درخت حوزه‌های تخصصی

This study compared the performance of the partial least squares-structural equation modelling (PLS-SEM) and the robust partial least squares -structural equation modelling (RPLS-SEM) methods through Winsorisation approach The inputs and the outputs used in this model were based on the electricity generation data, derived from the Al-Zawiya Steam Power Plant, Libya. Furthermore, the researchers compared the novel RPLS-SEM approach with the traditional PLS-SEM approach and noted that the novel RPLS-SEM method was more efficient compared to PLS-SEM.

Accelerating digital transformation and the basic needs of businesses to adapt to environmental transformations and complexities have doubled the necessity for extensive stakeholder interactions. Gamification is a powerful tool that facilitates stakeholder interaction and a common understanding of the vision and contributes to the sustainability of businesses. Businesses' sustainability in the digital transformation age depends on the continuous interaction of stakeholders and a comprehensive understanding of all the business pillars, which will be possible through the flow of ideas inside and outside the workplace and by providing innovative processes. In this regard, this study explores the effect of gamification on corporate sustainability by explaining the role of digital transformation, open innovation, and value co-creation. The statistical population of this study comprises top managers and experts in e-businesses who use gamification in their processes. The statistical sample included 117 managers and experts active in this field, and they were selected through convenience sampling. The data collection tool was a questionnaire reliability of which was 0.763 using Cronbach's alpha. Using Smart PLS software, the gathered data were analyzed by structural equation modeling (SEM). Gamification, with a factor of 74%, has a positive effect on open innovation, while a factor of 85% has a positive effect on digital transformation. Gamification, both directly and indirectly, creates value co-creation in businesses, and finally, value co-creation at 78% affects corporate sustainability.

The article examines the development of theoretical and methodological approaches and practical recommendations for improving the ecological and economic foundations of land management and land management at the local level in the example of the European Union. In practice, regional, spatial, strategic, landscape, and integrated types of land use development planning are most often used in the European Union. With the development of the market economy of European society, environmental problems arose, which prompted the development of landscape planning, both landscape-ecological and socio-economic aspects of territorial development. The experience of landscape planning was introduced in Germany, which later became used in almost all European countries. The study and direct comparison of existing landscape planning systems in different European countries, and their reduction to several basic features are not possible and impractical because these systems are very different. This is due to a number of reasons: history, features of political systems, cultural traditions, level of economic development, the nature of legal systems and property relations. Landscape planning in the European Union is going to be one of the tensest areas of activity in the future, and Ukraine must take an active part in this process.

To ensure the continuity of the training activity within the framework of the specific precau-tions for the Covid 19 health crisis , the regional centers for training and education profes-sions, such as universities and schools, have radically reversed their training strategy, from face-to-face training to alternating training mode known as `` hybrid training mode '', the training of which is provided remotely one week and face-to-face during the following week and vice versa. In this sense, digital platforms of social networks and videoconferencing soft-ware have been mobilized to succeed in the mission of trainers.  The objective of this article is to verify the contribution of videoconferencing software '' on training practices on the one hand and on the development of trainees' skills on the other hand, in order to trace a training methodology based on technological choices aimed at guar-anteeing maximum socio-professional performance. Finally, we confirm that despite the satisfaction of the majority of trainees 87% with respect to the advantages of videoconferencing tools in the process of assessing skills, technical, spa-tiotemporal and organizational disparities were noted among trainers compared to the use of digital tools in the evaluation (only 53% agree), or also in relation to the transition from one technological choice to another. 

This paper presents the design, simulation and realization of a patch antenna for IOT applications. The patch antenna consists of a radiating element printed on one face of a dielectric substrate, when the ground plane is placed on the other face. In this work, two techniques are used to design a miniaturized patch antenna: the set-up of slots on the radiating element and the use of defective ground plane. Also, the slot’s radius and Length of inset point effects on the performances of the antenna is illustrated. All the simulated results are performed with FEKO, a solver based on a Moments Method and measurement is made using Vector Network Analyzer Anritsu MS2026C. The propose antenna resonates in three frequency bands 3.91, 4.86 and 5.16GHz for different characteristics such as radiation pattern, gain, return loss, which makes it suitable for many wireless communication applications such as IoT applications.

The Moroccan education system has undergone several reforms in recent years, thus concluded by the strategic vision, especially project 9 which has valued the continuing training of educational actors and framework law 51-17 in articles 34 and 35 which valued the continuous development of skills of teachers in Morocco, In this perspective the Ministry of National Education has reserved significant budgets to operationalize the continuing training of teachers on the one hand and it is committed to a Moroccan-American partnership within the framework of the Millennium project Challenge Account-Morocco. By committing to the establishment-challenge project, the ministry has set up a set of priorities, in particular the operationalization of the digital strategy for schools declared by decree 2.20.474, thus benefiting from the contribution of digital and while trying to overcome some constraints related to face-to-face training, a new regime that is continuous online training is implemented, it is the e-takwine platform. In addition, given the interest that evaluation represents for maintaining and improving the quality of a digital training device, we opted in this research, by evaluating the technopedagogical modality of e-takwine reserved at the start of the continuing training of novice teachers in Morocco and then developed for the training of all second cycle teachers from the start of the 2021-2022 school year. The sampling of our research is represented by 362 professors from the region of Fés –Meknes belonging to two different teaching cycles, and who followed their training for three months in six training modules in a more or less assiduous manner, Similarly, to conduct our survey, we administered two online questionnaires to participating teachers, in order to judge the functionality and ergonomics of the platform being researched. Finally, confirming to us that despite the satisfaction of the majority of participants with respect to the functional quality and the contribution of the device, some difficulties were noted at the levels: technical (compatibility, accessibility), functional or even with regard to technological knowledge.

Despite all blockchain's advantages, it has experienced some challenges and disadvantages. Identifying these challenges and minimizing them are important steps in advancing the goals of this technology. The purpose of this study is to investigate and identify the challenges of using blockchain technology in international markets with regard to the risks of such newfound technologies. This is an applied research in terms of purpose and a mixed research in terms of methodology. The effective factors in using blockchain technology have been discovered by the meta-synthesis method at first; then the ranking of these factors has been done by the fuzzy pairwise comparison. A systematic search was performed in the database of Science Direct, Emerald, and Google Scholar. Also, the seven-step method of Sandlowski and Barroso (2007) was applied. All the related articles and the challenges of using blockchain in international markets were identified and coded. The comparison method between the two coders was used to adjust the reliability and the kappa coefficient was used to calculate the correlation coefficient between the two codes. The findings of the extracted sources were finally proposed and 41 challenges were classified into 6 categories of technical, educational, structural, market, infrastructure and legal which divided in both internal and external areas.

Limited energy capacity, physical distance between two nodes and the stochastic link quality are the major parameters in the selection of routing path in the internet of things network. To alleviate the problem of stochastic link quality as channel gain reinforcement based Q-learning energy balanced routing is presented in this paper. Using above mentioned parameter an optimization problem has been formulated termed as reward or utility of network. Further, formulated optimization problem converted into Markov decision problem (MDP) and their state, value, action and reward function are described. Finally, a QRL algorithm is presented and their time complexity is analyses. To show the effectiveness of proposed QRL algorithm extensive simulation is performed in terms of convergence property, energy consumption, residual energy and reward with respect to state-of-art-algorithms.

The aim of this study is to evaluate the performance of the pre-trained models and compare them with the probability percentage of prediction in terms of execution time. This study uses the COCO dataset to evaluate both pre-trained image recognition and object detection, models. The results revealed that Tiny-YoloV3 is considered the best method for real-time applications as it takes less time. Whereas ResNet 50 is required for those applications which require a high probability percentage of prediction, such as medical image classification. In general, the rate of probability varies from 75% to 90% for the large objects in ResNet 50. Whereas in Tiny-YoloV3, the rate varies from 35% to 80% for large objects, besides it extracts more objects, so the rise of execution time is sensible. Whereas small size and high percentage probability makes SqueezeNet suitable for portable applications, while reusing features makes DenseNet suitable for applications for object identification.

People are used to exploring grayscale images in their family albums but it is difficult to grasp the reality without colours. Luckily, with advancements in Machine Learning it has been possible to solve problems previously thought impossible. The authors aim to automatically colourize grayscale images using a subset of Machine Learning called Deep Learning. The system will be trained on an image dataset and given an input grayscale image the model will be able to assign aesthetically believable colours. A grayscale photograph has been provided; our approach solves the problem of visualizing a reasonable colour version of the grayscale picture. This issue is undoubtedly under controlled; therefore earlier methods to this problem have either counted majorly on user interaction or it leads to in unsaturated colourizations. The authors put forward a completely automatic approach that will try to produce realistic and vibrant colourizations as much as possible. The proposed system has been applied as a feed-forward in a Convolutional Neural Network and has been trained on over twenty thousand colour images currently.

Falls are a serious concern among the elderly people, causing severe physical pain to them and placing a strain on medical infrastructure. The global elderly population is expected to grow significantly in the coming years, as advances in healthcare allow lifespans to increase globally. This will bring more chances for falls to occur. With this in mind, there is a need for new research to be conducted on finding ways to reduce this problem. One area which shows promise is the use of Machine Learning to perform fall detection. Machine Learning is a rapidly growing field, and it has many applications in various fields such as finance, technology and medicine. When it comes to fall detection, Machine Learning systems are often able to detect falls much better and efficiently than a human can, given the same input data. The goal of this paper is to conduct a survey study on the main and most common machine learning algorithms implemented in the field of early fall detection for elderly people and the characteristics. The paper will discuss the different types of fall detection systems, algorithms, tools, datasets, applications, and challenges.  By conducting this research, a better understanding of the context, progress and trends in the field will be possible so that future research will have a guide to build upon.

This paper surveys recent publications on maritime logistics with a focus on blockchain capabilities. Maritime logistics plays an important role in global trade and an important part of the global economy depends on it. The growth of world trade has led to the creation of various systems that are prone to error and fraud, the current supply chain needs to connect and trust many intermediaries which not only increase abuse and errors but also it will increase cost and time in the chain. Hence, transparency and stability are highly demanded. On the other hand, with the onset of the fourth industrial revolution, a new approach called “blockchain” was introduced which can have very important applications in maritime logistics. As this novel technology is in its early stages of development, its applications in maritime logistics have not been specifically investigated. So, this paper takes a deep dive into blockchain technology capabilities for maritime logistics by reviewing relevant scientific journal papers since 2014. The paper also discusses and challenges the current operational paradigms and by categorizing efforts, provides a taxonomy of research topics in maritime logistics. Finally, the paper in addition to presenting challenges, identifies new avenues for academic research based on current trends and development.

This paper presents an approach that uses both genetic algorithm (GA) and fuzzy inference system (FIS), for feature selection for descriptor in a quantitative structure activity relationships (QSAR) classification and prediction problem. Unlike the traditional techniques that employed GA, the FIS is used to evaluate an individual population in the GA process. So, the fitness function is introduced and defined by the error rate of the GA and FIS combination. The proposed approach has been implemented and tested using a data set with experimental value anti-human immunodeficiency virus (HIV) molecules. The statistical parameters q2 (leave many out) is equal 0.59 and r (coefficient of correlation) is equal 0.98. These results reveal the capacity for achieving subset of descriptors, with high predictive capacity as well as the effectiveness and robustness of the proposed approach.