The aim of this review was to evaluate possibilities of implementing concepts of Industry 4.0 including big data optimization, integration of sensors, artificial intelligence, and Internet of things (IoT), leading to the lowest possible costs and the highest possible output through smart control of the process, sustainable production, and monitoring. Sensors are vital components of Industry 4.0 and different types of detectors and sensors are used in the food packaging industry to convey information about food quality and to increase food safety. The paper will discuss time-temperature indicators, visual (color) indicators, O2 and CO2 indicators, freshness indicators, pH indicators, poison indicators and a radio frequency indicator that automatically detect and track the product. Based on credit card use it is possible to reveal the behavior of the customers and consumers. Virtual shops are popular for office workers. By using artificial intelligence, it is possible to create a robotic kitchen, which can cook according to the wishes of users, while 3-D printing technologyis used to cut food in certain shapes.

It is a well-known fact that the changes on the world industrial and digital scene were named the fourth industrial revolution at the WEF –World Economic Forum (held in Davos in 2016). Almost all developed countries in the world have designed their own programs to implement the fourth industrial revolution. Thus, the German government promotes Industry 4.0 (first appeared at the Hannover Fair in Germany), USA promotes Smart Manufacturing Leadership Coalition (SMLC), the Japanese government established the Center for the Fourth Industrial Revolution Japan in 2018, while the Chinese government adopted the strategy ‘’Made in China 2025’’.It is necessary to make a detailed assessment of the fourth industrial revolution in order to raise awareness of its breadth of influence, and increase its application. The current competitiveness that is present in the world is the reason for the rapid implementation of Industry 4.0 in almost all companies in the world. Robotic technology is the core technology of Industry 4.0, and its application accelerates its application. The number of installed units of robots, both industrial and service robots, is increasing every year in the world. The paper presents the analysis of the implementation of both industrial and service robots worldwide, including the trend of implementation of Industry 4.0. The paper provides an analysis of the implementation of robots in the countries that installed the most robots in 2019, as well as the number of robots per 10,000 workers in the manufacturing industry in the same year, based on which we have an insight in the automation process of each country.In the future, the implementation of robotic technology in the industry will increase in order to achieve greater representation of Industry 4.0, making it easier to come up with "smart manufacturing processes" or "smart factories"

Vještačka inteligencija (VI) je veoma disruptivna tehnologija, koja u kombinaciji sa snažnim hardverom za procesiranje otvara mogućnosti za opšti napredak u industriji. Postoji hitna potreba za sistemskim razvojem i implementacijom VI radi njenog učinka u industrijskim sistemima, posebno u četvrtoj industrijskoj revoluciji (Industrija 4.0). Tržišni subjekti koji ne usvoje VI neće biti u mogućnostiodržati svoju konkurentnost na tržištu. Ova publikacija pruža uvid u glavne paradigme VI korištene u Industriji 4.0, stavljajući naglasak na ključne digitalne tehnologije i njihove izazove. Pored toga, u ovoj publikaciji smo napravili pregled trenutnog stanja u VI, te pregled najvažnijih algoritama korištenih u Industriji 4.0. Pored navedenih tema, u ovoj publikaciji diskutujemo i o trendovima vezanim za usvajanje VI u kontekstu ugradbenih aplikacija i softverskih arhitektura uopšteno.

Artificial intelligence (AI) is a very disruptive technology, which combined with powerful computational hardware have opened new possibilities for world-wide technological progress in industry. There is an urgent need for systematic development and implementation of AI to see its real impact in the next generation of industrial systems, namely Industry 4.0. Organisations that do not do so will fail to maintain their competitiveness. This paper provides an insight into the main paradigms of AI technologies used in Industry 4.0, by giving emphasis to the key enabling digitalization technologies and their challenges. In addition, we present an overview of AI current state and the most important AI algorithms used in Industry 4.0. Finally, we discuss trends related to adoption of AI in the context of software embedded applications and software architectures for embedded systems.

Important quality indicators for products made by Additive Manufacturing (AM) methods include dimensional accuracy, tolerances, and surface roughness. This paper describes a novel benchmark part designed and manufactured by a Low Force Stereolithography (LFS) 3D printer and controlled on a coordinate measuring machine. The benchmark part contains basic geometric shapes used to check the deviation from the nominal dimensions and given features from the 3D CAD model. We have developed an automatic measurement strategy based on a 3D object model and a control plan based on previously defined allowable tolerances. We have measured the deviation from nominal dimensions, positions, and form, including cylindricity, flatness and parallelism. The results revealed large deviations in dimensions and acceptable deviations in the shape of a manufactured benchmark part.

Važni pokazatelji kvaliteta za proizvode proizvedene metodom aditivne proizvodnje (AP) uključuju tačnost dimenzija, tolerancije i hrapavost površine. Ovaj rad opisuje inovativni referentni standard koji je dizajniran i proizveden pomoću 3D printera koji koristi stereolitografiju niske snage (LFS) i koji je kontroliran na koordinatnoj mjernoj mašini. Referentni standard sadrži osnovne geometrijske oblike koji se koriste za provjeru odstupanja od nominalnih dimenzija i zadatih karakteristika iz 3D CAD modela. Razvili smo strategiju automatskog mjerenja baziranu na 3D modelu objekta i plan upravljanja na osnovu prethodno definisanih dozvoljenih tolerancija. Izmjerili smo odstupanje od nominalnih dimenzija, položaja i oblika, uključujući cilindričnost, ravnost i paralelnost. Rezultati su otkrili velika odstupanja od dimenzija i prihvatljiva odstupanja od oblika proizvedenog referentnog standarda.

For almost two years now, the world has been facing major economic challenges, precisely struggling how to adjust the business in a period of a pandemic. It can be said with great certainty that there are very few economic entities to which the pandemic has left no trace or caused visible consequences on business. However, we can perceive the crisis as a chance for further development or an opportunity to show that we can achieve better results compared to the competition. One of the ways to improve the usage of mobile application in catering facilities is the implementation of digital technologies. Accordingly, the main goal of this paper is to determine whether there is an impact of digital technologies on the usage of mobile application in catering facilities. The methods used for the research will be a survey as a method of quantitative research, and an interview as a method of qualitative research. The expected results of the research are to determine whether there is an impact of the of digital technologies on the usage of mobile application at catering facilities.

This paper is dedicated to machine learning, the branches of machine learning, which include the methods for solving this issue, and the practical implementation of the solution to the automatic image description generation. Automatic image caption generation is one of the frequent goals of computer vision. Image description generation models must solve a larger number of complex problems to have this task successfully solved. The objects in the image must be detected and recognized, after which a logical and syntactically correct textual description is generated. For that reason, description generation is a complex problem. It is an extremely important challenge for machine learning algorithms because it represents an impersonation of a complicated human ability to encapsulate huge amounts of highlighted visual pieces of information in descriptive language. The results of the generated descriptions are compared depending on the used pretrained convolutional networks. The BLEU metrics are used to calculate the quality of the image description. Although the solution to the problem of image description automatic generation does provide us with good results, there is yet room for improvement since there are images that are not adequately described.

The logistics performance index (LPI) represents an important indicator of the state of logistics and its development in countries. The LPI is directly linked to the level of economic system development, and as such provides an adequate basis for the improvement of economy, through logistics and trade. The aim of this paper is to determine the impact of sensitivity analysis on the evaluation and ranking of the LPI in the Balkan countries, according to the report of the World Bank. Sensitivity analysis implies the change of the importance of six criteria based on which the LPI ranking is done. The multi-criteria decision-making model (MCDM), which consists of CRITIC and MARCOS methods for determining the LPI rank in the Balkan countries, was previously used. Criteria weights are simulated through 36 scenarios, whereby the weights of the observed criteria change in the range of 15% - 90%. The final results show that criteria values play very important role in the ranking of the Balkan countries, when it comes to the LPI.

The cornelian cherry (Cornus mas L.) is considered to be one of those medicinal plants with important nutritional and therapeutic properties. The plant shows resistance against abiotic and biotic stressors in natural growing conditions and could be suitable to use in sustainable fruit production. This study was carried out on 22 local cornelian cherry (Cornus mas L.) genotypes, which were grown northwest of Bosnia and Herzegovina. Fruits of these 22 genotypes were harvested and analyzed during the ripening period in 2018/2019. Fruit weight, length, and width ranged from 1.38 to 3.01 g, 13.84 to 19.43 mm, and 10.92 to 14.79 mm, respectively. Dry matter content was determined to be the lowest at 11.67% and the highest at 21.89%. The genotypes had vitamin C content between 25.85 and 58.75 mg/100 g. Total phenolic and anthocyanin content were found to be quite variable among genotypes and ranged from 1240 to 6958 mg gallic acid equivalents (GAE) per 100 g fresh weight (FW) and 55.57 to 205.6 mg cyaniding-3-glucoside equivalents (CGE) per 100 g FW, respectively. The content of phosphorus and iron were between 155.52 to 263.06 mg per 100 g and 0.25 to 0.93 mg per 100 g, respectively. Principal Component Analysis (PCA) showed that the first and second components accounted for 44.05% and 60.50% of the total variance, and the major proportion of the first and second components were the morphometric properties and chemical traits of the cornelian cherry fruits. The results revealed that the characterized genotypes could be important for cornelian cherry breeders as ready crossing materials to obtain new cornelian cherry varieties and shows the potential of certain genotypes as a valuable source of natural antioxidants. The results may have served as a guide towards the development of sustainable production programs for cornelian cherries as well.

In the original publication of the article, the initial of Professor Stefan Priebe has been incorrectly published as ‘P’ instead of’S’. The correct author name is given in this erratum.

Objectives: To investigate country-specific drivers and barriers of positive COVID-19 vaccine intentions in the Federation of Bosnia and Herzegovina (FBiH), one of the two entities comprising Bosnia and Herzegovina. Methods: A cross-sectional study design was used, using an online behavioural insights survey tool adapted to the context of FBiH. Three survey waves, each including approximately 1,000 adults, were conducted in July, September and December 2020. Fixed-effects regression analysis was used to explore the drivers, barriers and attitudes towards accepting a future COVID-19 vaccine. Results: COVID-19 risk perception, trust in health institutions and negative affect were positive predictors of positive COVID-19 vaccine intentions, as were living in urban areas and having a college education (versus having primary or secondary education). Conversely, being female, feeling that the pandemic was overhyped by the media and the country of vaccine production were negative predictors. Conclusion: This study provided snapshots on the state of attitudes regarding a future COVID-19 vaccine acceptance and hesitancy in 2020. These findings provided useful insights into the efforts to introduce and roll out the COVID-19 vaccines in FBiH. Further efforts should focus on better understanding the demographic, cultural and behavioural contexts of COVID-related vaccination perceptions in FBiH.

Millimeter wave (mmWave) communication technique has been developed rapidly because of many advantages of high speed, large bandwidth, and ultra-low delay. However, mmWave communications systems suffer from fast fading and frequent blocking. Hence, the ideal communication environment for mmWave is line of sight (LOS) channel. To improve the efficiency and capacity of mmWave system, and to better build the Internet of Everything (IoE) service network, this paper focuses on the channel identification technique in LOS and non-line of sight (NLOS) environments. Considering the limited computing ability of user equipments (UEs), this paper proposes a novel channel identification architecture based on eigen features, i.e. eigenmatrix and eigenvector (EMEV) of channel state information (CSI). Furthermore, this paper explores clustered delay line (CDL) channel identification with mmWave, which is defined by the 3rd generation partnership project (3GPP). The experimental results show that the EMEV based scheme can achieve identification accuracy of 99.88% assuming perfect CSI. In the robustness test, the maximum noise can be tolerated is $\text{SNR} = 16 \mathbf{dB}$, with the threshold $acc\geq$ 95%. What is more, the novel architecture based on EMEV feature will reduce the comprehensive overhead by about 90%.

Optimization of logistics processes and activities in the function of supply-chain sustainability is a great challenge for logistics companies. It is necessary to rationalize processes in accordance with the strict requirements of the market, while respecting aspects of sustainability, which is not an easy task. Multicriteria decision making can be a tool that contributes to the optimization of logistics processes in terms of making the right decisions and evaluating different strategies in different logistics subsystems. In this paper, we considered the warehousing system as one of the most important logistics subsystems in a company. Conditions and the possibility of implementing barcode technology in order to optimize warehousing processes were evaluated. We formed a strengths, weaknesses, opportunities, and threats (SWOT) matrix consisting of a total of 27 elements. In order to determine the weights of all factors at the first level of decision making and its indicators at the second level of the decision making hierarchy, an original model was developed. This model involved the creation of a novel grey full-consistency method (FUCOM-G) and integration with a SWOT analysis. Since it was a matter of group decision making, we developed a novel grey Hamy aggregator that, by adequately treating uncertainties and ambiguities, contributed to making more precise decisions. The original grey FUCOM-SWOT model based on the grey Hamy aggregator represents a contribution to the entire field of decision making and optimization of logistics processes. Based on the applied model, the obtained results showed that Weaknesses, as part of the SWOT matrix, are currently the most dominant indicators, and that the implementation of barcode technology in a warehousing system is justified.