Aggressive fibromatosis, also known as desmoid tumor, is a locally invasive soft tissue lesion arising from connective tissues. Reports in children less than 10 years of age is rare. We report a case of a  desmoid tumor located at the middle and lower third of the left rectus abdominis, in a 2-year-old-boy. Partial resection of the muscle segment and simultaneous reconstruction of the abdominal wall by abdominal fascia was done. The patient had an uneventful recovery. At one year of follow-up neither recurrence nor functional or aesthetic complications were seen.

We address the problem of attack detection and isolation for a class of discrete-time nonlinear systems under (potentially unbounded) sensor attacks and measurement noise. We consider the case when a subset of sensors is subject to additive false data injection attacks. Using a bank of observers, each observer leading to an Input-to-State Stable (ISS) estimation error, we propose two algorithms for detecting and isolating sensor attacks. These algorithms make use of the ISS property of the observers to check whether the trajectories of observers are "consistent" with the attack-free trajectories of the system. Simulations results are presented to illustrate the performance of the proposed algorithms.

The standard process of data science tasks is to prepare features inside a database, export them as a denormalized data frame and then apply machine learning algorithms. This process is not optimal for two reasons. First, it requires denormalization of the database that can convert a small data problem into a big data problem. The second shortcoming is that it assumes that the machine learning algorithm is disentangled from the relational model of the problem. That seems to be a serious limitation since the relational model contains very valuable domain expertise. In this paper we explore the use of convex optimization and specifically linear programming, for modelling machine learning algorithms on relational data in an integrated way with data processing operators. We are using SolverBlox, a framework that accepts as an input Datalog code and feeds it into a linear programming solver. We demonstrate the expression of common machine learning algorithms and present use case scenarios where combining data processing with modelling of optimization problems inside a database offers significant advantages.

The work reported in this paper aims to present possibility distribution model of soft data used for corporate client credit risk assessment in commercial banking by applying Type 2 fuzzy membership functions (distributions) for the purpose of developing a new expert decision-making fuzzy model for evaluating credit risk of corporate clients in a bank. The paper is an extension of previous research conducted on the same subject which was based on Type 1 fuzzy distributions. Our aim in this paper is to address inherent limitations of Type 1 fuzzy distributions so that broader range of banking data uncertainties can be handled and combined with the corresponding hard data, which all affect banking credit decision making process. Banking experts were interviewed about the types of soft variables used for credit risk assessment of corporate clients, as well as for providing the inputs for generating Type 2 fuzzy logic membership functions of these soft variables. Similar to our analysis with Type 1 fuzzy distributions, all identified soft variables can be grouped into a number of segments, which may depend on the specific bank case. In this paper we looked into the following segments: (i) stability, (ii) capability and (iii) readiness/willingness of the bank client to repay a loan. The results of this work represent a new approach for soft data modeling and usage with an aim of being incorporated into a new and superior soft-hard data fusion model for client credit risk assessment.

In recent years there is an increasing research attention on youth and their transition to adulthood. In that transition they have increasing demand for financial products and services. If they are not financial included it may leave long-lasting consequences for their future independence and stability.The main goal of this research is to investigate and explain barriers to poor financial inclusion of youth in Federation of Bosnia and Herzegovina (FBiH), and make some recommendations for increasing their financial inclusion, and indirectly for strengthening their social inclusion. Implications of this study suggest that the main reason for being unbanked is because someone else in the family already has an account, or because they do not have enough money to use services of financial institutions. The results have revealed statistically significant relation between need for financial services at a formal institution and having a bank account, category of students’ financial knowledge and having a bank account, having a debit card and having a credit card. Research results can serve the economic and social policy makers in the FBiH in policy and strategy design.