Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted prevention strategies. In this paper, we present an automatic tool that uses machine learning techniques to predict the development of type 2 diabetes mellitus (T2DM). Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. This study collected plasma glucose and insulin concentrations before glucose intake and at three time-points thereafter (30, 60 and 120 min). Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the dataset. Using 11 oral glucose tolerance test (OGTT) measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using Minimum Redundancy Maximum Relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. This research shows that an individual’s plasma glucose levels, and the information derived therefrom have the strongest predictive performance for the future development of T2DM. Significantly, insulin and demographic features do not provide additional performance improvement for diabetes prediction. The results of this work identify the parsimonious clinical data needed to be collected for an efficient prediction of T2DM. Our approach shows an average accuracy of 96.80 % and a sensitivity of 80.09 % obtained on a holdout set.

The interaction of robot teams and single human in teleoperation scenarios is beneficial in cooperative tasks, for example, the manipulation of heavy and large objects in remote or dangerous environments. The main control challenge of the interaction is its asymmetry, arising because robot teams have a relatively high number of controllable degrees of freedom compared to the human operator. Therefore, we propose a control scheme that establishes the interaction on spaces of reduced dimensionality taking into account the low number of human command and feedback signals imposed by haptic devices. We evaluate the suitability of wearable haptic fingertip devices for multi-contact teleoperation in a user study. The results show that the proposed control approach is appropriate for human–robot team interaction and that the wearable haptic fingertip devices provide suitable assistance in cooperative manipulation tasks.

AIM Present study analyses the co-localisation of RIP5 with FGFR1, FGFR2 and HIP2 in the developing kidney, as RIP5 is a major determinant of urinary tract development, downstream of FGF-signaling. METHODS Paraffin embedded human kidney tissues of 16 conceptuses between the 6th-22th developmental week were analysed using double-immunofluorescence method with RIP5/FGFR1/FGFR2 and HIP2 markers. Quantification of positive cells were performed using Kruskal-Wallis test. RESULTS In the 6th week of kidney development RIP5 (89.6%) and HIP2 (39.6%) are strongly expressed in the metanephric mesenchyme. FGFR1 shows moderate/strong expression in the developing nephrons (87.3%) and collecting ducts (70.5%) (p < 0.05). RIP5/FGFR1 co-localized at the marginal zone and the ureteric bud with predominant FGFR1 expression. FGFR2 (26.1%) shows similar expression pattern as FGFR1 (70.5%) in the same kidney structures. RIP5/FGFR2 co-localized at the marginal zone and the collecting ducts (predominant expression of FGFR2). HIP2 is strongly expressed in collecting ducts (96.7%), and co-localized with RIP5. In 10th week, RIP5 expression decrease (74.2%), while the pattern of expression of RIP5 and FGFR1 in collecting ducts (33.4% and 91.9%) and developing nephrons (21.9% and 32.4%) (p < 0.05) is similar to that in the 6th developmental week. Ureter is moderately expressing RIP5 while FGFR1 is strongly expressed in the ureteric wall. FGFR2 is strongly expressed in the collecting ducts (84.3%) and ureter. HIP2 have 81.1% positive cells in the collecting duct. RIP5/FGFR1 co-localize in collecting ducts and Henley's loop. CONCLUSIONS The expression pattern of RIP5, FGFR1, FGFR2 and HIP2 in the human kidney development might indicate their important roles in metanephric development and ureteric muscle layer differentiation through FGF signaling pathways.

We propose a linear state estimation (SE) model with complex coefficients and variables suitable for processing large-scale data in electric power systems observable by phasor measurement units. The presented model is based on factor graphs and solved using the belief propagation (BP) algorithm. The proposed algorithm is placed in the non-overlapping multi-area SE scenario without a central coordinator. The communication between areas is asynchronous, where neighboring areas exchange only “beliefs” about specific state variables. Presented architecture directly exploits system sparsity, can be flexibly paralellized and results in substantially lower computational complexity compared to traditional SE solutions. Finally, we discuss performances of the BP-based SE algorithm using power systems with 118, 1354 and 9241 buses.

It is known that physical activity levels (PA levels) decline during adolescence, but there is a lack of knowledge on possible predictors of changes in PA levels in this period of life. This study aimed to prospectively investigate the relationship between sociodemographic and behavioral factors (predictors), PA levels and changes in PA levels in older adolescents from Bosnia and Herzegovina. The sample comprised 872 participants (404 females) tested at baseline (16 years of age) and at follow-up (18 years of age). Predictors were sociodemographic characteristics (age, gender, socioeconomic status, urban/rural residence, paternal and maternal education level) and variables of substance misuse (consumption of cigarettes, alcohol and illicit drugs). The PA level, as measured by the Physical Activity Questionnaire for Adolescents (PAQ-A), was observed as a criterion. Boys had higher PAQ-A scores than girls at baseline and follow-up. Paternal education levels were correlated with PAQ-A scores at baseline (Spearman’s R: 0.18, 0.15 and 0.14, p < 0.05, for the total sample, females and males, respectively) and at follow-up (Spearman’s R: 0.12, p < 0.01 for the total sample). Logistic regression, which was used to calculate changes in PA levels between baseline and follow-up as a binomial criterion (PA decline vs. PA incline), evidenced a higher likelihood of PA incline in adolescents whose mothers were more educated (OR: 1.29, 95% CI: 1.05–1.60) and who live in urban communities (OR: 1.56, 95% CI: 1.16–2.10). The consumption of illicit drugs at baseline was evidenced as a factor contributing to the lower likelihood of PA incline (OR: 0.36, 95% CI: 0.14–0.92). The negative relationship between illicit drug consumption and PA decline could be a result of a large number of children who quit competitive sports in this period of life. In achieving appropriate PA-levels, special attention should be placed on children whose mothers are not highly educated, who live in rural communities, and who report the consumption of illicit drugs. The results highlighted the importance of studying correlates of PA levels and changes in PA levels during adolescence.

Outlier detection represents the problem of finding patterns in data that does not fit in expected behaviour. In this paper, outlier detection is done over real transactional data set of the distribution company. Outlier detection is done over time-series data, and over an ordered number of products that can be found within transactions. Unsupervised techniques and methods, S-H-ESD and LOF, are applied because data set is unlabelled. Implementation is performed in R language, and web application dashboard using R Shiny is made. Based on collected results, a proposal for creating the outlier detection and prevention system is made, and ideas for further improvements and additional analysis are given.

The identification of association rules is the problem of finding associations between different items in the same transactions. In this paper, performance comparison of different variants of Apriori, FP-Growth and ECLAT algorithms was performed over the real transactional data set of the distribution company by using R programming language and its appropriate packages, and the results obtained are later on explained. Then, the identification and visualization of the association rules of the said real data set was performed.

The 16 articles in this special section examine both licensed and unlicensed spectrum for 5G/B5G wireless networks. The incredible increase in connected appliances and downloaded applications has pushed mobile operators to the limits of their licensed spectrum bands. This has triggered the idea of evolving the current radio access network to use the underutilized unlicensed spectrum to extend spectrum resources beyond current usage charts. This mode of cellular access has raised a lot of questions about use cases, enabling technologies, and fairness to other native unlicensed users, such as WiFi. Nevertheless, unlicensed access is being accepted as one of the most significant solutions to improve the resource availability and system scalability in future fifth generation (5G)/beyond 5G (B5G) networks.

This paper presents a new approach for solving decentralized bi-level multi-objective linear fractional programming problems. The main goal was to find a simple algorithm with high confidence of decision-makers in the results. First, all the linear fractional programming models on the given set of constraints were solved separately. Next, all the linear fractional objective functions were linearized, membership functions of objective functions and decision variables controlled by decision-makers at the highest level calculated, and a fuzzy multi-objective linear programming model formed and solved as linear goal programming problem by using simplex algorithm. The efficiency of the proposed algorithm was investigated using an economic example, and the obtained results compared with those obtained using an existing method.

Increasing student motivation and engagement in classroom (and during the study in general) is the aim of every lecturer. Never stopping development of new digital tools and media present a new challenge in the educational process. The goal of this research is to increase the knowledge and understanding of the influence of Bring Your Own Device (BYOD) approach (and use of the mobile devices in classrooms in general) on: teachers’ practice and students’ classroom activities, students’ attitude about bringing the mobile phones in the class and mobile phone applications in education processes. This research focuses on undergraduate and postgraduate mechanical engineering students. Personal reflection of the lecturers and online survey for students was used as a tool to investigate participants’ attitude towards mobile applications as a method of promotion of active learning in engineering education.

The paper presents an approach to web-based visualization of automatically generated data models, which combines visualization principles used in desktop applications, and usage of web-based technologies. The proposed approach is implemented and integrated with an existing online system for automatic synthesis of conceptual database models based on business process models. The approach and implemented online system named AMADEOS are illustrated by an example of visualization of the UML class diagram representing the conceptual database model automatically generated based on the source BPMN model.

Objective. Childhood-onset systemic lupus erythematosus (cSLE) is usually a more severe and aggressive disease than adult-onset SLE (aSLE), but cellular and subcellular reasons for these differences are not well understood. The present study analyzed Th subsets, STAT1/STAT5 signaling response, and cytokine profiles of cSLE. Methods. FOXP3+ regulatory (Treg) and effector Th subsets, expression and phosphorylation of STAT1/STAT5 in Th, and cytokine profiles were measured in the peripheral blood of patients with cSLE and healthy controls (HC), using flow cytometry and immunoassay on a biochip. Results. Significant correlation between expression of the activation marker HLA-DR and decreased Th counts, an increase in the percentage of FOXP3+ Th, and a decrease in the activated Treg (aTreg) subset among them were found in cSLE. In contrast to our previous findings in aSLE, no significant differences in percentages and a significant decrease in the numbers of the naive-resting Treg (rTreg) subset compared to HC were found. The percentages of CD25− cells, possibly reflecting interleukin 2 depletion, were significantly increased in cSLE aTreg, but not in the rTreg subset. Consistent with the results of our previous studies in aSLE, increased expression of STAT1, along with significant correlation between decreased Th counts and their increased basal phosphorylation of STAT5, were also found in cSLE. Conclusion. Our results suggest that the key difference in Treg homeostasis between cSLE and aSLE is in the rTreg subset. However, perturbed aTreg homeostasis, increased levels of STAT1 protein, and homeostatic STAT5 signaling appear to be intrinsic characteristics of the disease, present in cSLE and aSLE alike.

OBJECTIVES To provide an overview of the paediatric rheumatology (PR) services in Europe, describe current delivery of care and training, set standards for care, identify unmet needs and inform future specialist service provision. METHODS An online survey was developed and presented to national coordinating centres of the Paediatric Rheumatology International Trials Organisation (PRINTO) (country survey) and to individual PR centres (centre and disease surveys) as a part of the European Union (EU) Single Hub and Access point for paediatric Rheumatology in Europe project. The survey contained components covering the organization of PR care, composition of teams, education, health care and research facilities and assessment of needs. RESULTS Response rates were 29/35 (83%) for country surveys and 164/288 (57%) for centre surveys. Across the EU, approximately one paediatric rheumatologist is available per million population. In all EU member states there is good access to specialist care and medications, although biologic drug availability is worse in Eastern European countries. PR education is widely available for physicians but is insufficient for allied health professionals. The ability to participate in clinical trials is generally high. Important gaps were identified, including lack of standardized clinical guidelines/recommendations and insufficient adolescent transition management planning. CONCLUSION This study provides a comprehensive description of current specialist PR service provision across Europe and did not reveal any major differences between EU member states. Rarity, chronicity and complexity of diseases are major challenges to PR care. Future work should facilitate the development, dissemination and implementation of standards of care, treatment and service recommendations to further improve patient-centred health care across Europe.

BACKGROUND Clinical Chemistry is the backbone of medical treatment, diagnostics, and prevention. The laborato-ries are trying to improve the quality and to reduce diagnostic errors and processing time and safeguard trace-ability of all laboratory procedures to ensure patient safety. Six sigma belongs to statistical quality control and provides a new methodology for measuring and improving process performance in laboratory. METHODS Activities of AST, ALT, CK, LDH, Amy, and γ-GT were determined by standard kinetic methods on a Vitros 5600 biochemistry analyzer. Two daily quality controls (Verifier I and Verifier II) were run over 60 days. Total percent CV was calculated from routine daily QC. Between-instrument bias was also calculated from daily QC. RESULTS The calculated sigma metrics for AST were 6.9 and 3.8; for ALT 9.3 and 5.6; for CK 6.6 and 5.3; LDH 5.2 and 5.2; for γ-GT 4.9 and 2.7; and for amylase 8.7 and 7.1. Analytical performance for AST, ALT, CK, LDH, and Amylase is world class. On the other hand, γ-GT analytical performance is poor. CONCLUSIONS Six Sigma benefits from earlier quality management approaches that creates new challenges for medical laboratories.