In recent decades, the heterogeneous and dynamic behavior of Internet traffic has placed new demands on the adaptive resource allocation of the optical network infrastructure. However, the advent of multifiber elastic optical networks has led to a higher degree of spectrum fragmentation than conventional flexible grid networks due to the dynamic and random establishment and removal of optical connections. In this paper, we propose heuristic routing and dynamic slot allocation algorithms to minimize spectrum fragmentation and reduce the probability of blocking future connection requests by considering the power consumption in elastic multifiber elastic optical networks.

The aim of this paper is to examine the factors that could influence leadership development and employee retention in private sector organizations. In this research, we will seek to determine the nature and extent of the impact of professional training on leadership development. This research project is one of the new topics that some researchers have started in recent years. the novelty of this subject is the inclusion of the vocational training factor. This factor is likely to have a positive impact on leadership development. Some of the factors already known for their impact on leadership development such as personality, power, and material and moral incentives will be adopted. It is assumed that the results of this research will have a positive impact on the development of leadership within companies.

Economic transformations have led to an increase in the role of creative assets and their central role in public life. Changes in creative activity have led to a change in the organization of the work of institutes engaged in the training of specialists, in particular teachers of labor education. Methods and approaches to training determine the development of creative industries, being the basis for models of professional training of future teachers of labor training. The purpose of an article was to develop a modern model of professional training of future teachers of labor training based on the concept of creative economy. The methodology is based on the concepts of holistic craft and creative economy. Based on the integration of pedagogical learning models "Craft as design and problem-solving", "Craft as skill and knowledge building", "Craft as product-making" and "Craft as self-expression" developed and experimentally confirmed the conceptual model of professional training of future teachers of labor training. The proposed model forms a practitioner with professional, technical, digital and creative skills who is able to transfer the experience to students. The training course "Creativity and creative thinking" has been developed. The model provided for the development of a course based on the strategy of developing professional creativity, flexibility, improvisation, openness, student activity, joint practice, student-oriented approach. The practical value implies the adaptation of the developed model of professional training of future teachers of labor education during the training of teachers in higher education, which is confirmed in the experiment.

RNA sequencing (RNA-Seq) is a technology that facilitates transcriptome analysis using next-generation sequencing (NSG) tools. Information on the quantity and sequences of RNA is vital to relate our genomes to functional protein expression. RNA-Seq data are characterized as being high-dimensional in that the number of variables (i.e., transcripts) far exceeds the number of observations (e.g., experiments). Given the wide range of dimensionality reduction techniques, it is not clear which is best for RNA-Seq data analysis. In this paper, we study the effect of three dimensionality reduction techniques to improve the classification of the RNA-Seq dataset. In particular, we use PCA, SVD, and SOM to obtain a reduced feature space. We built nine classification models for a cancer dataset and compared their performance. Our experimental results indicate that better classification performance is obtained with PCA and SOM. Overall, the combinations PCA+KNN, SOM+RF, and SOM+KNN produce preferred results.

This paper presents a design of a speech to sign language converter for deaf and hard of hearing people. The device is low-cost, low-power consumption, and it can be able to work entirely offline. The speech recognition is implemented using an open-source API, Pocketsphinx library. In this work, we proposed a context-oriented language model, which measures the similarity between the recognized speech and the predefined speech to decide the output. The output speech is selected from the recommended speech stored in the database, which is the best match to the recognized speech. The proposed context-oriented language model can improve the speech recognition rate by 21% for working entirely offline. A decision module based on determining the similarity between the two texts using Levenshtein distance decides the output sign language. The output sign language corresponding to the recognized speech is generated as a set of sequential images. The speech to sign language converter is deployed on a Raspberry Pi Zero board for low-cost deaf assistive devices.

Detection of protein-protein interactions (PPIs) remains essential for the development of therapies against diseases. Experimental studies to detect PPI are longer and more expensive. Today, with the availability of PPI data, several computer models for predicting PPIs have been proposed. One of the big challenges in this task is feature extraction. The relevance of the information extracted by some extraction techniques remains limited. In this work, we first propose an extraction method based on correlation relationships between the physicochemical properties of amino acids. The proposed method uses a correlation matrix obtained from the hydrophobicity and hydrophilicity properties that it then integrates in the calculation of the bigram. Then, we use the SVM algorithm to detect the presence of an interaction between 2 given proteins. Experimental results show that the proposed method obtains better performances compared to the approaches in the literature. It obtains performances of 94.75% in accuracy, 95.12% in precision and 96% in sensitivity on human HPRD protein data.

Security is an important issue for wireless communications and poses many challenges. Most security schemes have been applied to the upper layers of communications networks. Since in a typical wireless communication, transmission of data is over the air, third party receiver(s) may have easy access to the transmitted data. This work examines a new security technique at the physical layer for the sake of enhancing the protection of wireless communications against eavesdroppers. We examine the issue of secret communication through Rayleigh fading channel in the presence of an eavesdropper in which the transmitter knows the channel state information of both the main and eavesdropper channel. Then, we analyze the capacity of the main channel and eavesdropper channel we also analyze for the symbol error rate of the main channel, and the outage probability is obtained for the main transmission. This work elucidate that the proposed security technique can safely complement other Security approaches implemented in the upper layers of the communication network. Lastly, we implement the results in Mat lab

Generating electromagnetic waves with Orbital Angular Momentum (OAM) properties is a fast-growing research subject in both radio and optics. This paper describes the generation of OAM carrying waves using a circular array of rectangular planar monopole antennas. The proposed design combines simplicity, compactness, and most importantly very wideband of operating frequencies (about 20-160 GHz, bandwidth ratio about 1:8) which makes it suitable for future applications.

In this paper we study the problem of implementation of security issues of blue tooth, especially secure simple pairing, with the help of an efficient four user authenticated key (4UAK) for an elliptic curve cryptography (ECC). This paper also deals with the design, implement and performance evaluation of secure simple pairing (SSP) using an elliptic curve cryptography, such as Diffie Hellman protocol when four users are involved. Here, we also compute the best, worst and average case step counts (time complexities). This work puts forth an efficient way of providing security in blue tooth. The time complexity of O(n⁴) is achieved using Rabin Miller Primality methodology. The method also reduces the calculation price and light communication loads.

The article discusses the dead reckoning of the traveled path based on the analysis of the video data stream coming from the optoelectronic surveillance devices; the use of relief data makes it possible to partially compensate for the shortcomings of the first method. Using the overlap of the photo-video data stream, the terrain is restored. Comparison with a digital terrain model allows the location of the aircraft to be determined; the use of digital images of the terrain also allows you to determine the coordinates of the location and orientation by comparing the current view information. This method provides high accuracy in determining the absolute coordinates even in the absence of relief. It also allows you to find the absolute position of the camera, even when its approximate coordinates are not known at all.

An anthological analysis of known literature and historical sources is carried out in the work. It was found that the development of foreign language training of future professionals was influenced by a number of factors: socio-economic (focus on the needs of the labor market, integration into the international space, scientific and technological progress); educational (updating legal documents in the field of education, standardization of educational content, development of methods of professional development of a specialist). The historical period is analyzed and the following stages are determined: ideological (realization of ideological imperative in language and professional training of future specialists; educational-methodical (preparation according to unified curricula, reading and translation as a leading type of speech activity); integration (integration of foreign language teaching and multicultural education)), methodological (use of traditional verbal methods, standardized textbooks). Thus, the research conducted in the article indicates the periods (stages) of formation, functioning and development of foreign language education.

COVID-19 poses a major risk to global health, highlighting the importance of faster and proper diagnosis. To handle the rise in the number of patients and eliminate redundant tests, healthcare information exchange and medical data are transmitted between healthcare centres. Medical data sharing helps speed up patient treatment; consequently, exchanging healthcare data is the requirement of the present era. Since healthcare professionals share data through the internet, security remains a critical challenge, which needs to be addressed. During the COVID-19 pandemic, computed tomography (CT) and X-ray images play a vital part in the diagnosis process, constituting information that needs to be shared among hospitals. Encryption and image steganography techniques can be employed to achieve secure data transmission of COVID-19 images. This study presents a new encryption with the image steganography model for secure data transmission (EIS-SDT) for COVID-19 diagnosis. The EIS-SDT model uses a multilevel discrete wavelet transform for image decomposition and Manta Ray Foraging Optimization algorithm for optimal pixel selection. The EIS-SDT method uses a double logistic chaotic map (DLCM) is employed for secret image encryption. The application of the DLCM-based encryption procedure provides an additional level of security to the image steganography technique. An extensive simulation results analysis ensures the effective performance of the EIS-SDT model and the results are investigated under several evaluation parameters. The outcome indicates that the EIS-SDT model has outperformed the existing methods considerably.

The study aimed to define all the main determinants of writing a research paper in an integrated manner within the requirements of the science network journals, by introducing scientific research and the steps of writing a research paper, starting from choosing the title to writing the abstract, identifying the research problem, questions, importance and objectives of the study, how to choose previous studies, research methodology and tools, discussion and results In addition to determining the acceptable percentage of plagiarism for science network journals, recommendations, choosing appropriate references and choosing the appropriate references, in addition to determining the acceptable percentage of plagiarism for the journals of the Network of Science, the researcher used the method of analytical investigative research to find out the axes of the study and analyze different reference citation methods to deal with different sources of information (articles-books-theses-conference works-reviews), In addition to studying the most important international programs for measuring plagiarism ratios that are accredited within the journals of the Science Network, and the study concluded that the researcher must present original and innovative results, add an integrated and logical work, take into account all the scientific conditions in the design of the research, its steps and ethics, and take into account the international standards in citing Reference, taking into account the rates of plagiarism within Web of Science journals.

Obesity affects individuals of all gender and ages worldwide; consequently, several studies have performed great works to define factors causing it. This study develops an effective method to trace obesity levels based on supervised data mining techniques such as Random Forest and Multi-Layer Perception (MLP), so as to tackle this universal epidemic. Notably, the dataset was from countries like Mexico, Peru, and Colombia in the 14- 61year age group, with varying eating habits and physical conditions. The data includes 2111 instances and 17 attributes labelled using NObesity, which facilitates categorization of data using Overweight Levels l I and II, Insufficient Weight, Normal Weight, as well as Obesity Type I to III. This study found that the highest accuracy was achieved by Random Forest algorithm in comparison to the MLP algorithm, with an overall classification rate of 96.7%.

Social media has become a global means of communication in people's lives. Most people are using Twitter for communication purposes and its inappropriate use, which has negative effects on people's lives. One of the widely common misuses of Twitter is cyberbullying. As the resources of dialectal Arabic are rare, so for cyberbullying most people are using dialectal Arabic. For this reason, the ultimate goal of this study is to detect and classify cyberbullying on Twitter in the Arabic context in Saudi Arabia. To help in the detection and classification of tweets, Pointwise Mutual Information (PMI) to generate a lexicon, and Support Vector Machine (SVM) algorithms are used. The evaluation is performed on both methods in terms of the F1-score. However, the F1-score after applying the PMI is 50%, while after the SVM application on the resampling data it is 82%. The analysis of the results shows that the SVM algorithm outperforms better.

The article analyzes the possibility of using information and innovation experience in training public administration specialists, and also explores the system of training public administration and management specialists abroad. It was determined that in the European Union, Japan and other developed countries, three concepts of qualified personnel training will be developed: the concept of specialized training is focused on the present or near future and is relevant for the respective workplace; the concept of multidisciplinary training is effective from an economic point of view, as it increases intra-production and non-production mobility of an employee; the concept of learner-centered learning with the aim of developing human qualities.

Today, phishing attacks represent one of the biggest security threats targeting users of the digital world. They consist of an attempt to steal sensitive information, such as a user's identity or credit and debit card details, using various methods that include fake emails, fake websites, and fake social media messages. Protecting the user's security and privacy therefore becomes complex, especially when those users are children. Currently, children are participating in Internet activity more frequently than ever before. This activity includes, for example, online gaming, communication, and schoolwork. However, children tend to have a less well-developed knowledge of privacy and security concepts, compared to adults. Consequently, they often become victims of cybercrime. In this paper, the effects of security awareness on users who are children are investigated, looking at their ability to detect phishing attacks in social media. In this approach, two Experiments were conducted to evaluate the effects of security awareness on WhatsApp application users in their daily communication. The results of the Experiments revealed that phishing awareness training has a significant positive effect on the ability of children using WhatsApp to identify phishing messages and thereby avoid attacks.

The development of technology speeds up the process of obtaining information and its analysis to track the level of corruption in different countries and develop countermeasures. This study examines the role of information and analytical support of anti-corruption policy as a tool for government accountability and analysis, evaluation, combating corruption in Eastern Europe. The purpose of the article is to identify the components of the information-analytical system that help reduce the level of corruption. The research methodology is based on a qualitative content analysis of the functioning of information and analytical systems of Ukraine used by anti-corruption bodies. A quantitative analysis of the CPI score was conducted, according to Transparency International, to identify the effectiveness of anti-corruption policies in developing countries. The results show similar trends in countries developing on the effect of the use of information and analytical systems in the implementation of anti-corruption policies, strategies and measures. The strategy to combat corruption mainly involves increasing the independence and powers of anti-corruption bodies. Therefore, the development of information and analytical support is aimed at automating the processes of pre-trial investigations and criminal proceedings, information protection. As a tool for accountability, information and analytical systems may be ineffective due to the abuse of power by higher anti-corruption bodies due to political pressure from elite structures. Restrictions on political will are a major problem for the effectiveness of anti-corruption policies.

Modern concepts of management of construction production systems require solving the problems of harmonization of the distribution of structural elements on the way to overcoming global destabilization processes. An effective ratio of functional subsystems of production in an environment of mutual influence create sustainable opportunities for production management and contribute to the logical development of the system as a whole in achieving the main goal of harmonious management. The purpose of the study is to develop the concept of effective management of production systems in construction with the harmonious formation of organizational structures. The results of the study allowed to reveal the harmonization approach in improving the interaction of structural elements of production and accelerating their functional sensitivity to changes in the environment. Harmonious production system more effectively adapts to the diversity of interests, goals and actions at all levels of management of different subsystems in any environment.

In the Recent times, various technological enhancements in the field of artificial intelligence and big data has been noticed. This advancement coupled with the evolution of the 5G communication and Internet of Things technologies, has helped in the development in the domain of smart mine construction. The development of unmanned vehicles with enhanced and smart scheduling system for open-pit mine transportation is one such much needed application. Traditional open-pit mining systems, which often cause vehicle delays and congestion, are controlled by human authority. The number of sensors has been used to operate unmanned cars in an open-pit mine. The sensors haves been used to prove the real-time data in large quantity. Using this data, we analyses and create an improved transportation scheduling mechanism so as to optimize the paths for the vehicles. Considering the huge amount the data received and aggregated through various sensors or sources like, the GPS data of the unmanned vehicle, the equipment information, an intelligent, and multi-target, open-pit mine unmanned vehicle schedules model was developed. It is also matched with real open-pit mine product to reduce transport costs, overall unmanned vehicle wait times and fluctuation in ore quality. To resolve the issue of scheduling the transportation, we prefer to use algorithms based on artificial intelligence. To improve the convergence, distribution, and diversity of the classic, rapidly non-dominated genetic trial algorithm, to solve limited high-dimensional multi-objective problems, we propose a decomposition-based restricted genetic algorithm for dominance (DBCDP-NSGA-II).

The increasing importance of such fields as embedded systems, pervasive computing, and hybrid systems control is increasing attention to the time-dependent aspects of system modeling. In this paper, we focus on modeling conceptual time. Conceptual time is time represented in conceptual modeling, where the notion of time does not always play a major role. Time modeling in computing is far from exhibiting a unified and comprehensive framework, and is often handled in an ad hoc manner. This paper contributes to the establishment of a broader understanding of time in conceptual modeling based on a software and system engineering model denoted thinging machine (TM). TM modeling is founded on a one-category ontology called a thimac (thing/machine) that is used to elaborate the design and analysis of ontological presumptions. The issue under study is a sample of abstract modeling domains as exemplified by time. The goal is to provide better understanding of the TM model by supplementing it with a conceptualization of time aspects. The results reveal new characteristics of time and related notions such as space, events, and system behavior.

Vehicular Ad hoc Network (VANET) is an extension paradigm of moving vehicles to communicate with wireless transmission devices within a certain geographical limit without any fixed infrastructure. The vehicles have most important participation in this model is usually positioned quite dimly within the certain radio range. Fuzzy based multi-hop broadcast protocol is better than conventional message dissemination techniques in high-mobility VANETs, is proposed in this research work. Generally, in a transmission range the existing number of nodes is obstacle for rebroadcasting that can be improved by reducing number of intermediate forwarding points. The proposed protocol stresses on transmission of emergency message projection by utilization subset of surrounding nodes with consideration of three metrics: inter-vehicle distance, node density and signal strength. The proposed protocol is fuzzy MHB. The method assessment is accomplished in OMNeT++, SUMO and MATLAB environment to prove the efficiency of it.

This study is conducted with an educational institution to minimize the potential security hazards to an acceptable level where the vulnerabilities and threats are revealed in this institution. The paper provides a general structure for the risk management process in educational institutions, and some recommendation that increases security levels is presented.

All over the world, people are affected by many chronic diseases and medical practitioners are working hard to find out the symptoms and remedies for the diseases. Many researchers focus on the feature detection of the disease and trying to get a better health recommendation system. It is necessary to detect the features automatically to provide the most relevant solution for the disease. This research gives the framework of Health Recommendation System (HRS) for identification of relevant and non-redundant features in the dataset for prediction and recommendation of diseases. This system consists of three phases such as Pre-processing, Feature Selection and Performance evaluation. It supports for handling of missing and noisy data using the proposed Imputation of missing data and noise detection based Pre-processing algorithm (IMDNDP). The selection of features from the pre-processed dataset is performed by proposed ensemble-based feature selection using an expert's knowledge (EFS-EK). It is very difficult to detect and monitor the diseases manually and also needs the expertise in the field so that process becomes time consuming. Finally, the prediction and recommendation can be done using Support Vector Machine (SVM) and rule-based approaches.

The article provides a literary and analytical review in the following areas of search: problems and prerequisites for changes in the field of education, innovations and innovative models in education, the use of new technologies in teaching. A proposal for a business plan and accompanying documentation for a new methodology based on blockchain technologies were developed, to assess the economic efficiency of the project. The main systems of the new model were modeled on the basis of the proposed methodology, to develop a prototype based on the project documentation.

Ensuring the economic security of agro-industrial complexes of Ukrainian regions has become a top-priority task of state regional policy, as their stable functioning is an essential element of economic security of the whole country. It is overcoming threats to the development of the agro-industrial complex that ensures its further effective functioning and has a significant impact on the economic security of our state. Methods: logical method; methods of system analysis; synthesis; economic and statistical method; method of expert assessment; SWOT analysis; economic and mathematical modelling and planning. Results. Characteristic features of economic security have been given. The essence and significance of the agro-industrial complex in improving the economic security of the state have been determined. It has been noted that in recent years, the agro-industrial complex, which acts as a driver of the domestic economy and has a direct impact on the development of the country, has been growing (in 2019 the cereal and legume harvest exceeded 75 million tons, 20,269 thousand tons of potatoes were dug, more than 15 million tons of sunflower, 9,688 thousand tons of vegetables and 2,119 thousand tons of fruits and berries were harvested, meat and egg production increased by 137.5 thousand tons (or 5.8%) and 545.5 million pieces (or 3.4%), respectively, the number of employed population in agriculture increased by 139.8 thousand people (or 4.9%), the labour productivity in crop production increased by UAH 294.4 thousand (or 44.6%), in livestock production - by UAH 311.3 thousand (or 61.8%)). Based on the system of production and economic indicators, the analysis of the state of the agro-industrial complex has been carried out. Taking into account the results of the obtained data and using SWOT-analysis, the major threats to the development of the agro-industrial complex have been identified. Ways of overcoming threats enhancing the economic security of Ukraine have been proposed.

For twenty years, in the EU there has been a trend of a lack of maritime infrastructure and a redundance of the road one, which has a negative impact on the economy. The intermodal transport market structure in the EU has not changed over the past ten years. The stability of transport systems due to the lack of changes in the transport market remains under threat, affecting supply chains and networks through the optimization of warehousing and transportation costs. The research methodology is based on a quantitative assessment of cause-and-effect relations between economic growth and transport and logistics in the EU. A statistical analysis of security indicators, intermodal and modal transport, international trade in goods within the EU and in the world trade in goods, the dynamics of GDP of the EU countries, the level of openness of the EU economy, investment and maintenance costs of different modes of transport and infrastructure has been carried out. The results show that in 2000- 2010 there were positive changes in the transport and logistics infrastructure of the EU, which had a positive effect on trade, openness of the economy of the EU, GDP growth. However, at that time, negative effects of environmental impact and the load on road and rail transport were accumulating. Investment in different modes of transport is limited, and technical maintenance and infrastructure maintenance costs form a significant part of GDP of the EU. A slowdown in economic growth leads to budget constraints and infrastructure financing gap. As a result, the freight and passenger intermodal and modal transport market structure remains virtually unchanged. The load on rail and road transport remains stable, despite the reduced level of transport hazards. Transport productivity has declined over the past ten years. Herewith, the intensification of trade and the openness of the EU economies require constant modernization and innovative renewal. The EU policy in this direction remains normative, uncontrolled, which is reflected in investment differences within the EU and maintenance costs.

Internet users are increasingly invited to express their opinions on various subjects in social networks, e-commerce sites, news sites, forums, etc. Much of this information, which describes feelings, becomes the subject of study in several areas of research such as: "Sensing opinions and analyzing feelings". It is the process of identifying the polarity of the feelings held in the opinions found in the interactions of Internet users on the web and classifying them as positive, negative, or neutral. In this article, we suggest the implementation of a sentiment analysis tool that has the role of detecting the polarity of opinions from people about COVID-19 extracted from social media (tweeter) in the Arabic language and to know the impact of the pre-processing phase on the opinions classification. The results show gaps in this area of research, first of all, the lack of resources when collecting data. Second, Arabic language is more complexes in pre-processing step, especially the dialects in the pre-treatment phase. But ultimately the results obtained are promising.

Low-power and Lossy Networks (LLNs) have become the common network infrastructure for a wide scope of Internet of Things (IoT) applications. For efficient routing in LLNs, IETF provides a standard solution, namely the IPv6 Routing Protocol for LLNs (RPL). It enables effective interconnectivity with IP networks and flexibly can meet the different application requirements of IoT deployments. However, it still suffers from different open issues, particularly in large-scale setups. These include the node unreachability problem which leads to increasing routing losses at RPL sink nodes. It is a result of the event of memory overflow at LLNs devices due to their limited hardware capabilities. Although this can be alleviated by the establishment of multi-sink topologies, RPL still lacks the support for effective load balancing among multiple sinks. In this paper, we address the need for an efficient multi-sink load balancing solution to enhance the performance of PRL in large-scale scenarios and alleviate the node unreachability problem. We propose a new RPL objective function, Multi-Sink Load Balancing Objective Function (MSLBOF), and introduce the Memory Utilization metrics. MSLBOF enables each RPL node to perform optimal sink selection in a way that insure better memory utilization and effective load balancing. Evaluation results demonstrate the efficiency of MSLBOF in decreasing packet loss and enhancing network stability, compared to MRHOF in standard RPL.

The adoption of new technology in any organization will represent change, and such change needs user acceptance for its successful implementation. Saudi Arabian health centers are no exception; therefore, the current study will investigate the adoption of new technology, namely that of virtual reality (VR), within health centers in Saudi Arabia and specifically in Riyadh City. This study explores the current state of VR technology adoption, factors that influence such adoption, and the extent of this technology's efficiency when it is used for vaccinating children. The data were collected from two samples: workers in vaccination clinics who responded to a survey and a group of children who participated in the VR technology experiment. The current study proposed a model based on the unified theory of acceptance and use of technology 2 (UTAUT2), with the addition of two variables: personal innovativeness and satisfaction. The results indicated that the respondents' perceptions regarding the health centers' infrastructure in terms of adopting VR were moderate. Among the factors affecting VR adoption, satisfaction, personal innovativeness, and behavioral intention were identified as vastly influential factors. From the eight hypotheses, six were found to be supported, with their factors significantly influencing behavioral intention with regard to VR technology adoption. Besides, the experiment concerning the use of VR technology on children verified the technique's high efficiency in terms of providing pain management and fear removal. These findings support the continuity of VR technology use, expand its future application fields, and integrate this study into the literature on technology acceptance models for VR adoption, as limited studies have covered this topic; consequently, this will benefit future research in this field.

The explosive growth of video-based services is considered as the dominant contributor to Internet traffic. Hence it is very important for video service providers to meet the quality expectations of end-users. In the past, the Quality of Service (QoS) was the key performance of networks but it considers only the network performances (e.g., bandwidth, delay, packet loss rate) which fail to give an indication of the satisfaction of users. Therefore, Quality of Experience (QoE) may allow content servers to be smarter and more efficient. This work is motivated by the inherent relationship between the QoE and the QoS. We present a no-reference (NR) prediction model based on Deep Neural Network (DNN) to predict video QoE. The DNN-based model shows a high correlation between the objective QoE measurement and QoE prediction. The performance of the proposed model was also evaluated and compared with other types of neural network architectures, and three known machine learning methodologies, the performance comparison shows that the proposed model appears as a promising way to solve the problems.

The change of the development of transport and logistics systems occurs with the active change of technology and the advent of the era of Industry 4.0. It requires modernization of approaches to the development of transport and logistics systems at the macro and micro levels. The present study aims to identify perspective directions of development and evolution, find out the existing obstacles in the integration of technological solutions of transport and logistics systems at the macro and micro levels. This study is based on a quantitative and qualitative methodology for assessing the level of integration of technologies into transport and logistics systems to study the prospects for their development at the micro level. Macroeconomic indicators of transport and logistics in the context of different regions of the world were used to quantify the development prospects. For a qualitative assessment of the development of the transport and logistics system, the case study method was used. The object of the study was selected logistics company Sensco Logistics Inc., Austin TX. At the macro level, countries with more innovative logistics sectors have stronger mechanisms for coordinating private sector activities. Simplification of administrative procedures of control and regulation by the public sector in order to facilitate trade between countries is a promising direction for the development of transport and logistics systems. Such reforms are more effective in developing a "rigid" transport infrastructure. The integration of Industry 4.0 technology solutions into the international logistics sector is defined by political and legal barriers, especially in developing countries. In low-income countries, hard and soft infrastructure reforms are hindering the development of logistics companies that provide transport services. This determines the national level of development of transport and logistics systems, and in general the global level of development of transport and logistics. In developed countries, the legal barriers to the development of new technological logistics are environmental requirements for the integration of technologies into the transport system. These trends are slowing down the development of International Logistics, which, compared to other industries, is slower to integrate Industry 4.0 technologies. This study combines macroeconomic factors that determine the prospects for the development of transport and logistics systems at the micro level.

Poor logistical infrastructure and agri-food supply chain management leads to significant food waste in logistic system. The concept of the sustainable value added agri-food chains requires defined approach to the analysis of the existing situation, possible improving strategies and also assessment of these changes impact on further development. The purpose of research is to provide scientific substantiation of theoretical and methodological principles and develop practical recommendations for the improvement of the agri-food logistics distribution system. A case study methodology is used in this article. The research framework is based on 4 steps: Value Stream Mapping (VSM), Gap and Process Analysis, Validation and Improvement Areas Definition and Imitation Modelling. This paper presents the appropriateness of LEAN logistics tools using, in particular, Value Stream Mapping (VSM) for minimizing logistic losses and Simulation Modeling of possible logistics distribution system improvement results. The algorithm of VSM analysis of the agri-food supply chain, which involves its optimization by implementing the principles of sustainable development at each stage, is proposed. The methodical approach to the analysis of possible ways for optimizing the operation of the logistics system of the agri-food distribution is developed. It involves the application of Value Stream Mapping, i.e. designing of stream maps of the creation of the added value in the agri-food supply chain for the current and future state based on the minimization of logistic losses. Simulation modeling of the investment project on time optimization in the agri-food supply chain and economic effect of proposed improvements in logistics product distribution system functioning at the level of the investigated agricultural enterprise has been determined. Improvement of logistics planning and coordination of operations in the supply chain and the innovative pre-cooling system proposed to be introduced have a 3-year payback period and almost 75-80% probability. Based on the conducted VSM analysis of losses in the agri-food supply chain, there have been determined the main points, where it is advisable to conduct optimization changes for the achievement of positive results and the significant economic effect from the proposed measures has been confirmed. In further studies, it is recommended to focus on identifying the synergistic effect of the agri-food supply chain optimization on the basis of sustainable development.

Modern methods of regulating migration processes in EU states include a wide variety of adapted, transformed under the sway of globalization tools in order to influence the movement of human capital within the European space. The main purpose of the regulatory policy on migration flows is the redistribution of professionally competent professionals between different spheres of life. Herewith, the determining factor in the effectiveness of such distribution is a rational combination of stimulating and disincentive levers of influence on the movement of citizens of different EU countries and taking into account the motives of such mobility. Modernization of migration management approaches can be a major economic, social, political and cultural progress of European countries. The purpose of the research is to conduct a detailed analysis of existing practices of migration flow management, in particular their stimulation or containment, and to outline key migration trends formed under the influence of multicomponent approaches to migration regulation, transformation of regulatory legislation and changing priorities of modern society. The research methods: statistical-analytical method; ARIS method; method of tabular, graphical and analytical modeling; comparative analysis; systematization, generalization. Results. Current pan-European methods of regulating migration processes are insufficiently adapted to the multinational socialeconomic space; consequently, there are some disparities in the distribution of migrants between EU countries, although the overall dynamics of migration is positive. Fluctuations in the population of European countries during 2000-2019 and trends in the transformation of social-economic space confirm the insufficient level of influence of current methods of regulating migration flows. Along with this, the presence of a characteristic asymmetry in the distribution of migrants requires a greater focus on the modernization of regulatory instruments, in particular, the regulatory mechanism for managing migration processes. As a result of the conducted study, further prospects for the implementation of alternative methods of regulating migration processes in EU states have been outlined; the current and projected limits for increasing the level of observance of migrants' rights at the European level have been clarified through the adoption of appropriate regulatory acts; effective solutions for intensifying the influx of high-quality labor resources from different countries to EU have been identified. The research results can be used to study methods of regulating migration processes in the countries in the global dimension.

The study covers three components of the facility for protecting public utilities market participants in the public utilities market: prevention of potential poverty, reduction of existing poverty and compensation to the injured party in a case of tort that exacerbates or threatens to exacerbate its poverty. The analysis is based on official statistical information on the activities of the public utilities sector. Operational information of public utility service providers regarding certain indicators of their activity in the work was not studied. This approach narrows the empirical basis of the study, but at the present stage in the context of different rates of implementation of changes in regions, sectors and at the level of individual entities, as well as lack of uniformity in the structure of indicators published by service providers, analysis allows to identify «bottlenecks» of legal regulation, which are systemic in nature and largely independent of the subjective factor.

As a result of the vast amount of data that is geographically found in different locations. Distributed data mining (DDM) has taken a center stage in data mining. The use of mobile agents to enhance efficiency in DDM has gained the attention of industries, commerce and academia because it offers serious suggestions on how to solve inherent problems associated with DDM. In this paper, a novel DDM model has been proposed by using a mobile agent to enhance efficiency. The main idea behind the model is to use the Naive Bayes algorithm to give the mobile agent the ability to learn, compare, get and store the results on it from each server which has different datasets and we found that the accuracy increased roughly by 0.9% which is our main target.

Modern small farms are important link components in the structure of the world agro-industrial complex. It ensures the food and nutritional sustainability of the country exclusively at the local regional level. The purpose of the research is to examine the role of farming in ensuring nutritional security and food stability based on the analysis of the Food Sustainability Index (FSI). Research methods: modeling, abstraction, analogy, analysis, synthesis, formalization, logical abstraction, theoretical cognition, systematization and classification, abstract-logical, etc. Results. Having analyzed the Food Sustainability Index for 2018, it has been established that there is a lack of a clear relationship between the pace of economic development and the level of food and nutritional sustainability. In addition, this study has identified the countries with the largest number of small farms, as well as the number of farms within the region. The correlation between the size of the farm and the area of agricultural land that it cultivates has been determined. The problems faced by small farms in the process of their activity have been analyzed. The programs implemented in the field of agro-industrial complex development by international profile institutions have been systematized. Particularly, the regional structure of agricultural development programs under the guidance of IFAD is defined, as well as the areas to which they are directed. Specific measures taken by governments to stimulate the development of small farms have been outlined. Reasonable conclusions have been formed based on the study. The direction of future research is seen in the assessment of the export potential of small farms in terms of range, volume of export deliveries and geographical direction of movement of their products.

The relevance of the research is due to the fact that the issue of poverty is one of the most acute social problems of the beginning of the third millennium. The phenomenon of poverty is widespread in third world countries as well as it is observed in relatively developed countries. Poverty rates in Latin America are threatening. Consequently, the issue of social and economic inequality in these countries has become extremely acute. The purpose of the research: to identify the causes of poverty and social inequality and substantiate the main directions of poverty reduction in third world countries. The research methods: comparative analysis; index method; systematization; grouping; generalization. Results. The classification of the causes of poverty has been carried out and the directions of its overcoming in the countries of Latin America on groups of indicators have been defined, namely: 1) political; 2) economic; 3) demographic; 4) regional-geographical; 5) social; 6) qualification; 7) personal. Based on the Net Domestic Product indicator, a comparison of economic indicators of the studied countries has been carried out. It has been revealed that from 1990 to 2018 income inequality increased in 52 of 119 countries studied, and decreased in 57 states. Inequality has increased in the world's most populous countries, particularly China and India. In general, countries with growing inequality are home to more than two-thirds (71%) of the world's population. Trends in the distribution of income in the world have been investigated by applying the Gini index, the high level of which is observed in Latin America (Colombia 48,9%, Panama 46,1%, Chile and Mexico 45,9%). The forecast of the impact of the Covid-19 pandemic on this issue has been outlined; the ways of its impact on the economies of the countries have been studied. As a result of the study, the main directions and mechanisms of the strategy for poverty reduction and social inequality in the third world countries have been identified. The implementation of the poverty reduction strategy presented in this academic paper may have a positive impact on the economic situation of the population of Latin American countries.

The paper presents problems of optimization of the synthesis of embedded systems, in particular Pareto optimization. The model of such a system for its design for high-level of abstract is based on the classic approach known from the theory of task scheduling, but it is significantly extended, among others, by the characteristics of tasks and resources as well as additional criteria of optimal system in scope structure and operation. The metaheuristic algorithm operating according to this model introduces a new approach to system synthesis, in which parallelism of task scheduling and resources partition is applied. An algorithm based on a genetic approach with simulated annealing and Boltzmann tournaments, avoids local minima and generates optimized solutions. Such a synthesis is based on the implementation of task scheduling, resources identification and partition, allocation of tasks and resources and ultimately on the optimization of the designed system in accordance with the optimization criteria regarding cost of implementation, execution speed of processes and energy consumption by the system during operation. This paper presents examples and results for multi-criteria optimization, based on calculations for specifying non-dominated solutions and indicating a subset of Pareto solutions in the space of all solutions.