The implementation of disruptive technologies of Industry 4.0 is carried out in all segments of society, but we still do not fully understand the breadth and speed of its application. We are currently witnessing major changes in all industries, so that new business methods are emerging, as well as transformation of production systems, new form of consumption, delivery and transport. All this is happening due to the implementation of disruptive technological discoveries that include: the Internet of Things (IoT), advanced robotics, smart sensors, Big Data, analytics, cloud computing, 3D printing, machine learning, virtual and augmented reality (AR), artificial intelligence, and productive maintenance. Advanced robotics is one of the most important technologies in Industry 4.0. The robotic application in the automation of production processes, with the support of information technology, leads us to ‘’smart automation’’, i.e., ‘’smart factory’’. The changes are so profound that, from the perspective of human history, there has never been a time of greater promise or potential danger. New generation robots have many advantages compared to the firstgeneration industrial robots such as: they work alongside with workers, workers perform their tasks in a safe environment, robots take up less space, robots do not need to be separated by fences, robots are easy to manipulate and cheaper to implement. The paper analyzes the trend of implementation of collaborative and service robots for logistics, which make the automation of production processes more flexible. Robotic technology is the basic technology of Industry 4.0, because without its application, the implementation of Industry 4.0 would not be possible. The trend of application of new generation robots will have an increasing character in the future, because the goals of the fourth industrial revolution cannot be achieved without collaborative robots. In other words, the objective is to achieve a ‘’smart production process’’ or ‘’smart factory’’.

Since the introduction of the concept of Industry 4.0 until today, the world is facing a series of changes resulting from intensive scientific, technical and technological innovations. Research, innovation and development changes are aimed at improving production, business and everyday life through the application of basic technologies of Industry 4.0. In order for individuals, organizations, communities and states to be able to use the benefits of these improvements, it is necessary to rapidly adapt to all innovative trends: developing the necessary skills of individuals and groups for the adoption and use of these technologies, the implementation of technologies in companies, organizations and institutions, and the development of appropriate strategies that these processes would be managed and directed. In the developed world, these I 4.0 implementation processes are already reaching their maturity: educational programs are adapted to the needs of monitoring technical-technological changes, companies deal with solving challenges related to these processes after the implementation of Industry 4.0 technologies, and states and communities are working on devising further directions of development and a strategy that will further accelerate changes. In Bosnia and Herzegovina, the processes are somewhat slower: educational programs partially follow the needs of education for Industry 4.0, companies struggle with the challenges of adopting and implementing Industry 4.0 without adequate institutional support, and strategies related to exploiting the opportunities of Industry 4.0 have not been developed, both due to the lack of initiative, as well as due to administrative restrictions related to the complex political system of Bosnia and Herzegovina. Considering that, this paper presents the results of research on the representation of Industry 4.0 technologies in the economy and education of Sarajevo Canton. The sample on which the research was conducted included 105 companies and 239 respondents from the general population. The results show that the highest level of application of Industry 4.0 technologies exists in the part related to the advanced management of company resources using planning and management support systems, and in communications. These findings, as well as the results related to the established level of knowledge of Industry 4.0 technologies in the general population, speak in favor of the need for the urgent development of various educational programs that will accelerate the learning of Industry 4.0 among all members of the community, as well as the establishment of state programs to support the implementation of technologies in companies, so that the economy of the Canton and the country as a whole would not fall behind in relation to the world driven by the fourth industrial revolution.

Digital twins represent a new paradigm that brings fundamental changes to business and asset management. The proliferation of connected devices and sensors has generated vast amounts of data from physical assets and processes. Digital twins leverage this data to create a virtual counterpart that reflects the behavior, performance, and characteristics of their physical counterparts in real-time. The definition of digital twins encompasses a wide range of applications and contexts. This paper provides an overview of existing literature on digital twins, including their definition, key characteristics, and classification. Additionally, it highlights potential challenges and limitations associated with digital twins and identifies the technologies that enable their implementation. By understanding the fundamental concepts and technological advancements in the field of digital twins, organizations can harness their potential to enhance their business, optimize resources, and foster innovation. Numerous examples of digital twin applications in various industries are highlighted in this paper, with a particular focus on the elevator industry. Therefore, this paper serves as a comprehensive source of information for researchers, practitioners, and decision-makers who wish to explore the application of digital twins in different industries and domains.

This paper presents an effective approach to enable performance improvement in human-robot collaboration scenarios. The problem is tackled from the perspective of speed and separation monitoring principle, which stems from the recently instituted safety standard. The proposed approach attempts to seek for performance gains, measured by the speed-up of the production cycle, without compromising the safety constraints consistent with the standard. The approach is based on the notion of braking surface - an abstraction of the swept volume described by the manipulator during braking motion. We address two types of braking behavior: general and path-consistent. In both cases, the braking surface can be evaluated in a receding horizon manner. The robot velocity is continuously scaled such that, in case of a controlled stop, the corresponding volume spanned by the robot (braking surface) does not interfere with the surrounding obstacles. The approach is entirely kinematic and does not require the knowledge of the robot's dynamic model. Simulation study indicates that the pro-posed approach offers performance improvements compared to other state of the art methods. Moreover, the experiments demonstrate the real-time applicability of the method with the real robot in human-shared environment.

OBJECTIVES Infection is still the leading cause of morbidity and mortality among burn patients worldwide. Isolation and identification of pediatric burn wound bacterial colonizers can prevent infection and improve burn trauma treatment. In this study, we explored early microbial colonizers within the burn wounds and the susceptibility of those isolates to antibiotics among hospitalized pediatric patients with minor and moderate burns, clinically significant infections and outcomes. METHODS A retrospective analysis of pediatric patients admitted to the inpatient pediatric surgical ward and treated for minor and moderate burns from 2009 to 2018 was performed. RESULTS One hundred six patients met the inclusion criteria. The mean age was 3.6 ± three years (0.2-14.1 years). The most common type of burn was scald burns (82.1%). The mean TBSA of the hospitalized pediatric burn cases was 8.5% (IQR, 6-12%). Seventy-nine (74.5%) patients had positive wound cultures at admission, regardless of the hospital admission day. Fifty-eight (73.4%) had one bacterial growth (mono isolate), while 21 (26.6%) had mixed growth or poly isolates. Among patients with mixed growth or poly isolate, 16 had two bacteria, three had three bacteria, and one had four bacteria isolated, totaling 105 isolated microorganisms (14 different species, 70.5% Gram-positive bacteria and 29.5% Gram-negative bacteria). Twelve patients (11%) developed clinically significant infections (eleven got burn wound infection, and one had septicemia). All patients received prophylactic systemic antibiotics. Only 35.2% of the isolated bacteria from the wounds were sensitive to the prophylactic antibiotics, and only ∼17% in case of clinically significant infections. We found a statistically significant difference in the length of hospital stay between patients with initially colonized samples of burn wounds compared with patients with initial negative samples (p = 0.008). All patients in the cohort survived hospital discharge. CONCLUSION Despite common bacterial colonization of acute burn wounds, only ∼10% of the patients developed clinically significant infections, a minority of which were sensitive to prophylactic antibiotics. Our findings indicate the need to refine the antibiotic approach in pediatric patients with minor/moderate burns in our local setting.

INTRODUCTION Early diagnosis and treatment of primary vesicoureteral reflux (VUR) are essential for preserving renal function. OBJECTIVES The study explored whether preoperative cystoscopic grading of refluxing ureteric orifices (UO) correlated with their shape in an institution with non-performance of hydrodistention of the UO in the diagnosis and grading of VUR. We also assessed the relationship between the UO shape and VUR grade with the effectiveness of endoscopic correction of primary VUR in children. METHODS This retrospective study included consecutive patients ≤15 years treated for primary VUR. The reflux grade was based on the results of preoperative voiding cystourethrography as mild, moderate, or severe. RESULTS Fifty-one patients with 77 renal refluxing units (RRU) underwent endoscopic treatment with Deflux®. VUR was bilateral in 51 % of patients. VUR was mild in 13 %, moderate in 53 %, and severe in 34 % of cases. The patients with mild and moderate VUR had stadium-shaped UOs in 60 % and 54 % RRUs, respectively. Horseshoe-shaped UOs constituted 42 % of UOs in patients with severe VUR, followed by 31 % of golf-hole UOs. The reflux resolution rate after the first endoscopic injection was 84 %. The preoperative VUR grade correlated with UOs shape (p < 0.001). No significant correlation between UOs configuration and the outcome of endoscopic treatment was seen (p = 0.452). The preoperative VUR grade negatively correlated with a favorable endoscopic treatment (p = 0.043). DISCUSSION AND CONCLUSION Our data indicate ureteral orifice shapes are closely related to preoperative VUR grade. There was no correlation between the UO configuration and the success rate of endoscopic treatment of VUR, in contrast to the significant negative correlation between the VUR grade and the success rate of endoscopic treatment.

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.

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.

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.

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.

Medicinal plants are potentialsources of bioactive compounds.One of the medicinal plants used in the traditional medicine of Bosnia and Herzegovina isendemic Satureja subspicataL. In this work, we examined the ability of Satureja subspicataL. essential oil and hot water and methanol extractsto inhibit the enzymes acetylcholinesterase(AChE) and butyrylcholinesterase(BChE) using Ellman’s method.The ability ofSatureja subspicataL. essential oil in concentration of 1 mg/mL and 2 mg/mL to inhibit enzymes was moderate: 72.82%, and 76.89% for AChE, and 51.51%, and 27.15% for BChE, respectively. Analyzed hot water and methanol extractsin concentration of 1 mg/mL showed weak ability of cholinesterase inhibition. Extracts were additionally analyzed regardingtoability to protect proteins from oxidation, during 1 h and 24 h incubation periods. After incubation for 1 hhot water extractshowed a very good protective effect(10.61%), while the methanolic extract showed prooxidative activity. After incubation for 24 h, both extracts showed prooxidative activity.The obtained results show that the examined essential oil and extracts of S. subspicataL. containcompounds withcholinesterase inhibition and antioxidant potential, and thereforecan be useful in treatment of Alzheimer's disease.KEYWORDS:Satureja subspicata, essential oil, extracts, cholinesterase inhibition, protein oxidation

Non-tuberculous mycobacteria (NTM) are opportunistic pathogens capable of causing infections in humans and animals. The aim of this study was to demonstrate the potential role of domestic and wild animals as a reservoir of multiple resistant, rapidly growing NTM strains representing a potential zoonotic threat to humans. A total of 87 animal isolates belonging to 11 rapidly growing species (visible colonies appear within three to seven days) were genotyped and tested for susceptibility to the 15 most commonly used antibiotics in the treatment of such infections in a human clinic. By determining the antimicrobial susceptibility, the most prevalent resistance was found to cephalosporins (>50%), followed by amoxicillin–clavulanate (31.0%), clarithromycin (23.0%), tobramycin (14.9%) and doxycycline (10.3%). Resistance to imipenem, ciprofloxacin, minocycline and linezolid was notably lower (<7.0%). All tested isolates were susceptible to amikacin and moxifloxacin. The most frequent resistance was proved in the most pathogenic species: M. fortuitum, M. neoaurum, M. vaccae and M. porcinum. Meanwhile, other species displayed a higher sensitivity rate. No significant resistance differences between domestic and wild animals were found. The established significant frequency of resistance highlights the significant zoonotic potential posed by circulating rapidly growing NTM strains, which could lead to challenges in the treatment of these infections.

In the pursuit of optimizing healthcare delivery and improving patient outcomes, the field of healthcare is undergoing a transformative shift from a reactive approach to a proactive one. This shift is facilitated by two upcoming technological developments: automation and artificial intelligence (AI). The abundance of data generated in the era of digital advancement presents both opportunities and challenges for healthcare. This paper explores the application of AI and machine learning in healthcare, focusing on the challenges posed by the exponential growth in data volume, the analysis of unstructured data, and the rapid pace of data refreshment. It examines the role of AI and machine learning in generating clinical decision support, uncovering disease subtypes and prognostic markers, and generating new hypotheses. In addition, this paper highlights the transformative potential of automation, AI, and robotics in healthcare, showcasing their ability to enhance efficiency, accuracy, and precision in patient care. By embracing these technological advancements, healthcare can achieve continuous progress in meeting the ever-growing demands and aspirations of the field while improving patient outcomes.