Early math skills are a good predictor of later academic success. Finding what factors influence math performance might help educators create better and more efficient math programs. The goal of the present study was to assess the relationship of math achievement and verbal fluency, selective attention, visual-motor integration and inhibitory control. An additional goal was to assess the effect of gender and grade on math achievement. The sample for this study comprised 210 children from grades 1 to 3 (107 boys, 103 girls). Children were individually administered a math test and tests of various predictor measures. The significant predictors of math achievement were verbal fluency, selective attention, visual-motor integration, and inhibitory control. The proposed model explained around 70% of the variance in the math scores. There were no gender differences in the math scores. Given the fact that all the predictors used in this study are very susceptible to inclusion in instruction, their incorporation in an early age curriculum might significantly improve math skills at a later age.

Introduction: Diabetes mellitus type 2 has become a global health-care problem of modern society due to a pronounced increase of prevalence to pandemic proportions and vascular complications. At present, glycated hemoglobin (HbA1c) is widely accepted as a measure of glycemic control in established diabetes. The aim of this study was to analyze the lipid profile in serum of patients with diabetes mellitus type 2, and its relationship with HbA1c levels. Methods: The observational cross-sectional study included 60 diabetic patients, 30 men, and 30 women, age 32–94 years. Patients were assigned into two groups based on HbA1c values; Group 1: HbA1c ≤ 7% (good glycemic control) and Group 2: HbA1c > 7% (poor glycemic control). We analyzed the concentration of glucose, HbA1c, and lipid profile including total cholesterol levels, triglycerides (TAG), low-density lipoproteins (LDL), and high-density lipoproteins (HDL). Results: Significantly lower values of glucose concentration, TAG and the ratio TAG/HDLc were obtained in the group of patients with good glycemic control. (p < 0.0005) Patients with good glycemic control had lower values of Castelli 1 and Castelli 2 index, and atherogenic index of plasma, compared to patients with poor glycemic control, but this difference was not significant. (p > 0.005) Our study revealed a significant positive correlation between HbA1c and triglyceride level (r = 0.375; p = 0.003) and HbA1c and ratio triglyceride/HDLc (r = 0.335; p = 0.009). Conclusion: HbA1c can also be used as a predictor of dyslipidemia in type 2 diabetics in addition to as a glycemic control parameter.

Product Line Engineering is an approach to reuse assets of complex systems by taking advantage of commonalities between product families. Reuse within complex systems usually means reuse of artifacts from different engineering domains such as mechanical, electronics and software engineering. Model-based systems engineering is becoming a standard for systems engineering and collaboration within different domains. This paper presents an exploratory case study on initial efforts of adopting Product Line Engineering practices within the model-based systems engineering process at Volvo Construction Equipment (Volvo CE), Sweden. We have used SysML to create overloaded models of the engine systems at Volvo CE. The variability within the engine systems was captured by using the Orthogonal Variability Modeling language. The case study has shown us that overloaded SysML models tend to become complex even on small scale systems, which in turn makes scalability of the approach a major challenge. For successful reuse and to, possibly, tackle scalability, it is necessary to have a database of reusable assets from which product variants can be derived.

We study the problem of maximizing privacy of quantized sensor measurements by adding random variables. In particular, we consider the setting where information about the state of a process is obtained using noisy sensor measurements. This information is quantized and sent to a remote station through an unsecured communication network. It is desired to keep the state of the process private; however, because the network is not secure, adversaries might have access to sensor information, which could be used to estimate the process state. To avoid an accurate state estimation, we add random numbers to the quantized sensor measurements and send the sum to the remote station instead. The distribution of these random variables is designed to minimize the mutual information between the sum and the quantized sensor measurements for a desired level of distortion - how different the sum and the quantized sensor measurements are allowed to be. Simulations are presented to illustrate our results.

This paper presents the detection method and analysis methodology of dynamic stray current effects on underground pipelines based on simultaneous multiparametric measurements in combination with continuous wavelet cross‐correlation and frequency plots. Measurements presented within this paper were conducted at two locations experiencing tram induced dynamic stray currents, in the first case, on unprotected pipeline and in the second case, on pipeline under cathodic protection. On both pipelines, measurements of pipe‐to‐soil potential, lateral potential gradient, corrosion probe current, and pipeline current were simultaneously done for time period of 20 min. For both locations, the two wavelet cross‐correlation spectrograms for the quantities: lateral potential gradient and probe current versus pipe‐to‐soil potential show that 5% statistical significance levels are in periods between 16 and 128 s. Time spans and period lengths of observed cross‐correlations on spectrograms reflect the stray current influence that may be linked to specific events, such as passing of trams. On the other hand, frequency plots of the measured quantities give clear visual representation of the time that pipeline spends in a certain state related to the possible harmful effect of stray current and also show the degree of cathodic protection beneficial effect.

This paper investigates the impact of accidental release of chlorine gas in surrounding areas consequences of chlorine gas leak studying the negative effects on both the environment and individuals. Chlorine and its consequences have a far more reaching effect in society that one may have imagined. The ALOHA software has been used in this paper to modelling of chlorine release. The modelling was performed for an accidental release of 3.373 tons chlorine gas from unsheltered single storied for one hour. For a typical average atmospheric condition in location, this accidental chlorine release would cause a red zone of 3.0 kilometres (AEGL-3=20 ppm), orange zone of 7.1 kilometres (AEGL-2=2 ppm) and yellow zone stretching to greater than 10.0 kilometres (AEGL-1=0.5 ppm) to downwind from the source.

Environmental noise pollution, a form of air pollution, is a threat to health and well-being. The primary aim of this study was to determine noise pollution in the urban part of the city of Banja Luka in Jovana Dučića Street (Republic of Srpska, Bosnia and Herzegovina (BiH)) by evaluating noise levels in the street. The aim of this research is also to compare the measured noise levels in the street with legislation. The measured values exceeded the level of noise allowed. Results indicated that noise level values in this area near health institution are alarming.

Urban forests are part of the public city space and have multiple significance. Various roles of greenery (health, social, aesthetic, cultural, educational, etc.) improve the quality of life in the city. Urban forests can play key roles in mitigate stormwater runoff, improve air quality, reduces noise level, store carbon, etc. The system of green areas in a continuous and dynamic interaction with the built structure in a city. Because of the above, subject of the research is determining the state of greenery in the Banja Luka area and the changes that accompany it and to suggest the planning of green areas and protect them.

Noise pollution, as a major environmental problem, is present in Banja Luka. The measured values exceeded the level of noise allowed, which is a great problem. Evaluation of the noise levels was carried out in the streets in the area with health institutions. Objectives of this research were to evaluate the environmental noise pollution in the City of Banja Luka due to traffic noise and to compare the measured noise levels in the city with legislation and to establish the connection between noise and the number of vehicles. Correlation between the noise level and number of vehicles was positive and significant during the study period (r=0.89). It is confirmed that, with the increase of the number of vehicle, the noise level increases, i.e. the decrease in the number of vehicle decreases the noise level.

Atmospheric pollutants have a negative effect on the plants; they can have direct toxic effects, or indirectly by changing soil pH followed by solubilization of toxic salts of metals. Large number of studies have investigated the possible effects of ambient air pollution on  vegetation and air pollution in stomata number and size and stomatal apparatus. The primary aim of this study was to impact air pollution from the aspect of presence SO2, NOX, CO2, O3 and PM10 on vegetation in the city of Banja Luka and stomatal response to air pollution, through a review of existing research.

We extend the Nystr\"om method for low-rank approximation of positive definite Mercer kernels to approximation of indefinite kernel matrices. Our result is the first derivation of the approach that does not require the positive definiteness of the kernel function. Building on this result, we then devise highly scalable methods for learning in reproducing kernel Kre\u{\i}n spaces. The main motivation for our work comes from problems with structured representations (e.g., graphs, strings, time-series), where it is relatively easy to devise a pairwise (dis)similarity function based on intuition/knowledge of a domain expert. Such pairwise functions are typically not positive definite and it is often well beyond the expertise of practitioners to verify this condition. The proposed large scale approaches for learning in reproducing kernel Kre\u{\i}n spaces provide principled and theoretically well-founded means to tackle this class of problems. The effectiveness of the approaches is evaluated empirically using kernels defined on structured and vectorial data representations.

As more attention is paid to security in the context of control systems and as attacks occur to real control systems throughout the world, it has become clear that some of the most nefarious attacks are those that evade detection. The term stealthy has come to encompass a variety of techniques that attackers can employ to avoid being detected. In this manuscript, for a class of perturbed linear time-invariant systems, we propose two security metrics to quantify the potential impact that stealthy attacks could have on the system dynamics by tampering with sensor measurements. We provide analysis mathematical tools (in terms of linear matrix inequalities) to quantify these metrics for given system dynamics, control structure, system monitor, and set of sensors being attacked. Then, we provide synthesis tools (in terms of semidefinite programs) to redesign controllers and monitors such that the impact of stealthy attacks is minimized and the required attack-free system performance is guaranteed.