bluuebunny/crossref_metadata_2025_split huggingface dataset:
Knowledge-based sentence semantic similarity: algebraical properties
Authors: Oussalah Mourad , Mohamed Muhidin 
Published: August 2021

AbstractDetermining the extent to which two text snippets are semantically equivalent is a well-researched topic in the areas of natural language processing, information retrieval and text summarization. The sentence-to-sentence similarity scoring is extensively used in both generic and query-based summarization of documents as a significance or a similarity indicator. Nevertheless, most of these applications utilize the concept of semantic similarity measure only as a tool, without paying importance to the inherent properties of such tools that ultimately restrict the scope and technical soundness of the underlined applications. This paper aims to contribute to fill in this gap. It investigates three popular WordNet hierarchical semantic similarity measures, namely path-length, Wu and Palmer and Leacock and Chodorow, from both algebraical and intuitive properties, highlighting their inherent limitations and theoretical constraints. We have especially examined properties related to range and scope of the semantic similarity score, incremental monotonicity evolution, monotonicity with respect to hyponymy/hypernymy relationship as well as a set of interactive properties. Extension from word semantic similarity to sentence similarity has also been investigated using a pairwise canonical extension. Properties of the underlined sentence-to-sentence similarity are examined and scrutinized. Next, to overcome inherent limitations of WordNet semantic similarity in terms of accounting for various Part-of-Speech word categories, a WordNet “All word-To-Noun conversion” that makes use of Categorial Variation Database (CatVar) is put forward and evaluated using a publicly available dataset with a comparison with some state-of-the-art methods. The finding demonstrates the feasibility of the proposal and opens up new opportunities in information retrieval and natural language processing tasks.
Sampling and analysis of coarsely shredded mixed commercial waste. Part II: particle size-dependent element determination
Authors: Viczek S. A. , Kandlbauer L. , Khodier K. , Aldrian A. , Sarc R. 
Published: August 2021

AbstractIn contemporary waste management, sampling of waste is essential whenever a specific parameter needs to be determined. Although sensor-based continuous analysis methods are being developed and enhanced, many parameters still require conventional analytics. Therefore, sampling procedures that provide representative samples of waste streams and enable sufficiently accurate analysis results are crucial. While Part I estimated the relative sampling variabilities for material classes in a replication experiment, Part II focuses on relative sampling variabilities for 30 chemical elements and the lower heating value of the same samples, i.e., 10 composite samples screened to yield 9 particle size classes (< 5 mm–400 mm). Variabilities < 20% were achieved for 39% of element-particle size class combinations but ranged up to 203.5%. When calculated for the original composite samples, variabilities < 20% were found for 57% of the analysis parameters. High variabilities were observed for elements that are expectedly subject to high constitutional heterogeneity. Besides depending on the element, relative sampling variabilities were found to depend on particle size and the mass of the particle size fraction in the sample. Furthermore, Part I and Part II results were combined, and the correlations between material composition and element concentrations in the particle size classes were interpreted and discussed. For interpretation purposes, log-ratios were calculated from the material compositions. They were used to build a regression model predicting element concentration based on material composition only. In most cases, a prediction accuracy of ± 20% of the expected value was reached, implying that a mathematical relationship exists.
Fixed point theorems via Generalized WF-Contractions with applications
Authors: Rodríguez-López Rosana , Tiwari Rakesh 
Published: August 2021

AbstractThe aim of this paper is to introduce a new class of mixed contractions which allow to revise and generalize some results obtained in [6] by R. Gubran, W. M. Alfaqih and M. Imdad. We also provide an example corresponding to this class of mappings and show how the new fixed point result relates to the above-mentioned result in [6]. Further, we present an application to the solvability of a two-point boundary value problem for second order differential equations.
The radiative transfer model for the greenhouse effect
Authors: Bardos Claude , Pironneau Olivier 
Published: August 2021

AbstractRadiative transfer is at the heart of the mechanism to explain the greenhouse effect based on the partial infrared opacity of carbon dioxide, methane and other greenhouse gases in the atmosphere. In absence of thermal diffusion, the mathematical model consists of a first order integro-differential equation coupled with an integral equation for the light intensity and the temperature, in the atmosphere. We revisit this much studied system from a mathematical and numerical point of view. Existence and uniqueness and implicit solutions of the Milne problem for grey atmospheres are stated. Numerical simulations are given for grey and non-grey atmospheres and applied to calculate the effect of greenhouse gases. In the context of a transparent atmosphere for sunlight, it is found that by doubling the absorption coefficient in the infrared absorption range of $$\texttt {CO}_2$$

CO
2

the temperature decreases by 2%. On the other hand, the same changes but in the low infrared range of the sunlight leads to an increase of temperature in the atmosphere. Several computer codes were written to cross-validate the results. The authors conclude that the radiative transfer model without thermal diffusion for an atmosphere transparent to the incident sunlight is not capable of explaining the greenhouse effect due to the greenhouse gases. A decreasing temperature due to an increasing proportion of $$\texttt {CO}_2$$

CO
2

has been observed in the high atmosphere (D.W.J. Thomson et al, nature11579). In the lower atmosphere thermal diffusion and convection cannot be neglected and since the absorption coefficient are highly dependent on the temperature, a full ocean–atmosphere–biosphere climate model is required. Hence, driving conclusions from this study on climate change should be cautiously avoided and a review of the hypothesis of the radiative transfer argument commonly found in textbooks should be revisited.
Insights from principal component analysis applied to Py-GCMS study of Indian coals and their solvent extracted clean coal products
Authors: Roy Abyansh , Dhawan Heena , Upadhyayula Sreedevi , Kodamana Hariprasad 
Published: August 2021

AbstractThe present work aims at studying five Indian coals and their solvent extracted clean coal products using Py-GCMS analysis and correlating the characterization data using theoretical principal component analysis. The pyrolysis products of the original coals and the super clean coals were classified as mono-, di- and tri-aromatics, while other prominent products that were obtained included cycloalkanes, n-alkanes, and alkenes ranging from C10–C29. The principal component analysis is a dimensionality reduction technique that reduced the number of input variables in the characterization dataset and gave inferences on the relative composition of constituent compounds and functional groups and structural insights based on scores and loading plots which were consistent with the experimental observations. ATR-FTIR studies confirmed the reduced concentration of ash in the super clean coals and the presence of aromatics. The Py-GCMS data and the ATR-FTIR spectra led to the conclusion that the super clean coals behaved similarly for both coking and non-coking coals with high aromatic concentrations as compared to the raw coal. Neyveli lignite super clean coal was found to show some structural similarity with the original coals, whereas the other super clean coals showed structural similarity within themselves but not with their original coal samples confirming the selective action of the e,N solvent in solubilizing the polycondensed aromatic structures in the coal samples.
Comparative Evaluation of Marking Methods on Cast Parts of Al–Si Alloy with Image Processing
Authors: Deng Fangtian , Li Rui , Klan Steffen , Volk Wolfram 
Published: August 2021

AbstractQuality issues caused by casting defects are commonly complicated to solve, because the part-specific process parameters are not traced to the individual cast part. This problem can be mitigated by the traceability of each cast part with an identifier code. Therefore, a study of the influence of marked surface topography and post-treatments on code symbol quality is desirable for a well-designed traceability system. In this work, the code symbol quality of laser, dot peen, and electrolytic marking methods on three as-cast surfaces of Al–Si alloy, after sandblasting and heat treatment, is evaluated comparatively with a customized image processing software. The result shows that the laser marking method produces the highest performance for different as-cast surfaces; electrolytic marking provides acceptable results only on the smooth surfaces of high-pressure die casting; dot peen marking produces the codes of high symbol contrasts, which are similar to those of laser marking, especially for rough as-cast surfaces of sand casting. However, for all marking methods, the code qualities of all surface topographies decrease substantially after post-treatments. Considering that dot peen marking has satisfying performances as well as low equipment and maintenance costs, this method is more suitable for small- and medium-size foundries to start to trace each cast part in an economical manner.
FEM Simulations for the Optimization of the Inlet Gate System in Rapid Investment Casting Process for the Realization of Heat Exchangers
Authors: Almonti D. , Baiocco G. , Mingione E. , Ucciardello N. 
Published: August 2021

AbstractOver the last decades, additive manufacturing (AM) has become the principal production technology for prototypes and components with high added value. In the production of metallic parts, AM allows producing complex geometry with a single process. Also, AM admits a joining of elements that could not be realized with traditional methods. In addition, AM allows the manufacturing of components that could not be realized using other types of processes like reticular structures in heat exchangers. A solid mold investment casting that uses printed patterns overcomes typical limitations of additive processes such as expensive machinery and challenging process parameter settings. Indeed, rapid investment casting provides for a foundry epoxy pattern reproducing the component to exploit in the lost wax casting process. In this paper, aluminium radiators with flat heat pipes seamlessly connected with a cellular structure were conceived and produced. This paper aims at defining and investigating the principal foundry parameters to achieve a defect-free heat exchanger. For this purpose, different device CAD models were designed, considering four pipes’ thickness and length. Finite element method numerical simulations were performed to optimize the design of the casting process. Three different gate configurations were investigated for each length. The numerical investigations led to the definition of a castability range depending on flat heat pipes geometry and casting parameters. The optimal gate configuration was applied in the realization of AM patterns and casting processes
Set-Based Adaptive Distributed Differential Evolution for Anonymity-Driven Database Fragmentation
Authors: Ge Yong-Feng , Cao Jinli , Wang Hua , Chen Zhenxiang , Zhang Yanchun 
Published: August 2021

AbstractBy breaking sensitive associations between attributes, database fragmentation can protect the privacy of outsourced data storage. Database fragmentation algorithms need prior knowledge of sensitive associations in the tackled database and set it as the optimization objective. Thus, the effectiveness of these algorithms is limited by prior knowledge. Inspired by the anonymity degree measurement in anonymity techniques such as k-anonymity, an anonymity-driven database fragmentation problem is defined in this paper. For this problem, a set-based adaptive distributed differential evolution (S-ADDE) algorithm is proposed. S-ADDE adopts an island model to maintain population diversity. Two set-based operators, i.e., set-based mutation and set-based crossover, are designed in which the continuous domain in the traditional differential evolution is transferred to the discrete domain in the anonymity-driven database fragmentation problem. Moreover, in the set-based mutation operator, each individual’s mutation strategy is adaptively selected according to the performance. The experimental results demonstrate that the proposed S-ADDE is significantly better than the compared approaches. The effectiveness of the proposed operators is verified.
BC tree-based spectral sampling for big complex network visualization
Authors: Hu Jingming , Chu Tuan Tran , Hong Seok-Hee , Chen Jialu , Meidiana Amyra , Torkel Marnijati , Eades Peter , Ma Kwan-Liu 
Published: August 2021

AbstractGraph sampling methods have been used to reduce the size and complexity of big complex networks for graph mining and visualization. However, existing graph sampling methods often fail to preserve the connectivity and important structures of the original graph. This paper introduces a new divide and conquer approach to spectral graph sampling based on graph connectivity, called the BC Tree (i.e., decomposition of a connected graph into biconnected components) and spectral sparsification. Specifically, we present two methods, spectral vertex sampling$$BC\_SV$$BC_SVand spectral edge sampling$$BC\_SS$$BC_SSby computing effective resistance values of vertices and edges for each connected component. Furthermore, we present$$DBC\_SS$$DBC_SSand$$DBC\_GD$$DBC_GD, graph connectivity-based distributed algorithms for spectral sparsification and graph drawing respectively, aiming to further improve the runtime efficiency of spectral sparsification and graph drawing by integrating connectivity-based graph decomposition and distributed computing. Experimental results demonstrate that$$BC\_SV$$BC_SVand$$BC\_SS$$BC_SSare significantly faster than previous spectral graph sampling methods while preserving the same sampling quality.$$DBC\_SS$$DBC_SSand$$DBC\_GD$$DBC_GDobtain further significant runtime improvement over sequential approaches, and$$DBC\_GD$$DBC_GDfurther achieves significant improvements in quality metrics over sequential graph drawing layouts.
Delay in payment effects on productivity of small and medium construction companies in Oman: exploration and ranking
Authors: Al Alawi Mubarak 
Published: August 2021

AbstractMaintaining a stable productivity rate in a construction project is a challenge. Many external and internal factors influence it. Delay in payment is one of the factors representing the project cash flow and mirrors the company’s financial stability status. This study explores the delay in payments effects on the construction productivity of the small and medium construction companies in Oman. Also, it ranks the delay in payment among other productivity factors. Sixty-five small and medium construction companies registered in Oman Tender Board participated in the questionnaire survey. The results showed that delay in payment significantly affects the financial stability of the companies. The delay in payment was ranked third out of 21 influencing productivity factors. The results were compared with a previous study that covered large construction companies in Oman. It was found that the rank of delay in payment in the small and medium construction is significantly higher than what was found in large companies.
Irreversible phase field models for crack growth in industrial applications: thermal stress, viscoelasticity, hydrogen embrittlement
Authors: Kimura Masato , Takaishi Takeshi , Alfat Sayahdin , Nakano Takumi , Tanaka Yoshimi 
Published: August 2021

AbstractThree new industrial applications of irreversible phase field models for crack growth are presented in this study. The phase field model for irreversible crack growth in an elastic material is derived as a unidirectional gradient flow of the Francfort–Marigo energy with the Ambrosio–Tortorelli regularization, which is known to be consistent with classic Griffith fracture theory. The obtained compact parabolic-elliptic system of PDEs including two regularization parameters for space and time enables us to simulate various kinds of crack behaviors with standard finite element software, without any geometric restriction on the crack path. We extend the irreversible phase field model to thermal cracking in solder and to cracking in a viscoelastic material, keeping the compact forms of the PDEs and the energy consistency. On the other hand, for hydrogen-assisted cracking in metal, we propose a compact phase field model focusing on a kinematic jamming effect of the hydrogen by a weak coupling approach. Several numerical experiments for these three models show not only their reasonableness and usefulness but also flexible extendability of the phase field approach in fracture mechanics.
Design and exploration of neural network microsystem based on SiP
Authors: Lv Hao , Zhang Shengbing , Deng Bao , Wang Jia , Jing Desheng , Chu Yaoqin , Zhang Kun 
Published: August 2021

AbstractIn recent years, microelectronics technology has entered the era of nanoelectronics/integrated microsystems. System in package (SiP) and system on chip (SoC) are two important technical approaches for the realization of microsystems. Deep learning technology based on neural networks is used in graphics and images. Computer vision and target recognition are widely used. The deep learning technology of convolutional neural network is an important research field in the miniaturization and miniaturization of embedded platforms. How to combine the lightweight neural network with the microsystem to achieve the optimal balance of performance, size, and power consumption is a difficult point. This article introduces a micro-system implementation scheme that combines SiP technology and FPGA-based convolutional neural network. It uses Zynq SoC and FLASH and DDR3 memory as the main components, and uses SiP high-density system packaging technology to integrate. PL end (FPGA) design Convolutional Neural Network, convolutional neural network accelerator, adopt the method of convolution multi-dimensional division and cyclic block to design the accelerator structure, design multiple multiplication and addition parallel computing units to provide the computing power of the system. Improving and accelerating perform on the YOLOv2_Tiny model. The test uses the COCO data set as the training and test samples. The microsystem can accurately identify the target. The volume is only 30 × 30 × 1.2 mm. The performance reaches 22.09GOPs and the power consumption is only 0.81 W under the working frequency of 150 MHz. Multi-objective balance (performance, size and power consumption) of lightweight neural network Microsystems has realized.
Changes in Heme Levels During Acute Vaso-occlusive Crisis in Sickle Cell Anemia
Authors: Cardoso Evilazio Cunha , Silva-Neto Pedro Vieira , Hounkpe Bidossessi Wilfried , Chenou Francine , Albuquerque Cintia Cristina Mateus Xerez , Garcia Nadja Pinto , Silva-Junior Alexander Leonardo , Malheiro Adriana , Cesar Purim , de Lima Franciele , De Paula Erich V. , Fraiji Nelson Abrahim 
Published: August 2021

Objective/Background:
Sickle cell anemia (SCA) is associated with increased levels of extracellular heme, which is a key mediator of inflammation in this condition. Despite abundant evidence supporting this concept in cell and animal models, few studies addressed the association between heme levels and the development and severity of acute vasoocclusive crises (VOC) in humans.

Methods:
A cross-sectional study was conducted in patients with acute VOC. Total extracellular heme levels were measured in both plasma and serum at admission and after convalescence, and correlated with other clinical and laboratory markers of SCA severity.

Results:
A total of 28 episodes of VOC in 25 patients were included. Heme levels were similar between admission and convalescence, and correlated with the difference between pre and post hemoglobin, and SCA severity estimated by a composite score of clinical and laboratory markers. Heme levels were neither associated with VOC severity nor with markers of hemostasis activation, and were similar to those reported in an independent population of SCA patients at steady state.

Discussion:
Acute VOC are not characterized by significant increases in total extracellular heme levels. Studies measuring the fraction of free extracellular heme unbound to proteins are warranted to further refine our understanding of the role of heme in acute VOC.
Comprehensive analysis of the association of seasonal variability with maternal and neonatal nutrition in lowland Nepal
Authors: Saville Naomi M , Cortina-Borja Mario , De Stavola Bianca L , Pomeroy Emma , Marphatia Akanksha , Reid Alice , Manandhar Dharma S , Wells Jonathan CK 
Published: August 2021

AbstractObjective:To provide a comprehensive seasonal analysis of pregnant mothers’ eating behaviour and maternal/newborn nutritional status in an undernourished population from lowland rural Nepal, where weather patterns, agricultural labour, food availability and disease prevalence vary seasonally.Design:Secondary analysis of cluster-randomised Low Birth Weight South Asia Trial data, applying cosinor analysis to predict seasonal patterns.Outcomes:Maternal mid-upper arm circumference (MUAC), BMI, dietary diversity, meals per day, eating down and food aversion in pregnancy (≥31 weeks’ gestation) and neonatal z-scores of length-for-age (LAZ), weight-for-age (WAZ) and head circumference-for-age (HCAZ) and weight-for-length (WLZ).Setting:Rural areas of Dhanusha and Mahottari districts in plains of Nepal.Participants:2831 mothers aged 13–50 and 3330 neonates.Results:We found seasonal patterns in newborn anthropometry and pregnant mothers’ anthropometry, meal frequency, dietary diversity, food aversion and eating down. Seasonality in intake varied by food group. Offspring anthropometry broadly tracked mothers’. Annual amplitudes in mothers’ MUAC and BMI were 0·27 kg/m2 and 0·22 cm, with peaks post-harvest and nadirs in October when food insecurity peaked. Annual LAZ, WAZ and WLZ amplitudes were 0·125, 0·159 and 0·411 z-scores, respectively. Neonates were the shortest but least thin (higher WLZ) in winter (December/January). In the hot season, WLZ was the lowest (May/June) while LAZ was the highest (March and August). HCAZ did not vary significantly. Food aversion and eating down peaked pre-monsoon (April/May).Conclusions:Our analyses revealed complex seasonal patterns in maternal nutrition and neonatal size. Seasonality should be accounted for when designing and evaluating public heath nutrition interventions.