C. 
Published: December 2021

AbstractMotivated by the long‐standing interest in understanding the role of location for firm performance, this paper provides a semiparametric methodology to accommodate locational heterogeneity in production analysis. Our approach is novel in that we explicitly model spatial variation in parameters in the production‐function estimation. We accomplish this by allowing both the input‐elasticity and productivity parameters to be unknown functions of the firm's geographic location and estimate them via local kernel methods. This allows the production technology to vary across space, thereby accommodating neighborhood influences on firm production. In doing so, we are also able to examine the role of cross‐location differences in explaining the variation in operational productivity among firms. Our model is superior to the alternative spatial production‐function formulations because it (i) explicitly estimates the cross‐locational variation in production functions, (ii) is readily reconcilable with the conventional production axioms and, more importantly, (iii) can be identified from the data by building on the popular proxy‐variable methods, which we extend to incorporate locational heterogeneity. Using our methodology, we study China's chemicals manufacturing industry and find that differences in technology (as opposed to in idiosyncratic firm heterogeneity) are the main source of the cross‐location differential in total productivity in this industry.
A definition and clinical grading of Modic changes
Authors: Udby Peter M. , Samartzis Dino , Carreon Leah Y. , Andersen Mikkel Østerheden , Karppinen Jaro , Modic Michael 
Published: December 2021

AbstractTo provide an up‐to‐date description of knowledge and pitfalls related to the classification, definition and grading of Modic changes (MC) visualized on magnetic resonance imaging (MRI). State‐of‐the‐art review of current knowledge regarding the definition and grading of MC on MRI. MC on MRI have been reported to be associated with low back pain and disability. However, previous studies have shown heterogeneous results in regards to the impact of MC and its clinical relevance in patients with back pain. MC is a term used with considerable variation in the literature. No strict definition has been provided previously, this has contributed to varying diagnostic inclusion criteria, heterogeneous study populations, and discrepancy in results. A definition of MC and a proposal for grading is provided in this state‐of‐the‐art review. MC are important, clinically relevant findings. However, issues with the nomenclature, definition and grading of these changes need to be addressed. Our current review highlights relevant issues related to MC, and provides a definition and grading score for the term MC that includes the Modic type and the extent of vertebral body involvement. Future studies should seek to validate the MC grading score in clinically relevant populations.
Soil methane (CH4) fluxes in cropland with permanent pasture and riparian buffer strips with different vegetation#
Authors: Dlamini Jerry Celumusa , Cárdenas Laura , Tesfamariam Eyob Habte , Dunn Robert , Hawkins Jane , Blackwell Martin , Evans Jess , Collins Adrian 
Published: December 2021

AbstractBackgroundMethane (CH4) has a global warming potential (GWP) 28 times that of carbon dioxide (CO2) over a 100‐year horizon. Riparian buffers strips are widely implemented for their water quality protection functions along agricultural land, but conditions prevailing within them may increase the production of radiative greenhouse gases (GHGs), including CH4. However, a few information is available regarding the dynamics of unintended emissions of soil CH4 in these commonplace features of agroecosystems and how the dynamics compare with those for agricultural land.AimsTo understand the dynamics of soil CH4 fluxes from a permanent upslope pasture and contiguous riparian buffer strips with different (grass, willow, and woodland) vegetation as well controls with no buffer vegetation, an experiment was carried out using the static chamber technique on a replicated plot‐scale facility.MethodsGas fluxes were measured periodically with soil and environmental variables between June 2018 and February 2019 at North Wyke, UK.ResultsSoils under all treatments were sinks of soil CH4 with the willow riparian buffer (–2555 ± 318.7 g CH4 ha–1) having the lowest soil CH4 flux followed by the grass riparian buffer (–2532 ± 318.7 g CH4 ha–1), woodland riparian buffer (–2318.0 ± 246.4 g CH4 ha–1), no‐buffer control (–1938.0 ± 374.4 g CH4 ha–1), and last, the upslope pasture (–1328.0 ± 89.0 g CH4 ha–1), which had a higher flux.ConclusionsThe three vegetated riparian buffers were more substantial soil CH4 sinks, suggesting that they may help reduce soil CH4 fluxes into the atmosphere in similar agroecosystems.
Is high adaptation to the Mediterranean diet effective in increasing ecological footprint awareness? A cross‐sectional study from Turkey
Authors: Yardimci Hülya , Demirer Büşra 
Published: December 2021

AbstractBACKGROUNDThe effect of diet type on sustainable environment is known. It is important to understand the adaptation of individuals with high ecological footprint awareness to the Mediterranean type diet, especially under the assumption that the Mediterranean diet will reduce the ecological footprint. In the study, it is aimed to examine the relationship between the level of adherence to the Mediterranean diet and Ecological Footprint awareness of adults.RESULTSThe sample of the study consisted of 395 individuals with a mean age of 26.2 ± 8.7 years: 293 females (74.2%) and 102 males (25.8%). As the Mediterranean Diet Adherence Scale (MDAS) score of individuals increased, the Ecological Footprint Awareness Scale score also increased. Similarly, a positive correlation was found between the participants' MDAS score and the awareness of food (r = 0.157; P = 0.002), energy (r = 0.133; P = 0.008), waste (r = 0.149; P = 0.003) and water consumption (r = 0.101; P = 0.045), which are sub‐factors of the Ecological Footprint Awareness Scale.CONCLUSIONAs far as is known, this cross‐sectional study is one of the first studies on compliance with the Mediterranean diet and awareness of the ecological footprint. The findings of the study show that as individuals' compliance with the Mediterranean diet increases, their awareness of their ecological footprint will also increase. To reach a conclusion on this issue, future studies should evaluate the relationship between adherence to the Mediterranean diet and awareness of food–environmental impact on different samples. © 2021 Society of Chemical Industry.
Development of a multivariate model with desirability‐based optimization for determination of atenolol and hydrochlorothiazide by eco‐friendly HPLC method with fluorescence detection
Authors: Hemdan Ahmed , Al‐Tannak Naser F. , Mohamed Ekram H. 
Published: December 2021

AbstractDetermination of a widely used antihypertensive combination of atenolol and hydrochlorothiazide was achieved by rapid and eco‐friendly high‐performance liquid chromatography method combined with fluorescence detection. The response surface methodology is conducive to the complete separation of the two drugs in a shorter analysis time. The separation of the mixture was achieved using an Inertsil C18 analytical column (150 × 4.6 mm, 5 µ). The mobile phase used was ethanol: potassium dihydrogen phosphate at pH 3 (65:35 v/v) and the flow rate was 0.7 mL/min. The fluorescence detector operated at excitation and emission wavelengths of 230 and 310 nm (atenolol) and 270 and 375 nm (hydrochlorothiazide). The linearity of the developed method covered a concentration of atenolol of 0.05–5 µg/mL and a concentration of hydrochlorothiazide of 0.02–1 µg/mL. The greenness of the developed method was evaluated by analytical eco‐scale and the recently reported evaluation method, that is, green analytical procedure index, and it was found to be an excellent, sensitive, and green alternative to the reported methods. The developed method was validated according to the ICH guidelines and compared with the reference method. No significant difference was found in terms of accuracy.
Melt‐Extruded Sensory Fibers for Electronic Textiles
Authors: Tabor Jordan , Thompson Brendan , Agcayazi Talha , Bozkurt Alper , Ghosh Tushar K. 
Published: December 2021

AbstractTextile‐based flexible sensors are key to the development of personal wearable electronic devices and systems for a wide range of applications including physiological monitoring, communication, and entertainment. Textiles, for their many desirable characteristics and use, offer a natural interface between electronics and the human body. A wide range of fabrication techniques have been explored for textile‐based sensors; however, most are not compatible or readily adaptable to textile manufacturing processes. Here, a practical and scalable method of producing textile‐based sensory fibers using a common manufacturing technique, melt extrusion, is proposed. An overview of the fabrication method as well as the mechanical and electrical properties of the fibers is presented. Subsequently, the fibers’ ability to sense changes in pressure is studied in detail using assembled fibers. Methods to improve the sensor performance by altering the geometry of the fiber assembly are also presented. As a proof‐of‐concept demonstration, the fibers are woven into a pressure‐sensing fabric mat consisting of 64 sensing elements. The woven substrate can detect the location and level of pressure, thereby illustrating the fibers' potential use as sensors in textile structures.
Technical note: 3D‐printed phantom for dedicated cardiac protocols and geometries in nuclear medicine
Authors: Green Samantha , Grice Jared 
Published: December 2021

AbstractPurposeThe purpose of this study was to create and validate a 3D‐printed nuclear cardiac phantom for low cost, user‐friendly design and easy implementation with modern cardiac SPECT systems. This new phantom design aims to address common problems with commercial phantoms such as lengthy setup, prohibitive cost, and overly large size, while improving the overall functionality of the phantom.MethodsThe phantom was developed using computer aided design software and fabricated with a 3D printer using optimized watertight printing protocols. The phantom design includes six low perfusion lesions within a stylized myocardium of the left ventricle that are placed in the common quantitation sectors for polar maps. The validation of this phantom was completed with two dedicated cardiac SPECT systems; a dual head gamma camera and a multi‐pinhole CZT system. Multiple SPECT acquisitions were used to demonstrate the functionality of the phantom. Polar maps were reconstructed and used to score the contrast detectability based on the number of visible low contrast objects representing "lesions."ResultsThe images reconstructed from the various acquisitions on both SPECT systems closely resemble a clinical examination. Lesion visibility followed the expected relationships between protocol changes affecting contrast and spatial resolution. Lesion visibility improved with iterative reconstruction against filtered back projection.ConclusionA phantom of a stylized left ventricle with fillable myocardium was developed, 3D printed, and implemented for cardiac nuclear medicine. The phantom simulates the task of perfusion imaging and successfully demonstrates differences in image quality depending on imaging protocol. This study validates the 3D‐printed design as a low cost and user‐friendly phantom that can be easily scanned and scored using various systems, in particular those implementing a nontraditional cardio‐centric geometry.
Reduced order models for material non‐linearities in FEM applications
Authors: Özmen Yasemin , Scheunemann Lisa , Nigro Paulo S. B. , Schröder Jörg 
Published: December 2021

AbstractIn this paper, an adaptive reduced order method for small strain elasto‐plasticity is discussed. This method works purely online, meaning no preliminary offline calculations have to be conducted. Using a GALERKIN projection, the high‐fidelity problem is reduced to a lower vector space. For this, the basis is extracted from an initial POD after the first few load steps of the simulation. Later on, POD is used again to check the relevance of new snapshots gained from the load stepping. Corrections of the basis ensure the best performance of the current basis.
Physics informed neural networks for continuum micromechanics
Authors: Henkes Alexander , Wessels Henning , Mahnken Rolf 
Published: December 2021

AbstractThe present work proposes a Physics Informed Neural Network (PINN) for solving boundary value problems in continuum micromechanics. The presented technique is therefore an alternative to the finite element method or Fourier transform based methods. In this context, a neural network is used to approximate the function solving the partial differential equation. The theory of PINN in the context of micromechanics is developed.
Simulations of a multiscale bone material model
Authors: Blaszczyk Mischa , Hackl Klaus 
Published: December 2021

AbstractAn important application of simulating wave propagation in bone is the early detection of osteoporosis. During the course of this bone disease cortical bone is reduced and replaced by bone marrow, weakening the bone and increasing the likelihood of fractures [1]. Commonly, in modeling the problem is reduced to only mechanical effects. Recent research includes electrical and magnetic effects as well [2‐4]. In our previous contributions we introduced a two‐phase material model for bone, which considers mechanical, electrical and magnetic effects [5,6].In this contribution we use the introduced material model to perform multiscale simulations of bone. For this purpose we resort to the multiscale finite element method (FE2). To apply this method, we constructed a representative volume element (RVE) incorporating both material phases. We show numerical results of a cylinder model and a true to scale model of a human femur bone.
Probability of Hitting the Target with an Imperfectly Manufactured Projectile
Authors: Trzun Zvonko , Vrdoljak Milan 
Published: December 2021

AbstractThe effect of individual projectile production errors on the probability of hitting a target is analyzed. The intensity of errors is linked to the level of production quality. The consequences of manufacturing errors are estimated by a comprehensive Monte Carlo (MC) simulation where a 3D CAD model of a missile and a modified 6DOF model of its flight are coupled. A 122 mm rocket is selected as a case study. The MC simulation gives the estimation of the probability that rocket will destroy the target.
Efficient two‐scale simulations of microstructured materials using deep material networks
Authors: Gajek Sebastian , Schneider Matti , Böhlke Thomas 
Published: December 2021

AbstractDeep material networks (DMN) are a promising piece of technology for accelerating concurrent multiscale simulations. DMNs are identified by linear elastic pre‐computations on representative volume elements, and serve as high‐fidelity surrogates for full‐field simulations on microstructures with inelastic constituents. The offline training phase is independent of the online evaluation, such that a pre‐trained DMN may be applied for varying material behavior of the constituents. In this contribution, we investigate a two‐scale component simulation of industrial complexity accelerated by DMNs. To this end, a DMN is solved implicitly at every Gauss point to include the microstructure information into the macro simulation.
Efficient Computation of the Sharpest Bounds on the Probability of Failure of a Sheet Metal Forming Process
Authors: Miska Niklas , Balzani Daniel 
Published: December 2021

AbstractThe determination of the sharpest bounds on the probability of failure (PoF) based on the Optimal Uncertainty Quantification (OUQ) framework relies on the solution of non‐convex global optimization problems. These problems are subjected to non‐linear constraints to reflect moment constraints from available data, which impedes the numerical efficiency of the method. A different parameterization approach of the optimization problem utilizing canonical moments allows the transformation to only bound constraints and hence, an improved numerical efficiency can be reached. The performance of this new approach is contrasted with the performance of the original approach on the basis of a numerical simulation of a sheet metal forming process.
Space‐time stabilized FEM applied on the hyperbolic heat conduction equation for high speed frictional contact
Authors: Feike Marvin C. , Mundt Christian 
Published: December 2021

AbstractFrictional contact results in dissipation of energy, wherein one part is transformed into thermal energy. This leads to heating under the surface, where the resulting transient temperature fields at short times and high Peclet numbers are of interest. Therefore, the numerical method of finite elements with space‐time stabilization technique is applied on the 2D hyperbolic heat conduction equation of Cattaneo‐Vernotte with respect to a moving frame. It is shown, that the combination of the θ‐Method with Streamline‐Upwind‐Petrov‐Galerkin‐FEM gives satisfying solutions for a wide broadband of Peclet numbers.
Efficient simulation of heterogeneous materials with the finite cell method
Authors: Gorji Mahan , Düster Alexander 
Published: December 2021

AbstractThe finite cell method (FCM) represents a combination of high‐order finite elements with the fictitious domain approach. Due to it's simple mesh generation the FCM reduces the pre‐processing effort. For heterogeneous problems of solid mechanics however, discontinuities occur at the material interfaces, which cannot be captured by the smooth shape functions of the FCM anymore, whereby the convergence deteriorates. Thus, to recover the optimal convergence rate, we extend the FCM ansatz by specially designed shape functions, which are able to capture discontinuities at the material interfaces. To get more insight into the problem, we study the performance of the proposed method by means of a two‐dimensional example.
Influence of thermally induced changes in lubricating gap clearance and oil viscosity on nonlinear oscillations of hydrodynamically supported rotors
Authors: Irmscher Cornelius , Ziese Christian , Woschke Elmar 
Published: December 2021

AbstractDespite many advantages, hydrodynamic bearings tend to induce nonlinear vibrations on rotor systems at low external loads. The occurrence and also the amplitudes of these vibrations are very sensible to the operating conditions of the system such as occurrence of gaseous cavitation, oil viscosity (strongly dependent on oil temperature), bearing clearance (dependent on metal temperature and thermal expansion coefficients) or rotor imbalance. This contribution shows the calculation of nonlinear rotor oscillations with the help of a detailed transient 3D‐thermo‐hydrodynamic bearing model. Referring to experimental data of a real exhaust turbocharger with semi‐floating ring bearings, this holistic approach is used for a separate investigation of a) the influences of the temperature‐dependent oil viscosity in the lubricating films and b) the influences of temperature‐related changes in the lubricating gap on the nonlinear oscillations of the rotor.
A parallel matrix‐free finite element solver for phase separation in electrode particles of lithium ion batteries
Authors: Castelli Giuseppe Fabian , Dörfler Willy 
Published: December 2021

AbstractWe consider a Cahn–Hilliard‐type phase field model for phase separation in electrode particles of lithium ion batteries. For the numerical solution we employ a finite element solver together with a variable‐step, variable‐order time integration method. To overcome the large computational requirements we use a parallel matrix‐free approach with a specialized preconditioner. Numerical experiments demonstrate the capabilities of our solver. In particular, we show the dependence of the phase morphology on the applied insertion rate in a two‐dimensional circular particle example.
Stability Score for Local Solutions of Unconstrained Parametric Nonlinear Programs
Authors: Mykhailiuk Ivan , Schäfer Kai , Büskens Christof 
Published: December 2021

AbstractIn many application areas that involve solving parametric optimization problems, it is desirable that solutions of perturbed problems do not deviate too much from the original ones. In this work, we develop a criterion which characterizes the stability of a local solution of an unconstrained parametric nonlinear program with respect to parameter perturbations. It is defined as the solution of an optimization problem, which we approximate using parametric sensitivity analysis. The presented approach is illustrated with the help of an example.