Thanks to the climatic and geographical conditions, the area of the Northwestern part of Bosnia and Herzegovina has a long tradition of producing honey and other bee products. However, there is little or no literature data on the physico-chemical properties and biological activity of different types of honey and other bee products from Bosnia and Herzegovina. Five different types of honey were analyzed: monofloral honey (acacia, chestnut, linden), meadow honey and forest honey. Physico-chemical parameters, sensory analysis, color of honey, antioxidant activity, and content of total phenols were analyzed in five types off collected honey samples. The analyzes performed showed that chestnut honey contains the highest and acacia honey has the lowest content oftotal phenolic compounds. The forest honey showed the best antioxidant activity. The color of the honey was measured according to the CIELab system and the estimated L, a, bparameters show that all types of honey from this area can be characterized asdark types of honey (L50) with the presence of a yellow color. The obtained results show that the analyzed samples of five different types of honey are rich in polyphenolic components and represent a good source of antioxidants in the human diet.KEYWORDS:honey,physico-chemical parameters, color, antioxidant activity, total phenols

The review and analysis of a timeline work and stoppage/failure of transportational complex on separation in SC coal mine „Gračanica“ LLC Gornji Vakuf – Uskoplje has been given in this work. The work is based on collecting and analysing data. Collecting data lasted for one year and it is analysed and shown in this work. Rightfully determined the state of work and stoppage/failure, allows precautions and choice of strategy for the next period. Conclusions about which stoppage/failure affected the stoppage of transportational system and separation in full are derived from the research, and based on those conclusions, suggestions about activities which would minimize these stoppages on acceptable value are given. Key words: mine, coal, effective work, stoppage, failure, transportational complex, separation, belt conveyor, scraper.

We propose a novel strategy to construct optimal controllers for continuous-time nonlinear systems by means of linear-like techniques, provided that the optimal value function is differentiable and quadratic-like. This assumption covers a wide range of cases and holds locally around an equilibrium under mild assumptions. The proposed strategy does not require solving the Hamilton–Jacobi–Bellman equation, i.e., a nonlinear partial differential equation, which is known to be hard or impossible to solve. Instead, the Hamilton–Jacobi–Bellman equation is replaced with an easy-solvable state-dependent Lyapunov matrix equation. We exploit a linear-like factorization of the underlying nonlinear system and a policy-iteration algorithm to yield a linear-like policy-iteration for nonlinear systems. The proposed control strategy solves optimal nonlinear control problems in an asymptotically exact, yet still linear-like manner. We prove optimality of the resulting solution and illustrate the results via four examples.

Abstract Congenital malformations are defined as structural or functional anomalies that occur in utero or at birth and can be detected at an early age. They are also known as birth defects, disabilities or congenital malformations. Congenital malformations are accompanied by hereditary or developmental disabilities or disease. From the establishment of the registry in early 2019 until the end of 2021, the total number of reported congenital malformations is 449. According to available data from EUROCAT (European network of population-based registries for the epidemiological surveillance of congenital anomalies), the average rate of congenital malformations in the countries of the European Union (EU) is 262/per 10,000 live births, while the registered rate of congenital malformations in the Federation of Bosnia and Herzegovina is 261/per 10,000 live births. In the Federation of Bosnia and Herzegovina, the highest incidence rate was registered in Sarajevo Canton (175 cases with a rate of 416/10,000 live births) and Tuzla Canton (122 cases with a rate of 356/10,000 live births). The most common congenital malformations are heart defects, cleft lip and palate, musculoskeletal deformities and Down syndrome. In the Federation of Bosnia and Herzegovina (FBiH) in 2020, 135 children under the age of 5 died, among which 18 children (13.3%) died from congenital malformations, deformations and chromosomal abnormalities (Q00-Q99). Congenital malformations can lead to chronic diseases and disabilities, death of infants and children up to five years of age. Congenital malformations represent a significant public health problem, given that they lead to disability, incapacity and pressure on the health system, as well as the problem of social integration of patients. Key messages • The registered rate of congenital malformations in the Federation of Bosnia and Herzegovina is 261/per 10,000 live births. • Congenital malformations can lead to chronic diseases and disabilities, death of infants and children up to five years of age.

We provide observational evidence that suggests the presence of a molten silicate layer above the core of Mars, which is overlain by a partially molten layer, indicating that the core of Mars is smaller than previously thought. The detection of deep reflected S waves on Mars inferred a core size of 1,830 ± 40 km (ref. ^ 1 ), requiring light-element contents that are incompatible with experimental petrological constraints. This estimate assumes a compositionally homogeneous Martian mantle, at odds with recent measurements of anomalously slow propagating P waves diffracted along the core–mantle boundary^ 2 . An alternative hypothesis is that Mars’s mantle is heterogeneous as a consequence of an early magma ocean that solidified to form a basal layer enriched in iron and heat-producing elements. Such enrichment results in the formation of a molten silicate layer above the core, overlain by a partially molten layer^ 3 . Here we show that this structure is compatible with all geophysical data, notably (1) deep reflected and diffracted mantle seismic phases, (2) weak shear attenuation at seismic frequency and (3) Mars’s dissipative nature at Phobos tides. The core size in this scenario is 1,650 ± 20 km, implying a density of 6.5 g cm^−3, 5–8% larger than previous seismic estimates, and can be explained by fewer, and less abundant, alloying light elements than previously required, in amounts compatible with experimental and cosmochemical constraints. Finally, the layered mantle structure requires external sources to generate the magnetic signatures recorded in Mars’s crust.

Radio-frequency fingerprint (RFF), which comes from the imperfect hardware, is a potential feature to ensure the security of communication. With the development of deep learning (DL), DL-based RFF identification methods have made excellent and promising achievements. However, on one hand, existing DL-based methods require a large amount of samples for model training. On the other hand, the RFF identification method is generally less effective with limited amount of samples, while the auxiliary data set and the target data set often needs to have similar data distribution. To address the data-hungry problems in the absence of auxiliary data sets, in this article, we propose a supervised contrastive learning (SCL)-based RFF identification method using data augmentation and virtual adversarial training (VAT), which is called “SCACNN.” First, we analyze the causes of RFF, and model the RFF identification problem with augmented data set. A nonauxiliary data augmentation method is proposed to acquire an extended data set, which consists of rotation, flipping, adding Gaussian noise, and shifting. Second, a novel similarity radio-frequency fingerprinting encoder (SimRFE) is used to map the RFF signal to the feature coding space, which is based on the convolution, long short-term-memory, and a fully connected deep neural network (CLDNN). Finally, several secondary classifiers are employed to identify the RFF feature coding. The simulation results show that the proposed SCACNN has a greater identification ratio than the other classical RFF identification methods. Moreover, the identification ratio of the proposed SCACNN achieves an accuracy of 92.68% with only 5% samples.

Wi-Fi-based passive sensing is considered as one of the promising sensing techniques in advanced wireless communication systems due to its wide applications and low deployment cost. However, existing methods are faced with the challenges of low sensing accuracy, high computational complexity, and weak model robustness. To solve these problems, we first propose a robust channel state information (CSI)-based Wi-Fi passive sensing method using attention mechanism deep learning (DL). The proposed method is called as convolutional neural network (CNN)-ABLSTM, a combination of CNNs and attention-based bi-directional long short-term memory (LSTM). Specifically, CSI-based Wi-Fi passive sensing is devised to achieve the high precision of human activity recognition (HAR) due to the fine-grained characteristics of CSI. Second, CNN is adopted to solve the problems of computational redundancy and high algorithm complexity which are often occurred by machine learning (ML) algorithms. Third, we introduce an attention mechanism to deal with the weak robustness of CNN models. Finally, simulation results are provided to confirm the proposed method in three aspects, high recognition performance, computational complexity, and robustness. Compared with CNN, LSTM, and other networks, the proposed CNN-ABLSTM method improves the recognition accuracy by up to 4%, and significantly reduces the calculation rate. Moreover, it still retains 97% accuracy under the different scenes, reflecting a certain robustness.

The communications between vehicle-to-vehicle (V2V) with high frequency, group sending, group receiving and periodic lead to serious collision of wireless resources and limited system capacity, and the rapid channel changes in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. For the Internet of Vehicles (IoV), it is a fundamental challenge to achieve low latency and high reliability communication for real-time data interaction over short distances in a complex wireless propagation environment, as well as to attenuate and avoid inter-vehicle interference in the region through a reasonable spectrum allocation. To solve the above problems, this paper proposes a resource allocation (RA) method using dueling double deep Q-network reinforcement learning (RL) with low-dimensional fingerprints and soft-update architecture (D3QN-LS) while constructing a multi-agent model based on a Manhattan grid layout urban virtual environment, with communication links between V2V links acting as agents to reuse vehicle-to-infrastructure (V2I) spectrum resources. In addition, we extend the amount of transmitted data in our work, while adding scenarios where spectrum resources are relatively scarce, i.e. the number of V2V links is significantly larger than the amount of spectrum, to compensate for some of the shortcomings in existing literature studies. We demonstrate that the proposed D3QN-LS algorithm leads to a further improvement in the total capacity of V2I links and the success rate of periodic secure message transmission in V2V links.

Alpine skiing is a sport and recreational physical activity which requires fine postural control to maintain balance in challenging conditions. Theoretically, balance dominates in alpine skiing, but coordinated action of the whole body of the skiers is equally important. The aim of this research was to determine the effects of experimental short-term program of intensive training of alpine skiing techniques to postural stability (on Biodex Balance System) of students. The sample is divided into an experimental (31 students, age 21.4±1.0 and body height 180.7±6.3 cm) and control group (34 students, age 20.6±0.8 and body height 180.3±6.8 cm). The results of ANCOVA within variables for the evaluation of postural stability show statistically significant effects of the applying experimental program in all applied variables at the level of significance p=.000. From the mean value results (M) it is obvious that the experimental group achieved better results compared to the identical tests applied to the control group. The results of this research show that learning to ski can improve the ability to maintain balance, especially if it is conducted under the expert supervision of a ski instructor, which can have the effect of reducing the risk of injury.

Purpose Lean Management (LM) is a process improvement approach with growing interest from healthcare organizations. Obtaining a culture of continuous improvement is a primary objective of LM, and a culture of continuous improvement indicates a mature LM approach, and here leadership plays a central role. However, a comprehensive overview of leadership activities influencing LM maturity is lacking. This study aims to identify leadership activities associated with continuous improvement and, thus, LM, maturity. Methods Following the PRISMA guidelines, a scoping literature review of peer-reviewed articles was conducted in twenty healthcare management journals. The search provided 466 articles published up until 2023. During the selection process, 23 studies were included in the review. The leadership activities related to continuous improvement maturity were identified using the grounded theory approach and data coding. Results The analysis highlighted a total of 58 leadership activities distributed across nine themes of LM leadership. Next, analysing leadership activities concerning the different maturity levels revealed three maturity stages: beginner, intermediate, and expert. Based on the findings, we propose a framework that guides suitable leadership activities at the various stages of LM maturity. The framework provides leaders in healthcare with a practical overview of actions to facilitate the growth of the LM approach, and the related propositions offer academics a theoretical basis for future studies. Conclusion This review presents the first comprehensive overview of LM leadership activities in relation to continuous improvement and LM maturity. To enhance LM maturity, leaders are encouraged to consider their leadership style, (clinical) stakeholder involvement, alignment with the organizational strategy, and their role in promoting employee autonomy.