Molecular Dynamics Computer Simulation of Crystal Growth and Melting in Al50Ni50

The melting and crystallization of Al50Ni50} are studied by means of molecular dynamics computer simulations, using a potential of the embedded atom type to model the interactions between the particles. Systems in a slab geometry are simulated where the B2 phase of AlNi in the middle of an elongated simulation box is separated by two planar interfaces from the liquid phase, thereby considering the (100) crystal orientation. By determining the temperature dependence of the interface velocity, an accurate estimate of the melting temperature is provided. The value k=0.0025 m/s/K for the kinetic growth coefficient is found. This value is about two orders of magnitude smaller than that found in recent simulation studies of one-component metals. The classical Wilson-Frenkel model is not able to describe the crystal growth kinetics on a quantitative level. We argue that this is due to the neglect of diffusion processes in the liquid-crystal interface.Comment: 6 pages, 6 figure

Amorphous silicon under mechanical shear deformations: shear velocity and temperature effects

Mechanical shear deformations lead, in some cases, to effects similar to those resulting from ion irradiation. Here we characterize the effects of shear velocity and temperature on amorphous silicon (\aSi) modelled using classical molecular dynamics simulations based on the empirical Environment Dependent Inter-atomic Potential (EDIP). With increasing shear velocity at low temperature, we find a systematic increase in the internal strain leading to the rapid appearance of structural defects (5-fold coordinated atoms). The impacts of externally applied strain can be almost fully compensated by increasing the temperature, allowing the system to respond more rapidly to the deformation. In particular, we find opposite power-law relations between the temperature and the shear velocity and the deformation energy. The spatial distribution of defects is also found to strongly depend on temperature and strain velocity. For low temperature or high shear velocity, defects are concentrated in a few atomic layers near the center of the cell while, with increasing temperature or decreasing shear velocity, they spread slowly throughout the full simulation cell. This complex behavior can be related to the structure of the energy landscape and the existence of a continuous energy-barrier distribution.Comment: 10 pages, 17 figure

Image Captioning based on Feature Refinement and Reflective Decoding

Image captioning is the process of automatically generating a description of an image in natural language. Image captioning is one of the significant challenges in image understanding since it requires not only recognizing salient objects in the image but also their attributes and the way they interact. The system must then generate a syntactically and semantically correct caption that describes the image content in natural language. With the significant progress in deep learning models and their ability to effectively encode large sets of images and generate correct sentences, several neural-based captioning approaches have been proposed recently, each trying to achieve better accuracy and caption quality. This paper introduces an encoder-decoder-based image captioning system in which the encoder extracts spatial features from the image using ResNet-101. This stage is followed by a refining model, which uses an attention-on-attention mechanism to extract the visual features of the target image objects, then determine their interactions. The decoder consists of an attention-based recurrent module and a reflective attention module, which collaboratively apply attention to the visual and textual features to enhance the decoder's ability to model long-term sequential dependencies. Extensive experiments performed on Flickr30K, show the effectiveness of the proposed approach and the high quality of the generated captions

Drones for smart cities

Smart cities and unmanned aerial vehicles (UAVs) are two relatively recent concepts and also hot topics in research. The combination of these two technologies is expected to propel their capabilities even further for enabling revolutionary applications that will improve our quality of life. This Special Issue focuses on novel work done on the application of UAVs where state-of-the-art technologies in sensing, information dissemination, communications, and artificial intelligence (AI) are applied within the context of smart cities..

Ageing effects in supercooled silica: a molecular dynamics investigation

he two-, three- and four-body effective collision induced scattering spectral line shapes are calculated for dense gaseous krypton using the pairwise additivity (PA) approximation and different polarizability models. These spectra and several interaction induced spectra calculated at various densities are compared with the experimental measurements of Barocchi et al. [1988, Europhys. Lett., 5, 607]. The potential effect on the spectrum is found to be weak. The results obtained with the Meinander et al. [1986, J. chem. Phys., 84, 3005] empirical polarizability model and molecular dynamics fit well the experimental two- and three-body spectral shapes. The irreducible contribution to the spectral shape is evaluated using the dipole induced dipole irreducible polarizability [buckingham, A. D., and Hands, I. D., 1991, Chem. Phys. Lett., 185, 544]. This contribution is found to be relatively weak for the anisotropic spectra in the frequency and density range studied, explaining the good agreement between the pairwise approximation calculations and the experimental data. The spectra radiated by the quasi-molecules Kr2, Kr3, and Kr4 (the total spectrum within the PA approximation) are also simulated

An Approach for Link Prediction in Directed Complex Networks based on Asymmetric Similarity-Popularity

Complex networks are graphs representing real-life systems that exhibit unique characteristics not found in purely regular or completely random graphs. The study of such systems is vital but challenging due to the complexity of the underlying processes. This task has nevertheless been made easier in recent decades thanks to the availability of large amounts of networked data. Link prediction in complex networks aims to estimate the likelihood that a link between two nodes is missing from the network. Links can be missing due to imperfections in data collection or simply because they are yet to appear. Discovering new relationships between entities in networked data has attracted researchers' attention in various domains such as sociology, computer science, physics, and biology. Most existing research focuses on link prediction in undirected complex networks. However, not all real-life systems can be faithfully represented as undirected networks. This simplifying assumption is often made when using link prediction algorithms but inevitably leads to loss of information about relations among nodes and degradation in prediction performance. This paper introduces a link prediction method designed explicitly for directed networks. It is based on the similarity-popularity paradigm, which has recently proven successful in undirected networks. The presented algorithms handle the asymmetry in node relationships by modeling it as asymmetry in similarity and popularity. Given the observed network topology, the algorithms approximate the hidden similarities as shortest path distances using edge weights that capture and factor out the links' asymmetry and nodes' popularity. The proposed approach is evaluated on real-life networks, and the experimental results demonstrate its effectiveness in predicting missing links across a broad spectrum of networked data types and sizes

A Sequence-to-Sequence Approach for Arabic Pronoun Resolution

This paper proposes a sequence-to-sequence learning approach for Arabic pronoun resolution, which explores the effectiveness of using advanced natural language processing (NLP) techniques, specifically Bi-LSTM and the BERT pre-trained Language Model, in solving the pronoun resolution problem in Arabic. The proposed approach is evaluated on the AnATAr dataset, and its performance is compared to several baseline models, including traditional machine learning models and handcrafted feature-based models. Our results demonstrate that the proposed model outperforms the baseline models, which include KNN, logistic regression, and SVM, across all metrics. In addition, we explore the effectiveness of various modifications to the model, including concatenating the anaphor text beside the paragraph text as input, adding a mask to focus on candidate scores, and filtering candidates based on gender and number agreement with the anaphor. Our results show that these modifications significantly improve the model's performance, achieving up to 81% on MRR and 71% for F1 score while also demonstrating higher precision, recall, and accuracy. These findings suggest that the proposed model is an effective approach to Arabic pronoun resolution and highlights the potential benefits of leveraging advanced NLP neural models

Screening dependence of the dynamical and structural properties of BKS silica

Molecular dynamics simulations of amorphous silica are carried out on a large temperature range using a modified version of the BKS inter-atomic potential. We investigate the dependence on the screening procedure of the structural and dynamical properties of amorphous silica. We show that an increased screening of the electrostatic interaction leads to a decrease of the diffusion constants and then to better agreement with experimental data, while structural properties are unchanged. We show that the Arrhenius dependence of the diffusion constants may be reproduced in this case up to a temperature of 4000 K with activation energies very similar to the experimental data

NOTRINO: a NOvel hybrid TRust management scheme for INternet-Of-vehicles

Internet-of-Vehicles (IoV) is a novel technology to ensure safe and secure transportation by enabling smart vehicles to communicate and share sensitive information with each other. However, the realization of IoV in real-life depends on several factors, including the assurance of security from attackers and propagation of authentic, accurate and trusted information within the network. Further, the dissemination of compromised information must be detected and vehicle disseminating such malicious messages must be revoked from the network. To this end, trust can be integrated within the network to detect the trustworthiness of the received information. However, most of the trust models in the literature relies on evaluating node or data at the application layer. In this study, we propose a novel hybrid trust management scheme, namely, NOTRINO, which evaluates trustworthiness on the received information in two steps. First step evaluates trust on the node itself at transport layer, while second step computes trustworthiness of the data at application layer. This mechanism enables the vehicles to efficiently model and evaluate the trustworthiness on the received information. The performance and accuracy of NOTRINO is rigorously evaluated under various realistic trust evaluation criteria (including precision, recall, F-measure and trust). Furthermore, the efficiency of NOTRINO is evaluated in presence of malicious nodes and its performance is benchmarked against three hybrid trust models. Extensive simulations indicate that NOTRINO achieve over 75% trust level as compared to benchmarked trust models where trust level falls below 60% for a network with 35% malicious nodes. Similarly, 92% precision and 87% recall are achieved simultaneously with NOTRINO for the same network, comparing to benchmark trust models where precision and recall falls below 87% and 85% respectively.N/