This paper deals with the design and optimization of a 1.2 MVA medium-voltage (MV) power electronic traction transformer (PETT) for an AC 15 kV/16.7 Hz railway grid, in which a simple two-stage multi-cell PETT topology consisting of a bidirectional 170 kW, 2.5 kV AC rms to 6 kV DC power factor corrected (PFC) converter stage followed by a bidirectional isolated 46 kHz, 6 kV to 1.5 kV series resonant DC/DC converter for each cell is presented. This paper presents a methodology that maximizes the converter"s efficiency and minimizes the converter"s size and weight. Accordingly, the first stage employs 10 kV SiC MOSFETs based on the integrated Triangular Current Mode (iTCM). The second stage uses 10 kV SiC MOSFETs on the MV-side, 3.3 kV SiC MOSFETs on the LV-side, and a medium frequency (MF) MV transformer operating at 46 kHz. MF transformers offer a way to reduce weight and improve energy efficiency, particularly in electric multiple-unit applications. The MF MV transformer requires power electronic converters, which invert and rectify the voltages and currents at the desired operating frequency. The development of high voltage SiC MOSFETs, which can be used instead of Si IGBTs in PETT topologies, increases the operating frequency without reducing the converter"s efficiency. The designed MV PETT achieves 98.95% efficiency and 0.76 kVA/kg power density.

The growth of manufacturing industries and the huge competitive environment forced manufacturing organizations to develop advanced improvement strategies and enhance their sustainability performance. The integration of sustainable Manufacturing in industrial operations leads to enhanced process performances through the reduction of wastes, cost, and environmental impacts and satisfies ergonomic conditions. For this reason, various firms have adopted sustainable manufacturing concepts to enhance their performances and hold a prestigious competitive position. The purpose of this research is to develop an integrated Pythagorean Fuzzy MCDM model to enhance the application process of the conventional Lean Manufacturing approach (LM). Firstly, an extended Value Steam Mapping is proposed to assess the sustainability of the manufacturing process and identify the causes of waste from a sustainability viewpoint. Secondly, Pythagorean Fuzzy Decision-Making Trial And Evaluation Laboratory (PF-DEMATEL) is employed to analyze the interrelationship among the identified. Thirdly, Pythagorean Fuzzy Technique for Order Preference by Similarity to Ideal Solution (PF-TOPSIS) is introduced to prioritize a set of solutions in order to overcome the investigated causes and improve the durability of the manufacturing operations. Finally, sensitivity analysis is conduced to assess the effectiveness of the obtained results. The proposed method has several attractive features. It can address the drawbacks of the conventional LM and enhance its analysis and improvement tasks. However, the proposed approach offers an advanced application process for Lean Manufacturing in a sustainability context. Additionally, the suggested strategy facilitates the leaders to assess the current state of the manufacturing processes and select the appropriate solutions for successful sustainability implementation. The validity of the proposed approach was investigated in a real case study. The results confirm its effectiveness and indicate that using MCDM approaches in LM application process offers a consistent and flexible demarche for sustainable manufacturing implementation.

Condition monitoring (CM) of industrial processes is essential for reducing downtime and increasing productivity through accurate Condition-Based Maintenance (CBM) scheduling. Indeed, advanced intelligent learning systems for Fault Diagnosis (FD) make it possible to effectively isolate and identify the origins of faults. Proven smart industrial infrastructure technology enables FD to be a fully decentralized distributed computing task. To this end, such distribution among different regions/institutions, often subject to so-called data islanding, is limited to privacy, security risks, and industry competition due to the limitation of legal regulations or conflicts of interest. Therefore, Federated Learning (FL) is considered an efficient process of separating data from multiple participants to collaboratively train an intelligent and reliable FD model. As no comprehensive study has been introduced on this subject to date, as far as we know, such a review-based study is urgently needed. Within this scope, our work is devoted to reviewing recent advances in FL applications for process diagnostics, while FD methods, challenges, and future prospects are given special attention.

Over the past few decades, Lean Manufacturing (LM) has been the pinnacle of strategies applied for cost and waste reduction. However as the search for competitive advantage and production growth continues, there is a growing consciousness towards environmental preservation. With this consideration in mind this research investigates and applies Value Stream Mapping (VSM) techniques to aid in reducing environmental impacts of manufacturing companies. The research is based on empirical observation within the Chassis weld plant of Company X. The observation focuses on the weld operations and utilizes the cross member line of Auxiliary Cross as a point of study. Using various measuring instruments to capture the emissions emitted by the weld and service equipment, data is collected. The data is thereafter visualised via an Environmental Value Stream Map (EVSM) using a 7-step method. It was found that the total lead-time to build an Auxiliary Cross equates to 16.70 minutes and during this process is emitted. It was additionally found that the UPR x LWR stage of the process indicated both the highest cycle time and carbon emissions emitted and provides a starting point for investigation on emission reduction activity. The EVSM aids in the development of a method that allows quick and comprehensive analysis of energy and material flows. The results of this research are important to practitioners and academics as it provides an extension and further capability of Lean Manufacturing tools. Additionally, the EVSM provides a gateway into realising environmental benefits and sustainable manufacturing through Lean Manufacturing.

An effective Food Traceability System (FTS) in a Food Supply Chain (FSC) should adequately provide all necessary information to the consumer(s), meet the requirements of the relevant agencies, and improve food safety as well as consumer confidence. New information and communication technologies are rapidly advancing, especially after the emergence of the Internet of Things (IoT). Consequently, new food traceability systems have become mainly based on IoT. Many studies have been conducted on food traceability. They mainly focused on the practical implementation and theoretical concepts. Accordingly, various definitions, technologies, and principles have been proposed. The “traceability” concept has been defined in several ways and each new definition has tried to generalize its previous ones. Nevertheless, no standard definition has been reached. Furthermore, the architecture of IoT-based food traceability systems has not yet been standardized. Similarly, used technologies in this field have not been yet well classified. This article presents an analysis of the existing definitions of food traceability, and thus proposes a new one that aims to be simpler, general, and encompassing than the previous ones. We also propose, through this article, a new architecture for IoT-based food traceability systems as well as a new classification of technologies used in this context. We do not miss discussing the applications of different technologies and future trends in the field of IoT-based food traceability systems. Mainly, an FTS can make use of three types of technologies: Identification and Monitoring Technologies (IMT), Communication Technologies (CT), and Data Management Technologies (DMT). Improving a food traceability system requires the use of the best new technologies. There is a variety of promising technologies today to enhance FTS, such as fifth-generation (5G) mobile communication systems and distributed ledger technology (DLT).

Machine learning prognosis for condition monitoring of safety-critical systems, such as aircraft engines, continually faces challenges of data unavailability, complexity, and drift. Consequently, this paper overcomes these challenges by introducing adaptive deep transfer learning methodologies, strengthened with robust feature engineering. Initially, data engineering encompassing: (i) principal component analysis (PCA) dimensionality reduction; (ii) feature selection using correlation analysis; (iii) denoising with empirical Bayesian Cauchy prior wavelets; and (iv) feature scaling is used to obtain the required learning representations. Next, an adaptive deep learning model, namely ProgNet, is trained on a source domain with sufficient degradation trajectories generated from PrognosEase, a run-to-fail data generator for health deterioration analysis. Then, ProgNet is transferred to the target domain of obtained degradation features for fine-tuning. The primary goal is to achieve a higher-level generalization while reducing algorithmic complexity, making experiments reproducible on available commercial computers with quad-core microprocessors. ProgNet is tested on the popular New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) dataset describing real flight scenarios. To the extent we can report, this is the first time that all N-CMAPSS subsets have been fully screened in such an experiment. ProgNet evaluations with numerous metrics, including the well-known CMAPSS scoring function, demonstrate promising performance levels, reaching 234.61 for the entire test set. This is approximately four times better than the results obtained with the compared conventional deep learning models.

Permanent Ground Deformations (PGD) that follow slope failures caused catastrophic damages on buried pipelines. This paper presents a two-dimensional numerical analysis of the behavior of an 800mm water transport pipeline buried in the Aine-Tine slope (Mila, Algeria) subjected to shallow PGD triggered by the recent earthquake of August 07th, 2020 (M= 4.9). The soil-pipeline interaction was simulated focusing on the effect of (1) the magnitudes of the PGD and (2) the rigidity of the pipeline on the structural response of the pipeline. The pipeline deformations (i.e., translation and ovalization) and radial internal efforts (i.e., axial forces F_A, shear forces F_S, and bending moments M_B) result highlighted that shallow PGD can cause additional loads on pipelines that are proportional to the magnitude of PGD. Moreover, it was found that rigid pipelines are more performant than flexible pipelines. Through a simplified numerical simulation, the study helps engineers and planners to predict the actual causes of pipeline leaks and ruptures leading to severe disruption of their normal operations.

تهدف الدراسة الى معرفة أثر استخدام بعض الوسائل البصرية (صور وأفلام فيديو) المساعدة في تحسين مهارة التمرير من أعلى لدى تلاميذ التعليم المتوسط، وذلك من خلال مواقف تعليمية تعرض في درس التربية البدنية، لمعرفة مدى تحسن هذه المهارة بالنسبة لتلاميذ (10-11) سنة. وبعد تنفيذ البرنامج خاص بتعلم المهارة وإجراء الاختبارات القبلية والبعدية الحصول على البينات ومعالجتها إحصائياً والحصول على النتائج، وتوصل الباحثون إلى أهم الاستنتاجات وهي: كان لاستخدام بعض الوسائل البصرية أثر إيجابي وفعال في تحسن أداء مهارة التمرير من اعلى بالكرة الطائرة. وإن أفضلية نتائج الإختبارات البعدية في إختبار مهارة التمرير من أعلى التي حققتها المجموعة التجريبية على المجموعة الضابطة. ويوصي الباحثون بما يأتي: استخدام بعض وسائل البصرية في هذه المرحلة السنية تلعب دورا كبيرا في تحسين أداء مهارة التمرير من أعلى بالكرة الطائرة. وضرورة توفير الوسائل التعليمية والأدوات من أجل تسهيل عملية التحسين وتشويق الأطفال، وخلق جو من المرح أثناء العمل. وإجراء دورات تدريبية للقائمين بالعمل في مجال الرياضة، وكيفية التعامل مع التلميذ وكيفية تعليم الأنشطة الحركية.

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.