Abstract: In this paper we examine the scientific production of Serbian scientists in the area of computing, published between year 2008 and year 2020, extracted from the Web of Science (WoS) database. Our dataset includes papers published in journals with impact factor, which are classified in at least one out of seven WoS categories with the prefix "Computer Science". The work includes identification of unique authors' and institutions' identity from the dataset, as well as creation of a database used for bibliometric and coauthorship analysis. As a result, we have created coauthorship network using Gephi, identified the leading authors and institutions, as well as the most popular journals, WoS categories and collaborators of Serbian authors in the field of computer science for the analyzed period.

Abstract: Compliant grippers are used in the macro and micro world for capturing and manipulating objects, when it is necessary to manipulate fragile objects, such as surgical instruments, in biomedicine for manipulating individual cells, etc. In addition, grippers with a rigid contact between the grippers and the rigid objects can lead to damage to the objects, so in these cases, a control system is required to control the contact forces. Development of compliant finger-like gripper for soft touch application is described in this paper. This paper provides an answer to the question how to design, model and implement such compliant gripper. The developed compliant fingers are inspired by the real human fingers. Machine learning methods where proposed for realization of non-distractive grasping of fragile objects where feedback information is acquired by computer vision system while intelligent algorithms where considered for motion control and grasping. The complexity of lays not only in non-destructive grasping but also in adequate force implementation in order to avoid dropping the grasped object. Hence, nonlinear simulation of compliant gripper, by means of finite elements method (FEM), is provided in this paper. Several experiment where done to provide real life conformation of such analysis.

Abstract: This research aims to propose a novel framework for the assessment of the consequences of hazardous events on a water resources system using dynamic resilience. The two main types of hazardous events considered are: a severe flood event, and an earthquake. Given that one or both hazards occur, this framework utilizes a digital twin based on a system dynamics (SD) model, backed by an Artificial Neural Network (ANN) to estimate the dynamic resilience. The ANN was trained using a large, simulated dataset ranging from very mild to extreme hazard combinations. The ANN’s efficacy was quantified using the average relative error metric which equals 2.14% and 1.77% for robustness and rapidity, respectively.