Supporting real-time flows with delay and throughput constraints is an important challenge for future wireless networks. In this paper, we develop an optimal scheduling scheme to optimally choose the packets to transmit. The optimal transmission strategy is based on an observable Markov decision process. The novelty of the work focuses on a priority-based probabilistic packet scheduling strategy for efficient packet transmission. This helps in providing guaranteed services to real time traffic in Heterogeneous Wireless Networks. The proposed scheduling mechanism is able to optimize the desired performance. The proposed scheduler improves the overall end-to-end delay, decreases the packet loss ratio, and reduces blocking probability even in the case of congested network.

The relevance of the topic is determined by the existence of inconsistencies between economic and environmental aspects of innovative development of urban logistics infrastructure, as well as the conflict of public, state and private commercial interests in this issue. The purpose of the article is to determine the principles and effective tools for innovative development of urban logistics infrastructure, taking into account pandemic constraints. The influence of the environmental aspect on the dynamics of transport flows was analyzed in detail and characterized, the scheme of determining the optimal solution for the development of the city was proposed considering the compromise between economic and ecological interests of society, state and business entities. A scheme of innovative development of urban logistics infrastructure under pandemic constraints was also developed. Materials of the article are relevant for developers of city plans, managers of urban logistics infrastructure, employees of enterprises participating in transport and logistics markets, scientists, and doctoral students.

This article defines the process of formation of anti-corruption consciousness as one of the areas of interaction between public authorities and civil society institutions. It is concluded that the implementation of the state anti-corruption policy in Ukraine may be more promising if the institutions of civil society are actively involved. The degree of citizen engagement can be increased by increasing social trust, as well as by increasing the level of political and legal education and culture. Particular attention should be paid to the organization of public control and monitoring of the activities of public authorities, impartial coverage of information on the fight against corruption in the media, as well as the joint conduct of an information and educational campaign and the promotion of the formation of anti-corruption consciousness among a large number of citizens.

The purpose of the article is to analyze students' self-assessment of the level of soft skills development and the prospects for its improvement in a pandemic. For an adequate and objective study of the stated issues, comparative analysis was most often used. For this purpose, theoretical studies of national and foreign experts were analyzed, which included specific sociological surveys. Prospects for further work are assumed in a detailed study of the factors, conditions and mechanisms for the formation and development of soft skills in students of natural, engineering and technological, medical, social and humanitarian specialties.

In deep learning classification tasks, most models frequently assume that all labels are available for the training datasets. As such strategies to learn new concepts from unlabeled datasets are scarce. In fingerprint classification tasks, most of the fingerprint datasets are labelled using the subject/individual and fingerprint datasets labelled with finger type classes are scarce. In this paper, authors have developed approaches of classifying fingerprint images using the majorly known fingerprint classes. Our study provides a flexible method to learn new classes of fingerprints. Our classifier model combines both the clustering technique and use of deep learning to cluster and hence label the fingerprint images into appropriate classes. The K means clustering strategy explores the label uncertainty and high-density regions from unlabeled data to be clustered. Using similarity index, five clusters are created. Deep learning is then used to train a model using a publicly known fingerprint dataset with known finger class types. A prediction technique is then employed to predict the classes of the clusters from the trained model. Our proposed model is better and has less computational costs in learning new classes and hence significantly saving on labelling costs of fingerprint images.

Plug-in Hybrid electric vehicles (PHEV) show great potential to reduce gas emission, improve fuel efficiency and offer more driving range flexibility. Moreover, PHEV help to preserve the eco-system, climate changes and reduce the high demand for fossil fuels. To address this; some basic components and energy resources have been used, such as batteries and proton exchange membrane (PEM) fuel cells (FCs). However, the FC remains unsatisfactory in terms of power density and response. In light of the above, an electric storage system (ESS) seems to be a promising solution to resolve this issue, especially when it comes to the transient phase. In addition to the FC, a storage system made-up of an ultra-battery UB is proposed within this paper. The association of the FC and the UB lead to the so-called Fuel Cell Battery Electric Vehicle (FCBEV). The energy consumption model of a FCBEV has been built considering the power losses of the fuel cell, electric motor, the state of charge (SOC) of the battery, and brakes. To do so, the implementing a reinforcement-learning energy management strategy (EMS) has been carried out and the fuel cell efficiency has been optimized while minimizing the hydrogen fuel consummation per 100km. Within this paper the adopted approach over numerous driving cycles of the FCBEV has shown promising results.

To make the research and development in IoT networks witness a significant improvement and last for a long period, it is always important to attract new researchers to work on this area and be a part of it. The best way to attract researchers to work in any research area and have their interest is to give them a clear background and roadmap about it. In this way, researchers can easily find a deep point to start their research based on their interest. This paper presents an overview and roadmap about IoT technologies from the most five vital aspects: IoT architecture, communication technologies, type of IoT applications, IoT applications protocols and IoT challenges.

The article examines the problems of ensuring the effectiveness of the economic security system in the face of increasing competition. The relevance of the study is determined by the intensification of competition between enterprises and the threats arising from this phenomenon. The methodological basis is the methods used: analysis and synthesis - to identify the main trends in the development of tools for economic security of the enterprise; systematization - to highlight the main characteristics of the economic security of the enterprise in the intensification of competition; generalization - to form the general conclusions of the study. The purpose of scientific research is to substantiate the feasibility of using the tools of the economic security system to increase its efficiency in the face of intensifying competition. The main components of the economic security system of the enterprise, which have the greatest impact on the formation of an effective level of economic security. The defining characteristics of the system of economic security of the enterprise are investigated. Special characteristics of the economic security system of the enterprise are considered. The offered directions of tools of maintenance of system of economic safety of the enterprise in competitive conditions: objective, subjective external, subjective internal. For each area, specific tools for ensuring the system of economic security, which affect its efficiency, have been identified.

Object classification is one of the main fields in neural networks and has attracted the interest of many researchers. Although there have been vast advancements in this area, still there are many challenges that are faced even in the current era due to its inefficiency in handling large data, linguistic and dimensional complexities. Powerful hardware and software approaches in Neural Networks such as Deep Neural Networks present efficient mechanisms and contribute a lot to the field of object recognition as well as to handle time series classification. Due to the high rate of accuracy in terms of prediction rate, a neural network is often preferred in applications that require identification, segmentation, and detection based on features. Neural networks self-learning ability has revolutionized computing power and has its application in numerous fields such as powering unmanned self-driving vehicles, speech recognition, etc. In this paper, the experiment is conducted to implement a neural approach to identify numbers in different formats without human intervention. Measures are taken to improve the efficiency of the machines to classify and identify numbers. Experimental results show the importance of having training sets to achieve better recognition accuracy.

COVID-19 struck labor markets around the world, exposing and exacerbating the gender inequalities within the human capital structure. The last, in its turn, jeopardizes the return of the national economies to the growth trajectory undermined by pandemic impact. The authors assume that COVID-19 disproportionately affected the employment rates of women and men, which led to increased gender inequality in the labor market, which, in turn, affected GDP growth rates in the EU. To prove this hypothesis two research questions are discovered: 1) whether there was a different correlation between the number of COVID-19 cases in the EU and indicators of the labor market for women and men; and 2) whether there was a link between the growth of gender inequality in the EU labor market and the GDP dynamics in these countries. The analysis of the correlation between the number of cases of COVID-19 and indicators of the labor market in the EU revealed faster growth of women's unemployment rates compared to men's ones as the COVID-19 incidence unfolded. Multiple linear regression and factor analysis have been used to investigate the influence of gender inequality in the labor market on GDP dynamics. Despite the methodological limitations, the proposed model is both a sound argument and an analytical basis in favor of gender-responsive economic recovery backed by the systematic and consistent gender equality policy of a government.

In response to the significant COVID-19 outbreak, countries have enforced the use of E-learning systems as an alternative to traditional learning; to contain the virus and minimize the infection rate while maintaining the continuity of the learning experience. However, the effective adoption of E-learning systems requires a well-understanding of critical factors, especially in times of crisis. In this regard, this study intends to assess the success of the E-learning system adoption by Higher Education Institutions (HEIs) during the crisis of COVID-19 by utilizing the Information Systems Success (ISS) model. This study's adopted model consists of nine interdependent dimensions, namely: Technical System Quality, Information Quality, Service Quality, Learner Quality, Perceived Satisfaction, Perceived Usefulness, System Use, Intention to Use, and System Success. An electronic survey was distributed among higher education students from different universities in Saudi Arabia to explore each model's dimension. Structural Equation Modeling (SEM) has been applied via SmartPLS software to test the causal relationships between dimensions. This study's main results revealed that students' Service Quality, Learner Quality, and the Intention to Use by students are essential drives for E-learning System Use during the Covid-19 pandemic. Meanwhile, the Intention to Use the system is significantly influenced by Perceived Satisfaction and Perceived Usefulness dimensions. Further, Perceived Satisfaction, Perceived Usefulness, and System Use are interdependent, and all three have a significant positive impact on E-learning System Success.

The Saudi Vision 2030 defined the directions of the national economy and market towards diversifying sources of income, and developing energy to become less dependent on oil. The study sought through a theoretical review to identify the reality of the energy sector and the areas of investment available in the field of renewable energy. Findings showed that investment in the renewable energy sector is a promising source according to solar, wind, hydrogen, geothermal energy and burning waste than landfill to extract biogas for less emission. The renewable energy sector faces challenges related to technology, production cost, price, quantity of production and consumption, and markets. The study revealed some recommendations providing and suggested electronic marketing system to provide investors and consumers with energy available from renewable sources.

Cloud computing platform is a shared pool of resources and services with various kind of models delivered to the customers through the Internet. The methods include an on-demand dynamically-scalable form charged using a pay-per-use model. The main problem with this model is the allocation of resource in dynamic. In this paper, we have proposed a mechanism to optimize the resource provisioning task by reducing the job completion time while, minimizing the associated cost. We present the Cooperative Agents Dynamic Resource Allocation and Monitoring in Cloud Computing CADRAM system, which includes more than one agent in order to manage and observe resource provided by the service provider while considering the Clients' quality of service (QoS) requirements as defined in the service-level agreement (SLA). Moreover, CADRAM contains a new Virtual Machine (VM) selection algorithm called the Node Failure Discovery (NFD) algorithm. The performance of the CADRAM system is evaluated using the CloudSim tool. The results illustrated that CADRAM system increases resource utilization and decreases power consumption while avoiding SLA violations.

The Saudi Arabian Special Education Regulations define the role and requirements from teaching assistants within the educational process. Although all public special education programs are subject to such regulations, their implementation in practice sometimes appears contradictory. Therefore, special educators frequently encounter a range of problems when they fail to comply with such regulations. This article discusses how teaching assistants influence the teaching practices delivered to students with disabilities in special education settings. A qualitative case study approach was conducted using 22 semi-structured interviews. The results suggest a need to focus on the role of the teaching assistant in special education classes to ensure exposure to effective learning practices for students with disabilities. Based on these findings, a number of important implications for future practice, in terms adopting appropriate provisions are suggested.

The automotive industry is motivated to provide more and more amenities to its customers. The industry is taking advantage of artificial intelligence by increasing different sensors and gadgets in vehicles machoism is forward collision warning, at the same time road accidents are also increasing which is another concern to address. So there is an urgent need to provide an A.I based system to avoid such incidents which can be address by using artificial intelligence and global positioning system. Automotive/smart vehicles protection has become a major study of research for customers, government and also automotive industry engineers In this study a two layered novel hypothetical approach is proposed which include in-time vehicle/obstacle detection with auto warning mechanism for collision detection & avoidance and later in a case of an accident manifestation GPS & video camera based alerts system and interrupt generation to nearby ambulance or rescue-services units for in-time driver rescue.

This research emphasizes on the role of congruence between the sponsor and the sponsored unit that effect sponsor's brand image, attitudes towards it and the memorization of the sponsor's name. The contribution of this research is highlighted especially during the sponsorship of national football team during the 2018 football World Cup. Because of the increasing cost of sponsorship, it has become essential for sponsors to understand how the direct and indirect audience responds to their congruence with the sponsored sports team; and which of the two types of audiences is more and more receptive to their actions. Specifically researchers were not conscious about the level of congruence that optimizes the effects on the sponsor. Therefore, this research aims to characterize the congruence between the marks of the sponsors and the football team through a survey administered face to face. Then, we test the global model to determine if the sponsorship operation has more impact on the sponsor in case of high, low or average congruence? To respond to our research questions we selected two samples from customers that were exposed to the sponsoring actions two ascertain the exploratory and confirmatory steps. The first sample consists of 200 interviewees and the second 400 are considered as an acceptable representation guarantee for our research. In addition, we propose to validate the research model and test the inherent hypotheses that analysis the relationship between the chosen constructs.

The primary goal of any classroom is effective teaching and student learning, with an emphasis on changing negative students' behaviors into positive ones. This change can occur when students' behavior is always at the forefront of teachers' educational goals. The current study aimed to reveal the experiences of special education teachers in dealing with the behavioral problems that students with disabilities show by identifying behavioral problems among students with disabilities and investigating the efficacy of the application of behavior modification plans and strategies designed to address the behavioral problems presented by students with disabilities. The researcher used a qualitative phenomenological approach, and the study tool consisted of semi-structured interviews that consisted of (13) questions directed to (10) special education male and female teachers who work in special education centres and institutes. The data were analyzed using the thematic analysis method. The findings of the study showed that the most undesirable behaviors prevalent among students with disabilities in the classroom were aggression, bullying and ADHD. The actions taken by teachers in confronting these behaviors were not systematic and were carried out randomly, as no behavior modification plans were applied. The most important strategies used by teachers in facing behavioral problems were applied behavioral analysis (ABA) strategies such as reinforcement, punishment and ignoring. The results have many important implications for future practice, including the need to conduct practical training courses for special education teachers to implement behavior modification plans and develop methods of measuring inappropriate behavior.

Scientific and technological progress is also fundamental to the evolving merchant shipping industry, both in terms of the size and speed of modern ships and in the level of their technical capabilities. While the freight performance of ships is growing, the number of crew on board is steadily decreasing, as more work processes are being automated through the implementation of information technologies, including ship management systems. Although there have been repeated appeals from international maritime organizations to focus on building effective maritime security defenses against cyber attacks, the problems have remained unresolved. Owners of shipping companies do not disclose information about cyberattack attempts or incidents against them due to fear of commercial losses or consequences, such as loss of image, customer and insurance claims, and investigations by independent international organizations and government agencies. Issues of cybersecurity of control systems in the world today have gained importance, due to the fact that existing threats concern not only the security of technical means and devices, but also issues of environmental safety and safety of life at sea. The article examines the implementation of cyber risk management in the shipping industry, providing recommendations for the safe ship operation and its systems in order to improve vulnerability to external threats related to cyberattacks, and to ensure the safety and security of such a technical object as a seagoing ship.

In modern educational practices, the issue of dependence on the experience of using multimedia by students and the adoption of technologies in education, the perception of their benefits and effectiveness in blended learning is little covered. The purpose of the academic paper lies in assessing the audiovisual context of multimedia technologies, its acceptance by students in practice on the example of using video lectures in blended learning. The methodology is based on an online survey of 120 students of Ukrainian universities who have assessed the experience level in using video lectures, as well as the constructs as follows: Technology Characteristics, Fit, Perceived Usefulness, Perceived Ease of Use, Attitude, Intention to Use, Actual Use. The results show that the majority of students use video lectures to a certain extent in their training (20,8% have used technology to a certain extent, 49,2% have often used technology in training, 20% are regular users of technology). It has been revealed that most students agree with the relevance of video lectures, the accuracy of lectures, the brevity of lectures, the clarity of lectures, as well as the high quality of lecture videos. It has been estimated that 42,5% believe that lecture videos are an effective tool towards supporting students in hybrid learning. 26,7% of students consider video lectures to be appropriate technologies for online / hybrid courses. In general, 37,5% of respondents find video lectures useful; however, 35,0% do not agree with this statement. 83,3% of students have rated the high level of ease of access to video. In total, 95% of students find lecture videos easy to use. In general, positive attitude of students to video lectures has been revealed.

Actually, brand personality is more and more important in marketing field. Considering the employee as consumer this paper will study the effect of employee brand personality on the intention to send a job application. In this research we tried to make the difference by following the qualitative methodology. we did ten interviews with IT engineering student in terminal classes and after thematique analysis tow thems emerged in relation with the brand personality factors which are sincerity and excitement. people are attracted by firms inspiring confidence and sincerity an exciting environment this is an important finding for firms who want to attract talented people. however, it important for enterprises in the IT filed to focus all their communication actions on the sincerity and excitement

Over the last 10 years, there has been rapid growth in the use of Machine Learning (ML) techniques to automate the process of intrusion threat detection at a scale never imagined before. This has prompted researchers, software engineers, and network specialists to rethink the applications of machine ML techniques particularly in the area of cybersecurity. As a result there exists numerous research documentations on the use ML techniques to detect and block cyber-attacks. This article is a systematic review involving the identification of published scholarly articles as found on IEEE Explore and Scopus databases. The articles exclusively related to the use of machine learning in Intrusion Detection Systems (IDS). Methods, concepts, results, and conclusions as found in the texts are analyzed. A description on the process taken in the identification of the research articles included: First, an introduction to the topic which is followed by a methodology section. A table is used to list identified research articles in the form of title, authors, methodology, and key findings.

The interconnection of an enormous number of devices into the Internet at a massive scale is a consequence of the Internet of Things (IoT). As a result, tasks offloading from these IoT devices to remote cloud data centers become expensive and inefficient as their number and amount of its emitted data increase exponentially. It is also a challenge to optimize IoT device energy consumption while meeting its application time deadline and data delivery constraints. Consequently, Fog Computing was proposed to support efficient IoT tasks processing as it has a feature of lower service delay, being adjacent to IoT nodes. However, cloud task offloading is still performed frequently as Fog computing has less resources compared to remote cloud. Thus, optimized schemes are required to correctly characterize and distribute IoT devices tasks offloading in a hybrid IoT, Fog, and cloud paradigm. In this paper, we present a detailed survey and classification of of recently published research articles that address the energy efficiency of task offloading schemes in IoT-Fog-Cloud paradigm. Moreover, we also developed a taxonomy for the classification of these schemes and provided a comparative study of different schemes: by identifying achieved advantage and disadvantage of each scheme, as well its related drawbacks and limitations. Moreover, we also state open research issues in the development of energy efficient, scalable, optimized task offloading schemes for Fog computing.

Healthcare organizations are overwhelmingly embracing smart value-based care strategies, which focuses on providing superior treatment at a significantly lower cost and quality of service (QoS). For these purposes, fifth generation (5G) of mobile service provides an innumerable improvement that clearly outperforms previous generations e.g., 3G and 4G. However, as with most advancements, 5G is projected to introduce new challenges, prompting the community to think about what comes next. This research was conducted to examine the most recent smart 5G technology applications and the solutions they provide to the healthcare industry. Finally, the paper discusses how the upcoming 6G technology has the potential to transform the future of healthcare sector even beyond the current 5G systems.

Educators have been making strides in the research into and practices supporting the policy of mainstreaming students with disability. A move towards including students with intellectual disability in community schools with all the other students can be seen in many countries' education systems, including that of the Kingdom of Saudi Arabia. The 'rights of the child' has been the main argument put forward by advocates of this policy in an attempt to move from the medical to the social model. This study argues that, although mainstreaming can be viewed as a positive trend toward effective education, its implementation remains somewhat problematic. It is believed that more investigative research into professionals' attitudes is needed to improve service provision and inform the administration of mainstreaming practices. The attitudes of special education teachers on the policy of mainstreaming are examined and emerging key themes discussed. Furthermore, challenges that continue to inhibit mainstreaming practices in Saudi Arabia are identified.

In today's digital world, smartphones and web-based applications have gained remarkable importance throughout the globe. These smart applications are playing a very significant role in maintaining a powerful business. As well as, they are helping a lot to expand these businesses via social networks. Social media networks such as Instagram, Facebook, Twitter, and LinkedIn are playing a prominent role to promote the companies. In the hospitality sector, most of the companies are running their hotel booking systems by utilizing mobile applications and a web-based infrastructure, but usability issues still exist. This study has been conducted specifically to tackle the usability issues of hotel booking systems and the best utilization of social networks to promote the business. TripAdvisor was selected as an authentic source for selecting those systems and two international hotels are selected for this study. The first step is to identify different hotel booking systems. In the second step, the user's satisfaction level was measured for the selected systems by performing the System Usability Scale (SUS, Quick & Dirty) approach. Additionally, by which source (social media or personal relations) they found these hotels. It is found that the SUS rating for both systems is below the acceptable level of usability. The Mean SUS for hotel 1 is found at 55.25 and 51.2 for hotel 2. The third step was to identify the user interface (UI) issues, and heuristic evaluation is performed for this. The experts identified the UI issues on the basis of their experience. The major issues were related to the visibility of system status, error prevention, flexibility and efficiency of use. Depending upon the identified issues, an interactive UI (prototype) for the selected web-based applications was proposed. This prototype is mainly based on the user's perspective. This prototype can be used for improving the UI of the selected systems which is based on the user's perspective. During the process of verifying the satisfaction level, it is revealed that the targeted audience is not able to use these systems efficiently and effectively. The reason behind this is the negligence of usability guidelines throughout the process of design and development of these hotel booking systems. Therefore, it is highly recommended that the usability of these systems should be evaluated and redesigned, based on expert opinions. It has also been observed that the reviews/ feedback of customers has spread a negative impact through social networks.

Prior to the emergence of Covid-19, Saudi Arabia (SA) had never faced the challenge of dealing with a global pandemic. Significantly, the current crisis has impacted all industries and sectors in the country, including the healthcare system, and has led to an emphasis on human life being more precious and valuable than economic profit. This study focuses on the impact of Covid-19 on the health of nurses, including their quality of life, during 2020. Understanding the position of the nursing profession during the pandemic, including the most effective methods of preventing work-related stress is important. Information was acquired through an online survey method (i.e. self-completion), known as the Expanded Nursing Stress Scale (ENSS), which was distributed to nurses in all regions of SA. It was found that the main aspects impacting nurses' work-related stress include gender, employment type, training, and dealing with infected patients. In addition, they highlight that such stress plays a substantial role in patient safety and nurses' satisfaction at work, as well as the future survival of organizations. The emergence of Covid-19 as a novel infectious disease has increased nurses' uncertainty and work-related stress. The results of this research will provide insights into the views of both nurses and their managers, in order to identify the main indicators of stress.

The main purpose of the study is to determine the main aspects of the introduction of modern management technologies into the security system in the context of socio-economic development and digitalization of the economy. Socio-economic development and a high level of security include growth in income, labor productivity, production volumes, increased competitiveness, changes in the institutional environment, consciousness, activity, social security, the quality of the education system, healthcare, etc. Despite the root cause of economic development, it is not an end in itself, but a tool for ensuring social development. Gaining access for citizens to education, health care, observance of the principles of equality and justice, ensuring protection are directly dependent on the level of economic well-being, the level of economic potential of the country or regions. The research methodology involved the use of both theoretical and practical methods. As a result of the study, the key elements of the introduction of modern management technologies into the security system in the context of socio-economic development and digitalization of the economy were identified.

The directed tests produce an expectation model to assist the organization's heads and professionals with settling on the right and speedy choice. A directed deep learning strategy has been embraced and applied for SCADA information. In this paper, for the load shedding expectation overall power organization of Libya, a convolutional neural network with multi neurons is utilized. For contributions of the neural organization, eight convolutional layers are utilized. These boundaries are power age, temperature, stickiness and wind speed. The gathered information from the SCADA data set were pre-handled to be ready in a reasonable arrangement to be taken care of to the deep learning. A bunch of analyses has been directed on this information to get a forecast model. The created model was assessed as far as precision and decrease of misfortune. It tends to be presumed that the acquired outcomes are promising and empowering. For assessment of the outcomes four boundary, MSE, RMSE, MAPE and R2 are determined. The best R2 esteem is gotten for 1-overlap and it was 0.98.34 for train information and for test information is acquired 0.96. Additionally for train information the RMSE esteem in 1-overlap is superior to different Folds and this worth was 0.018.

The process of correcting, upgrading, and improving software products after they have been handed over to the consumer is known as software maintenance. Offshore software maintenance outsourcing (OSMO) clients benefit from cost savings, time savings, and improved quality software through OSMO. In most circumstances, the OSMO vendor makes a lot of money but not in all the cases. Especially, when the OSMO project offer is not properly assessed. An efficient outsourcing contract might yield successful outcomes for outsourced projects. But before sending a detailed proposal to bid on the OSMO project the vendor must have to assess the client's project (business offer) requirements. The purpose of this study is to find out common trends within the assessment of a OSMO project. A case study approach along with semi-structured interviews from eight companies concluded ten common practices and several roles. Among these practices, four (code structure, requirements, communication barriers and required infrastructure) were consistent amongst the responses .The findings, limitations and future work are discussed.

Network Mobility Basic Support (NEMO BS) protocol has been accredited and approved by Internet Engineering Task Force (IETF) working group for mobility of sub-networks. Trains, aircrafts and buses are three examples of typical applications for this protocol. The NEMO BS protocol was designed to offer Internet access for a group of passengers in a roaming vehicle in an adequate fashion. Furthermore, in NEMO BS protocol, specific gateways referred to Mobile Routers (MRs) are responsible for carrying out the mobility management operations. Unfortunately, the main limitations of this basic solution are pinball suboptimal routing, excessive signaling cost, scalability, packet delivery overhead and handoff latency. In order to tackle shortcomings of triangular routing and Quality of Service (QoS) deterioration, the proposed scheme (Diff-FH NEMO) has previously evolved for end-users in moving network. In this sense, the article focuses on an exhaustive analytic evaluation at Home Agent (HA) entity of the proposed solutions. An investigation has been conducted on the signaling costs to assess the performance of the proposed scheme (Diff-FH NEMO) in comparison with the standard NEMO BS protocol and MIPv6 based Route Optimization (MIRON) scheme. The obtained results demonstrate that, the proposed scheme (Diff-FH NEMO) significantly improves the signaling cost at the HA entity in terms of the subnet residence time, number of mobile nodes, the number of DMRs, the number of LFNs and the number of CNs.

In the context of the pandemic, educational institutions had to ensure an instant transition to remote technological models of communication within the new conditions of the educational environment. The purpose of the academic paper lies in determining the role of the communicative model of educational transformations in the realities of (post) modernity. The research methodology is based on a survey of 120 students from 10 higher educational institutions (HEIs) of Ukraine through an online form regarding the importance of live communication during a pandemic. Results. The communicative model changed significantly during the pandemic - the interaction was mainly due to technologies. The research has identified four communication models of educational transformations under the conditions of the pandemic, depending on learning models. The first traditional model of distance learning involves distance learning; the second model involves contact remote training using remote educational technologies; the third model is blended learning, which combines remote and traditional learning formats, synchronous and asynchronous modes of interaction; the fourth model is traditional contact training. The empirical study of the effectiveness of communication models proves that live communication remains extremely important for learning and understanding of educational materials by students, and technology has provided support for such communication. Along with this, seminars and video lectures with presentations combining live communication and communication technologies are as important as digital learning tools. The most effective teaching method for mastering and memorizing educational material was a live dialogue with a teacher at seminars in ZOOM, followed by individual written assignments on the studied topic.

Globalization, development of the information society, intensification of migration processes, and internationalization of education in recent years have significantly affected the international labour market and increased competition between professionals in various fields. Academic, research and teaching staff are currently facing rapid changes and growing demand for quality educational services. Under such conditions, educational institutions around the world pay particular attention to improving the quality of the educational process in order for their graduates to be able to compete in the international labour market. The study of the experience of teachers' professional training in advanced countries opens new opportunities for improving the system of pedagogical training in Ukraine in order to adapt it to the requirements of the European educational space. The progressive achievements of countries demonstrating a high level of teachers' professional training in accordance with international standards, having rich historical educational traditions are of considerable scientific interest; consequently, this contributes to their leadership in science and education at the regional and global levels. The purpose of the present academic paper lies in analysing the latest trends in exploring teaching experience in the European countries and performance review of passing by students and teachers of the program on studying of pedagogical practice and experience of teaching in the countries of Europe. Methodology. Analytical and empirical (questionnaire) methods were used in the research process. Results. According to the obtained results of the research, the advantages, the most common areas of educational programs and the wishes of the participants regarding the practical implementation of the experience were identified.

This research aims to increase understanding of family participation in special education programs, to gain a deeper understanding of the programs themselves, and to determine the consequences of the research findings. It addresses the opportunities for families to participate in their children's learning journey and focuses on enhancing the experience of families participating in the education of students with intellectual disabilities. This study interviews four teachers of students with intellectual disabilities, and three important themes emerge from their discussion of whether parents should participate in special education programs for their children. The findings of this study have several important implications for future practice.

Private specialized institutions differ from public ones in that they mostly act independently. This paper reports a study designed to assess the provision of specialized institutions for children with intellectual disabilities in Saudi Arabia. The approach taken in this study was qualitative, involving a case study strategy that enabled the researcher to gain rich and in-depth information based on the shared experiences of participants comprising institution leaders, educators and families from two specific specialized settings, one public and one private. The study aimed to examine the existing disparities in service delivery so as to develop a clear picture of the service quality provided by public specialized institutions for children with intellectual disabilities in Saudi Arabia. The results suggest that the weak relationship with inpatient and specialized institutions is a consequence of the parents' poor responsiveness, which may result in these institutes developing a negative impression of the parents. Conversely, the lack of active initiative on the part of the public specialized institutions led to a negative parental attitude towards these institutions. A sensible approach to resolving this problem might be to recognize that these institutions have a significant responsibility to encourage parents of children with intellectual disabilities to become involved in their children's learning, to promote positive attitudes.

Deep Learning is used nowadays in Nuclei segmentation. While recent developments in theory and open-source software have made these tools easier to implement, expert knowledge is still required to choose the exemplary model architecture and training setup. We compare two popular segmentation frameworks, U-Net and Mask-RCNN, in the nuclei segmentation task and find that they have different strengths and failures. we compared both models aiming for the best nuclei segmentation performance. Experimental Results of Nuclei Medical Images Segmentation using U-NET algorithm Outperform Mask R-CNN Algorithm.

The research aims to study the dynamics of academic procrastination and its impact on the mental health of students during the transition to distance learning during the COVID-19 pandemic. At the beginning of the COVID-19 pandemic, it was identified a declining tendency of overall rates of academic procrastination and at the same time increase in the number of carriers of mid and high levels of academic procrastination. The decline in the general rates of academic procrastination at the beginning of 2021 testifies to the adaptation processes experienced by students to the conditions of distance learning. It was documented that students' academic procrastination is accompanied by a steady negative emotional tension. During the transition to distance learning, the intensity of students' learning activity has increased, which altogether causes stress as one of the main reasons for the academic procrastination among future psychologists. The study identified a risk of academic procrastination manifestation among students for their mental health, which provides a basis for developing and testing a program to prevent the phenomenon of academic procrastination among degree-seeking students.

WSN is the major component for information transfer in IoT environments. Source Location Privacy (SLP) has attracted attention in WSN environments. Effective SLP can avoid adversaries to backtrack and capture source nodes. This work presents a Two-Level Randomized Sector-based Routing (TLRSR) model to ensure SLP in wireless environments. Sector creation is the initial process, where the nodes in the network are grouped into defined sectors. The first level routing process identifies sector-based route to the destination node, which is performed by Ant Colony Optimization (ACO). The second level performs route extraction, which identifies the actual nodes for transmission. The route extraction is randomized and is performed using Simulated Annealing. This process is distributed between the nodes, hence ensures even charge depletion across the network. Randomized node selection process ensures SLP and also avoids depletion of certain specific nodes, resulting in increased network lifetime. Experiments and comparisons indicate faster route detection and optimal paths by the TLRSR model.

The radar tomographic imaging is based on the Radar Cross-Section "RCS" of the materials of a shape under examination and investigation. The RCS varies as the conductivity and permittivity of a target, where the target has a different material profile than other background objects in a scene. In this research paper, we use Hierarchical Performance Modeling "HPM" and a framework developed earlier to determine/spot bottleneck(s) for pattern recognition of materials using a combination of the Single Layer Perceptron (SLP) technique and tomographic images in radar systems. HPM provides mathematical equations which create Objective Functions "OFs" to find an average performance metric such as throughput or response time. Herein, response time is used as the performance metric and during the estimation of it, bottlenecks are found with the help of OFs. The obtained results indicate that processing images consumes around 90% of the execution time.

The tremendous growth of the Internet of things is unbelievable. Many IoT devices have emerged on the market over the last decade. This has made our everyday life easier inside our homes. The technology used at home has changed significantly over the past several decades, leading to what is known today as the smart home. However, this growth has also brought new challenges to our home security and privacy. With the smart home becoming more mainstream, cybersecurity issues have become a fundamental concern. The smart home is an environment where heterogeneous devices and appliances are interconnected through the Internet of Things (IoT) to provide smart services to residents. These services include home climate control, energy management, video on demand, music on-demand, remote healthcare, remote control, and other similar services in a ubiquitous manner. Smart home devices can be controlled via the Internet using smartphones. However, connecting smart home appliances to wireless networks and the Internet makes individuals vulnerable to malicious attacks. Remote access within the same environment or over the Internet requires an effective access control mechanism. This paper intends to shed light on how smart home devices are working as well as the type of security and privacy threats of the smart home. It also illustrated the types of authentication methods that can be used with smart home devices. In addition, a comparison of Smart home IoT-based security protocols was presented along with a security countermeasure that can be used in a smart home environment. Finally, a few open problems were mentioned as future research directions for researchers.

From the past two eras, artificial intelligence has gained the attention of researchers of all research areas. Video editing is a task in the list that starts leveraging the blessing of Artificial Intelligence (AI). Since AI promises to make technology better use of human life although video editing technology is not new yet it is adopting new technologies like AI to become more powerful and sophisticated for video editors as well as users. Like other technologies, video editing will also be facilitated by the majestic power of AI in near future. There has been a lot of research that uses AI in video editing, yet there is no comprehensive literature review that systematically finds all of this work on one page so that new researchers can find research gaps in that area. In this research we conducted a statically approach called, systematic mapping study, to find answers to pre-proposed research questions. The aim and objective of this research are to find research gaps in our topic under discussion.

By using the Blackboard (BB) system in the education sector, the educational process for both academics and students is facilitated. Two data resources were used to evaluate the use of the BB system by students of Umm Al-Qura University: statistical reports issued by the university and an online questionnaire. A total of 989 students from all colleges and different programmes provided by the university responded to the questionnaire survey. According to our findings, most students did not use the BB before the pandemic. Therefore, the sudden conversion to the BB system required intensive training courses. After the data analysis, the relationship between the use of the BB system before the pandemic and the problems students faced during the lockdown was revealed. The most critical issues raised by the respondents were: (1) "The voice of the lecturer went on and off during BB collaborate class", (2) "internet connection of the lecturer went on and off during BB collaborate class" and (3) "High possibility of IT problems during exams".

This study aimed to explore science teachers' perceptions of obstacles that have affected their teaching during the COVID-19 pandemic. The sample comprised 204 science teachers in public schools within a Western district of Saudi Arabia. Results revealed that these teachers experienced obstacles across six areas: administration, students, teaching methods, assessment, technology, and learning aids. The study also explored teachers' perceptions of their current experience and offers suggestions for dealing with the identified obstacles.

The article analyzes the features of innovation and investment activities of enterprises in the context of digitalization to increase their competitiveness in the international market. Ukraine's position on the Global Innovation Index is assessed. The interrelation of management functions in the context of innovation and investment activities of enterprises is substantiated. The structure of sources of financing of innovative activity of industrial enterprises is analyzed. Trends in changes in the volume of foreign direct investment and capital investment in Ukraine are assessed. It is determined that the level of innovative development of enterprises is determined by the level of their investment support, which is determined by the level of their investment attractiveness. The components of the strategy of investment attractiveness of enterprises are outlined. Determining factors in the implementation of innovation and investment policy of enterprises are identified and the main stages that should include the processes of managing innovation and investment activities of enterprises in the context of digitalization.

Ombudsmanship is the framework to provide the speedy administrative justice to poor man which is originated in Sweden in the 19^th Century and the modus operandi with modernized arrangement of public grievance redressal. Pakistan has thirteen different Ombudsman Institutions at both Federal and Provincial level with limited public accessibility. This paper presents a collaboration framework for extending the outreach of Ombudsmanship at proverbial doorstep of complainants. This framework has three main components as collaborative arrangements, Proceedings and Resolution of the complaints that increased the public trust. A pilot project titles Swift Complaint Resolution (SCR) was constructed and executed by Federal Ombudsman Secretariat to materialize the concept of speedy redressal of complaints. The SCR project first affirmed the strength of the components of the framework. The concept of SCR was first presented in the Federal Advisory committee for Administrative Justice, which comprised of highest level of the Parliamentarians, Public Officials, NGOs and Civil Society where presented guidelines to be adopted for the free and speedy redressal of grievances at proverbial doorsteps in tehsils and district headquarters ultimately to the extended to the union councils. In SCR, the complaints were decided within 25 days even though the Law permitted 60 days that is itself a record in any judicial /quasi-judicial forum.

This paper presents the modeling for islanded hybrid AC/DC microgrid and the verification of the proposed supervisory controller for energy management for this microgrid. The supervisory controller allows the microgrid system to operate in different power flows through the proposed control algorithm, it has several roles in the management of the energy flow between the different components of the microgrid for reliable operation. The proposed microgrid has both essential objectives such as the maximum use of renewable energies resources and the reduction of multiple conversion processes in an individual AC or DC microgrids. The microgrid system considered for this study has a solar photovoltaic (PV), a wind turbine (WT), a battery (BT), and a AC/DC loads. A small islanded hybrid AC/DC microgrid has been modeled and simulated using the MATLAB-Simulink. The simulation results show that the system can maintain stable operation under the proposed supervisory controller when the microgrid is switched from one operating mode of energy flow to another.

Health information systems (HIS) are facing security challenges on data privacy and confidentiality. These challenges are based on centralized system architecture creating a target for malicious attacks. Blockchain technology has emerged as a trending technology with the potential to improve data security. Despite the effectiveness of this technology, still HIS are suffering from a lack of data privacy and confidentiality. This paper presents a blockchain-based data storage security architecture integrated with an e-Health care system to improve its security. The study employed a qualitative research method where data were collected using interviews and document analysis. Execute-order-validate Fabric's storage security architecture was implemented through private data collection, which is the combination of the actual private data stored in a private state, and a hash of that private data to guarantee data privacy. The key findings of this research show that data privacy and confidentiality are attained through a private data policy. Network peers are decentralized with blockchain only for hash storage to avoid storage challenges. Cost-effectiveness is achieved through data storage within a database of a Hyperledger Fabric. The overall performance of Fabric is higher than Ethereum. Ethereum's low performance is due to its execute-validate architecture which has high computation power with transaction inconsistencies. E-Health care system administrators should be trained and engaged with blockchain architectural designs for health data storage security. Health policymakers should be aware of blockchain technology and make use of the findings. The scientific contribution of this study is based on; cost-effectiveness of secured data storage, the use of hashes of network data stored in each node, and low energy consumption of Fabric leading to high performance.

The main objective of an Information Retrieval System (IRS) is to obtain suitable information within a reasonable time to satisfy a user need. To achieve this purpose, an IRS should have a good indexing system that is based on natural language processing.In this context, we focus on the available Arabic language processing techniques for an IRS with the goal of contributing to an improvement in the performance. Our contribution consists of integrating morphological analysis into an IRS in order to compare the impact of morphological analysis with that of stemming algorithms.

Satellite communication for high altitude platform stations (HAPS) and low earth orbit (LEO) systems suffer from dust and sand (DU&SA) storms in the desert regions such as Saudi Arabia. These attenuations have a distorting effect on signal fidelity at high frequency of operations. This results signal to noise ratio (SNR) to dramatically decreasing and leads to wireless transmission error. The main focus in this paper is to propose common relations between HAPS and LEO for the atmospheric impairments affecting the satellite communication networks operating above Ku-band crossing the propagation path. A double phase three dimensional relationship for HAPS and LEO systems is then presented. The comparison model present the analysis of atmospheric attenuation with specific focus on sand and dust based on particular size, visibility, adding gaseous effects for different frequency, and propagation angle to provide system operations with a predicted vision of satellite parameters' values. Skillful decision and control system (SD&CS) is proposed to control applied parameters that lead to improve satellite network performance and to get the ultimate receiving wireless signal under bad weather condition.

This quantitative study explored the extent to which convenience enhances professional development (PD) online. The sample of the study consisted of 421 teachers and supervisors. Findings evinced that convenience stimulated online PD. Another salient finding is the absence of significant differences among participants' responses concerning position and gender. Nonetheless, a significant difference exists in the variable of teaching experience. Teachers with more than 26-year experience were motivated to use online PD, which evidently could be attributed to the convenience they had over teachers with fewer years of experience.

Online social networks contain a large amount of data that can be converted into valuable and insightful information. Text mining approaches allow exploring large-scale data efficiently. Therefore, this study reviews the recent literature on text mining in online social networks in a way that produces valid and valuable knowledge for further research. The review identifies text mining techniques used in social networking, the data used, tools, and the challenges. Research questions were formulated, then search strategy and selection criteria were defined, followed by the analysis of each paper to extract the data relevant to the research questions. The result shows that the most social media platforms used as a source of the data are Twitter and Facebook. The most common text mining technique were sentiment analysis and topic modeling. Classification and clustering were the most common approaches applied by the studies. The challenges include the need for processing with huge volumes of data, the noise, and the dynamic of the data. The study explores the recent development in text mining approaches in social networking by providing state and general view of work done in this research area.