bluuebunny/crossref_metadata_2025_split huggingface dataset:
Particle size and substrate wettability dependent patterns in dried pendant drops
Authors: Kumar P Logesh , Thampi Sumesh P , Basavaraj Madivala G 
Published: September 2020

Abstract
The particle laden sessile drops when dried on solid surfaces under certain conditions leave a deposit pattern wherein all the particles are confined to a narrow region close to the edge of the deposit. Such patterns which often form when coffee drops dry are referred to as the coffee ring patterns or the coffee stains. Recent research points to the formation of intriguing patterns when colloidal particle laden drops are dried in configurations other than sessile mode. In this article, the combined effect of particle size and wettability of the substrate on the patterns formed by drying drops in sessile and pendant configurations is investigated via experiments. Our results demonstrate a transition from coffee ring to central dome-like deposit morphology with decrease in wettability of the substrates when drops containing 3 μm diameter particles are dried in pendent mode. A similar transition in the deposit morphology is observed with increase in the diameter of the particles in pendant drops dried on substrates of near neutral wettability (θ = 86 ± 3°). The influence of particles size, substrate wettability and drop configuration on the kinetics of deposition of particles at the three phase contact line will also be discussed. We compare our experimental observations with particle based simulations wherein the dried patterns are generated by accounting for three particle transport modes, namely, advective particle transport resulting from capillary flow, gravity driven settling of particles and particle capture by descending interface.
Development of MnO2 hollow nanoparticles for potential drug delivery applications
Authors: Greene Allison , Hashemi Jonathon , Kang Yunqing 
Published: September 2020

Abstract
This study reports the development of hollow nanoparticles, formed from manganese dioxide (δ-MnO2) sheets, that are coated with polydopamine for potential immobilization of chemical agents. The biodegradability and colloidal stability of the uncoated hollow MnO2 nanoparticles were investigated in comparison to commercially synthesized solid MnO2 nanoparticles and graphene oxide sheets. The MnO2 hollow nanoparticles degraded at a faster rate and seem to have a higher surface area and better colloidal dispersion than solid MnO2 nanoparticles. Xanthan gum (as a dispersant) was proven to improve colloidal dispersion of these hollow nanoparticles and were used for further cell studies. In this study, cancer and healthy cells were treated with coated hollow nanoparticles, and the studies indicate that this novel nanoparticle can internalize cells. Particle aggregation has shown to inhibit cell growth. Further studies with this novel hollow nanoparticle may lead to a groundbreaking solution to new drug delivery systems for cancers or other applications.
Silver nanoparticle on aluminum mirror: active spectroscopy and decay rate enhancement
Authors: Kurochkin N S , Eliseev S P , Gritsienko A V , Sychev V V , Vutukhnovsky A G 
Published: September 2020

Abstract
Recent advances in nanotechnology and optics have paved the way for new plasmonic devices. One of them are nanopatch antennas that are simple and, at the same time, effective devices for localizing the electromagnetic field on a scale of less than 10 nm and can be used in photonic integrated circuits as effective sources of photons, including single-photon sources. In the present study, we investigate the radiative characteristics of a submonolayer of colloidal CdSe/CdS quantum dots that form island structures in a resonator: a cubic silver nanoparticle on an aluminum mirror. For detecting plasmonic nanoparticles on glass or metal surfaces, we propose a new technique involving a tunable laser and a confocal microscope. We provide a comparative study of the luminescence enhancement factors for QDs in the NPAs upon off-resonance excitation and at a wavelength close to the resonance; a significant difference in the luminescence enhancement factors (by order of magnitude) is demonstrated. A 60-fold reduction in the spontaneous emission time, as well as an increase in the radiation intensity by a factor of 330, has been obtained in the experiments. The increase in the spontaneous emission rate demonstrated for the quantum dots is explained by the Purcell effect. Full-wave simulations of electromagnetic fields were carried out for the model of the developed nanopatch antenna; luminescence enhancement factors and radiative efficiencies were calculated as well.
A controllable mechanical motion rectifier-based semi-active magnetorheological inerter for vibration control
Authors: Ning Donghong , Christie Matthew Daniel , Sun Shuaishuai , Du Haiping , Li Weihua , Wang Yafei 
Published: September 2020

Abstract
The conventional semi-active variable damping (VD) device can only dissipate the vibration energy, which inherently limits its performance in vibration control. This paper proposes a novel semi-active inerter concept and develops a prototype with magnetorheological (MR) dampers, which can store the vibration energy in a flywheel and then release it to suppress vibration. The new idea is inspired by a variable inertance (VI) device, which has a VD device and a passive inerter in serial, and a passive mechanical motion rectifier (MMR), which can convert the reciprocating input into a unidirectional output. The controllable MMR (CMMR) applies two VD devices to replace the two one-way roller clutches in the MMR. By equipping the CMMR, the semi-active inerter gets more controllability than the original VI device because it can switch the torque and motion transmitting routes between the device terminals and the flywheel in it. The frequency-domain analysis validates the versatile of the semi-active inerter, which can work in VD and VI modes. The test results of the semi-active MR inerter prototype are used to identify the device parameters, which are applied for the control simulation. The semi-active MR inerter in CMMR mode has the best torque tracking performance with a given test condition, and it has a 20.13% improvement than in the VD mode. Then, a seat suspension with the semi-active MR inerter is applied to validate the effectiveness of the device in vibration control. The results show that the vibration reduction of the seat suspension in the CMMR mode is 39.3% higher than in the VD mode, which indicates a significant improvement of ride comfort. The new concept of the semi-active device has excellent potential in vibration control and is promising in practical applications.
Deep learning for impact detection in composite plates with sparsely integrated sensors
Authors: Damm Andreas M , Spitzmüller Claudio , Raichle Andreas T S , Bühler Andre , Weißgraeber Philipp , Middendorf Peter 
Published: September 2020

Abstract
In this paper, both location and energy of impacts on an anisotropic carbon fiber reinforced plate (CFRP) are detected with the help of deep learning. We introduce sparse low-cost sensor array integration in CFRP plates that allows for structural monitoring of lightweight structures. Using a resin transfer moulding process microelectromechanical systems (MEMS) and piezoelectric transducers (PZT) sensors are integrated into CFRP plates. We developed an automated test bench to perform weight drop impact loadings with impact energies ranging between 0.22–0.56 mJ on a 1 × 1 cm2-grid with 441 locations. The obtained sensor signals were processed by means of a short-time fourier transformation and used as input for the training of a deep learning model. This model was implemented with a convolutional neural network. To accelerate the training phase we introduce a coarse analytical model that generates artificial sensor signals we use for pretraining of the neural network. Yielding high prediction accuracies of 99.82% and 98.68% for a correct classification of impact location and energy, respectively, the capability of the proposed approach was demonstrated. Despite their limited resolution the low-cost MEMS accelerometers were able to correctly locate an impact and its energy with 99.76% and 97.04%, respectively. The pretraining led to an increased robustness of the training process. Additionally, for the case of PZT sensors, it also reduced the number of required epochs for convergence significantly.
Development and damping properties of a seismic linear motion damper with MR fluid porous composite rotary brake
Authors: Nakano Masami , Yang Jian , Sun Shuaishuai , Totsuka Atsushi , Fukukita Akira 
Published: September 2020

Abstract
The particle sedimentation of the dispersed particles in magnetorheological (MR) fluids when off-working has been a challenging problem, causing adverse effect to their practical applications. In order to solve this problem, ‘MR fluid porous composite’ made of porous materials impregnated with MR fluid has been fabricated in this work. Its effect to prevent the particle sedimentation has been proved through contrast experiment. Its MR effect has also been measured and verified using a hand-made oscillatory rheometer. Utilizing the MR fluid porous composite, a small-scale seismic linear MR damper has been developed. The unique design of this seismic linear MR damper has enabled it to convert the linear motion (damping force) of the MR damper to the rotations (braking torque) of the MR brake with almost no transmission loss. The damping properties of the developed small-scale seismic MR damper were investigated experimentally to be found that the design target with the damping force to be 20 kN has been achieved when applying an electric current of 0.5 A. Then a theoretical model was derived to estimate the damping force. The simulation results demonstrate that the proposed model is good at describing the properties of the seismic linear MR damper.
A twist piezoelectric beam for multi-directional energy harvesting
Authors: Hu Guobiao , Liang Junrui , Lan Chunbo , Tang Lihua 
Published: September 2020

Abstract
This paper develops a twist beam for multi-directional energy harvesting using piezoelectric transduction. The working principle of the multi-directional ability is explained by the theory of a pre-twisted beam. A simulation study is performed for concept verification and revealing the potential advantages of the proposed twist-beam vibration energy harvester (VEH). A physical prototype is fabricated and an experimental study is conducted to evaluate the actual performance of the proposed twist-beam VEH for validation. The results show that for the excitation comes from any direction in the plane in parallel with the beam cross-section, the proposed twist-beam VEH can produce a substantial amount of power. The optimal resistance is independent of the excitation direction. Moreover, it is found that the relationships between the output voltage amplitude and the excitation direction are different for the cases around the first and second resonances of the twist-beam VEH. The underlying physics behind this phenomenon is that the dominant motion of the two resonant modes are contrarily different. In addition, the experimental study shows that the first two natural frequencies of the prototyped twist-beam VEH are 22.37 and 41.47 Hz, respectively, which are much closer to each other as compared with those of a conventional plain beam. This feature benefits multi-modal energy harvesting within a targeted frequency range. The prospects regarding further optimization of the proposed twist-beam VEH are discussed.
Strong flux pinning and anomalous anisotropy of Sr0.6K0.4Fe2As2 superconducting tapes
Authors: Lin He , Dong Chiheng , Pan Xifeng , Yao Chao , Zhang Xianping , Zhao Yong , Ma Yanwei 
Published: September 2020

Abstract
A comprehensive study on the resistivity, magnetization and microstructure is carried out to understand the superconductivity and flux pinning behavior of hot-pressed Sr0.6K0.4Fe2As2 tapes. The Sr0.6K0.4Fe2As2 tapes possess excellent properties of high T
c = 36.2 K, large transport J
c of ∼105A cm−2 at 10 T and 4.2 K, and high pinning potential U
0/k
B with small field-dependence, demonstrating a great potential in high-field applications. The magnetic J
c at various temperatures and flux pinning mechanism are exhibited for both B//ab and B//c directions. Notably, a point pinning in combination with surface pinning appears in our samples, resulting in a strong flux pinning force. And the characteristic ratio B
irr
//c(0 K)/B
irr
//ab(0 K) is as low as 1.2, indicating that the vortex pinning abilities are nearly isotropic. Further transmission electron microscope characterization reveals that the anomalous pinning behavior is originated from the largely increased small-sized defects and the morphological differences along two directions. These positive results indicate that tailoring the pinning characteristic by creating micro-defects is crucial to improving the J
c value and suppressing the anisotropy in iron pnictides.
Target finding in fibrous biological environments
Authors: Gomez David , Teomy Eial , Lesman Ayelet , Shokef Yair 
Published: September 2020

Abstract
We use a lattice model to study first-passage time distributions of target finding events through complex environments with elongated fibers distributed with different anisotropies and volume occupation fractions. For isotropic systems and for low densities of aligned fibers, the three-dimensional search is a Poisson process with the first-passage time exponentially distributed with the most probable finding time at zero. At high enough densities of aligned fibers, elongated channels emerge, reducing the dynamics dimensionality to one dimension. We show how the shape and size of the channels modify the behavior of the first-passage time distribution and its short, intermediate, and long time scales. We develop an exactly solvable model for synthetic rectangular channels, which captures the effects of the tortuous local structure of the elongated channels that naturally emerge in our system. For arbitrary values of the nematic order parameter of fiber orientations, we develop a mapping to the simpler situation of fully aligned fibers at some other effective volume occupation fraction. Our results shed light on the molecular transport of biomolecules between biological cells in complex fibrous environments.
Chaos and regularity of radionuclides with maximum likelihood estimation method
Authors: Majarshin A Jalili , Sabri H , Mobarakeh S K Mousavi , Pan Feng , Luo Yan-An , Zhang Yu , Draayer Jerry P 
Published: September 2020

Abstract
In this study, we considered the fluctuation properties of some energy levels of even and odd mass radionuclides, which are used in complex phenomena. Different sequences are prepared by using all the available experimental data and analyzed by using the maximum likelihood estimation technique to get the chaoticity parameter of Abul-magd distribution. The dependence of chaoticity degrees of different radionuclides to their mass regions, their decay modes, and also their physical half-lives are studied. Our results show more chaotic behavior of odd-mass radionuclides in comparison with even–even mass and also the most Poisson-like behavior for even–even mass in the A > 150 mass region. The results offer the most regular behavior for long-lived, even mass radionuclides in comparison to other categories of half-lives. Also, we got an obvious difference between the chaoticity degrees for nuclei which undergo β
+ decay in comparison with radionuclides which show electron capture mode.
Fault Diagnosis of Multi-level Principal Component Space Based on High-Dimensional Feature Representation
Authors: Zhang Caixia , Hu Shaolin , Guo Jing , Wang Xiangdong 
Published: September 2020

AbstractDue to the influence of space equivalent representation and pattern composition, traditional principal component analysis (PCA) is faced with challenges in identifying small faults. This paper proposes a fault diagnosis method is proposed based on multi-levels principal component space based on high-dimensional feature representation. Firstly, based on the original PCA, the dimensions are expanded first and then reduced, so that the information that cannot be expressed in the original space is fully expressed. This method further divides the principal element space based on the eigenvalues, and projects the occurrence of the event and the size of the identification abnormality according to the projection to different sub-spaces. Compared with the usual fault detection methods, simulations verify that this algorithm can effectively measure the level of faults and improve the correct detection rate of minor faults.
Motion Retargeting for Virtual Human Based on Vector Features of Lower Limbs
Authors: Qu Shi , Liu Jianxun , Xu Tinxin , Ma Liang 
Published: September 2020

Abstract
In view of the contradiction that limited motion data can not meet the requirements of rich and changeable motion, motion reuse technology emerges as the times require, which can drive different human skeleton models with the same motion data to generate corresponding motion. However, when the motion data is used to drive the human models with different heights and bone proportions, motion deformation often occurs. In order to solve this problem, the concept of human lower limb vector is proposed in this paper. By analyzing human motion, it is pointed out that the lower limb vector can maintain the main characteristics of motion, and then a human motion redirection method based on the invariance of lower limb vector feature is proposed. This method can redirect the motion data from the original bone model to the target bone model with different bone length ratio, while maintaining the main characteristics of the original motion. The experimental results show that this method has good motion redirection effect and fast computational efficiency.
Bearing-Only Target Tracking Based on Piecewise Backward Smoothing Kalman Filter
Authors: Zheng Yi , Wang Mingzhou 
Published: September 2020

Abstract
In order to solve the problem that the Unscented Kalman Filter algorithm is sensitive to the initial value selection and the noise is easy to expand the error in the recursive process, a piecewise backward smoothing Kalman filter method is proposed in this paper. To reduce the influence of noise, the observation data is optimized in sections, and the forward and reverse use of historical data is increased. The simulation results under different initial distance error, different observation angle variance and different initial velocity error show that the proposed method is effective in underwater bearings only target tracking, and the effect of Unscented Kalman filter is obviously improved, the estimation error is greatly reduced, and the estimation accuracy and stability are improved.
Research on the Reconstruction and Parameterization of Virtual Costume Draping Mannequin in OpenGL Environment
Authors: Wang Xiaobo , Qian Xiaoming , Wang Lijing 
Published: September 2020

Abstract
In the 3D virtual design of clothing, the accuracy of virtual mannequin will significantly affect the fitness of the final garment. At the same time, parameterized mannequin can also improve the efficiency and effect of virtual clothing design. The traditional mannequin modeling method has been improved and the reconstruction and parameterized deformation of virtual costume draping mannequin has been realized in OpenGL environment. The parameterized reconstruction process of mannequin is mainly divided into four parts, namely the establishment of the basic mannequin, the reading of the model data, the parameterized deformation of the grid model and the rendering and drawing of the modal data. Finally, the new parameterized virtual human platform is built and the interactive interface is developed. And the virtual human mannequin can be edited and previewed in real time. The modeling result shows that the parametric reconstruction of the virtual draping mannequin is effective and can be applied to virtual costume designing and virtual fitting system.
Research on the Construction and Operation of the Statistical Analysis System of Cultural Tourism Industry of Shenyang
Authors: Zheng Xinna , Zhou Caiying 
Published: September 2020

Abstract
This paper analyses the development status of the cultural tourism industry in Shenyang by extracting Shenyang’s cultural tourism industry data, establishing a statistical index system for the development of Shenyang tourism industry based on big data tools, and uses big data tools to collect massive amounts of data and break the traditional data sample size. Based on the big data mathematical model, the tourism market in Shenyang was predicted and analysed, and the bottlenecks of small, time delay and low accuracy were obtained. Finally, it proposes to improve the management strategy of Shenyang’s cultural tourism industry, enhance the marketing strategy of cultural travel, strengthen the strategy of cultural travel services, and promote the high-quality development of the cultural tourism industry in Shenyang.
A 3D Design System of Skirt Profile Based on OpenGL
Authors: Xu Jun , Wang Xiaobo 
Published: September 2020

Abstract
Personalized customization is inseparable from effective communication between designers and consumers, which is often aided by 3D software. Based on the OpenGL graphics interface, this paper studies and developed a three-dimensional interactive skirt design system. And This system can help designers and consumers quickly agree on the skirt profile and record the profile parameters. What’s more, it can help designers design and produce clothes that are more in line with consumers’ needs. The research process can be divided into four parts: the establishment of the skirt point cloud model, the triangular meshing of the point cloud model, the rendering of the triangular meshing model, and the development of the interactive interface. The design of skirt length, contour, and pleats can be parametric now with the realistic virtual visual effect. Therefore, this paper provides new ideas and methods for achieving the “what you see is what you get” skirt design concept.
Enhancing Deep Learning-Based Multi-label Text Classification with Capsule Network
Authors: Yan Siyi 
Published: September 2020

Abstract
Given a piece of text, multi-label text classification (MLTC) is designed to mark the most relevant one label or multiple labels for the text. Most of the existing MLTC models use convolutional neural network (CNN) as feature extractor, but CNN will lose information when dealing with MLTC task. In this paper, we explore the CNN combined with capsule network for MLTC. We use capsule network instead of pool layer in CNN to extract information related to classification results in high-dimensional features. We also explore the way of combining recurrent neural network (RNN) and CNN to model the characteristics of time and space for capsule network to complete classification. In two open MLTC datasets, our model achieves the better results as the baseline system, which shows the effectiveness of the combination of capsule network and CNN for MLTC.