تهدف هذه الدراسة للتعرف على العلاقة بين بعض المهارات الحياتية وكفايات التدريس عند أساتذة التربية البدنية، استخدمنا المنهج الوصفي، تكونت عينة الدراسة من 174 فرد، استعملنا إستمارة تقيس كفايات التدريس واستمارة تقيس بعض المهارات، توصلت الدراسة لوجود علاقة ارتباطية بين بعض المهارات الحياتية عند أساتذة التربية البدنية والرياضية وكفايات التدريس لديهم.

تهدف هذه الدراسة إلى التعرف على العلاقة بين السمات الشخصية للمدرب و تماسك الفريق الرياضي"، ولإنجاز هذه الدراسة تم استخدام المنهج الوصفي بأسلوبه الارتباطي، وذلك بتطبيق مقياس فرايبورغ لقياس السمات الشخصية الذي أعد نسخته العربية محمد حسن علاوي 1998، وكذلك مقياس التماسك الاجتماعي داخل الفريق الرياًّضي هو من تصميم محمد حسن علاوي 1994، على عينة تتكون من 60 مدربا و 900 لاعب موزعين على 60 فريقا في بعض ولايات الشرق الجزائري ( باتنة، قسنطينة، أم البواقي، خنشلة، تبسة) للموسم الرياضي 2018/2019. وضحت النتائج أن هناك علاقة ذات دلالة إحصائية عند مستوى الدلالة 0.05 بارتباط موجب قوي بين التماسك الاجتماعي و سمتي الاجتماعية والهدوء، وهناك ارتباط قوي عكسي بين التماسك الاجتماعي و سمتي العصبية و العدوانية، بينما لا يوجد أي ارتباط بين التماسك الاجتماعي وسمة الكف على ضوء هذه النتائج قدمت الدراسة مجموعة من التوصيات، أهمها الاهتمام بالجانب الاجتماعي داخل الفرق الرياضية، واعتماد النمط القيادي الديمقراطي في تسييرها. الكلمات المفتاحية: السمات الشخصية؛ تماسك الفريق الرياضي؛ أنماط القيادة؛ الفريق الرياضي؛ المدرب. 

هدف البحث إلى تصميم برنامج ألعاب صغيرة لتطوير بعض عناصر اللياقة البدنية (المداومة، السرعة، المرونة) لتلاميذ المرحلة الابتدائية بعمر(10-11) سنة ،فضلا عن معرفة تأثير هذه الألعاب ، وتم إجراء هذا البحث في المدة من 19/02 /2014 ولغاية 08/05/2014 وعلى عينة من تلاميذ مدرسة سلالي فرحات الابتدائية للعام الدراسي (2013-2014) وبلغ عددهم(30) تلميذ تم تقسيمهم إلى مجموعتين متساويتين إحداهما تجريبية عملت ببرنامج الألعاب الصغيرة والأخرى ضابطة عملت بالأسلوب التقليدي، وتكونت كل مجموعة من(15) تلميذ وفي ضوء هذه الاستنتاجات أوصى الباحث بضرورة تطبيق مجموعة الألعاب الصغيرة في درس التربية الرياضية بالمدارس الابتدائية وكذلك ضرورة تهيئة البيئة التعليمية بالإمكانات والأدوات اللازمة لتطبيق الألعاب الصغيرة مع إعداد ألعاب ترويحية تعليمية للأنشطة الرياضية المختلفة التي تعمل على تطوير عناصر اللياقة البدنية لدى تلاميذ هذه المرحلة. 

يهدف هذا البحث الى معرفة وجود فروق ذات دلالة إحصائية في مستوى تقدير الذات بين ذوي الإعاقة الوراثية والمكتسبة لدى اللاعبين كرة السلة على الكراسي المتحركة، وذلك باستخدام مقياس تقدير الذات على عينة قصدية متكونة من 45 لاعب معاق حركيا ذوي الإعاقة المكتسبة والوراثية منخرطين في مختلف الفرق المستوى الوطني الأول، ولقد استخدمنا المنهج الوصفي لملائمته هذا البحث متبعين أسلوب الاحصائي الوصفي باستخدام المتوسط الحسابي والانحراف المعياري معامل الارتباط بيرسون واختبار (ت)، و أسفرت النتائج البحث بعدم وجود فروق ذات دلالة إحصائية في متوسط الدرجات النمط الأول والثاني لتقدير الذات، وبوجود فروق ذات دلالة إحصائية في النمط الثالث والرابع لتقدير الذات بين اللاعبين ذوي الإعاقة الوراثية والمكتسبة لكرة السلة على الكراسي المتحركة، ومن بعض الاقتراحات والتوصيات التي يوصي بها الباحث، استخدام طرق ارشادية نفسية تساعد اللاعبين المعاقين حركيا على بلوغهم تقدير الذات الايجابي وتوفير الوسائل والأجهزة الخاصة منها الكراسي المتحركة ذات الجودة الرفيعة.

In this article, we propose a new class of search directions based on new kernel function to solve the monotone semidefinite linear complementarity problem by primal-dual interior point algorithm. We show that this algorithm based on this function benefits from the best polynomial complexity, namely O( √ n(log n) 2 log n ). The implementation of the algorithm showed a great improvement concerning the time and the number of iterations.

In this paper, we investigate linear codes over the ring R5,3 = F5 + u1F5 + u2F5 + u3F5, and we determine the homogeneous weight of this ring, to derive some properties corresponding to these codes.

This paper is about the study of the well-posedness of a stochastic Cahn-Hilliard equation driven by white noise induced by a Q-Brownian motion. The proof of the existence of a unique global solution relies on the Galerkin method together with a monotonicity method.

Le présent article traite de la problématique de la poésie et de la prose comme deux productions artistiques unies par l’appartenance à un domaine commun, mais différenciées par des caractéristiques singulières. Dans cette optique, nous opterons pour une démarche chronologique qui atteste de la dynamique de la pensée humaine en matière de production artistique. Pour nous inscrire dans la mobilité en question et respecter le principe de la contextualisation de tout discours, nous partirons de «La Poétique» et «La Rhétorique» d’Aristote pour passer en revue les conceptions données à ces deux genres artistiques par Barthes, Genette, Jakobson, Sartre, Todorov, et tant d’autres théoriciens.

Purpose The purpose of this paper is to develop a model for sustainable manufacturing by adopting a combined approach using AHP, fuzzy TOPSIS and fuzzy EDAS methods. The proposed model aims to identify and prioritize the sustainable factors and technical requirements that help in improving the sustainability of manufacturing processes. Design/methodology/approach The proposed approach integrates both AHP, Fuzzy EDAS and Fuzzy TOPSIS. AHP method is used to generate the weights of the sustainable factors. Fuzzy EDAS and Fuzzy TOPSIS are applied to rank and determine the application priority of a set of improvement approaches. The ranks carried out from each MCDM approach is assessed by computing the spearman’s correlation coefficient. Findings The results reveal the proposed model is efficient in sustainable factors and the technical requirements prioritizing. In addition, the results carried out from this study indicate the high efficiency of AHP, Fuzzy EDAS and Fuzzy TOPSIS in decision making. Besides, the results indicate that the model provides a useable methodology for managers’ staff to select the desirable sustainable factors and technical requirements for sustainable manufacturing. Research limitations/implications The main limitation of this paper is that the proposed approach investigates an average number of factors and technical requirements. Originality/value This paper investigates an integrated MCDM approach for sustainable factors and technical requirements prioritization. In addition, the presented work pointed out that AHP, Fuzzy EDAS and Fuzzy TOPSIS approach can manipulate several conflict attributes in a sustainable manufacturing context.

Smart grid is an emerging system providing many benefits in digitizing the traditional power distribution systems. However, the added benefits of digitization and the use of the Internet of Things (IoT) technologies in smart grids also poses threats to its reliable continuous operation due to cyberattacks. Cyber–physical smart grid systems must be secured against increasing security threats and attacks. The most widely studied attacks in smart grids are false data injection attacks (FDIA), denial of service, distributed denial of service (DDoS), and spoofing attacks. These cyberattacks can jeopardize the smooth operation of a smart grid and result in considerable economic losses, equipment damages, and malicious control. This paper focuses on providing an extensive survey on defense mechanisms that can be used to detect these types of cyberattacks and mitigate the associated risks. The future research directions are also provided in the paper for efficient detection and prevention of such cyberattacks.

Fuel cell technology has been rapidly developed in the last decade owing to its clean characteristic and high efficiency. Proton exchange membrane fuel cells (PEMFCs) are increasingly used in transportation applications and small stationary applications; however, the cost and the unsatisfying durability of the PEMFC stack have limited their successful commercialization and market penetration. In recent years, thanks to the availability and the quality of emerging data of PEMFCs, digitization is happening to offer possibilities to increase the productivity and the flexibility in fuel cell applications. Therefore, it is crucial to clarify the potential of digitization measures, how and where they can be applied, and their benefits. This paper focuses on the degradation performance of the PEMFC stacks and develops a data-driven intelligent method to predict both the short-term and long-term degradation. The dilated convolutional neural network is for the first time applied for predicting the time-dependent fuel cell performance and is proved to be more efficient than other recurrent networks. To deal with the long-term performance uncertainty, a conditional neural network is proposed. Results have shown that the proposed method can predict not only the degradation tendency, but also contain the degradation behaviour dynamics.

Logistics is one of the main tactics that countries and businesses are improving in order to increase profits. Another prominent theme in today’s logistics is emerging technologies. Today’s developments in logistics and industry are how to profit from collected and accessible data to use it in various processes such as decision making, production plan, logistics delivery programming, and so on, and more specifically deep learning methods. The aim of this paper is to identify the various applications of deep learning in logistics through a systematic literature review. A set of research questions had been identified to be answered by this article.

The Ki-67 proliferation index is an essential biomarker that helps pathologists to diagnose and select appropriate treatments. However, automatic evaluation of Ki-67 is difficult due to nuclei overlapping and complex variations in their properties. This paper proposes an integrated pipeline for accurate automatic counting of Ki-67, where the impact of nuclei separation techniques is highlighted. First, semantic segmentation is performed by combining the Squeez and Excitation Resnet and Unet algorithms to extract nuclei from the background. The extracted nuclei are then divided into overlapped and non-overlapped regions based on eight geometric and statistical features. A marker-based Watershed algorithm is subsequently proposed and applied only to the overlapped regions to separate nuclei. Finally, deep features are extracted from each nucleus patch using Resnet18 and classified into positive or negative by a random forest classifier. The proposed pipeline’s performance is validated on a dataset from the Department of Pathology at Hôpital Nord Franche-Comté hospital.

Strengthening production plants and process control functions contribute to a global improvement of manufacturing systems because of their cross-functional characteristics in the industry. Companies established various innovative and operational strategies; there is increasing competitiveness among them and increasing companies’ value. Machine learning (ML) techniques become an intelligent enticing option to address industrial issues in the current manufacturing sector since the emergence of Industry 4.0 and the extensive integration of paradigms such as big data and high computational power. Implementing a system able to identify faults early to avoid critical situations in the production line and its environment is crucial. Therefore, powerful machine learning algorithms are performed for fault diagnosis, real-time data classification, and predicting the state of functioning of the production line. Random forests proved to be a better classifier with an accuracy of 97%, compared to the SVM model’s accuracy which is 94.18%. However, the K-NN model’s accuracy is about 93.83%. An accuracy of 80.25% is achieved by the logistic regression model. About 83.73% is obtained by the decision tree’s model. The excellent experimental results reached on the random forest model demonstrated the merits of this implementation in the production performance, ensuring predictive maintenance and avoiding wasting energy.

Many machine learning-based methods have been widely applied to Coronary Artery Disease (CAD) and are achieving high accuracy. However, they are black-box methods that are unable to explain the reasons behind the diagnosis. The trade-off between accuracy and interpretability of diagnosis models is important, especially for human disease. This work aims to propose an approach for generating rule-based models for CAD diagnosis. The classification rule generation is modeled as combinatorial optimization problem and it can be solved by means of metaheuristic algorithms. Swarm intelligence algorithms like Equilibrium Optimizer Algorithm (EOA) have demonstrated great performance in solving different optimization problems. Our present study comes up with a Novel Discrete Equilibrium Optimizer Algorithm (NDEOA) for the classification rule generation from training CAD dataset. The proposed NDEOA is a discrete version of EOA, which use a discrete encoding of a particle for representing a classification rule; new discrete operators are also defined for the particle’s position update equation to adapt real operators to discrete space. To evaluate the proposed approach, the real world Z-Alizadeh Sani dataset has been employed. The proposed approach generate a diagnosis model composed of 17 rules, among them, five rules for the class “Normal” and 12 rules for the class “CAD”. In comparison to nine black-box and eight white-box state-of-the-art approaches, the results show that the generated diagnosis model by the proposed approach is more accurate and more interpretable than all white-box models and are competitive to the black-box models. It achieved an overall accuracy, sensitivity and specificity of 93.54%, 80% and 100% respectively; which show that, the proposed approach can be successfully utilized to generate efficient rule-based CAD diagnosis models.

Reliability and security of power distribution and data traffic in smart grid (SG) are very important for industrial control systems (ICS). Indeed, SG cyber-physical connectivity is subject to several vulnerabilities that can damage or disrupt its process immunity via cyberthreats. Today’s ICSs are experiencing highly complex data change and dynamism, increasing the complexity of detecting and mitigating cyberattacks. Subsequently, and since Machine Learning (ML) is widely studied in cybersecurity, the objectives of this paper are twofold. First, for algorithmic simplicity, a small-scale ML algorithm that attempts to reduce computational costs is proposed. The algorithm adopts a neural network with an augmented hidden layer (NAHL) to easily and efficiently accomplish the learning procedures. Second, to solve the data complexity problem regarding rapid change and dynamism, a label autoencoding approach is introduced for Embedding Labels in the NAHL (EL-NAHL) architecture to take advantage of labels propagation when separating data scatters. Furthermore, to provide a more realistic analysis by addressing real-world threat scenarios, a dataset of an electric traction substation used in the high-speed rail industry is adopted in this work. Compared to some existing algorithms and other previous works, the achieved results show that the proposed EL-NAHL architecture is effective even under massive dynamically changed and imbalanced data.

The green conversion of proton exchange membrane fuel cells (PEMFCs) has received particular attention in both stationary and transportation applications. However, the poor durability of PEMFC represents a major problem that hampers its commercial application since dynamic operating conditions, including physical deterioration, have a serious impact on the cell performance. Under these circumstances, prognosis and health management (PHM) plays an important role in prolonging durability and preventing damage propagation via the accurate planning of a condition-based maintenance (CBM) schedule. In this specific topic, health deterioration modeling with deep learning (DL) is the widely studied representation learning tool due to its adaptation ability to rapid changes in data complexity and drift. In this context, the present paper proposes an investigation of further deeper representations by exposing DL models themselves to recurrent expansion with multiple repeats. Such a recurrent expansion of DL (REDL) allows new, more meaningful representations to be explored by repeatedly using generated feature maps and responses to create new robust models. The proposed REDL, which is designed to be an adaptive learning algorithm, is tested on a PEMFC deterioration dataset and compared to its deep learning baseline version under time series analysis. Using multiple numeric and visual metrics, the results support the REDL learning scheme by showing promising performances.

Federated learning (FL) is a data-privacy-preserving, decentralized process that allows local edge devices of smart infrastructures to train a collaborative model independently while keeping data localized. FL algorithms, encompassing a well-structured average of the training parameters (e.g., the weights and biases resulting from training-based stochastic gradient descent variants), are subject to many challenges, namely expensive communication, systems heterogeneity, statistical heterogeneity, and privacy concerns. In this context, our paper targets the four aforementioned challenges while focusing on reducing communication and computational costs by involving recursive least squares (RLS) training rules. Accordingly, to the best of our knowledge, this is the first time that the RLS algorithm is modified to completely accommodate non-independent and identically distributed data (non-IID) for federated transfer learning (FTL). Furthermore, this paper also introduces a newly generated dataset capable of emulating such real conditions and of making data investigation available on ordinary commercial computers with quad-core microprocessors and less need for higher computing hardware. Applications of FTL-RLS on the generated data under different levels of complexity closely related to different levels of cardinality lead to a variety of conclusions supporting its performance for future uses.

In modern Smart Grids (SGs) ruled by advanced computing and networking technologies, condition monitoring relies on secure cyberphysical connectivity. Due to this connection, a portion of transported data, containing confidential information, must be protected as it is vulnerable and subject to several cyber threats. SG cyberspace adversaries attempt to gain access through networking platforms to commit several criminal activities such as disrupting or malicious manipulation of whole electricity delivery process including generation, distribution, and even customer services such as billing, leading to serious damage, including financial losses and loss of reputation. Therefore, human awareness training and software technologies are necessary precautions to ensure the reliability of data traffic and power transmission. By exploring the available literature, it is undeniable that Machine Learning (ML) has become the latest in the timeline and one of the leading artificial intelligence technologies capable of detecting, identifying, and responding by mitigating adversary attacks in SGs. In this context, the main objective of this paper is to review different ML tools used in recent years for cyberattacks analysis in SGs. It also provides important guidelines on ML model selection as a global solution when building an attack predictive model. A detailed classification is therefore developed with respect to data security triad, i.e., Confidentiality, Integrity, and Availability (CIA) within different types of cyber threats, systems, and datasets. Furthermore, this review highlights the various encountered challenges, drawbacks, and possible solutions as future prospects for ML cybersecurity applications in SGs.

The purpose of this paper is to propose an improvement strategy based on multi-criteria decision making approaches, including fuzzy analytic hierarchy process (AHP), preference ranking organisation method for enrichment evaluation II (PROMETHEE) and vi\v sekriterijumsko kompromisno rangiranje (VIKOR) for the objective of simplifying and organising the improvement process in complex manufacturing processes. Firstly, the proposed strategy started with the selection of decision makers’, such as company leaders, to determine performance indicators. Then fuzzy AHP is used to quantify the weight of each defined indicators. Finally, the weights carried out from fuzzy AHP approach are used as input in VIKOR and PROMETHE II to rank the operations according to their improvement priority. The results obtained from each outranking method are compared and the best method is determined.