Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation into a comprehensive forming limit curve considering multi-parameters effects for semi-cured FMLs through uniform pressure loading test

Recently, hydroforming has paid dividends in composite forming due to its uniform pressure distribution, which helps in reducing the delamination defects. Considering the need to develop an appropriate criterion for evaluating the formability of FMLs, inspired by sheet metal forming and testing, the uniform pressure loading test was adopted to characterize the formability of semi-cured FMLs by forming limit curve (FLC). In this paper, both theoretical analysis models considering and ignoring the tangential cohesive effect were established, and the stress expression of the critical zone was given. The results show that the tangential cohesive stress demonstrates a non-negligible influence on the FLC. A new form of comprehensive FLC was given by defining global equivalent strain and global strain ratio and analyzed by the finite element method. In addition, the numerical simulation model and the experimental parameters were verified. A simplified Hashin criterion was proposed and developed into the numerical simulation as a subroutine based on theoretical analysis. Parameters such as blank holder pressure, pressure rate, and temperature were considered both in experiments and simulation. Only fiber stretching damage was distinguished in the cross, single, and arc crack but no wrinkling. As predicted theoretically, a lower forming limit is expected when FMLs are subjected to greater tangential cohesive stress. Finally, with the help of numerical simulation, the influence of different boundary conditions on the comprehensive FLC was studied. Instead of a simple evaluation of FMLs, this comprehensive FLC could be used directly to optimize hydroforming processes in the engineering field.
The ionization structure of the Orion nebula: Infrared line observations and models

Observations of the (O III) 52 and 88 micron lines and the (N III) 57 micron line have been made at 6 positions and the (Ne III) 36 micron line at 4 positions in the Orion Nebula to probe its ionization structure. The measurements, made with a -40'' diameter beam, were spaced every 45'' in a line south from and including the Trapezium. The wavelength of the (Ne III) line was measured to be 36.013 + or - 0.004 micron. Electron densities and abundance ratios of N(++)/O(++) have been calculated and compared to other radio and optical observations. Detailed one component and two component (bar plus halo) spherical models were calculated for exciting stars with effective temperatures of 37 to 40,000K and log g = 4.0 and 4.5. Both the new infrared observations and the visible line measurements of oxygen and nitrogen require T sub eff approx less than 37,000K. However, the double ionized neon requires a model with T sub eff more than or equal to 39,000K, which is more consistent with that inferred from the radio flux or spectral type. These differences in T sub eff are not due to effects of dust on the stellar radiation field,more » but are probably due to inaccuracies in the assumed stellar spectrum. The observed N(++)/O(++) ratio is almost twice the N(+)/O(+) ratio. The best fit models give N/H = 8.4 x 10 to the -5 power, O/H = 4.0 x 10 to the -4 power, and Ne/H = 1.3 x 10 to the -4 power. Thus neon and nitrogen are approximately solar, but oxygen is half solar in abundance. From the infrared O(++) lines it is concluded that the ionization bar results from an increase in column depth rather than from a local density enhancement. « less
Consecutive Interpretation: A Discourse Approach. Towards a Revision of Gile’s Effort Model

In reality, expert interpreters from time to time do successfully tide over the gap between the capacity required and capacity available in dealing with extraordinarily large segments in consecutive interpretation. These exceptional cases imply that Gile’s Effort Model does not always hold and requires to be supplemented. This paper attempts to: 1) advance a solution to the dilemma that, in processing large segments in consecutive interpreting, the working memory capacity available is more often than not smaller than the capacity required, hence supplementing Gile’s Effort Model; 2) specify the rules of discourse transformation in consecutive interpretation; based upon the features of memory and consecutive interpretation, we deem that each segment, be it large or small, shall be processed as a discourse, the transformation of which is presumed to be the said solution; 3) and subsequently identify the optimal discourse transformation model, which is both capable of embodying the source text to the largest extent possible and achievable in terms of memory load. In addition, the author, through an observational study, justified the hypothesis. The validity of this theory, however, still requires further experimental evidence.
A MODEL FOR THE TRANSFORMATION OF SATELLITE VECTORS TO THE PLANE OF THE MAP.

AbstractGlobal Positioning System (GPS) measurements, processed with standard vendor software result in vectors and their covariance matrices. It is convenient to transform these vectors to the plane of the map. The transformation is into plane distance, azimuth or grid bearing and height difference or zenith angle. This paper presents a model for the above transformation, which is performed via a cartesian topocentric coordinate system to the ellipsoid and to the plane ofthe map. The transformation is performed without loss of information or accuracy and makes it easy to separate height and planimetry.
A DFT Study of the Reaction of Acrylamide with L-Cysteine and L-Glutathione

Thermal processing of certain foods implies the formation of acrylamide, which has been proven to provoke adverse effects on human health. Thus, several strategies to mitigate it have been developed. One of them could be the application of organosulfur compounds obtained from natural sources to react with the acrylamide, forming non-toxic adducts. A DFT study of the acrylamide reaction with the organosulfur model compounds L-cysteine and L-glutathione by Michael addition and a free radical pathway complemented by a kinetic study of these model molecules has been applied. The kinetic evaluation results demonstrate that the L-glutathione reaction exhibited a higher rate constant than the other studied compound.
The Phenomena of Rupture and Flow in Solids

In the course of an investigation of the effect of surface scratches on the mechanical strength of solids, some general conclusions were reached which appear to have a direct bearing on the problem of rupture, from an engineering standpoint, and also on the larger question of the nature of intermolecular cohesion. The original object of the work, which was carried out at the Royal Aircraft Estab­lishment, was the discovery of the effect of surface treatment—such as, for instance, filing, grinding or polishing—on the strength of metallic machine parts subjected to alternating or repeated loads. In the case of steel, and some other metals in common use, the results of fatigue tests indicated that the range of alternating stress which could be permanently sustained by the material was smaller than the range within which it was sensibly elastic, after being subjected to a great number of reversals. Hence it was inferred that the safe range of loading of a part, having a scratched or grooved surface of a given type, should be capable of estimation with the help of one of the two hypotheses of rupture commonly used for solids which are elastic to fracture. According to these hypotheses rupture may be expected if (a) the maximum tensile stress, ( b ) the maximum extension, exceeds a certain critical value. Moreover, as the behaviour of the materials under consideration, within the safe range of alternating stress, shows very little departure from Hooke’s law, it was thought that the necessary stress and strain calculations could be performed by means of the mathematical theory of elasticity.
Skin Cancer Detection Based on Deep Learning

Background: The conventional procedure of skin-related disease detection is a visual inspection by a dermatologist or a primary care clinician, using a dermatoscope. The suspected patients with early signs of skin cancer are referred for biopsy and histopathological examination to ensure the correct diagnosis and the best treatment. Recent advancements in deep convolutional neural networks (CNNs) have achieved excellent performance in automated skin cancer classification with accuracy similar to that of dermatologists. However, such improvements are yet to bring about a clinically trusted and popular system for skin cancer detection. Objective: This study aimed to propose viable deep learning (DL) based method for the detection of skin cancer in lesion images, to help physicians in diagnosis. Material and Methods: In this analytical study, a novel DL based model was proposed, in which other than the lesion image, the patient’s data, including the anatomical site of the lesion, age, and gender were used as the model input to predict the type of the lesion. An Inception-ResNet-v2 CNN pretrained for object recognition was employed in the proposed model. Results: Based on the results, the proposed method achieved promising performance for various skin conditions, and also using the patient’s metadata in addition to the lesion image for classification improved the classification accuracy by at least 5% in all cases investigated. On a dataset of 57536 dermoscopic images, the proposed approach achieved an accuracy of 89.3%±1.1% in the discrimination of 4 major skin conditions and 94.5%±0.9% in the classification of benign vs. malignant lesions. Conclusion: The promising results highlight the efficacy of the proposed approach and indicate that the inclusion of the patient’s metadata with the lesion image can enhance the skin cancer detection performance.
On the performance of a software reliability model for grouped failure data

In previous publications, an alternative prediction model, namely, compound Poisson (CP) software reliability model in software testing was studied. Later, two different estimation methods, i) compound Poisson maximum likelihood estimation (CPMLE), and ii) compound Poisson nonlinear regression (CPNLR) estimation methods were derived for time-failure data, generally in terms of CPU seconds. For time-between failure data, the competing Musa Okumoto (MO) method usually proved better due to little clumping within each CPU second. However, the proposed compound Poisson model proved better for grouped failure data where within each hour, day or week, the data accumulated considerably to the advantage of the compound Poisson. Owing to the non-influencing of the failures within sampling time-units (hour, day or week), the compounding distribution was popularly selected to be geometric, hence the name Poisson geometric. In addition to the ARE (averaged relative error) measure, Kolmogorov-Smirnov (K-S) test statistics are employed for comparing the goodness of fit to the actual empirical distribution. The K-S statistics indicated that for grouped failure data (weekly data sets WD1-WD5), CPMLE or CPNLR performed better. <<ETX>>
A vision guided life-science laboratory robot

The automatic plaque selection and culture inoculation robot (APSCIR) is a prototype machine vision directed system that will select a particular set of biological specimens from a mixed random array and harvest each in turn for discrete location in a predetermined spatial format for further processing. The authors describe the image processing aspects of APSCIR, which was originally designed to automate plaque harvesting during the cloning step of the DNA sequencing process. In this device a modified histogram-based segmentation technique is combined with a feature extraction algorithm and rapid classification scheme. This results in a trainable image processing system that will select from petri dishes white plaques growing in a disordered array on a mixed bacterial lawn of blue and white plaques. The candidate plaque coordinates are passed to an XYZ robot that performs sampling and transfer of the selected specimens discretely into separate containers for further culturing.
Multipass amplifiers of POLARIS

Advanced high intensity laser matter interaction experiments always call for optimized laser performance. In order to further enhance the POLARIS laser system, operational at the University of Jena and the Helmholtz-Institute Jena, in particular its energy, bandwidth and focusability, new amplifier technologies have been developed and are reported here. Additionally, existing sections were considerably improved. A new multi-pass amplification stage, which is able to replace two currently used ones, was developed in close collaboration with the MPQ (Garching). The new basic elements of this amplifier are well homogenized pump modules and the application of a successive imaging principle. By operating the amplifier under vacuum conditions a top hat beam profile with an output energy of up to 1.5 J per pulse is foreseen. The already implemented POLARIS amplifier A4 was further improved by adapting an advanced method for the homogenization of the multi-spot composed pump profile. The new method comprises a computer-based evolutionary algorithm which optimizes the position of the different spots regarding its individual size, shape and intensity. The latter allowed a better homogenization of the POLARIS near field profile.
A hybrid fluid master–apprentice evolutionary algorithm for large-scale multiplicity flexible job-shop scheduling with sequence-dependent set-up time

In this article, a large-scale multiplicity flexible job-shop scheduling problem (FJSP) with sequence-dependent set-up time is studied. In this problem, the large production demand for each type of job yields the large-scale multiplicity manufacturing feature. To address the problem, a hybrid fluid master–apprentice evolutionary algorithm (HFMAE) is presented to minimize the makespan. In the first step, a fluid relaxation initialization method (FRI) and an initialize procedure are proposed to obtain high-quality initial solutions. In the FRI, an online fluid tracking policy is presented to improve the assignment decision and the sequencing decision of operations. In the second step, an improved master–apprentice evolutionary method (IMAE) is presented based on the generated initial solutions. In the IMAE, two neighbourhood structures and three makespan estimation approaches are presented to accelerate the solution space search efficiency. Numerical results show that the proposed HFMAE outperforms the comparison algorithms in solving large-scale multiplicity FJSPs.
LEARNING OVERLAPPING COMMUNITIES IN COMPLEX NETWORKS VIA NON-NEGATIVE MATRIX FACTORIZATION

Community structure is an important topological phenomenon typical of complex networks. Accurately unveiling communities is thus crucial to understand and capture the many-faceted nature of complex networks. Communities in real world frequently overlap, i.e. nodes can belong to more than one community. Therefore, quantitatively evaluating the extent to which a node belongs to a community is a key step to find overlapping boundaries between communities. Non-negative matrix factorization (NMF) is a technique that has been used to detect overlapping communities. However, previous efforts in this direction present: (i) limitations in the interpretation of meaningful overlaps and (ii) lack of accuracy in predicting the correct number of communities. In this paper, a hybrid method of NMF to overcome both limitations is presented. This approach effectively estimates the number of communities and is more interpretable and more accurate in identifying overlapping communities in undirected networks than previous approaches. Validations on synthetic and real world networks show that the proposed community learning framework can effectively reveal overlapping communities in complex networks.
A swirling spiral wave solution in pipe flow

Abstract A numerically exact full Navier–Stokes counterpart of the asymptotic nonlinear solution in Hagen–Poiseuille flow proposed by Smith & Bodonyi (Proc. R. Soc. A, vol. 384, 1982, pp. 463–489) is discovered. The solution takes the form of a spiral travelling wave, with a novel feature being a strong induced component of swirl. Our solution shows excellent quantitative agreement with the asymptotic theory at Reynolds numbers of the order of $1{0}^{8} $ .
EpiReSIM: A Resampling Method of Epistatic Model without Marginal Effects Using Under-Determined System of Equations

Simulation experiments are essential to evaluate epistasis detection methods, which is the main way to prove their effectiveness and move toward practical applications. However, due to the lack of effective simulators, especially for simulating models without marginal effects (eNME models), epistasis detection methods can hardly verify their effectiveness through simulation experiments. In this study, we propose a resampling simulation method (EpiReSIM) for generating the eNME model. First, EpiReSIM provides two strategies for solving eNME models. One is to calculate eNME models using prevalence constraints, and another is by joint constraints of prevalence and heritability. We transform the computation of the model into the problem of solving the under-determined system of equations. Introducing the complete orthogonal decomposition method and Newton’s method, EpiReSIM calculates the solution of the underdetermined system of equations to obtain the eNME model, especially the solution of the high-order model, which is the highlight of EpiReSIM. Second, based on the computed eNME model, EpiReSIM generates simulation data by a resampling method. Experimental results show that EpiReSIM has advantages in preserving the biological properties of minor allele frequencies and calculating high-order models, and it is a convenient and effective alternative method for current simulation software.
Factors Associated with Undertaking Health-Promoting Activities by Older Women at High Risk of Metabolic Syndrome

Background: The complexity of health problems concerning women aged ≥60 years makes it necessary to develop effective, low-cost strategies involving biopsychosocial interventions. The aim of this study is to identify the factors associated with undertaking health-promoting activities by older women at high risk of metabolic syndrome (MetS) with or without depressive symptoms. Methods: The study group consisted of 70 older women (62–84 years old) undertaking regular physical activity. A self-developed questionnaire (used to determine the living situation, selected lifestyle components and health problems), the Perceived Stress Questionnaire (PSQ) and the Geriatric Depression Scale (GDS) were used. Results: In the study group undertaking regular physical activity, 40% had increased symptoms of depression (D group), and 60% were classified as non-depressed (ND group). The D group had a higher general stress level (t = −6.18, p = 0.001). Improving and/or maintaining physical fitness was identified as the greatest motivation in both groups. Willingness to spend time with other people significantly differed between the two groups (χ2 = 4.148, p = 0.042). The sole factor significantly differentiating between both groups was lack of time (χ2 = 8.777, p = 0.003). Conclusions: Motivations and barriers to undertaking health-promoting activities and levels of perceived stress were significantly different between the depressed and non-depressed groups. It is important to encourage primary care physicians to perform screening tests for late-life depression and to provide information on where therapeutic interventions are available for patients with symptoms of MetS and coexisting depressive symptoms.
A Comparative Analysis of Chemical, Plasma and In Situ Modification of Graphene Nanoplateletes for Improved Performance of Fused Filament Fabricated Thermoplastic Polyurethane Composites Parts

The limited number of materials and mechanical weakness of fused deposition modeling (FDM) parts are deficiencies of FDM technology. The preparation of polymer composites parts with suitable filler is a promising method to improve the properties of the 3D printed parts. However, the agglomerate of filler makes its difficult disperse in the matrix. In this work, graphene nanoplatelets (GnPs) were surface modified with chemical, low-temperature plasma and in situ methods, in order to apply them as fillers for thermoplastic polyurethane (TPU). Following its modification, the surface chemical composition of GnPs was analyzed. Three wt% of surface-modified GnPs were incorporated into TPU to produce FDM filaments using a melting compounding process. Their effects on rheology properties and electrical conductivity on TPU/GnPs composites, as well as the dimensional accuracy and mechanical properties of FDM parts, are compared. The images of sample facture surfaces were examined by scanning electron microscope (SEM) to determine the dispersion of GnPs. Results indicate that chemical treatment of GnPs with zwitterionic surfactant is a good candidate to significantly enhance TPU filaments, when considering the FDM parts demonstrated the highest mechanical properties and lowest dimensional accuracy.
A high-precision image classification network model based on a voting mechanism

ABSTRACT With the development of satellite remote sensing technology, image classification task, as the basis of remote sensing data interpretation, has received wide attention to improving accuracy and robustness. At the same time, in-depth learning technology has been widely used in remote sensing and has a far-reaching impact. Since the existing image classification methods ignore the feature that the general image semantics are the same as the semantics of a single pixel, this paper presents an algorithm that uses the semantics of an image to achieve high-precision image classification. Based on the idea of partial substitution for global, this algorithm designs a split result voting mechanism and builds a Vgg-Vote network model. This mechanism votes on the semantically segmented result of an image and uses the maximum filtering function to select the category containing the most significant number of pixels as the prediction category of the image. Experiments on UC Merced Land-User complete datasets and five types of incomplete datasets with varying degrees of interference, including noise, data occlusion and loss, show that the Vote mechanism dramatically improves the classification accuracy, robustness and anti-jamming capability of Vgg-Vote.
Drug-Drug Interactions among Patients Hospitalized with COVID-19 in Greece

The modulation of the pharmacological action of drugs due to drug-drug interactions (DDIs) is a critical issue in healthcare. The aim of this study was to evaluate the prevalence and the clinical significance of potential DDIs in patients admitted to the University Hospital of Heraklion in Greece with coronavirus disease 2019 (COVID-19). Cardiovascular disorders (58.4%) and diabetes (types I and II) (29.6%) were the most common comorbidities. A high occurrence of DDIs was observed, and clinically significant DDIs that may hamper response to treatment represented 40.3% of cases on admission, 21% during hospitalization, and 40.7% upon discharge. Polypharmacy and comorbidities were associated with a higher prevalence of DDIs in a statistically significant way (p < 0.05, 95% CI). Clinically significant DDIs and increased C-reactive protein values upon admission were associated with prolonged hospitalization. The results reveal that patients admitted due to COVID-19 in Greece often have an additional burden of DDIs that healthcare teams should approach and resolve.