Introduction: A new disease coronavirus disease 2019 (COVID-19) is with insufficiently known epidemiological characteristics and spectrum of clinical expression in childhood. Children have a lower incidence of this disease with a predominance of mild forms but severe clinical forms, such as among others, acute respiratory distress syndrome, and multisystem inflammatory syndrome may occur, according to current findings. In children with atypical symptomatology and positive or suspicious epidemiological survey, practitioners should consider the possibility of COVID-19.Methods: This study formed the group of 70 children previously healthy or with no pre-existing heart disease from Sarajevo with positive post-COVID history. Following the history of disease and epidemiological data, establishing the 1st day of disease or contact, a detailed cardiovascular examination was performed, including parameters of body weight, height, oxygen saturation, pulse, blood pressure, 12 leads electrocardiogram (ECG) done on Schiller machine, values of polymerase chain reaction (PCR), or serological test on corona: Immunoglobulin (Ig) G and IgM. Echocardiographic examination was done using M, B mode, color, continuous wave, and pulse wave Doppler in standard views. Laboratory blood tests included: Full blood count, creatinine phosphokinase myofibril, creatinine phosphokinase, lactate dehydrogenase; liver enzymes, D dimer, C reactive protein, and urine.Results: Majority of children (64.3%) were asymptomatic. ECG was normal in relation to patients’ age except in eight patients (intermittent palpitations on exertion) who had short PR interval 0.120–0.140 ms, with no delta wave, with heart rate within the normal range according to age, so 24 h ECG Holter was performed without any significant arrhythmias, incomplete right branch block has been documented in 12%, monofocal ventricular ectopic extrasistoly in 15%. Mean IgG, as a marker of infection, showed a statistical significance when compared between age Groups I and II (<5) and older groups: III, IV, and V (>5) (p < 0.05; p = 0.043). PCR test was negative in 9 (70 children), although they showed symptoms, COVID-19 infection clinical data, and positive laboratory findings. Echocardiogram was normal in all patients with normal ejection fraction of the left ventricle.Conclusion: The possibility of COVID-19 in children with atypical symptomatology and positive or suspicious epidemiological survey should be in the focus of every pediatrician at primary care institutions nowadays. Cardiovascular assessment should always be an option in post-COVID patients. Immunological assessment is necessary in post-COVID patients in order to gain a further understanding of PTS status. With more serological testing for severe acute respiratory syndrome coronavirus 2 physicians would be able to make a diagnosis of COVID-19 timely and more accurately, as well as to evaluate the role of asymptomatic children in disease transmission and to assess the importance of protective antibodies and the distribution of COVID-19.

The development intelligence tools is gaining a new dimension every day, and this is an area of dynamic development. The importance of this paper is to select the tool that represents the best solution for the given needs in public administration. A multi-stage, broad- based survey with clear selection criteria leads to the choice of three tools. Criteria for tool evaluation were set, virtual machines were created, tests and analyzes were performed. The evaluations carried out give the choice of the most accepTable business intelligence tool for use and interoperability in public administration.

Making informed driving decisions requires reliable prediction of other vehicles' trajectories. In this paper, we present a novel learned multi-modal trajectory prediction architecture for automated driving. It achieves kinematically feasible predictions by casting the learning problem into the space of accelerations and steering angles - by performing action-space prediction, we can leverage valuable model knowledge. Additionally, the dimensionality of the action manifold is lower than that of the state manifold, whose intrinsically correlated states are more difficult to capture in a learned manner. For the purpose of action-space prediction, we present the simple Feed-Forward Action-Space Prediction (FFW-ASP) architecture. Then, we build on this notion and introduce the novel Self-Supervised Action-Space Prediction (SSP-ASP) architecture that outputs future environment context features in addition to trajectories. A key element in the self-supervised architecture is that, based on an observed action history and past context features, future context features are predicted prior to future trajectories. The proposed methods are evaluated on real-world datasets containing urban intersections and roundabouts, and show accurate predictions, outperforming state-of-the-art for kinematically feasible predictions in several prediction metrics.

Aim: Global expenditure on medicines is rising up to 6% per year driven by increasing prevalence of non-communicable diseases (NCDs) and new premium priced medicines for cancer, orphan diseases and other complex areas. This is difficult to sustain without reforms. Methods: Extensive narrative review of published papers and contextualizing the findings to provide future guidance. Results: New models are being introduced to improve the managed entry of new medicines including managed entry agreements, fair pricing approaches and monitoring prescribing against agreed guidance. Multiple measures have also successfully been introduced to improve the prescribing of established medicines. This includes encouraging greater prescribing of generics and biosimilars versus originators and patented medicines in a class to conserve resources without compromising care. In addition, reducing inappropriate antibiotic utilization. Typically, multiple measures are the most effective. Conclusion: Multiple measures will be needed to attain and retain universal healthcare.

Karst aquifers are important sources of thermal and groundwater in many parts of the world, such as the Alpine–Dinaric–Carpathian region in Europe. The Upper Triassic dolomites are regionally recognized thermal and groundwater aquifers but also hydrocarbon reservoirs. They are characterized by predominantly fractured porosity, but the actual share of depositional and diagenetic porosity is rarely investigated. In this research, we presented the geometric characterization of the measured microporosity of the Upper Triassic dolomites of the Žumberak Mts (Croatia), through thin-section image processing and particle analysis techniques. Pore parameters were analyzed on microphotographs of impregnated thin sections in scale. A total of 2267 pores were isolated and analyzed. The following parameters were analyzed: pore area, pore perimeter, circularity, aspect ratio (AR), roundness, solidity, Feret AR, compactness, and fractal dimension. Furthermore, porosity was calculated based on the pore portion in each image. The effective porosity on rock samples was determined using saturation and buoyancy techniques as an accompanying research method. We analyzed distributions of each parameter, their correlation, and most of the parameters are characterized by an asymmetric or asymmetric normal distribution. Parameters that quantify pore irregularities have similar distributions, and their values indicate the high complexity of the pore geometry, which can significantly impact permeability.

Highlights • Based on antibody-based proteomics by proximity extension assays, pre-treatment levels of several proteins were predictive of shorter progression-free survival (PFS) after treatment with MAPK-inhibitors in metastatic cutaneous melanoma (CM), including IL6, IL10, CCL-2/-3/-4, LGALS1, and CSF1.• By in-depth mass-spectrometry-based proteomic analysis of 1,160 proteins in a subset of metastatic CM patients receiving BRAF- and MEK- inhibitors, we discovered alterations in plasma proteins involved in cell adhesion-, neutrophil degranulation-, and proteolysis- during BRAFi and MEKi treatment.• CPB1 had the highest increase during BRAF- and MEK- inhibitors’ treatment and was associated with longer PFS.• Most of the proteins altered in plasma during MAPKi treatment were traceable to BRAFV600E-mutated metastatic CM tissue at mRNA level, based on expression patterns in 154 patients from the TCGA cohort.

Motivation/Background: positive staphylococci (CPS) are common contaminants of raw milk. Before it is used, various heat treatments are applied to destroy microorganisms, inactivate enzymes and improve technological properties and concentration of dry matter of milk. This work aimed to determine the influence of commonly used heat treatments in diary on presence and number CPS in raw milk from Bosnia and Herzegovina area and to affirm whether there is a difference in efficacy between different treatments. Method: Using the standard method, 40 samples of raw milk from farms were inoculated for counting the initial number of CPS in raw milk. Samples were then exposed to heat treatments in vapor sterilizer and CPS number was counted using the same standard method. Results: Applied treatments included heat treatments at: 68 °C/40 s, 70 °C/15 s, 72 °C/without holding, 63 °C/30 min and 72 °C/15 s. CPS presence was detected in all tested samples of raw milk in numbers ranging from 2,82 to 5,32, with an average of 4,30, calculated as log10 /ml. Conclusions: Raw milk samples collected in the field initially registered a high CPS number. The applied heat treatments were effective to a large extent. The initial CPS count of milk seems to be the most important factor determining the number of CPS after heat treatments as well as traits of the strains.

Rank $1$ modules are the building blocks of the category ${\rm CM}(B_{k,n}) $ of Cohen-Macaulay modules over a quotient $B_{k,n}$ of a preprojective algebra of affine type $A$. Jensen, King and Su showed in \cite{JKS16} that the category ${\rm CM}(B_{k,n})$ provides an additive categorification of the cluster algebra structure on the coordinate ring $\mathbb C[{\rm Gr}(k, n)]$ of the Grassmannian variety of $k$-dimensional subspaces in $\mathbb C^n$. Rank $1$ modules are indecomposable, they are known to be in bijection with $k$-subsets of $\{1,2,\dots,n\}$, and their explicit construction has been given in [8]. In this paper, we give necessary and sufficient conditions for indecomposability of an arbitrary rank 2 module in ${\rm CM}(B_{k,n})$ whose filtration layers are tightly interlacing. We give an explicit construction of all rank 2 decomposable modules that appear as extensions between rank 1 modules corresponding to tightly interlacing $k$-subsets $I$ and $J$.

The molecular strong-field approximation is employed to study high-order harmonic generation by linear and planar polyatomic molecules exposed to an orthogonally polarized two-color laser field, which consists of two orthogonal linearly polarized components with commensurable frequencies. For such a driving field, we find that the harmonic emission rate and the shape of the spectrum strongly depend on the laser-field parameters including the relative phase and the ratio of the intensities of the two components. The values of the relative phase that correspond to the optimal harmonic emission rate, as well as the cutoff position, can be assessed using a classical model. The possible production of an isolated attosecond pulse is investigated. For suitable symmetry of the laser field an attosecond pulse train with only one attosecond pulse per cycle can be generated. Depending on the frequencies of the two field components, the molecular symmetry properties and the orientation of the molecule with respect to the field, the even harmonics can be absent from the spectrum, which can be used to determine the molecular orientation. The emitted harmonics are elliptically polarized and their ellipticity depends on the molecular orientation.

With the advent of modern embedded systems, logging as a process is becoming more and more prevalent for diagnostic and analytic services. Traditionally, storage and managing of the logged data are generally kept as a part of one entity together with the main logic components. In systems that implement network connections, this activity is usually handled over a remote device. However, enabling remote connection is still considered a limiting factor for many embedded devices due to the demanding production cost. A significant challenge is presented to vendors who need to decide how the data will be extracted and handled for an embedded platform during the design concept phase. It is generally desirable that logging memory modules are able to be addressed as separate units. These devices need to be appropriately secured and verifiable on a different system since data compromise can lead to enormous privacy and even financial losses. In this paper, we present two patterns. First, a pattern that allows flexible logging operation design in terms of module and interface responsibility separation. Second, a pattern for the design of secure logging processes during the utilization of constrained embedded devices. The introduced patterns fulfil the following conditions: (i) flexibility – design is independent of the chip vendors making the logging memory modules easily replaceable, (ii) self-sufficiency – every logging controller is maintained as a separate entity in a decentralized topology, (iii) security – through providing authenticity, confidentiality, and integrity by means of using a dedicated security module.

Efficiently approximating local curvature information of the loss function is a key tool for optimization and compression of deep neural networks. Yet, most existing methods to approximate second-order information have high computational or storage costs, which can limit their practicality. In this work, we investigate matrix-free, linear-time approaches for estimating Inverse-Hessian Vector Products (IHVPs) for the case when the Hessian can be approximated as a sum of rank-one matrices, as in the classic approximation of the Hessian by the empirical Fisher matrix. We propose two new algorithms as part of a framework called M-FAC: the first algorithm is tailored towards network compression and can compute the IHVP for dimension $d$, if the Hessian is given as a sum of $m$ rank-one matrices, using $O(dm^2)$ precomputation, $O(dm)$ cost for computing the IHVP, and query cost $O(m)$ for any single element of the inverse Hessian. The second algorithm targets an optimization setting, where we wish to compute the product between the inverse Hessian, estimated over a sliding window of optimization steps, and a given gradient direction, as required for preconditioned SGD. We give an algorithm with cost $O(dm + m^2)$ for computing the IHVP and $O(dm + m^3)$ for adding or removing any gradient from the sliding window. These two algorithms yield state-of-the-art results for network pruning and optimization with lower computational overhead relative to existing second-order methods. Implementations are available at [9] and [17].