International Conference on Computer Science and Communication Engineering

UBT Annual International Conference is the 8th international interdisciplinary peer reviewed conference which publishes works of the scientists as well as practitioners in the area where UBT is active in Education, Research and Development. The UBT aims to implement an integrated strategy to establish itself as an internationally competitive, research-intensive university, committed to the transfer of knowledge and the provision of a world-class education to the most talented students from all background. The main perspective of the conference is to connect the scientists and practitioners from different disciplines in the same place and make them be aware of the recent advancements in different research fields, and provide them with a unique forum to share their experiences. It is also the place to support the new academic staff for doing research and publish their work in international standard level. This conference consists of sub conferences in different fields like: – Computer Science and Communication Engineering– Management, Business and Economics– Mechatronics, System Engineering and Robotics– Energy Efficiency Engineering– Information Systems and Security– Architecture – Spatial Planning– Civil Engineering , Infrastructure and Environment– Law– Political Science– Journalism , Media and Communication– Food Science and Technology– Pharmaceutical and Natural Sciences– Design– Psychology– Education and Development– Fashion– Music– Art and Digital Media– Dentistry– Applied Medicine– Nursing This conference is the major scientific event of the UBT. It is organizing annually and always in cooperation with the partner universities from the region and Europe. We have to thank all Authors, partners, sponsors and also the conference organizing team making this event a real international scientific event. Edmond Hajrizi, President of UBTUBT – Higher Education Institutio

Proceedings XXIII Congresso SIAMOC 2023

Il congresso annuale della Società Italiana di Analisi del Movimento in Clinica (SIAMOC), giunto quest’anno alla sua ventitreesima edizione, approda nuovamente a Roma. Il congresso SIAMOC, come ogni anno, è l’occasione per tutti i professionisti che operano nell’ambito dell’analisi del movimento di incontrarsi, presentare i risultati delle proprie ricerche e rimanere aggiornati sulle più recenti innovazioni riguardanti le procedure e le tecnologie per l’analisi del movimento nella pratica clinica. Il congresso SIAMOC 2023 di Roma si propone l’obiettivo di fornire ulteriore impulso ad una già eccellente attività di ricerca italiana nel settore dell’analisi del movimento e di conferirle ulteriore respiro ed impatto internazionale. Oltre ai qualificanti temi tradizionali che riguardano la ricerca di base e applicata in ambito clinico e sportivo, il congresso SIAMOC 2023 intende approfondire ulteriori tematiche di particolare interesse scientifico e di impatto sulla società. Tra questi temi anche quello dell’inserimento lavorativo di persone affette da disabilità anche grazie alla diffusione esponenziale in ambito clinico-occupazionale delle tecnologie robotiche collaborative e quello della protesica innovativa a supporto delle persone con amputazione. Verrà infine affrontato il tema dei nuovi algoritmi di intelligenza artificiale per l’ottimizzazione della classificazione in tempo reale dei pattern motori nei vari campi di applicazione

Machine-learning of liquids: A multi-scale framework for liquid physics and coarse-graining using deep learning

Liquids are a condensed and non-crystallized phase of matter and their properties are typically governed by many-body effects. The physics and properties of liquids are a mixture of gas and solid phases, but quite different from any of them. The application of theoretical models developed for solid and gas phases to liquids is difficult and development of theoretical models for liquid is hindered by many-body effects. Computer simulation and experimental methods have played a crucial role in understanding of liquids. In fact, most of our knowledge about liquids are obtained using computer simulation, especially molecular dynamics (MD) and Monte Carlo simulations. MD simulations are successful in finding thermodynamic, structural, dynamical, and transport properties of liquids. However, MD simulation requires accurate potential parameters to model physical phenomena. It is noteworthy to state that with known potential parameters structural properties not only describe local arrangement of atoms, but they are enough to calculate various thermodynamic properties such as pressure and isothermal compressibility. The more intriguing part about liquids is the relationship between structural properties and potential parameters. This question can be seen as the inverse problem of liquid-state theory or as a coarse-graining problem, where the objective is to parameterize a force field to reproduce a reference structure. In this study, a deep neural network is used for atom-agnostic parametrization of the Lennard-Jones potential at different thermodynamic states. The DNN demonstrates good performance for two cases – parameterization of LJ particles and development of single-bead CG LJ potentials for simple multi-atom molecules through transfer learning obtained from LJ particles. The transferability and generalizability of the method are investigated by computing the total variation in the radial distribution function and Kullback-Leibler divergence for the coarse-grained model development. Our results indicate that deep learning can compute the solution to the inverse-problem of liquid-state theory (DeepILST) under the assumption of a predetermined pair potential in a coarse-grained model. In a follow-up study, statistical and deep learning-based methods are employed to obtain insights into the quasi-universal properties of simple liquids. In the first part, a statistical model is employed to provide a probabilistic explanation for the similarity in the structure of simple liquids interacting with different pair potential forms, collectively known as simple liquids. The methodology works by sampling the radial distribution function and the number of interacting particles within the cut-off distance and it produces the probability density function of the net force. We show that matching the probability distribution of the net force can be a direct route to parameterize simple liquids pair potentials with a similar structure, as the net force is the main component of the Newtonian equations of motion. The statistical model is assessed and validated against various cases. The physics and quasi-universality of simple liquids are also studied through deep learning by finding structurally-equivalent Lennard-Jones liquids with similar reduced RDFs, i.e., isomorphs. Structurally-equivalent Lennard-Jones liquids identify systems with constant order parameters in the space of non-dimensional temperature and density of Lennard-Jones liquids consistent with the approximate theoretical solution derived in the current study and other theoretical models. Considering various investigations performed in this study, we show the successful employment of statistical and deep learning approaches and coarse-graining methods in the physics of simple liquids. As shown in above examples, MD simulation is a popular and strong computational tool to compute microscopic and macroscopic properties of liquids. However, to determine properties of atomic systems to a good level of accuracy with minimal noise or fluctuation, MD simulations are performed over a long time ranging from a few nanoseconds to several tens to hundreds of nanoseconds depending on the system and the properties of interest. There have been attempts to go around this issue with enhanced sampling and theoretical models. In this study, by considering simple liquids, we explore the feasibility of significantly reducing the MD simulation time to compute various properties of monoatomic systems such as the structure, pressure, and isothermal compressibility. A deep denoising autoencoder network is trained to obtain structural and thermodynamic properties of Lennard-Jones liquids at various thermodynamic states using a single snapshot RDF as input. The algorithm is successful not only in predicting the RDF of a Lennard-Jones pair potential, but also it is generalizable to other simple liquid pair potentials such as exponential, Yukawa, and inverse-power-law potentials. In terms of computational efficiency, the number of snapshots required from MD simulation to obtain the accuracy of DAE predicted RDF is at least hundred snapshots, making the network highly efficient. The pressure and isothermal compressibility from DAE based RDFs are also comparable with those obtained from longtime MD simulation. To expand our frameworks for more complex liquids, we investigate development of coarse-grained (CG) models of water, which is far more complex than simple liquids. In this study, we train a neural network-based force field with two- and three-body interactions, which makes the developed force field interpretable. Within our framework, the requirement for accurate forces and energies is eliminated by using the local search algorithm instead of backpropagation. We successfully develop coarse-grained models of both classical and ab initio water models. We also investigate the dependency of the coarse-grained force field of water on the number of expansions, which shows that the double-well interaction, known as a signature of water-like behavior among spherically symmetric pairwise interactions, vanishes with the inclusion of three-body interactions. We also notice that the two-body interaction fails to reproduce the angular distribution of water, especially over a short range. Based on our findings, we conclude that water-like behavior is better captured using the three-body interaction, which is consistent with the directional dependency of interactions in water. Finally, we employ graph neural network in the phase identification of water. Due to the high dimensionality and uninterpretable nature of atomistic simulation data, researchers have developed a wide variety of order parameters to reduce dimensionality and connect data with the phase and structural properties. Motivated by the importance of water in various areas, water is studied through various order parameters such as bond-order parameter (BOP), tetrahedral order parameter, and local-structure index. Even though these order parameters are widely adapted in various studies ranging from ice nucleation, phase discrimination/identification, free energy calculation, and as collective variables of enhanced sampling simulation, however, they are far from perfect. In several cases, it requires lots of domain expertise and effort to combine multiple order parameters to reach conclusive findings. Our phase classification works by collecting all the pairwise distances of high dimensional data within a cut-off distance, followed by feeding these data into an edge-conditioned convolutional graph neural network. The high accuracy and no need for pre-calculation of other order parameters make our methods more rigorous and generalizable for more complex cases such as confined water

Model Order Reduction

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This three-volume handbook covers methods as well as applications. This third volume focuses on applications in engineering, biomedical engineering, computational physics and computer science

Handbook of Mathematical Geosciences

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences