This paper studies elasto-plastic large deformation behaviour of thin shell structures using the isogeometric computational approach with the main focus on the efficiency in modelling the ...multi-patches and arbitrary material formulation. In terms of modelling, we employ the bending strip method to connect the patches in the structure. The incorporation of bending strips allows to eliminate the strict demand of the C1 continuity condition, which is postulated in the Kirchhoff-Love theory for thin shell, and therefore it enables us to use the standard multi-patch structure even with C0 continuity along the patch boundaries. Furthermore, arbitrary nonlinear material models such as hyperelasticity and finite strain plasticity are embedded in the shell formulation, from which a unified thin shell formulation can be achieved. In terms of analysis, the Bézier decomposition concept is used to retain the local support of the traditional finite element. The performance of the presented approach is verified through several numerical benchmarks.
•Unified thin shell formulation allowing arbitrary mateiral nonlinearity.•Multi-patch shell structure applicable.•C1 continuity at patch boundaries by bending strip method.•Bézier decomposition concept to retain local support of the traditional FE.
•A polygonal finite element method (PFEM) based on C0-type higher-order shear deformation theory (C0-HSDT) is proposed for static and free vibration analyses of laminated composite plates.•A ...piecewise-linear shape function which is constructed based on sub-triangles of polygonal element is considered.•A simple numerical integration over polygonal elements is devised.•Shear locking is addressed by a simple Timoshenko's beam model.•The numerical results show the efficiency and reliability of the present approach.
In this study, a polygonal finite element method (PFEM) is extended and combined with the C0-type higher-order shear deformation theory (C0-HSDT) for the static and free vibration analyses of laminated composite plates. Only the piecewise-linear shape function which is constructed based on sub-triangles of polygonal element is considered. By using the analogous technique which relies on the sub-triangles to calculate numerical integration over polygonal elements, the procedure becomes remarkably efficient. The assumption of strain field along sides of polygons being interpolated based on Timoshenko's beam leads to the fact that the shear locking phenomenon can be naturally avoided. In addition, the C0-HSDT theory, in which two additional variables are included in the displacement field, significantly improves the accuracy of the displacements and transverse shear stresses. Numerical examples are provided to illustrate the efficiency and reliability of the proposed approach.
Display omitted Several mode shapes of a three-layer CCCC laminated composite plate with a complicated cutout.
The phytochemical investigation of the lichen Parmotrema sancti‐angelii afforded three racemic compounds, sanctis A–C, which feature an original dibenzo‐2,8‐dioxabicyclo3.3.1nonane scaffold. These ...compounds were structurally characterized by extensive NMR spectroscopy analyses, comparison between experimental and theoretical NMR spectroscopic data, and X‐ray crystallography. These metabolites are similar to procyanidin A and display a methyl group instead of a pendant aromatic ring at C‐9, a so far unprecedented structural feature. A biosynthetic route to sanctis A–C is proposed.
Three racemic pairs of procyanidin analogues (sanctis A–C) are isolated from the lichen Parmotrema sancti‐angelii along with eight other known compounds. These new metabolites all display a new carbon skeleton.
ABSTRACT
We develop a new analysis approach towards identifying related radio components and their corresponding infrared host galaxy based on unsupervised machine learning methods. By exploiting ...Parallelized rotation and flipping INvariant Kohonen maps (pink), a self-organizing map (SOM) algorithm, we are able to associate radio and infrared sources without the a priori requirement of training labels. We present an example of this method using 894 415 images from the Faint Images of the Radio-Sky at Twenty centimeters (FIRST) and Wide-field Infrared Survey Explorer (WISE) surveys centred towards positions described by the FIRST catalogue. We produce a set of catalogues that complement FIRST and describe 802 646 objects, including their radio components and their corresponding AllWISE infrared host galaxy. Using these data products, we (i) demonstrate the ability to identify objects with rare and unique radio morphologies (e.g. ‘X’-shaped galaxies, hybrid FR I/FR II morphologies), (ii) can identify the potentially resolved radio components that are associated with a single infrared host, (iii) introduce a ‘curliness’ statistic to search for bent and disturbed radio morphologies, and (iv) extract a set of 17 giant radio galaxies between 700 and 1100 kpc. As we require no training labels, our method can be applied to any radio-continuum survey, provided a sufficiently representative SOM can be trained.
The article uses the latest firm-level data in Vietnam, from 2011 to 2015, to find fresh evidence on productivity spillovers from foreign direct investment across six regions in Vietnam. The finding ...indicates negative horizontal spillover as the most dominant channel in all regions. The positive backward spillover is compensated for by the large magnitude of negative horizontal and forward spillovers. Besides, absorptive capability really matters in productivity spillovers. Furthermore, total factor productivity growth at domestic firms within 100 sq. km. of foreign capital-intensive and administrative centers is similar to that of external firms under the effects of productivity spillover.
This paper develops a novel framework to defeat a super-reactive jammer, one of the most difficult jamming attacks to deal with in practice. Specifically, the jammer has an unlimited power budget and ...is equipped with the self-interference suppression capability to simultaneously attack and listen to the transmitter's activities. Consequently, dealing with super-reactive jammers is very challenging. Thus, we introduce a smart deception mechanism to attract the jammer to continuously attack the channel and then leverage jamming signals to transmit data based on the ambient backscatter communication technology. To detect the backscattered signals, the maximum likelihood detector can be adopted. However, this method is notorious for its high computational complexity and requires the model of the current propagation environment as well as channel state information. Hence, we propose a deep learning-based detector that can dynamically adapt to any channels and noise distributions. With a Long Short-Term Memory network, our detector can learn the received signals' dependencies to achieve a performance close to that of the optimal maximum likelihood detector. Through simulation and theoretical results, we demonstrate that with our approaches, the more power the jammer uses to attack the channel, the better bit error rate performance the transmitter can achieve.
This two-part paper aims to provide a comprehensive survey on how emerging technologies, e.g., wireless and networking, artificial intelligence (AI) can enable, encourage, and even enforce social ...distancing practice. In Part I, an extensive background of social distancing is provided, and enabling wireless technologies are thoroughly surveyed. In this Part II, emerging technologies such as machine learning, computer vision, thermal, ultrasound, etc., are introduced. These technologies open many new solutions and directions to deal with problems in social distancing, e.g., symptom prediction, detection and monitoring quarantined people, and contact tracing. Finally, we discuss open issues and challenges (e.g., privacy-preserving, scheduling, and incentive mechanisms) in implementing social distancing in practice. As an example, instead of reacting with ad-hoc responses to COVID-19-like pandemics in the future, smart infrastructures (e.g., next-generation wireless systems like 6G, smart home/building, smart city, intelligent transportation systems) should incorporate a pandemic mode in their standard architectures/designs.
Anti-vaccination attitudes have been an issue since the development of the first vaccines. The increasing use of social media as a source of health information may contribute to vaccine hesitancy due ...to anti-vaccination content widely available on social media, including Twitter. Being able to identify anti-vaccination tweets could provide useful information for formulating strategies to reduce anti-vaccination sentiments among different groups. This study aims to evaluate the performance of different natural language processing models to identify anti-vaccination tweets that were published during the COVID-19 pandemic. We compared the performance of the bidirectional encoder representations from transformers (BERT) and the bidirectional long short-term memory networks with pre-trained GLoVe embeddings (Bi-LSTM) with classic machine learning methods including support vector machine (SVM) and naïve Bayes (NB). The results show that performance on the test set of the BERT model was: accuracy = 91.6%, precision = 93.4%, recall = 97.6%, F1 score = 95.5%, and AUC = 84.7%. Bi-LSTM model performance showed: accuracy = 89.8%, precision = 44.0%, recall = 47.2%, F1 score = 45.5%, and AUC = 85.8%. SVM with linear kernel performed at: accuracy = 92.3%, Precision = 19.5%, Recall = 78.6%, F1 score = 31.2%, and AUC = 85.6%. Complement NB demonstrated: accuracy = 88.8%, precision = 23.0%, recall = 32.8%, F1 score = 27.1%, and AUC = 62.7%. In conclusion, the BERT models outperformed the Bi-LSTM, SVM, and NB models in this task. Moreover, the BERT model achieved excellent performance and can be used to identify anti-vaccination tweets in future studies.
In the context of increasing climate change, fishery-based livelihoods as major means of income and well-beings for millions of population in coastal communities around the world are most affected. ...Yet, available information how fishery-based livelihood system at local level are vulnerable to climate change, especially in developing countries is very limited. Using an indicator-based vulnerability assessment framework, this study examined the household-level vulnerability of fishery-based livelihoods in two coastal communities in Central Vietnam. The results showed that the nature and degree of livelihood vulnerability to climate change among fishing households depend on their own characteristics and conditions as well as accessibility to livelihood diversification opportunities. Developing appropriate adaptation policies and coastal management measures to reduce livelihood vulnerability should enhance positive indicators of household's adaptive capacity and create a better environment for alternative livelihood opportunities.
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