With the rapid development of sensing, communication, and computing technologies and infrastructure, today’s manufacturing industry is marching towards a big data era and a new generation of digitalization and intelligence. The availability of big data provides us with a golden opportunity to promote smart manufacturing. Nevertheless, the deployment and popularization of big data analytics in manufacturing is still at its nascent stage. One critical challenge results from the lack of high-performance computing (HPC) capability, which is crucial for responsive and intelligent decision-making in the modern manufacturing industry. To address this challenge, this paper proposes a framework and some general guidelines for implementing big data analytics in an HPC environment. The details of the whole workflow, from the prototype to the final application, are high-lighted. A case study for intelligent 3D sensing with real-world manufacturing data is presented to demonstrate the effectiveness of the proposed framework.

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.

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.

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.

Since the first widespread use of depleted uranium in military in the 1991 Gulf War, the so-called "Gulf War Syndrome" has been a topic of ongoing debate. However, a low number of reliable scientific papers demonstrating the extent of possible contamination as well as its connection to the health status of residents and deployed veterans has been published. The authors of this study have therefore aimed to make a selection of data based on strict inclusion and exclusion criteria. With the goal of clarifying the extent of DU contamination after the Gulf Wars, previously published data regarding the levels of DU in the Middle East region were analyzed and presented in the form of a meta-analysis. In addition, the authors attempted to make a correlation between the DU levels and their possible effects on afflicted populations. According to results observed by comparing 234U/238U and 235U/238U isotopic activity ratios, as well as 235U/238U mass ratios in air, water, soil and food samples among the countries in the Middle East region, areas indicating contamination with DU were Al Doha, Manageesh and Um Al Kwaty in Kuwait, Al-Salman, Al-Nukhaib and Karbala in Iraq, Beirut in Lebanon and Sinai in Egypt. According to these data, no DU contamination was observed in Algeria, Israel, Afghanistan, Oman, Qatar, Iran, and Yemen. Due to the limited number of reliable data on the health status of afflicted populations, it was not possible to make a correlation between DU levels and health effects in the Middle East region.